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Remember learning about genetics for the first time in biology class? I myself dont remember much, aside from the traumatic time we had to dissect a pregnant rat (I vividly recall the smell of formaldehyde and do not wish to smell it ever again). The only other thing I vaguely remember were those squares we had to fill in. Punnett squares. Remember those? What a pain in the ass. These are two of many reasons why I never became a doctor.

Real genetics are a lot more complicated and dont fall quite so neatly into those Punnett squares, unfortunately. You might think that your genetic composition would be as simple as being an even quarter mix of each of your grandparents blended into one human being. But in reality, processes like genetic recombination shake things up considerably.

Science Alert used gummy bears to show a graphic representation of how genetics can work down the line, inspired by this tweet from NYU neuroscience prof Jay Van Bavel, who tweets as @jayvanbavel:

It is pretty adorable. And delicious. Because who doesnt love the idea of using a handful of gummy bears to depict your ancestry? Its not exactly perfect because, according to Science Alert, gummy bears dont convey dominant or recessive traits (the uppercase letters in a Punnett square are dominant, while the lowercase ones are the recessive ones, if youve forgotten). Still, its something, and in the end, youll be shoving your weirdo genetic mishmash monster gummy bears right into your face; really, there are few things better than a science experiment that you can end up eating later. Plus candy is a great way to get kids (and adults, for that matter) to pay attention. Maybe if wed used them in my biology class, I would be a doctor today.

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What better way to learn genetics than with gummy bears? - The Takeout

Genetic testing can provide crucial information about what's causing a patient's problems and what lies ahead.

But for healthy patients?It's a lottricker.

With the growing popularity of consumer genetic tests, patients are increasingly asking their doctors about whether a test could predict their vulnerability to certain conditions, such as cancer, dementia and heart disease. Acase studypublished Mondayin the Annals of Internal Medicine by researchers at Columbia University warns that doctors and patients alike should proceed carefully in understanding the context in which these tests provide useful information.

RELATED: How physicians should answer questions from patients about consumer genetic tests

Genetic testing was developed for diagnosing somebody who has a condition, or whose doctor suspects they have a condition, saidAli Gharavi, MD, professor of medicine and chief of the Division of Nephrology at Columbia University College of Physicians and Surgeons and one of the article's authors.

In that case, doctors start with a prior suspicion of a condition that can be confirmed, he told Fierce Healthcare.

But with a healthy patient, the probability of a currently undiagnosed disease is lower, which makes the probability of a false-positive result higher, he said.

As a result, physicians may need to do additional work to confirm a diagnosis. The amount of additional work required depends on the degree to which a given genetic variance has been positively correlated with a specific condition.

A relatively small number of genetic mutations are both well-studied and well-understood. Certain cancer mutations, for example, correlate strongly with the risk of developing breast cancer or ovarian cancer. However, a positive result on those tests doesnt mean an individual has one of these cancers, or even that they definitively will get them. Instead, it may simply indicate they have a higher risk of developing that cancer.

With other genetic mutations, potential variations could be falsely correlated with a disease or they could occupy a gray area classified as a variant of unknown significance. Labs tend to use this classification for variants that require more study before they can be classified either as benign or malignant.

RELATED: Ancestry rolls out more advanced DNA testing to flag risk of heart disease, breast cancer

Gharavi says its important for physicians to take the consequences of a potential false-positive into account when helping patients make a decision on whether or not to undertake a given genetic test.

Its probably better to whittle things down to what youre concerned aboutif youre concerned about Alzheimers and you conduct a test that scans the entire genome and come back with variants of unknown significance for heart disease and cancer, et cetera, then suddenly your anxiety level goes much higher.

For patients, Gharavi recommends talking to your doctor about the conditions best suited to a genetic test and ensuring you understand beforehand what the potential consequences of a positive test might be. And if the test comes up negative, remember that it doesnt mean you have no risk of contracting the disease in question.

For practitioners, Gharavi suggests bearing in mind that not all genetic tests have the same predictive capability. Understanding which tests are well-studied and correlate with specific diseases can help provide patients the education they need to decide which tests they really want, given their family history or other specific concerns.

Since research is ongoing, physicians should be aware that it can be a lot of information to stay on top of. Its all changing very quickly, so conferring with a genetic counselor or clinical geneticist also helpssomebody whos really familiar with these tests, Gharavi advises.

Excerpt from:
More healthy patients are taking genetic test results to their docs. But there are some pitfalls - FierceHealthcare

average: (in science) A term for the arithmetic mean, which is the sum of a group of numbers that is then divided by the size of the group.

cell: The smallest structural and functional unit of an organism. Typically too small to see with the unaided eye, it consists of a watery fluid surrounded by a membrane or wall. Depending on their size, animals are made of anywhere from thousands to trillions of cells. Mostorganisms, such as yeasts, molds, bacteria and some algae, are composed of only one cell.

develop: To emerge or to make come into being, either naturally or through human intervention, such as by manufacturing. (in biology) To grow as an organism from conception through adulthood, often undergoing changes in chemistry, size, mental maturity, size or sometimes even shape.

development: (in biology) The growth of an organism from conception through adulthood, often undergoing changes in chemistry, size and sometimes even shape.

DNA: (short for deoxyribonucleic acid) A long, double-stranded and spiral-shaped molecule inside most living cells that carries genetic instructions. It is built on a backbone of phosphorus, oxygen, and carbon atoms. In all living things, from plants and animals to microbes, these instructions tell cells which molecules to make.

egg: The unfertilized reproductive cell made by females.

embryo: The early stages of a developing organism, or animal with a backbone, consisting only one or a few cells. As an adjective, the term would be embryonic and could be used to refer to the early stages or life of a system or technology.

fraternal: Of our relating to brothers, or others with whom people develop close friendships and affection. (in genetics) The term for a type of twin birth where each baby comes from a separate fertilized egg. This is in contrast to identical twins, which result from a single fertilized egg (creating two separate but nearly identical babies).

genetic: Having to do with chromosomes, DNA and the genes contained within DNA. The field of science dealing with these biological instructions is known as genetics. People who work in this field are geneticists.

genome: The complete set of genes or genetic material in a cell or an organism. The study of this genetic inheritance housed within cells is known as genomics.

Iceland: A largely arctic nation in the North Atlantic, sitting between Greenland and the western edge of Northern Europe. Its volcanic island was settled between the late 800s and 1100 by immigrants from Norway and Celtic lands (ones governed by the Scots and Irish). It is currently home to roughly a third of a million people.

mutation: (v. mutate) Some change that occurs to a gene in an organisms DNA. Some mutations occur naturally. Others can be triggered by outside factors, such as pollution, radiation, medicines or something in the diet. A gene with this change is referred to as a mutant.

replicate: (in biology) To copy something. When viruses make new copies of themselves essentially reproducing this process is called replication.

trait: A characteristic feature of something. (in genetics) A quality or characteristic that can be inherited.

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Some identical twins dont have the exact same DNA - Science News for Students

Pharmacogenetics, or precision medicine, is still new to many pediatric providers despite its documented benefits, according to an article the Sanford Childrens Genomic Medicine Consortium published recently in The Pharmacogenomics Journal.

Pharmacogenetics uses a persons DNA to help providers choose the best medicine and dosage of medicine. And there are plenty of opportunities to use genetic traits in pediatrics, the group wrote.

Theres evidence that pharmacogenetics benefit pediatric oncology, pain management, organ transplantation, and immunosuppression, according to the journal article. Additionally, advances in technology have made it easier to study complete genomes, and providers can use that information to improve health care for children.

Ten hospitals have signed on to the consortium to rapidly integrate genetics and genomics into primary and specialty pediatric care.

Above all, the mission of the consortium is to efficiently manage resources in genetics and genomics, perform cutting-edge research and education and bring genomic medicine into pediatric practice. This will help set the standard for precision medicine in childrens health care.

The 10 member hospitals are:

A previous innovation project funded by the consortium was a study of the outcomes of rapid whole genomic sequencing in critically ill newborn infants. Another previous study evaluated the routine use of an extensive, pediatric-focused, next generation sequencing panel in the diagnosis of childhood cancers.

Posted In Children's, Company News, Genetics

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Sanford genetics group shares benefits of custom kids' care - Sanford Health News

Introduction

The Brazilian population is one of the most heterogeneous in the world, showing considerable genetic admixture among Europeans, Africans, and Native Americans.1 Among the three main groups forming the Brazilian population, Native Americans have the scarcest genetic data.

The Amazon region concentrates a greater part of the Native American populations of Brazil: there are more than 180 communities, apart from several isolated groups living in the biome, which represents approximately 200 thousand people, 86 languages, and 650 dialects.2 Specifically addressing the state of Par, the second largest state in the Brazilian Amazon region, the last census reported more than 50 thousand indigenous people and 42 different Native American groups.3

The epidemiological profile of Native American populations is very little known, which stems from the scarcity of investigations, the absence of surveys and censuses, as well as the precariousness of information systems on morbidity and mortality, which complicates any discussion about the health/disease process of indigenous peoples.4 As far as information on genetic data for that population, data availability is even more scarce.

The population paradigm of PGx is based on the frequency of numerous polymorphisms in pharmacogenes that vary widely among human populations (Suarez-Kurtz, 2010). The guidelines formulated by regulatory drug agencies for the accuracy of therapies cannot be fully applied to Native Americans or even to populations with a high degree of genetic admixture with this group, such as the Brazilian population.5 This study aims to investigate a panel of 117 polymorphisms in 35 pharmacogenes, including label recommendations or clinical evidence from international drug regulatory agencies in an Amazonian Native American population, and to compare the results obtained with global population data. Relevant pharmacogenetic biomarkers were selected from the Pharmacogenomics Knowledge Base database.7

A total of 109 Native American individuals from the Brazilian Amazon region were selected from a database of an epidemiological study investigating indigenous populations of Par. The study population was composed of 65 men and 44 women and collected from adult individuals (between 18 and 50 years old). Twenty-five samples were obtained from Asurini do Koatinemo (KOA), 41 Asurini do Trocar (ASU), and 43 Kayap-Xicrin (KAY). All the Native Americans groups are in the state of Par: the Kayap-Xikrin is located in the Catet and Trincheira Bacaj regions, both indigenous protective lands (geographic coordinates: 6.241917, 50.804833), it counts with a total population of 1800 individuals; the Asurini do Trocar settlement is located east of the Tocantins River (geographic coordinates: 3.567694, 49.711039), summing a total population of 546 individuals; and, Asurini do Koatinemo is situated on the right bank of the Xingu River (geographic coordinates: 4.230970, 52.298335), with a total population of 182 individuals. The three Native American groups are isolated from each other, located at a mean distance of 390 km between them, and do not share family relationships. For some analyses, the three Amazonian Native American populations were gathered in a group called Native Americans (NAM). The genomic data for each marker investigated in the continental populations were obtained from the Ensembl Phase 3 Project.8

Relevant pharmacogenetic biomarkers were selected from the database of the Pharmacogenomics Knowledge Base,9 a publicly available online knowledgebase website whose main objective is collecting, curating, integrating, and disseminating basic pharmacogenetic data. Here, we define biomarker as the function of a gene to code enzymes responsible for processes of pharmacokinetics or pharmacodynamics that may interfere in drug pathways, consequently affecting drug response.10

Pharmacogenetic biomarkers are labeled by levels of evidence regarding their importance to drug response. Levels of evidence rank from 1A, which denotes a variant-drug combination in a medical society-endorsed PGx guideline, or already implemented in a major health system, to level 4, which denotes annotation based on a case report, nonsignificant study or in vitro, molecular or functional assay evidence only. For the current analysis of the Native American populations, 117 biomarkers (ranked from level 1A to 2A and 3) from a total of 35 different genes, including absorption, distribution, metabolism, and excretion genes (ADME) and pharmacodynamic genes, were selected. All the biomarkers selected for this study are shown in Table 1.

Genetic material was extracted from peripheral blood using the BiopurKit Mini Spin Plus-250 commercial kit (Biopur, Brazil) according to the manufacturers recommendations. DNA concentration and purity were measured with a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE). The genotyping of single nucleotide polymorphisms (SNPs) was performed by allelic discrimination using TaqMan OpenArray Genotyping with a panel of 120 customized assays on the QuantStudio 12K Flex Real-Time PCR System (Applied Biosystems, Life Technologies, Carlsbad, USA) according to the protocol recommended by Applied Biosystems. Three of the 117 selected biomarkers were triallelic, specifically rs2032582 for ABCB1, rs5030865 for CYP2D6, and rs7900194 for CYP2C9, requiring two different probes per biomarker in the array, making a total of 120 assays to be analyzed (Table 1). To ensure the correct assessment of the genotypes, native samples were analyzed together with negative and positive internal quality controls. Data were analyzed with TaqMan Genotyper software v1.2.2. Copy number variation for CYP2D6 was analyzed by using TaqMan commercial probes according to the TaqMan Copy Number assay protocol recommended by Applied Biosystems and to a final volume of 10 L per reaction. Three different regions were analyzed, intron 2, intron 6, and exon 9, together with an internal 2-copy control (RNAse P). Analysis of the three regions allowed us to detect hybrids CYP2D6/2D7 and CYP2D6*36. Data were analyzed with CopyCaller software v.2 by using a two-copy as a positive control. The predicted copy number was assessed for the three probes, and the mean and standard deviation were also calculated.

Ancestry analysis was performed as described by Ramos et al 2016,11 using 61 autosomal ancestry informative markers (AIMs). Three multiplex PCR reactions were performed using the insertion/deletion markers (INDEL) and the PCR amplifications were analyzed by electrophoresis using the ABI Prism 3130 sequencer and the GeneMapper ID v.3.2 software. The individual proportions of European, African, and Native Americans genetic ancestries were estimated using the STRUCTURE v.2.3.3 software, assuming three parental populations (European, African, and Native Americans).

To compare genetic frequencies for the genes involved in the ADME processes between the three Native American populations and other reference populations, data from the 1000 Genomes Project Consortium6 were downloaded from the website, and pharmacogenetic biomarkers were carefully selected. A total of 2.613 individuals from Africa (AFR), Europe (EUR), East Asia (EAS), South Asia (SAS), and America (AMR) were used to perform discriminant analysis of principal components (DAPC) using R software with the Adegenet package.12

DAPC maximizes discrimination between the populations included in the analysis and, in this way, enables us to characterize the proximity of the NAM populations to the reference populations. Moreover, DAPC provided an informative description of the contribution of the alleles to the discriminant functions used to differentiate the populations. Two R libraries were used to obtain summary tables with descriptive information for each SNP: SNPassoc (Minor Allele Frequency, Hardy-Weinberg Equilibrium, and call rate) and GenABEL (Minor Allele Frequency, Hardy-Weinberg Equilibrium, call rate, and genotype frequencies).13,14

Haplotypes were inferred by using Pharmgkb website and the software AlleleTyper v1.0. This software interprets the real-time PCR analysis data and determines the star-allele results based on specific tables designed from haplotypes tables from Pharmgkb website. Allele Typer software allows to encompass results from SNPs and copy number variation to give a joint genotype prediction.

Furthermore, frequencies of genotypes, haplotypes, and metabolizer phenotypes were compared by Fishers exact tests. Call rates higher than 90% were obtained when analyzed with OpenArray.15

The distribution of 12 haplotypes in the three representative groups of the Native American populations of Brazil is shown in Figure 1. Of these genes, five had a significantly different distribution among the three Native Americans groups: CYP2D6 (p = 0.0047), CYP3A5 (p = 0.043), CYP4F2 (p = 0.0105), CYP2B6 (p = 0.00018), and DPYD (p = 0.0012) (Figure 1). To define the possible CYP2D6 genotypes, 22 polymorphisms were investigated, determining 11 different genotypes, of which *1/*1, *2A/*2A, *1/*4, and *1/*2A were present in all three populations investigated. The wild-type homozygous genotype (*1/*1) was the most frequently found (33%) followed by the *1/*2A genotype (32%). Wild-type homozygotes (*1/*1) were highly common in ASU (17%) and KAY (14%), while KOA presented a considerably lower frequency (4%). Some of the genotypes were detected at low frequencies and in only one of the three populations investigated, such as *2A/*9 and *1/*5 genotypes that occurred only in ASU; *1/*1xN found only in KAY; *4/*29 and *2A/*29 only in KOA. The haplotypes *9 (present in genotype *2A/* 9) and *5 (*1/*5) were exclusive in the ASU.

Figure 1 Genotype distribution of haplotype-forming genes in the three Native American populations of Brazil.

Abbreviations: A_T, Asurini do Trocar (ASU); K_X, Kayap-Xikrin (KAY); (K), Koatinemo (KOA).

Notes: *p<0.05; **p<0.01; ***p<0.001.

Another gene that also presented a distribution of genotypes with significant differences in the three indigenous communities was CYP3A5, which has five possible genotypes. Haplotype *3 was the most frequent in the Native Americans. The *1/*3 genotype was observed with high frequencies in ASU and KAY (42% and 58% individuals, respectively), in contrast with the frequency values observed in the KOA community (20% of individuals). Another genotype that confirms the prevalence of haplotype *3 in the groups is the *3/*3 genotype, which has a relatively more frequent frequency in the groups, summing 32%, 35%, and 56% of individuals in the ASU, KAY, and KOA communities, respectively. On the other hand, the*3/*6 genotype presented a rare frequency, being observed only in 4% of KOA individuals.

The CYP4F2 gene has three possible genotypes, which also presented significant differences regarding its distribution in the Brazilian Native American populations. The wild-type genotype *1/*1 was the most frequent in the groups, being found in 93%, 63%, and 84% of individuals of the ASU, KAY, and KOA communities, respectively. The genotype *1/*3 had a high frequency in the KAY population (35% individuals), whereas in the ASU, it was only found in 7% of individuals and the KOA group in 16% of individuals. Regarding the *3/*3 genotype, it was observed only in 2% of the individuals in the KAY group.

The CYP2B6 gene also presents three possibilities of genotypes, which were also different in the Native Americans evaluated. In general, the wild-type genotype (*1/*1) was observed most frequently, being observed in 68%, 28%, and 48% of individuals of the ASU, KAY, and KOA groups, respectively. The haplotype *6 presented a high frequency, mainly in the Kay population, being found in the genotypes *1/*6 (49% individuals) and *6/*6 (14% individuals).

Finally, the DPYD gene also presented three genotypes with significant differences in the studied populations. The most common genotype was the wild-type genotype (*1/*1), summing 59% of individuals in the ASU group, 49% of individuals in the KAY group, and 76% of individuals in the KOA community. The haplotype *9 was observed in both genotypes *1/*9 and *9/*9. The *1/*9 genotype was frequent in the KAY group (44% of individuals) and was also found in 12% and 20% of individuals from the ASU and KOA groups. The genotype *9/*9 exhibited low frequency, being observed only in 7% and 2% of KAY and ASU populations.

Figure 2 shows the distribution of the metabolization profile of eight genes found in the Brazilian Native American populations. For seven genes, CYP2C19, CYP2C9, CYP3A5, CYP4F2, DPYD, TPMT, and SLCOB1, we considered the assignment of phenotype-based on genotypes: poor function, decreased function, and normal function.7 For the CYP2D6 gene, the activity score (AS) classification was considered.16,17 Two genes had a significantly different profile distribution among the three communities analyzed: CYP2D6 (p = 0.0306) and CYP4F2 (p = 0.0105).

Figure 2 Metabolism profile distribution for the genes investigated in Native American populations of Brazil. For CYP2C19, CYP2C9, CYP3A5, CYP4F2, DPYD, TPMT, and SLCOB1, we considered the assignment of genotypes poor metabolizers (PM), intermediate metabolizers (IM), and extensive metabolizers (EM). For the CYP2D6 gene, the activity score (AS) classification was considered.

Abbreviations: A_T, Asurini do Trocar (ASU); K_X, Kayap-Xikrin (KAY); (K), Koatinemo (KOA).

Note: *p<0.05.

According to the combination of the CYP2D6 genotypes, we can determine the enzyme metabolic profile and classify the predictive phenotype of each individual by the activity score (AS) rate, as defined previously by Gaedigk et al, 2008,16 associating this information with the efficacy of drugs or adverse reactions during pharmacological therapies.

For the other seven genes, the normal function profiles were the most frequent in the Native Americans. The KAY population was the only one to have two individuals with AS 3, equivalent to ultrafast metabolism classification, representing approximately 5% of the total group. In the KOA, it was possible to exclusively observe one individual with poor function. The CYP4F2 gene also showed significant differences in metabolization profiles. In all the Native Americans studied, the most frequently observed profile was normal function followed by decreased function. The KAY group was the only one to present a single individual classified as poor function.

Although the distribution of the phenotypes was not statistically significant regarding the differences presented by the Native American groups, it is important to highlight the data found in two genes: CYP3A5 and SLCO1B1. Both genes showed high frequencies of poor function individuals. The poor function profile of the CYP3A5 gene was observed in 60% of individuals in the KOA community, 35% of individuals in the KAY group, and 32% of individuals in the ASU population. Regarding the SLCO1B1 gene, there were also high frequencies of poor function, approximately 20% and 19% of individuals in the ASU and the KAY communities and 16% of individuals in the KOA group.

The scatterplot shown in Figure 3 was obtained with a DAPC for the 117 PGx markers in 2613 individuals from eight global populations (EUR, AFR, EAS, SAS, AMR, KOA, ASU, and KAY). X- and Y-axis of the scatterplot describe the first and second linear discriminant (LD) function (LD1 and LD2 respectively). The AFR group formed an isolated cluster, clearly genetically differentiated from the rest of the world (x-axis). In the y-axis of the diagram, the divergence between the EUR and EAS cluster was highlighted. The SAS and AMR populations formed close clusters between themselves and the EUR cluster, demonstrating similarity between these populations for the PGx markers evaluated.

Figure 3 Discriminant analysis of principal components (DAPC) of 117 PGx. Scatterplot for the five groups of continental populations described in the 1000 Genomes Project (EUR, AFR, EAS, SAS, and AMR) and three populations of Native Americans of Brazil (KOA, ASU, and KAY).

The Native American populations formed close clusters among themselves and were closely situated between the EAS, AMR, and SAS groups, even though the ASU was the closest to the EAS group. The DAPC assigned 51% of individuals belonging to the ASU population to the EAS cluster, while the percentage of EAS-assigned individuals was lower in the other two Amerindians populations (37% for KAY and 20% for KOA). This result is in keeping with the highest percentage of Native American ancestry shown by ASU (mean value 97.4%), which was significantly higher than that shown by KOA (94.9%).

Because of the lack of clear discrimination between native American populations of Brazil in the previous analysis (Figure 3), DAPC was also performed using only the three Native American populations of Brazil (Figure 4). The ASU population forms a cluster isolated from the other two Native American populations in the x-axis (LD1) and consequently has more differences. KOA and KAY, although still forming different clusters (y-axis, LD2), have some intercession between them that shows a greater similarity of these regarding the ASU group.

Figure 4 Discriminant analysis of principal components (DAPC) of 117 PGx markers. Scatterplot for the three Native American populations of Brazil (KOA, ASU, and KAY).

Table 2 shows the list of the most contributing PGx markers to each discriminant function (LD1 and LD2) in both DAPC analysis. The first section of Table 2 shows the most important markers in the discrimination shown in Figure 3. LD1 corresponds to the x-axis demonstration of the scatterplot; this discriminant function allows differentiate the AFR population (Figure 3). Among the markers listed in LD1, we highlight CYP3A4 (rs2740574), GRIK4 (rs1954787), and OPRM1 (rs1799971), which are more relevant in differentiating AFR from other populations. The second linear discriminant (LD2) corresponds to the scatterplot demonstrative y-axis (Figure 3); along this axis, the rest of the populations are distributed. Among the markers listed in LD2, we highlight CYP1A2 (rs2069514), CYP2A6 (rs28399433), CYP2E1 (rs2070673), SLCO1B1 (rs2306283), and SOD2 (rs4880), which have been shown to have greater relevance in differentiating EUR from EAS.

Table 2 List of Most Contributing PGx Markers to Each Linear Discriminant Function (LD1 and LD2) in DAPC for Nam and 1000 Genome Populations (Top) and for the Three Native American Populations of Brazil (Bottom)

The second section of Table 2 shows the most important markers to differentiate the Brazilian Native American populations among themselves (KOA, ASU, and KAY). LD1 corresponds to scatterplots x-axis demonstration (Figure 4). Among the markers listed in LD1, we highlight ABCB1 (rs1128503), GSTP1 (rs1695), and UGT2B15 (rs1902023), which are of greater relevance in differentiating the ASU population from the other Native American populations investigated. LD2 corresponds to scatterplots y-axis demonstration (Figure 4). Among the markers listed in LD2, we highlight ABCG2 (rs2231142), CYP2E1 (rs2070673), and NAT2 (rs1041983), which are more relevant to differentiate KOA from the other Native American populations in Brazil.

The Amazonian Native American populations present low degrees of genetic admixture with non-indigenous population, a fact that is highly important for studies involving these groups, which remain genetically isolated from others and may offer advantages in genome-wide studies of hereditary diseases.18,19 The Amazonian Native Americans of this study presented mean values of Native Americans genomic ancestry of 96.2%, which confirms the low genetic admixture of these populations.

Several studies have shown large genetic variation for important PGx biomarkers between distinct populational groups.2022 The knowledge obtained to date in PGx genes in Native American populations is very limited to specific genes, failing to reach a wider genome context.23,24 The investigation of important PGx polymorphisms in the genes selected by our panel has the potential to provide powerful information regarding the predictivity of therapeutic response to the use of different drugs and xenobiotics in Amazonian Native Americans and/or admixed populations with this ethnic group. Although PGx biomarkers genotyping is useful to guarantee a more accurate prediction of the response to drugs in Amazonian Native Americans, it is also necessary to consider other factors such as ethnic origin and environmental factors of each population.

The pharmacogenomic data obtained from populations were compared to global populations from the 1000 Genomes Project Consortium.8 In our analyses, the DAPC identified a set of SNPs in PGx genes that most contributed to grouping global populations into clusters, making it possible to infer which populations have the highest level of similarity regarding PGx genes (Figure 1).

The distancing of AFR in the plot is due to the out-of-Africa hypothesis, in which modern human populations originated in Africa and migrated to other continents in the world; thus, the African populations show a greater genetic diversity that was reflected in the PGx data evaluated.25 The data demonstrate the formation of relatively close clusters among the three Amazonian Native American populations.

The SAS and AMR groups formed similar clusters regarding the PGx data evaluated. Our results showed that Amazon Native American populations are located between this cluster and the EAS grouping. The formation of the AMR population (Peru, Mexico, Puerto Rico, and Colombia) occurred through abundant mixing between European, African, and Native American groups.26,27 Therefore, the similarity between Amazonian Native American groups and AMR is possible due to the high level of admixture of these populations with indigenous peoples.19,27 Several authors have demonstrated genetic affinities between Native American and Asian populations,28,29 which corroborates the findings of our study. This similarity of PGx genes is based on the hypothesis of migration of Asian populations to the Americas through the Bering Strait.30

The DAPC analysis revealed in LD1 the most important polymorphisms capable of differentiating AFR and the rest of the world in the CYP3A4, GRIK4, and OPRM1 genes. The divergence found for these polymorphisms in AFR may influence the therapy of different drugs for the populations formed and derived from them. The CYP3A4 gene presents genetic information referenced by FDA and EMA agencies in package inserts of different drug classes, such as antineoplastics, antipsychotics, and antiretrovirals (Food and Drug Administration, n.d.; For et al, n.d.). Polymorphisms in the GRIK4 and OPRM1 genes are strongly associated with an altered response upon treatment with antidopaminergic and opioid-based drugs.3234

We observed that the PGx locus investigated could also separate EUR and EAS clusters (LD2) through the P450 family represented by three genes: CYP1A2, CYP2A6, and CYP2E1. The polymorphism in the CYP2A6 gene is particularly important because it defines the interindividual variability in the tolerability of the S1 antineoplastic therapy between European and Asian populations, which is considered a genetic-dependent scheme.35 Other genes that strongly contributed to differentiating global populations (EUR x EAS) were the SLCO1B1 and SOD2 genes. The FDA warns that higher plasma concentrations of the rosuvastatin have been seen in small groups of patients homozygous for the SLCO1B1 rs4149056 variant.31 The polymorphism in SOD2 is associated with adverse effects observed during the use of asparaginase in patients with acute lymphoid leukemia and cyclophosphamide as antineoplastic.36,37

Here, we will discuss the genotype/phenotype relationship of important PGx genes evaluated in Figures 3 and 4. Our results demonstrated significant differences at the genotype level of five genes among the Brazilian Native American groups (CYP4F2, CYP2D6, CYP2B6, DPYD, and CYP3A5) and the phenotypic profile of two genes (CYP2D6 and CYP4F2).

The CYP4F2 gene has great relevance in the evaluation of metabolism and dose adjustment of warfarin.38 A polymorphism (rs2108622) was investigated in this gene to define haplotype *3. The three Native American populations of the study demonstrated a high frequency of the wild-type homozygous genotype (*1/*1) followed by the heterozygous genotype (*1/*3). The KAY population demonstrated a differentiated metabolization profile since it was the only one to present the mutant homozygous genotype (*3/*3), which is determinant to define the PM profile. Moreover, this group also showed higher frequencies of the heterozygote genotype in comparison with the other populations investigated.

Populations from EUR, EAS, and AMR have frequencies of the CYP4F2*3 haplotype similar to the corresponding global population (24%), as described in the design from 1000 Genomes Project Consortium.8 AFR presented low frequencies of this haplotype (8%), which was similar to that found in our study for Native American populations (11%). Shendre et al showed that the warfarin dose varies according to ancestry background by the influence of the CYP4F2 gene.39 These researchers reported that the CYP4F2*3 variant was associated with higher doses of warfarin in European/American, Asian, and Hispanic populations, while Africans, Americans, and Brazilians, especially self-declared blacks, presented low frequencies of this mutation and therefore showed no need for warfarin dose adjustment.39

The CYP2D6 gene plays an important role in the metabolism of approximately 25% of clinically important drugs, including antidepressants, antipsychotics, antiarrhythmic drugs, antihistamines, -blockers, and antineoplastics.40 Polymorphisms of this gene have been extensively studied in several population groups; however, little is known about this gene in indigenous populations.41

Different studies in world populations describe a similar profile of CYP2D6 gene activity to that found in Amazonian Native Americans, with high frequencies of extensive metabolizer (EM) and low frequencies of ultra-rapid metabolizers (UM) or poor metabolizers (PM).27,40,42 In the Native American populations investigated, the alleles *1 and *2 (including *2A) were the most observed with frequencies of 58% and 32%, respectively. These alleles are associated with the normal metabolic function of the enzyme and therefore are decisive for the definition of EM, which was also the most frequent metabolic profile in the sample investigated (97%).43 These results are similar to other populations from the 1000 Genomes Project Consortium, except for AFR and EAS, which have lower frequencies of this metabolic profile.

The alleles associated with null enzyme activity (*4 and *5) were found in approximately 7% of the Native Americans, presenting in the heterozygous genotype. The PM profile was not found in any of the three Native American groups studied. This metabolic profile is considered rare in continental populations, except in Europeans.43 The frequency of PM described in the admixed population of Brazil is 4%.44 Studies have reported that other Native American populations have reduced frequencies of nonfunctional alleles in the CYP2D6 gene. In Venezuela and Mexico, mean frequencies of 3% of the *4 allele were reported,23,45 while in Costa Rica, the observed mean frequency was 7%.27 There were exceptions in Native Americans: Bribri, and Cabebar from Costa Rica, Bari from Venezuela, and Seris from Mexico presented high frequencies of the referred allele (31, 27, 42, and 21%, respectively).41,45

The intermediate metabolizer (IM) profile is defined by the presence of genotypes with reduced function alleles (*9 and *29). Data estimated by the 1000 Genomes Project demonstrate low frequencies of these alleles in world populations except for AFR, SAS, and EAS.8,43 In Native populations of the Brazilian Amazon, the IM profile was rare (1%), found exclusively in the KOA group. Our results differ from other studies with Native Americans that determined high frequencies of these alleles in Seris (41.2%) and Mayos (22.7%) from Mexico and Bari (35%) from Venezuela.41,45 Perez-Paramo et al have suggested that differentiated profiles of the null/reduced metabolic activity in the CYP2D6 gene in other indigenous populations of South America are the result of food selection and lifestyle processes that these populations have undergone.46 Patients with PM and IM profiles have a higher risk of developing adverse reactions to CYP2D6-substrate treatments.41 Therefore, the lack of PM and the low frequency of IMs in the Amazonian Native Americans of Brazil may represent a lower risk of toxicity development during these therapeutic schemes.41,45

The UM profile is determined by the presence of functional allele duplications, increasing the enzymes mechanism of action on metabolism. In the investigation of Amazonian Native Americans, the UM profile was found exclusively in the KAY population at low frequencies (2%). In the admixed population of Brazil, frequencies similar to the Amazonian Native Americans were reported (5%).16 In contrast, high percentages of UMs were described in Native Mexican populations (20%) and Guatuso from Costa Rica (18.8%).27 According to Lazalde-Ramos, the probable cause for the gain of active genes in these indigenous populations could be natural selection.41 Environmental factors, such as diet, could have exerted a selective advantage over duplicate CYP2D6 genes, increasing the survival rate of these individuals. It is believed that a similar phenomenon occurred in Ethiopia and Saudi Arabia, where the highest frequency of multiple active CYP2D6 genes has been described.41 Individuals with multiple active CYP2D6 copies metabolize drugs more rapidly; therefore, the therapeutic effect in standard doses is not achieved. For instance, reduced concentrations of drugs, such as tramadol, venlafaxine, morphine, and mirtazapine, were reported in patients with UM profiles.41

In conclusion, the Amazon Native Americans of Brazil presented high frequencies of EMs (97%), absence of PM, and low frequencies of IM (1%) and UM (2%). This population, thus, has a metabolic profile with normal CYP2D6 enzyme, mostly resulting in reduced adverse reactions and the obtention of adequate concentrations of drugs, thereby achieving the desired therapeutic effect.

The CYP2B6 gene is involved in the metabolism of several drugs, including antiretrovirals and opioids, such as efavirenz and methadone.47,48 The most frequently deficient allele of this gene is CYP2B6*6 (rs3745274), where homozygous and heterozygous carriers for this nonfunctional allele have demonstrated PM phenotypes for various drugs, such as those mentioned above.

In the Brazilian Native American populations, a relatively high frequency of the *6 alleles was observed in both the heterozygous genotype (*1/*6) and the homozygous genotype (*6/*6). The mean frequency of the *6 allele in the Amazonian indigenous populations was 27%. According to data from the 1000 Genomes Project Consortium, the mean frequency of this allele in continental populations is 32%, which is similar to the value found in the Native Americans of this study.

Due to the frequency of the *6, determinant allele for PM profile, found in the Amazonian Native American populations, it can be inferred that this population presents greater risks of developing toxicities if they are submitted to antiretroviral and opioid treatments. There are no studies investigating the CYP2B6*6 genotype in other Native American populations.

The DPYD gene is a biomarker for predicting severe toxicity in chemotherapeutic treatments, specifically fluoropyrimidine-based therapies. The guidelines of the Clinical Pharmacogenetics Implementation Consortium (CPIC) describe three DPYD haplotypes as the major nonfunctional variants (*2A [rs3918290], *13 [rs55886062], and rs67376798) and strongly recommend the use of alternative drugs or the reduction (in 50%) of the standard dose of fluoropyrimidines for patients who are homozygous or heterozygous for any of these variants.49,50 These polymorphisms were investigated in the Amazonian Native American populations, but their deleterious alleles were not observed.

Another polymorphic variant of DPYD is the *9 allele (rs1801265). This mutation induces an exchange of amino acids in the gene product (dihydropyrimidine dehydrogenase [DPD]), which can affect the enzymatic activity of the protein. The allele *9 was observed in both genotypes *1/*9 and *9/*9 in our Native American populations with an average allelic frequency of 16%. This frequency is not in agreement with that found in the continental populations described in the 1000 genomes database, where the MAF is 26%. Despite the change in amino acids in the DPD protein caused by the *9 allele, there are still divergences in the literature regarding the possible alterations that this allele may cause to the metabolizing phenotypes of the DPYD gene.49,51

Thus, as the Amazonian Native Americans investigated do not have deleterious alleles of the three main polymorphic variants of the DPYD gene and as the *9 allele has not been correlated as a potential interference in therapeutic conducts, they are classified as extensive metabolizer and may, if needed, benefit from fluoropyrimidine-based treatments.

The CYP3A5 gene is highly relevant to immunosuppressive therapies (Tacrolimus, Sirolimus, s and Everolimus), and dose adjustment is recommended for these drugs based on rs776746 SNP genotyping that characterizes the *3 allele.52 Amazonian Native Americans have a high frequency of the *3 allele in the three populations evaluated and, consequently, a large number of individuals with a PM profile. Data from the 1000 Genomes Project confirm that the deleterious allele *3 in the CYP3A5 gene is strongly influenced by population groups. The frequency of this polymorphism in Amazonian Native Americans (63%) resembles SAS and EAS populations with a frequency of 69%; however, it shows divergence with the EUR (94%) and AFR (18%) populations.8

A recent study evaluated the frequency of the rs776746 polymorphism and its association with hypertension in eight indigenous populations from Mexico.53 The analysis report that the CYP3A5*3/*3 genotype frequencies ranged from 23.5% in Mexicaneros to 93.3% in Mayos, and the mean observed in the Mexican indigenous groups was 67.5% (very similar to the frequency found in the Native Americans of our study). Also, Galaviz-Hernandez et al found that the CYP3A5*3/*3 genotype was more frequent in indigenous women with higher systolic and diastolic blood pressures values.

Birdwell et al have shown an increase in the chances of having the *3 allele for individuals with greater European ancestry and a reduction for those with a greater African ancestry influence.54 A study with miscegenated transplant recipients in Brazil identified benefit when adjusting tacrolimus dose according to the genotypes *3, *6, and *7.55 The Brazilian protocol is based on the European protocol, which considers the high frequencies of the *3 allele in its population. The design of the protocol for individuals carrying the *1 allele requires an increase in the dose of tacrolimus since this allele characterizes the extensive metabolism phenotype.54,56 The Native American populations combined showed a frequency of 12% of this phenotype; consequently, these individuals may have low therapeutic efficacy with the use of tacrolimus through a standard protocol.

Although the SLCO1B1 variants did not show significant differences between the Native Americans populations, they have high frequencies of phenotypes that confer decreased or poor function of the SLCO1B1 protein-coding, which is extremely important from the pharmacogenomic point of view. The FDA and EMA have clinical recommendations based on SLCO1B1 genotyping in the use of statin therapies.57 The FDA recommends against 80 mg daily simvastatin dosage.31 In patients with the C allele at SLCO1B1 rs4149056, there are modest increases in myopathy risk, even at lower simvastatin doses (40 mg daily); if optimal efficacy is not achieved with a lower dose, alternate agents should be considered.58

Our results indicate a high frequency of the PM phenotype in samples of Amazonian Native Americans. The PM profile was characterized in our study by the high frequency of the mutant allele in the 521T> C polymorphism (defined as haplotype *5 or *15) of 43% in Amazonian Native Americans, which differs from the frequency found in other world populations from the 1000 Genomes Project (9%).6 The high frequency of this allele in Native American populations may have an important impact on the therapeutic course with the use of different statin-based drugs in these populations due to the risk of myopathies and other adverse effects resulting from therapeutic conduction.

Finally, it is well-known that important PGx loci have great variation among world populations. Therefore, investigations that analyze the pharmacogenomic profile of understudied ancestral population groups, such as Native Americans and, consequently, populations admixed with them, will facilitate the implementation of protocols of precision medicine for these populations.

Most protocols of therapeutic conduct used in Brazilian populations are based on recommendations for populations of European origin. Thus, studies that show population differences for these important loci can assist in the design of targeted protocols for Native American populations and the populations admixed with them, as these groups are commonly underrepresented in pharmacogenomic studies.

The study was approved by the National Committee for Ethics in Research (CONEP) and by the Ethics and Research Committee of the Federal University of Par, with CAAE number 20,654,313.6.0000.5172. The informed consent was obtained from each study participant, as well as the ethnic group leaders, and all research methods in this study were performed in accordance with the Declaration of Helsinki.

The authors thank the Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Universidade de Santiago de Compostela (USC), Ncleo de Pesquisas em Oncologia and Laboratrio de Gentica Humana e Mdica (both from the Universidade Federal do Par) for collaborating in the development of the study; the Coordenao de Aperfeioamento de Pessoal de Nvel Superior (CAPES) for financing the first authors scholarship in Brazil and in the Doctorate Sandwich Program.

The authors acknowledge funding from the Research Support Program - Projetos temticos da Fundao Amaznia de Amparo a Estudos e Pesquisa do Par: Sade, N 006/2014 (FAPESPA/CNPq) and the Pr-Reitoria de Pesquisa e Ps-Graduao (PROPESP) of the Universidade Federal do Par (UFPA). The first authors scholarship in Brazil and the Doctorate Sandwich Program were financed by CAPES (N process: 99999.003676/2015-03). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

The authors declare no conflicts of interest in this work.

1. Santos NPC, Ribeiro-Rodrigues EM, Ribeiro-dos-Santos KC, et al. Assessing individual interethnic admixture and population substructure using a 48-insertion-deletion (INSEL) ancestry-informative marker (AIM) panel. Hum Mutat. 2010;31(2):184190. doi:10.1002/humu.21159

2. Comisso de Integrao Nacional, Desenvolvimento Regional e da Amaznia. Available from: www2.camara.leg.br/atividade-legislativa/comissoes/comissoes-permanentes/cindra/amazonia-legal/mais-informacoes-sobre-a-amazonia-legal. Accessed April 15, 2020.

3. Instituto Brasileiro de Geografia e Estatstica (IBGE). No Title. Available from: https://indigenas.ibge.gov.br/images/pdf/indigenas/folder_indigenas_web.pdf. Accessed September 27, 2020.

4. Carlos EA, Ricardo Ventura Santos ALE. Epidemiologia e Sade Dos Povos Indgenas No Brasil. Editora Fiocruz; 2003.

5. Suarez-Kurtz G. Pharmacogenetics in the Brazilian population. Front Pharmacol. 2010;1:118. doi:10.3389/fphar.2010.00118

6. Suarez-Kurtz G, Paula DP, Struchiner CJ. Pharmacogenomic implications of population admixture: brazil as a model case. Pharmacogenomics. 2014;15(2):209219. doi:10.2217/pgs.13.238

7. Whirl-Carrillo M, McDonagh EM, Hebert JM, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012;92(4):414417. doi:10.1038/clpt.2012.96

8. Auton A, Abecasis GR, Altshuler DM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):6874. doi:10.1038/nature15393

9. The pharmacogenomics knowledgebase. PharmGK. Available from: https://www.pharmgkb.org/. Accessed December 30, 2020.

10. U.S. Department of Health and Human Services FaDA. Available from: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm073162.pdf. Accessed September 27, 2020.

11. de Ramos BRA, MPB D, Amador MAT, et al. Neither self-reported ethnicity nor declared family origin are reliable indicators of genomic ancestry. Genetica. 2016;144(3):259265. doi:10.1007/s10709-016-9894-1

12. Jombart T, Devillard S, Balloux F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 2010;11:11. doi:10.1186/1471-2156-11-94

13. Gonzlez JR, Armengol L, Sol X, et al. SNPassoc: an R package to perform whole genome association studies. Bioinformatics. 2007;23(5):644645. doi:10.1093/bioinformatics/btm025

14. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R library for genome-wide association analysis. Bioinformatics. 2007;23(10):12941296. doi:10.1093/bioinformatics/btm108

15. Hartshorne T, Scientific TF, Le F, et al. A high-throughput real-time pcr approach to pharmacogenomics studies. J Pharmacogenomics amp. 2013;05. doi:10.4172/2153-0645.1000133

16. Gaedigk A, Simon SD, Pearce RE, Bradford LD, Kennedy MJ, Leeder JS. The CYP2D6 activity score: translating genotype information into a qualitative measure of phenotype. Clin Pharmacol Ther. 2008;83(2). doi:10.1038/sj.clpt.6100406

17. Gaedigk A, Sangkuhl K, Whirl-Carrillo M, Klein T, Steven Leeder J. Prediction of CYP2D6 phenotype from genotype across world populations. Genet Med. 2017;19(1):6976. doi:10.1038/gim.2016.80

18. Kuhn PC, Horimoto ARVR, Sanches JM, Vieira Filho JPB, Franco L, Fabbro AD. Genome-wide analysis in Brazilian Xavante Indians reveals low degree of admixture. PLoS One. 2012;7(8):e42702. doi:10.1371/journal.pone.0042702

19. Salzano FM, Sans M. Interethnic admixture and the evolution of Latin American populations. Genetics Molecular Biol. 2014;37(1 suppl 1):151170. doi:10.1590/s1415-47572014000200003

20. Mizzi C, Dalabira E, Kumuthini J, Dzimiri N, Balogh I, Baak NA. European spectrum of pharmacogenomic biomarkers: implications for clinical pharmacogenomics. PLoS One. 2016;11(9):e0162866. doi:10.1371/journal.pone.0162866

21. Jittikoon J, Mahasirimongkol S, Charoenyingwattana A, Chaikledkaew U, Tragulpiankit P, Mangmool S. Comparison of genetic variation in drug ADME-related genes in Thais with Caucasian, African and Asian HapMap populations. J Hum Genet. 2016;61(2):119127. doi:10.1038/jhg.2015.115

22. Rodrigues JCG, Fernandes MR, Guerreiro JF, Ribeiro-dos-Santos C, Santos S. Polymorphisms of ADME-related genes and their implications for drug safety and efficacy in Amazonian Amerindians. Sci Rep. 2019;9(1):7201. doi:10.1038/s41598-019-43610-y

23. Cuautle-Rodrguez P, Llerena A, Molina-Guarneros J. Present status and perspective of pharmacogenetics in Mexico. Drug Metabol Drug Interact. 2014;29(1):3745. doi:10.1515/dmdi-2013-0019

24. Chiurillo MA, Griman P, Santiago L, Torres K, Moran Y, Borjas L. Distribution of GSTM1, GSTT1, GSTP1 and TP53 disease-associated gene variants in native and urban Venezuelan populations. Gene. 2013;531(1):106111. doi:10.1016/j.gene.2013.08.055

25. Rito T, Vieira D, Silva M, Conde-Sousa E, Pereira L, Mellars P. A dispersal of Homo sapiens from southern to eastern Africa immediately preceded the out-of-Africa migration. Sci Rep. 2019;9(1):4728. doi:10.1038/s41598-019-41176-3.

26. Bryc K, Durand EY, Macpherson JM, Reich D, Mountain JL. The genetic ancestry of african americans, latinos, and european Americans across the United States. Am J Hum Genet. 2015;96(1):37. doi:10.1016/j.ajhg.2014.11.010

27. Cspedes-Garro C, Naranjo MEG, Ramrez R, Serrano V, Farias H, Barrantes R. Pharmacogenetics in Central american healthy volunteers: interethnic variability. Drug Metab Pers Ther. 2015;30(1):1931. doi:10.1515/dmdi-2014-0025

28. Shriner D, Tekola-Ayele F, Adeyemo A, Rotimi CN. Ancient human migration after out-of-Africa. Sci Rep. 2016;6:26565. doi:10.1038/srep26565

29. DM RR, DQB V, Crovella S, Brando LAC. On the use of Chinese population as a proxy of Amerindian ancestors in genetic admixture studies with Latin American populations. Eur J Hum Genet. 2016;24:326327. doi:10.1038/ejhg.2015.184

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[Full text] Genetic Diversity of Drug-Related Genes in Native Americans of the Bra | PGPM - Dove Medical Press

Weve all heard the conspiracy theoriesabout COVID-19. Now a whole new set is emerging around COVID vaccines and spreading as virulently as the pandemic they are meant to control.

Though the public health community tends to resort to reassurances about some of the more reasonable concerns yes, the vaccines have been developedincredibly quicklyand short-term side effects can occur this post aims to do something different.

Were going right to the heart of the matter. So no, COVID-19 vaccines arent delivery vehicles for government microchips. They arent tainted by material from aborted fetuses. And they wont turn us into GMOs though some of them do use genetic engineering, and all of them use genetics more broadly.

We think this is way cool something to celebrate, not shy away from. So, were doing the deep reveal on exactly how genetics and biotechnologyhavebeen a central component of the vaccine effort. Because we know the conspiracists dont care about evidence, anyway.

First up: mRNA. It wont reprogram your brain. But it does reprogram some of your cells, in a manner of speaking. And thats not a defect its intentional.

To get your head around this you need to understand what mRNA is for. Basically, its a single-stranded nucleic acid molecule that carries a genetic sequence from the DNA in the cells nucleus into the protein factories called ribosomes that sit outside the nucleus in the cellular cytoplasm.

Thats what the m in mRNA stands for: messenger. Messenger RNA just carries instructions for the assembly of proteins from the DNA template to the ribosomes. (Proteins do almost everything that matters in the body.) Thats it.

This is useful for vaccines because scientists can easily reconstruct specific genetic sequences that encode for proteins that are unique to the invading virus. In the COVID case, this is the familiar spike protein that enables the coronavirus to enter human cells.

What mRNA vaccines do is prompt a few of your cells near the injection site to produce the spike protein. This then primes your immune system to build the antibodies and T-cells that will fight off the real coronavirus infection when it comes.

Its not hugely different from how traditional vaccines work. But instead of injecting a weakened live or killed virus, the mRNA approach trains your immune system directly with a single protein.

Contrary to assertions made by the crazies, it wont turn you or anyone else into a GMO. mRNA stays in the cytoplasm, where the ribosomes are. It does not enter the nucleus and cannot interact with your DNA or cause any changes to the genome. No Frankencure here, either.

A variant of the mRNA approach is to go one step back in the process and construct a vaccine platform out of DNA instead. This DNA template constructed by scientists to encode for the coronavirus spike protein gets into cells where it is read into mRNA and well the rest is the same.

You might ask whether this DNA can genetically engineeryourcells. Once again, the answer is no. DNA is injected in little circular pieces called plasmids not to be confused with plastics and while these do enter the nucleus, the new DNA does not integrate into your cellular genome. Got it?

This one really is genetically engineered. But what does that actually mean?

The Oxford vaccine uses what is called a viral vector approach. The scientific team took an adenovirus a type of pathogen that causes a common cold and spliced in the same spike protein genetic sequence from the coronavirus.

The adenovirus simply serves as the vehicle to get the genetic sequence into your cells. Thats why its called a viral vector after all. Viruses have been designed by billions of years of evolution precisely to figure out ways to sneak into host cells.

Note that genetic engineering is an essential part of the development process. Firstly, vector viruses are stripped of any genes that might harm you and actually cause disease. Genes that cause replication are also removed, so the virus is harmless and cannot replicate.

Then the coronavirus spike protein genes are added a classic use of recombinant DNA. So yes, the Oxford/AstraZeneca vaccine does actually mean a genetically engineered virus is injected into your body.

And thats a good thing. In the past, for example with the polio vaccine, live viruses in the vaccine can sometimes mutate and revert to being pathogenic, causingvaccine-derived polio. You can see its far better to use a GM virus that cannot cause any such harm!

As we have reported before at the Alliance for Science, the anti-GMO and anti-vaccine movementssubstantially overlap. These groups tend to share an ideology that is suspicious of modern science and fetishsize natural approaches instead. Whatever natural means.

Note that these groups are not always marginalized to the fringe where they belong. In Europe, anti-GMO regulations have stymied any substantial use of crop biotechnology for nearly two decades, hindering efforts to to make agriculture more sustainable.

And back in July, the European Parliament actually had tosuspend the EUs anti-GMO rulesin order to allow the unimpeded development of COVID vaccines. Very embarrassing for Brussels!

Will the anti-GMO and anti-vaxxer movements use their usual scaremongering tactics to drum up fear, increase vaccine hesitancy and thereby prolong the hell of the COVID-19 pandemic? That remains to be seen. If they do succeed, then tragically many more people will die and our economies will continue to suffer. Its up to all of us the grassroots pro-science movement to stop them.

Mark Lynas is an environmental/science writer. Follow Mark on Twitter @mark_lynas

A version of this article was originally posted at the Cornell Alliance for Science and has been reposted here with permission. The Cornell Alliance for Science can be found on Twitter @ScienceAlly

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Genetic engineering gave us COVID vaccines. Here's how they work, and why you shouldn't be frightened - Genetic Literacy Project

A study carried out by the University of Malta shows that patients with the fatal neurological condition amyotrophic lateral sclerosis have different causative mutations to patients from Northern Europe.

The DNA results caught us by surprise. The most frequently mutated amyotrophic lateral sclerosis (ALS) genes were flawless in Maltese patients, said the studys lead researcher Ruben Cauchi, Ph.D., a senior lecturer at the University.

Instead, some of the 24 patients included in the study had mutations in genes occasionally associated with ALS including ATXN2, DAO, DCTN1, and ERBB4, among others.

As reported in the European Journal of Human Genetics, three of the 24 cases were familial and 21 were sporadic, with no known family history of the disease. Of the mutations seen in the sporadic cases, 40% were in genes with a previous link to ALS, whereas 60% were not. Only one of the familial cases had a mutation in a known ALS-associated gene.

Although Malta is part of Europe it is geographically and culturally isolated island population of just over 500,000 individuals, which makes it ideal for genetic biobanking studies. The Malta Biobank was set up at the university on the island in 1989 and now contains more than 100,000 samples.

Around 4 years ago, a national ALS registry was set up on the island to collect samples and data about those diagnosed with the condition to help scientists understand the condition better and help contribute to global research studies.

ALS is a rapid neurodegenerative condition with a strong genetic component, which currently has no cure. An effective treatment has proved difficult to develop, with many clinical trials failing over the last 10-15 years. However, research continues with the hope of finding a treatment or cure.

This study, which was carried out in collaboration with the University Medical Centre Utrecht in The Netherlands, sought to discover whether Maltese ALS patients had similar genetics and phenotypic characteristics to patients with the condition from elsewhere.

The researchers found that none of the Maltese patients had mutations in the genes C9orf72, SOD1, TARDBP and FUS, where the most common mutations associated with ALS are located, particularly in patients from a Northern European background. This agrees with other studies of Southern European countries, where rates of these mutations are also lower.

This finding confirms the presence of a NorthSouth gradient in the frequency of mutations within these genes across Europe, write the authors.

As with other populations, almost twice as many men were affected by ALS than women on Malta, although the women who were affected were diagnosed about 5 years earlier than the men at an average age of 59.5 years compared with 64 years. The overall incidence of 2-3 cases per 100,000 people was similar on Malta to elsewhere.

More familial cases of ALS (12.5%) were seen on Malta compared with elsewhere. Normally only 5-10% of cases are familial and 90-95% sporadic.

Our results underscore the unique genetics of the Maltese population, shaped by centuries of relative isolation. We also established that genetic factors play a significant role in causing ALS in Malta, noted Cauchi.

The researchers now plan to search for the disease triggers in the patients in the study who did not have mutations in known ALS-related genes.

Our preliminary data excludes the possibility that these patients have deleterious variants in a set of genes associated with other motor neuron disorders including hereditary ataxias, and hereditary motor and sensory neuropathies, writes the team.

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Maltese ALS Patients Have Different Genetic Mutations than Northern Europeans - Clinical OMICs News

GREENVILLE, S.C., Jan. 21, 2021 /PRNewswire/ --DIABETESpredict, a first of its kind predictive genetic test for type 2 diabetes, is now being administered to help improve patient outcomes during the COVID-19 pandemic. According to a recent study on the bidirectional relationship of type 2 diabetes and COVID-19, patients with type 2 diabetes are predisposed to adverse clinical outcomes from the COVID-19 virus. In addition, many COVID-19 patients are found to develop new onset diabetes after infection (1). DIABETESpredictproactively determines a patient's type 2 diabetes genetic risk. It then provides physicians with individualized lifestyle and diet recommendations for the prevention and management of type 2 diabetes, helping to reduce the probability of severe infection by COVID-19 or post infection disease onset.

A leading molecular diagnostics laboratory, Premier Medical Laboratory Services, recently introduced the DIABETESpredict test to the USas the first ever predictive genetic test for type 2 diabetes. Originally introduced in Europe and in Mexico, this test was developed by the European company, Patiain collaboration with world leading doctors and scientists of Harvard and MIT. The test is a disruptive innovation for the advancement of diabetes prevention and care which has become increasingly more valuable for patient management due to the complex pathophysiology of COVID-19 and diabetes (2).

"No two patients are the same, that's why, after rigorous analysis, we offer customized lifestyle recommendations to make DIABETESpredictas effective as possible for each unique person," states Dr. Mirella Zulueta,medical director atPatia. "Patia has analyzed in detail the results of the largest scientific studies and meta-analyses of the human genome in diabetics. Altogether such studies collected information from more than 110,000 diabetic and non-diabetic people to identify the genetic variants most associated with type 2 diabetes. We are now finding a second use for the DIABETESpredict test in providing vital information for healthcare professionals to help lessen the impact of COVID-19 in relation to diabetes."

The mortality rate in COVID-19 patients with diabetic hyperglycemia is found to be seven times higher than patients with no diabetes and no hyperglycemia (3). Having the ability to know sooner than ever before who is at risk for diabetes paired with access to lifestyle plans specifically made for individual patients based on their individual genetic profiles could hold a profound impact on the world's overall health.

DIABETESpredictand COVID-19 recommendations:

DIABETESpredictis available in the US through Premier Medical Laboratory Services. Family practitioners are encouraged to add this to their bloodwork during routine physicals as well as COVID-19 patient treatment to utilize the life changing knowledge that DIABETESpredictprovides.

Call 1.877.335.2455 or contact [emailprotected] for more information on how you can offer your patients the groundbreaking DIABETESpredict test.

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ABOUT PATIA

Patia's vision is to reduce the number of cases of diabetes and improve the quality of life of diabetic people, creating solutions and supporting a healthy lifestyle.Patiahas developed a platform of solutions to prevent, manage and intervene in type 2 diabetes and gestational diabetes. This uniquely and cost-effectively platform integrates a set of high-performance genotyping tests with predictive algorithms, digital applications and lifestyle intervention. Patia's activity starts by translating the knowledge from large genetic studies on diabetes and gestational diabetes performed at prestigious academic and research institutions.

SOURCE Premier Medical Laboratory Services

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Predictive Genetic Test for Type 2 Diabetes Now Being Implemented in COVID-19 Patient Care Protocols - PRNewswire

Experimental design

The teeth used for DNA extraction were obtained with relevant institutional permissions from the Institute of Ethnology and Anthropology of Russian Academy of Sciences (Russia), Cherepovets Museum Association (Russia), and Archaeological Research Collection of Tallinn University (Estonia). DNA was extracted from the teeth of 48 individuals: 3 from Stone Age HGs from western Russia (WeRuHG; 10,800 to 4250 cal BCE), 44 from Bronze Age Fatyanovo Culture individuals from western Russia (Fatyanovo; 2900 to 2050 cal BCE), and 1 from a Corded Ware Culture individual from Estonia (EstCWC; 2850 to 2500 cal BCE) (Fig. 1, data S1, table S1, and text S1). Petrous bones of 13 of the Fatyanovo Culture individuals have been sampled for another project. More detailed information about the archaeological periods and the specific sites and burials of this study is given below.

All of the laboratory work was performed in dedicated aDNA laboratories of the Institute of Genomics, University of Tartu. The library quantification and sequencing were performed at the Institute of Genomics Core Facility, University of Tartu. The main steps of the laboratory work are detailed below.

DNA extraction. The teeth of 48 individuals were used to extract DNA. One individual was sampled twice from different teeth. Apical tooth roots were cut off with a drill and used for extraction because root cementum has been shown to contain more endogenous DNA than crown dentine (62). The root pieces were used whole to avoid heat damage during powdering with a drill and to reduce the risk of cross-contamination between samples. Contaminants were removed from the surface of tooth roots by soaking in 6% bleach for 5 min, then rinsing three times with Milli-Q water (Millipore), and lastly soaking in 70% ethanol for 2 min, shaking the tubes during each round to dislodge particles. Last, the samples were left to dry under an ultraviolet light for 2 hours.

Next, the samples were weighed, [20 * sample mass (mg)] l of EDTA and [sample mass (mg) / 2] l of proteinase K were added, and the samples were left to digest for 72 hours on a rotating mixer at 20C to compensate for the smaller surface area of the whole root compared to powder. Undigested material was stored for a second DNA extraction if need be.

The DNA solution was concentrated to 250 l (Vivaspin Turbo 15, 30,000 MWCO PES, Sartorius) and purified in large-volume columns (High Pure Viral Nucleic Acid Large Volume Kit, Roche) using 2.5 ml of PB buffer, 1 ml of PE buffer, and 100 l of EB buffer (MinElute PCR Purification Kit, QIAGEN).

Library preparation. Sequencing libraries were built using NEBNext DNA Library Prep Master Mix Set for 454 (E6070, New England Biolabs) and Illumina-specific adaptors (63) following established protocols (6365). The end repair module was implemented using 30 l of DNA extract, 12.5 l of water, 5 l of buffer, and 2.5 l of enzyme mix, incubating at 20C for 30 min. The samples were purified using 500 l of PB and 650 l of PE buffer and eluted in 30 l of EB buffer (MinElute PCR Purification Kit, QIAGEN). The adaptor ligation module was implemented using 10 l of buffer, 5 l of T4 ligase, and 5 l of adaptor mix (63), incubating at 20C for 15 min. The samples were purified as in the previous step and eluted in 30 l of EB buffer (MinElute PCR Purification Kit, QIAGEN). The adaptor fill-in module was implemented using 13 l of water, 5 l of buffer, and 2 l of Bst DNA polymerase, incubating at 37C for 30 min and at 80C for 20 min. The libraries were amplified, and both the indexed and universal primers (NEBNext Multiplex Oligos for Illumina, New England Biolabs) were added by polymerase chain reaction (PCR) using HGS Diamond Taq DNA polymerase (Eurogentec). The samples were purified and eluted in 35 l of EB buffer (MinElute PCR Purification Kit, QIAGEN). Three verification steps were implemented to make sure library preparation was successful and to measure the concentration of double-stranded DNA/sequencing librariesfluorometric quantitation (Qubit, Thermo Fisher Scientific), parallel capillary electrophoresis (Fragment Analyzer, Agilent Technologies), and quantitative PCR. One sample (TIM004) had a DNA concentration lower than our threshold for sequencing and was hence excluded, leaving 48 samples from 47 individuals to be sequenced.

DNA sequencing. DNA was sequenced using the Illumina NextSeq 500 platform with the 75base pair (bp) single-end method. First, 15 samples were sequenced together on one flow cell. Later, additional data were generated for some samples to increase coverage.

Mapping. Before mapping, the sequences of adaptors and indexes and poly-G tails occurring due to the specifics of the NextSeq 500 technology were cut from the ends of DNA sequences using cutadapt 1.11 (66). Sequences shorter than 30 bp were also removed with the same program to avoid random mapping of sequences from other species. The sequences were mapped to reference sequence GRCh37 (hs37d5) using Burrows-Wheeler Aligner (BWA 0.7.12) (67) and command mem with reseeding disabled.

After mapping, the sequences were converted to BAM format, and only sequences that mapped to the human genome were kept with samtools 1.3 (68). Next, data from different flow cell lanes were merged and duplicates were removed with picard 2.12 (http://broadinstitute.github.io/picard/index.html). Indels were realigned with GATK 3.5 (69), and lastly, reads with mapping quality under 10 were filtered out with samtools 1.3 (68).

The average endogenous DNA content (proportion of reads mapping to the human genome) for the 48 samples is 29% (table S1). The endogenous DNA content is variable as is common in aDNA studies, ranging from under 1 to around 78% (table S1).

aDNA authentication. As a result of degrading over time, aDNA can be distinguished from modern DNA by certain characteristics: short fragments and a high frequency of CT substitutions at the 5 ends of sequences due to cytosine deamination. The program mapDamage2.0 (70) was used to estimate the frequency of 5 CT transitions.

mtDNA contamination was estimated using the method from (71).This included calling an mtDNA consensus sequence based on reads with mapping quality of at least 30 and positions with at least 5 coverage, aligning the consensus with 311 other human mtDNA sequences from (71), mapping the original mtDNA reads to the consensus sequence, and running contamMix 1.0-10 with the reads mapping to the consensus and the 312 aligned mtDNA sequences while trimming seven bases from the ends of reads with the option trimBases. For the male individuals, contamination was also estimated on the basis of chrX using the two contamination estimation methods first described in (72) and incorporated in the ANGSD software (73) in the script contamination.R.

The samples show 10% CT substitutions at the 5 ends on average, ranging from 6 to 17% (table S1). The mtDNA contamination point estimate for samples with >5 mtDNA coverage ranges from 0.03 to 2.02% with an average of 0.4% (table S1). The average of the two chrX contamination methods of male individuals with average chrX coverage of >0.1 is between 0.4 and 0.87% with an average of 0.7% (table S1).

Kinship analysis. A total of 4,375,438 biallelic single-nucleotide variant sites, with minor allele frequency (MAF) > 0.1 in a set of more than 2000 high-coverage genomes of Estonian Genome Center (EGC) (74), were identified and called with ANGSD (73) command --doHaploCall from the 25 BAM files of 24 Fatyanovo individuals with coverage of >0.03. The ANGSD output files were converted to .tped format as an input for the analyses with READ script to infer pairs with first- and second-degree relatedness (41).

The results are reported for the 100 most similar pairs of individuals of the 300 tested, and the analysis confirmed that the two samples from one individual (NIK008A and NIK008B) were indeed genetically identical (fig. S6). The data from the two samples from one individual were merged (NIK008AB) with samtools 1.3 option merge (68).

Calculating general statistics and determining genetic sex. Samtools 1.3 (68) option stats was used to determine the number of final reads, average read length, average coverage, etc. Genetic sex was calculated using the script sexing.py from (75), estimating the fraction of reads mapping to chrY out of all reads mapping to either X or Y chromosome.

The average coverage of the whole genome for the samples is between 0.00004 and 5.03 (table S1). Of these, 2 samples have an average coverage of >0.01, 18 samples have >0.1, 9 samples have >1, 1 sample have around 5, and the rest are lower than 0.01 (table S1). Genetic sexing confirms morphological sex estimates or provides additional information about the sex of the individuals involved in the study. Genetic sex was estimated for samples with an average genomic coverage of >0.005. The study involves 16 females and 20 males (Table 1 and table S1).

Determining mtDNA hgs. The program bcftools (76) was used to produce VCF files for mitochondrial positions; genotype likelihoods were calculated using the option mpileup, and genotype calls were made using the option call. mtDNA hgs were determined by submitting the mtDNA VCF files to HaploGrep2 (77, 78). Subsequently, the results were checked by looking at all the identified polymorphisms and confirming the hg assignments in PhyloTree (78). Hgs for 41 of the 47 individuals were successfully determined (Table 1, fig. S1, and table S1).

No female samples have reads on the chrY consistent with a hg, indicating that levels of male contamination are negligible. Hgs for 17 (with coverage of >0.005) of the 20 males were successfully determined (Table 1 and tables S1 and S2).

chrY variant calling and hg determination. In total, 113,217 haplogroup informative chrY variants from regions that uniquely map to chrY (36, 7982) were called as haploid from the BAM files of the samples using the --doHaploCall function in ANGSD (73). Derived and ancestral allele and hg annotations for each of the called variants were added using BEDTools 2.19.0 intersect option (83). Hg assignments of each individual sample were made manually by determining the hg with the highest proportion of informative positions called in the derived state in the given sample. chrY haplogrouping was blindly performed on all samples regardless of their sex assignment.

Preparing the datasets for autosomal analyses. The HO array dataset (https://reich.hms.harvard.edu/downloadable-genotypes-present-day-and-ancient-dna-data-compiled-published-papers) was used as the modern DNA background. Individuals from the 1240K dataset (https://reich.hms.harvard.edu/downloadable-genotypes-present-day-and-ancient-dna-data-compiled-published-papers) were used as the aDNA background.

The data of the comparison datasets and of the individuals of this study were converted to BED format using PLINK 1.90 (http://pngu.mgh.harvard.edu/purcell/plink/) (84), and the datasets were merged. Two datasets were prepared for analyses: one with HO and 1240K individuals and the individuals of this study, where 584,901 autosomal SNPs of the HO dataset were kept; the other with 1240K individuals and the individuals of this study, where 1,136,395 autosomal and 48,284 chrX SNPs of the 1240K dataset were kept.

Individuals with <10,000 SNPs overlapping with the HO autosomal dataset were removed from further autosomal analyses, leaving 30 individuals of this study to be used in autosomal analyses. These included 3 from WeRuHG, 26 from Fatyanovo, and 1 from EstCWC (table S1).

Principal components analysis. To prepare for PCA, a reduced comparison sample set composed of 813 modern individuals from 53 populations of Europe, Caucasus, and Near East and 737 ancient individuals from 107 populations was assembled (tables S3 and S4). The data were converted to EIGENSTRAT format using the program convertf from the EIGENSOFT 7.2.0 package (85). PCA was performed with the program smartpca from the same package, projecting ancient individuals onto the components constructed based on the modern genotypes using the option lsqproject and trying to account for the shrinkage problem introduced by projecting by using the option autoshrink.

Admixture analysis. For Admixture analysis (86), the same ancient sample set was used as for PCA, and the modern sample set was increased to 1861 individuals from 144 populations from all over the world (tables S3 and S4). The analysis was carried out using ADMIXTURE 1.3 (86) with the P option, projecting ancient individuals into the genetic structure calculated on the modern dataset due to missing data in the ancient samples. The HO dataset of modern individuals was pruned to decrease linkage disequilibrium using the option indep-pairwise with parameters 1000 250 0.4 in PLINK 1.90 (http://pngu.mgh.harvard.edu/purcell/plink/) (84). This resulted in a set of 269,966 SNPs. Admixture was run on this set using K = 3 to K = 18 in 100 replicates. This enabled us to assess convergence of the different models. K = 10 and K = 9 were the models with the largest number of inferred genetic clusters for which >10% of the runs that reached the highest log likelihood values yielded very similar results. This was used as a proxy to assume that the global likelihood maximum for this particular model was indeed reached. Then, the inferred genetic cluster proportions and allele frequencies of the best run at K = 9 were used to run Admixture to project the aDNA individuals, for which the intersection with the LD pruned modern dataset yielded data for more than 10,000 SNPs, on the inferred clusters. The same projecting approach was taken for all models for which there is good indication that the global likelihood maximum was reached (K3 to 18). We present all ancient individuals in fig. S2 but only population averages in Fig. 2B. The resulting membership proportions to K genetic clusters are sometimes called ancestry components, which can lead to overinterpretation of the results. The clustering itself is, however, an objective description of genetic structure and hence a valuable tool in population comparisons.

Outgroup f3 statistics. For calculating autosomal outgroup f3 statistics, the same ancient sample set as for previous analyses was used, and the modern sample set included 1177 individuals from 80 populations from Europe, Caucasus, Near East, Siberia and Central Asia, and Yoruba as outgroup (tables S3 and S4). The data were converted to EIGENSTRAT format using the program convertf from the EIGENSOFT 5.0.2 package (85). Outgroup f3 statistics of the form f3(Yoruba; West_Siberia_N/EHG/CentralRussiaHG/Fatyanovo/ Yamnaya_Samara/Poland_CWC/Baltic_CWC/Central_CWC, modern/ancient) were computed using the ADMIXTOOLS 6.0 program qp3Pop (87).

To allow chrX versus autosome comparison for ancient populations, outgroup f3 statistics using chrX SNPs were computed. To allow the use of the bigger number of positions in the 1240K over the HO dataset, Mbuti from the Simons Genome Diversity Project (88) was used as the outgroup. The outgroup f3 analyses of the form f3(Mbuti; West_Siberia_N/EHG/CentralRussiaHG/Fatyanovo/ Yamnaya_Samara/Poland_CWC/Baltic_CWC/Central_CWC, ancient) were run both using not only 1,136,395 autosomal SNPs but also 48,284 chrX positions available in the 1240K dataset. Because all children inherit half of their autosomal material from their father but only female children inherit their chrX from their father, then in this comparison chrX data give more information about the female and autosomal data about the male ancestors of a population.

The autosomal outgroup f3 results of the two different SNP sets were compared to each other and to the results based on the chrX positions of the 1240K dataset to see whether the SNPs used affect the trends seen. Outgroup f3 analyses were also run with the form f3(Mbuti; PES001/I0061/Sidelkino, Paleolithic/Mesolithic HG) and admixture f3 analyses with the form f3(Fatyanovo; Yamnaya, EF) using the autosomal positions of the 1240K dataset.

D statistics. D statistics of the form D(Yoruba, West_Siberia_N/EHG/CentralRussiaHG/Fatyanovo/ Yamnaya_Samara/Poland_CWC/Baltic_CWC/Central_CWC; Russian, modern/ancient) were calculated on the same dataset as outgroup f3 statistics (tables S3 and S4) using the autosomal positions of the HO dataset. The ADMIXTOOLS 6.0 package program qpDstat was used (87).

In addition, D statistics of the form D(Mbuti, ancient; Yamnaya_Samara, Fatyanovo/Baltic_CWC/ Central_CWC) and D(Mbuti, ancient; Poland_CWC/Baltic_CWC/ Central_CWC, Fatyanovo) were calculated using the autosomal positions of the 1240K dataset. However, comparing very similar populations directly using D statistics seems to be affected by batch biasesCentral_CWC comes out as significantly closer to almost all populations than Fatyanovo, while this is not the case when comparing less similar Fatyanovo and Yamnaya_Samara. Because of this, the results of D(Mbuti, ancient; Poland_CWC/Baltic_CWC/Central_CWC, Fatyanovo) are not discussed in the main text, but the data are included in table S19.

FST. Weir and Cockerham pairwise average FST (89) was calculated for the dataset used for outgroup f3 and D statistics using the autosomal positions of the HO dataset using a custom script.

qpAdm. The ADMIXTOOLS 6.0 (87) package programs qpWave and qpAdm were used to estimate which populations and in which proportions are suitable proxies of admixture to form the populations or individuals of this study. The autosomal positions of the 1240K dataset were used. Only samples with more than 100,000 SNPs were used in the analyses. Mota, Ust-Ishim, Kostenki14, GoyetQ116, Vestonice16, MA1, AfontovaGora3, ElMiron, Villabruna, WHG, EHG, CHG, Iran_N, Natufian, Levant_N, and Anatolia_N (and Volosovo in some cases indicated in table S15) were used as right populations. Yamnaya_Samara or Yamnaya_Kalmykia was used as the left population representing Steppe ancestry. Levant_N, Anatolia_N, LBK_EN, Central_MN, Globular_Amphora, Trypillia, Ukraine_Eneolithic, or Ukraine_Neolithic was used as the left population representing EF ancestry. In some cases, WHG, EHG, WesternRussiaHG, or Volosovo was used as the left population representing HG ancestry. Alternatively, one-way models between Fatyanovo, Baltic_CWC, and Central_CWC were tested. Also, PES001 was modeled as a mixture of WHG and AfontovaGora3, MA1, or CHG.

To look at sex bias, four models that were not rejected using autosomal data were also tested using the 48,284 chrX positions of the 1240K dataset. The same samples were used as in the autosomal modeling.

ChromoPainter/NNLS. To infer the admixture proportions of ancient individuals, the ChromoPainter/NNLS pipeline was applied (28). Because of the low coverage of the ancient data, it is not possible to infer haplotypes, and the analysis was performed in unlinked mode (option -u). The autosomal positions of the HO dataset were used. Only samples with more than 20,000 SNPs were used in the analyses. Because ChromoPainter (90) does not tolerate missing data, every ancient target individual was iteratively painted together with one representative individual from potential source populations as recipients. All the remaining modern individuals from the sample set used for Admixture analysis were used as donors (tables S3 and S4). Subsequently, we reconstructed the profile of each target individual as a combination of two or more ancient individuals, using the non-negative least square approach. Let Xg and Yp be vectors summarizing the proportion of DNA that source and target individuals copy from each of the modern donor groups as inferred by ChromoPainter. Yp = 1X1 + 2X2 + + zXz was reconstructed using a slight modification of the nnls function in R (91) and implemented in GlobeTrotter (92) under the conditions g 0 and g = 1. To evaluate the fitness of the NNLS estimation, we inferred the sum of the squared residual for every tested model (93). Models identified as plausible with qpAdm with Yamnaya_Samara and Globular_Amphora/Trypillia as sources were used. The resulting painting profiles, which summarize the fraction of the individuals DNA inherited by each donor individual, were summed over individuals from the same population.

DATES. The time of admixture between Yamnaya and EF populations forming the Fatyanovo Culture population was estimated using the program DATES (37). The autosomal positions of the 1240K dataset were used.

Phenotyping. To predict eye, hair, and skin color in the ancient individuals (tables S20 to S22), the HIrisPlex-S variants from 19 genes in nine autosomes were selected (9496), and the region to be analyzed was selected adding 2 Mb around each SNP, collapsing in the same region the variants separated by less than 5 Mb. A total of 10 regions (2 for chromosome 15 and 1 for each of the remaining autosomes) were obtained, ranging from about 6 to about 1.5 Mb. Similarly, to analyze the other phenotype-informative markers (diet, immunity, and diseases), 2 Mb around each variant was selected, and the overlapping regions were merged, for a total of 47 regions (45 regions in 17 autosomes and 2 regions on chrX). For the local imputation, we used a two-step pipeline (97) as follows: (i) variant calling, (ii) first imputation step using a reference panel as much similar as possible to the target samples, (iii) variant filtering, (iv) second imputation step using a larger worldwide reference panel, and (v) final variant filtering. This pipeline has been validated by randomly downsampling a high-coverage Neolithic sample (NE1) (98) to 0.05 and comparing the imputed variants in the low-coverage version with the called variants from the original genome. For a local imputation approach on 2 Mb, we obtained a concordance rate higher than 90% for all the variants, a figure that increased to 99% for frequent variants (MAF 0.3). The variants were called using ATLAS v0.9.0 (99) (task = call and method = MLE commands) (step 1) at biallelic SNPs with a MAF 0.1% in a reference panel composed of more than 2000 high-coverage Estonian genomes (EGC) (74). The variants were called separately for each sample and merged in one VCF file per chromosomal region. The merged VCFs were used as input for the first step of our two-step imputation pipeline [genotype likelihood update; -gl command on Beagle 4.1 (100)], using the EGC panel as reference (step 2). Then, the variants with a genotype probability (GP) less than 0.99 were discarded (step 3), and the missing genotype was imputed with the -gt command of Beagle 5.0 (101) using the large HRC as reference panel (102), with the exception of variants rs333 and rs2430561 [not present in the HRC (Haplotype Reference Consortium)], imputed using the 1000 Genomes as reference panel (step 4) (103). Last, a second GP filter was applied to keep variants with GP 0.85 (step 5). Then, the 113 phenotype-informative SNPs were extracted, recoded, and organized in tables, using VCFtools (104), PLINK 1.9 (http://pngu.mgh.harvard.edu/purcell/plink/) (84), and R (91) (tables S21 and S22). The HIrisPlex-S variants were uploaded on the HIrisPlex webtool (https://hirisplex.erasmusmc.nl/) to perform the pigmentation prediction, after tabulating them according to the manual of the tool. Out of 41 variants of the HIrisPlex-s system, two markers were not analyzed, namely, the rs312262906 indel and the rare (MAF = 0 in the HRC) rs201326893 SNP, because of the difficulties in the imputation of such variants.

The 28 samples analyzed here were compared with 34 ancient samples from surrounding geographical regions from literature, gathering them in seven groups according to their region and/or culture: (i) 3 Western Russian Stone Age HGs (present study); (ii) 5 Latvian Mesolithic HGs (34); (iii) 7 Estonian and Latvian Corded Ware Culture farmers [present study and (27, 34)]; (iv) 24 Fatyanovo Culture individuals (present study); (v) 10 Estonian Bronze Age individuals (28); (vi) 9 Estonian and Ingrian Iron Age individuals (28); (vii) 4 Estonian Middle Age individuals (28). For each variant, an analysis of variance (ANOVA) test was performed between the seven groups, applying Bonferronis correction by the number of tested variants to set the significance threshold (table S20). For the significant variant, a Tukey test was performed to identify the significant pairs of groups.

See the rest here:
Genetic ancestry changes in Stone to Bronze Age transition in the East European plain - Science Advances

Two issues negatively impacting COVID testing are false readings and the turnaround time it takes for results. False positive results can cause undue concern, whereas false negative readings have the potential to unwittingly add to the continued spread of COVID.

The waiting game is especially difficult; its the kind of time that most people dont really have to spare. They must then play the quarantine game which, in addition to the restrictions, often means a loss of income as they wait for their results.

To overcome these hurdles, Huntsville Hospital and Kailos Genetics have teamed up to offer a COVID-19 test option through its Assure Sentinel and Peace of Mind programs. The programs are designed for non-symptomatic individuals who want to know if they are carrying the COVID-19 virus.

Its the second program weve put into place with Huntsville Hospital, said Troy Moore, chief science officer at Kailos, which is headquartered at the HudsonAlpha Institute for Biotechnology. The first program was focused on a return to work or return to school testing, on a routine basis. Then, we learned there were quite a few people that had a son or daughter going back to school or to college, or theyve been around family members during the holidays, or they have a parent they are taking care of.

This is a place where they could go if they have concerns, but not necessarily a known exposure event.

In this partnership, the hospital staff will administer the test, collect samples, and deliver the results. Kailos will process the tests utilizing its Assure Sentinel program which can detect SARS-CoV-2, the virus that causes COVID-19.

Assure Sentinel testing is painless and affordable and can detect viral infections in individuals before they become symptomatic. By reducing the potential for exposure, Sentinel testing helps to minimize the impact in the workplace, as well as in the community.

The best news is the process is a saline swish and gargle the companys ViraWash to provide a viable sample. No long swab going up your nose and it can be easily done in the workplace.

For more information, contact the Huntsville Hospital Clinical Lab at: 256-265-2LAB (2522).

Continued here:
For COVID Peace of Mind - and No Swab Up Your Nose - Huntsville Hospital and Kailos Genetics Roll Out Assure Sentinel - Huntsville Business Journal

ROME (AP) The French doctor who discovered the genetic basis of Down syndrome but spent his career advocating against abortion as a result of prenatal diagnosis has taken his first major step to possible sainthood.

Pope Francis on Thursday approved the heroic virtues of Dr. Jerome Lejeune, who lived from 1926-1994 and was particularly esteemed by St. John Paul II for his anti-abortion stance.

The papal recognition of Lejeunes virtues means that he is considered venerable by the Catholic Church. The Vatican must now confirm a miracle attributed to his intercession for him to be beatified, and a second one for him to be declared a saint.

According to his official biography, Lejeune in 1958 discovered the existence of an extra chromosome on the 21st pair during a study of the chromosomes of a child. It was the first time scientists had found a link between an intellectual disability and a chromosomal anomaly; the condition is now known as trisomy 21.

Although the results of his research should have helped medicine to advance toward a cure, they are often used to identify children carrying these diseases as early as possible, usually with the aim of terminating pregnancy, the Jerome Lejeune Foundation wrote in its biography.

As soon as the pro-abortion laws were drafted in western countries, Lejeune began advocating for the protection of the unborn with Down syndrome: he gave hundreds of conferences and interviews across the globe in defense of life, the group said.

John Paul in 1974 made Lejeune a member of the Vaticans Pontifical Academy of Sciences think tank and later named him the first chairman of the Pontifical Academy for Life, the Holy Sees main bioethics advisory commission.

John Paul visited Lejeunes grave during the Paris World Youth Day in 1997.

Though John Paul made the churchs firm opposition to abortion a hallmark of his quarter-century papacy, Francis too has strongly denounced what he calls todays throwaway culture that considers the weak, disabled or sick disposable. He has likened abortion to hiring a hit man to take care of a problem.

Original post:
French doctor who discovered genetic basis of Down syndrome closer to sainthood - WTNH.com

Kimberly and Kelly Standard, who are twins, assumed that when they became sick with Covid-19 their experiences would be as identical as their DNA.

The virus had different plans.

Early last spring, the sisters from Rochester, Mich., checked themselves into the hospital with fevers and shortness of breath. While Kelly was discharged after less than a week, her sister ended up in intensive care, and spent almost a month in critical condition.

Nearly a year later, the sisters are bedeviled by the divergent paths their illnesses took.

I want to know, Kelly said, why did she have Covid worse than me?

Identical twins offer a ready-made experiment to untangle the contributions of nature and nurture in driving disease. With the help of twin registries in the United States, Australia, Europe and elsewhere, researchers are confirming that genetics can influence which symptoms Covid-19 patients experience.

These studies have also underscored the importance of the environment and pure chance: Even between identical twins, immune systems can look vastly different.

But at least some of the factors that influence the severity of a Covid-19 case are written into the genome. Recent studies suggest that people with type O blood, for example, may be at a slightly lower risk of becoming seriously sick (though experts have cautioned against overinterpreting these types of findings). Other papers have homed in on genes that affect how cells sound the alarm about viruses.

There even seems to be a measurable genetic influence on whether patients experience symptoms like fever, fatigue and delirium, said Tim Spector, an epidemiologist and the director of the TwinsUK registry based at St. Thomas Hospital in London.

Last year, he and his colleagues developed a symptom-tracking app. In a study that has not yet been published in a scientific journal, they reported that genetic factors might account for up to 50 percent of the differences between Covid-19 symptoms.

Still, Dr. Spector said, It would be wrong to think we can answer this if we just crack the genes.

Original post:
Twins with Covid-19 help scientists untangle the diseases genetic roots. - The New York Times

Cameroonian scientist Appolinaire Djikeng, Director of the Centre for Tropical Livestock Genetics ... [+] and Health (CTLGH), holding a chicken.

Cameroonian geneticist Appolinaire Djikeng grew up in a family that depended on livestock for their livelihood, now he is using Africa's livestock genetic diversity to help the 1.3 billion people who depend on livestock for food and income.

Djikeng, the Director of the Centre for Tropical Livestock Genetics and Health (CTLGH), a research alliance with scientific bases in Scotland, Kenya and Ethiopia, says livestock play a critical role in income generation, job creation, gender equity, food production and many other means to improve people's lives.

Djikeng says the focus of CTLGH's efforts is to improve the livestock production systems of smallholder farmers living in low- and middle-income countries, who generally farm less than two hectares and are among the poorest and most marginalized communities.

Djikeng says through the genetics work, smallholder farmers will be able to own animals that are better adapted, more resilient, healthier and able to reach their production potential.

"These animals are less also likely to incur extra inputs, due to poor health or inefficient feed conversion, for the farmers who own them and help mitigate climate change," he said, "Enhancing nutrition through the access to milk, meat and eggs is vital to improve human health and even more critical for infants in their first 1,000 days of life."

Djikeng says his long-standing research interests are centered on genomics, the study of genes and their functions, and specifically on livestock genomics.

"For the last 10 years I have been particularly interested in how genomics can be used to assess and exploit genetic diversity in order to address key agricultural productivity and sustainability challenges, with a focus on farmed animals and crops," he said.

"Our work is not about developing new breeds, " he said, adding that the goal to ensure that existing breeds are more resilient, productive and environmentally sustainable and are able to perform optimally in tropical production systems."

Djikeng believes a reasonable business model for tropical livestock development would be one where a systems approach is considered.

"Under such a model, an important consideration of livestock development must be given on its impact on the environment, deforestation, etcetera," he said, "This is an area where government regulation and incentives should be explored."

Cattle in the village of Idool, near Ngaoundere, Cameroon, Central Africa, Africa

Djikeng says his current work is heavily influenced by growing up in a household economically dependent on small-scale agriculture.

"I was born and grew up in small village in western (French-speaking) Cameroon," he said, adding that his parents farmed on a small piece of land and owned chickens and pigs as assets for income generation.

"You can imagine how very vulnerable my familys situation was, relying on three or four pigs and about 10 chickens to support a range of needs including education and healthcare for myself and my siblings," he said, "Growing up, I wanted to find a job that would give me a stable income so that I did not end up a subsistence farmer like my parents before me."

With the support of his family, he was able to complete his schooling and go to university.

"I wanted to choose a profession that would allow me to support my family and my own parents in the future, so studying medicine was my first option but I realized that advanced biological sciences would be a better path for me," he said, "It was when studying for my PhD that I realized the important link between agricultural development and human health which has led me to work in this area for close to 20 years now."

Another scientist from Cameroon who is making a big impact is Aristide Takoukam.

When Takoukam was in university, he'd never heard of the African Manatee (Trichechus senegalensis) and he didn't know how to swim, but he would go on to become the first person from Cameroon to earn a PhD studying this endangered mammal.

Takoukam would go on to complete his doctorate and become a National Geographic Explorer and founder of the African Marine Mammal Conservation Organization (AMMCO).

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This Scientist Is Harnessing The Rich Genetic Diversity Of African Cattle - Forbes

TEMPLE CITY, Calif., Jan. 06, 2021 (GLOBE NEWSWIRE) -- Fulgent Genetics, Inc. (NASDAQ: FLGT) (Fulgent Genetics or the company), a technology company providing comprehensive testing solutions through its scalable technology platform, today announced that its Chief Financial Officer Paul Kim, and Chief Commercial Officer Brandon Perthuis are scheduled to virtually participate in a presentation at the H.C. Wainwright BIOCONNECT 2021 Conference taking place January 11 to 14, 2021.

A webcast of thepresentation will be available beginning at 6:00 a.m. ET on January 11, 2021 on the Investor Relations section of the Fulgent Genetics website at ir.fulgentgenetics.com, and will be available for 90 days following the conference.

About Fulgent Genetics

Fulgent Genetics proprietary technology platform has created a broad, flexible test menu and the ability to continually expand and improve its proprietary genetic reference library while maintaining accessible pricing, high accuracy and competitive turnaround times. Combining next generation sequencing (NGS) with its technology platform, the Company performs full-gene sequencing with deletion/duplication analysis in an array of panels that can be tailored to meet specific customer needs. In 2019, the Company launched its first patient-initiated product, Picture Genetics, a new line of at-home screening tests that combines the Companys advanced NGS solutions with actionable results and genetic counseling options for individuals. Since March 2020, the Company has commercially launched several tests for the detection of SARS-CoV-2, the virus that causes the novel coronavirus (COVID-19), including NGS and reverse transcription polymerase chain reaction (RT-PCR) - based tests. The Company has received Emergency Use Authorization (EUA) from the U.S. Food and Drug Administration (FDA) for the RT-PCR-based tests for the detection of SARS-CoV-2 using upper respiratory specimens (nasal, nasopharyngeal, and oropharyngeal swabs) and for the at-home testing service through Picture Genetics. A cornerstone of the Companys business is its ability to provide expansive options and flexibility for all clients unique testing needs through a comprehensive technology offering including cloud computing, pipeline services, record management, web portal services, clinical workflow, sequencing as a service and automated lab services.

Investor Relations Contacts:The Blueshirt GroupNicole Borsje, 415-217-2633; nicole@blueshirtgroup.com

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Fulgent Genetics to Participate in the H.C. Wainwright BIOCONNECT 2021 Conference - GlobeNewswire

Cases of eggs from Cal-Maine Foods, Inc., await to be handed out by the Mississippi Department of Agriculture and Commerce employees to several hundred families along with over 1,400 boxes of meat products from Merchants Foodservice and 2,200 gallons of milk from Borden Dairy, at the Mississippi State Fairgrounds in Jackson, Miss., Aug. 7, 2020.

Rogelio V. Solis | AP

Check out the companies making headlines after the bell on Tuesday:

Cal-Maine Foods Cal-Maine Foods reported a surprise profit for its fiscal second quarter, sending the stock up more than 2% after-hours trading. The company posted earnings per share of 25 cents on revenue of $347.3 million. Analysts polled by FactSet expected a loss of 8 cents pre share on revenue of $333.5 million. The company's egg-dozen sales grew by 4.6% on a year-over-year basis.

Myriad Genetics Shares of the biotechnology company rose nearly 2% on news Myriad will explore "strategic alternatives" for its autoimmune business. The company is also restructuring its international operations.

Smart Global Holdings Smart Global posted fiscal first-quarter earnings per share that were better than expected, lifting the computer-memory manufacturer's stock up by 2.4%. Smart Global reported adjusted earnings per share of 78 cents, topping a FactSet estimate of 70 cents per share. The company also issued better-than-expected revenue guidance for the current quarter.

Nektar Therapeutics Nektar shares slipped about 1% after the company announced Dr. Brian Kotzin will take over as interim chief medical officer, effective immediately, replacing current CMO Wei Lin.

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Stocks making the biggest moves after the bell: Cal-Maine Foods, Myriad Genetics & more - CNBC

DUBLIN, Jan. 5, 2021 /PRNewswire/ -- The "Animal Genetics - Global Market Outlook (2019-2027)" report has been added to ResearchAndMarkets.com's offering.

According to this report, the Global Animal Genetics market accounted for $4.48 billion in 2019 and is expected to reach $8.60 billion by 2027 growing at a CAGR of 8.5% during the forecast period. Some of the key factors propelling the growth of the market are growing preference for animal protein, increasing population, and increasing adoption of advanced genetic technologies. However, the shortage of skilled veterinary research professionals is the restraining factor for the growth of the market.

Animal genetics is the study of heredity in animals. It includes the study of colour, genetics, gene expression, and animal breeding for a wide variety of applications and is primarily focused on the passing of traits from one generation to the next.

By live animal, the porcine segment is expected to grow at a significant market share during the forecast period owing to the large consumer base for pork meat, as well as growing penetration of advanced genetic research. Based on geography, North America is anticipated to hold considerable market share during the forecast period which is attributed to the research activities being carried out on animal genetics and the adoption of strategic activities by industry players.

Some of the key players in Animal Genetics Market include Animal Genetics Inc, Cogent, Crv Holding B.V., Alta Genetics Inc, Genus PLC, Neogen Corporation, Inguran LLC, Groupe Grimaud La Corbiere SA, Hendrix Genetics BV, Topigs Norsvin, Vetgen, Ew Group GmbH, Zoetis Inc, Envigo Inc, and Urus.

What the Report offers:

Key Topics Covered:

1 Executive Summary

2 Preface2.1 Abstract 2.2 Stake Holders 2.3 Research Scope 2.4 Research Methodology 2.4.1 Data Mining 2.4.2 Data Analysis 2.4.3 Data Validation 2.4.4 Research Approach 2.5 Research Sources 2.5.1 Primary Research Sources 2.5.2 Secondary Research Sources 2.5.3 Assumptions

3 Market Trend Analysis 3.1 Introduction 3.2 Drivers 3.3 Restraints 3.4 Opportunities 3.5 Threats 3.6 End User Analysis 3.7 Emerging Markets 3.8 Impact of Covid-19

4 Porters Five Force Analysis 4.1 Bargaining power of suppliers 4.2 Bargaining power of buyers 4.3 Threat of substitutes 4.4 Threat of new entrants 4.5 Competitive rivalry

5 Global Animal Genetics Market, By Live Animal 5.1 Introduction 5.2 Canine 5.3 Avian 5.4 Piscine 5.5 Poultry 5.6 Bovine 5.7 Porcine 5.8 Other Live Animals 5.8.1 Goat 5.8.2 Horse 5.8.3 Sheep

6 Global Animal Genetics Market, By Service 6.1 Introduction 6.2 DNA Typing 6.3 Genetic Disease Tests 6.4 Genetic Trait Tests 6.5 DNA Testing 6.6 Other Services 6.6.1 Forensic Testing 6.6.2 Prenatal Testing 6.6.3 Predictive and Presymptomatic Testing 6.6.4 Diagnostic Testing

7 Global Animal Genetics Market, By Genetic Material 7.1 Introduction 7.2 Embryos 7.2.1 Equine Embryos 7.2.2 Bovine Embryos 7.2.3 Other Animal Embryos 7.2.3.1 Porcine Embryos 7.2.3.2 Sheep Embryos 7.2.3.3 Goat Embryos 7.3 Semen 7.3.1 Canine Semen 7.3.2 Porcine Semen 7.3.3 Bovine Semen 7.3.4 Equine Semen 7.3.5 Other Animal Semen 7.3.5.1 Goat Semen 7.3.5.2 Sheep Semen

8 Global Animal Genetics Market, By End User 8.1 Introduction 8.2 Veterinary Hospitals & Clinics 8.3 Research Centers and Institutes 8.4 Diagnostic Centres

9 Global Animal Genetics Market, By Geography 9.1 Introduction 9.2 North America 9.2.1 US 9.2.2 Canada 9.2.3 Mexico 9.3 Europe 9.3.1 Germany 9.3.2 UK 9.3.3 Italy 9.3.4 France 9.3.5 Spain 9.3.6 Rest of Europe 9.4 Asia Pacific 9.4.1 Japan 9.4.2 China 9.4.3 India 9.4.4 Australia 9.4.5 New Zealand 9.4.6 South Korea 9.4.7 Rest of Asia Pacific 9.5 South America 9.5.1 Argentina 9.5.2 Brazil 9.5.3 Chile 9.5.4 Rest of South America 9.6 Middle East & Africa 9.6.1 Saudi Arabia 9.6.2 UAE 9.6.3 Qatar 9.6.4 South Africa 9.6.5 Rest of Middle East & Africa

10 Key Developments10.1 Agreements, Partnerships, Collaborations and Joint Ventures 10.2 Acquisitions & Mergers 10.3 New Product Launch 10.4 Expansions 10.5 Other Key Strategies

11 Company Profiling11.1 Animal Genetics Inc 11.2 Cogent 11.3 Crv Holding B.V. 11.4 Alta Genetics Inc 11.5 Genus PLC 11.6 Neogen Corporation 11.7 Inguran LLC 11.8 Groupe Grimaud La Corbiere SA 11.9 Hendrix Genetics BV 11.10 Topigs Norsvin 11.11 Vetgen 11.12 Ew Group GmbH 11.13 Zoetis Inc 11.14 Envigo Inc 11.15 Urus

For more information about this report visit https://www.researchandmarkets.com/r/gk37es

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

U.S. Fax: 646-607-1907 Fax (outside U.S.): +353-1-481-1716

SOURCE Research and Markets

http://www.researchandmarkets.com

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Worlwide Animal Genetics Industry to 2027 - Impact Analysis of COVID-19 - PRNewswire

Livestock Improvement Corporation (LIC) say continued investment in gene discovery and genetic analysis technology is allowing their farmer shareholders to improve cow production valued in the millions.

Investment into the understanding of bovine genetics undertaken by LIC scientists indicates farmers could be missing out on production to the tune of up to the tune of up to $10 million each year.

The co-operative spent $16 million on research and development during the 2019/20 season.

The discovery of genetic variations have been made from the farmer-owned co-operative's database of genotyped cows and bulls and validated through on-farm inspections.

LIC chief scientist Richard Spelman says that despite a relatively low frequently, the hidden impacts on production from these variants can be substantial.

Spelman says they are recessive genetic variations, which means an animal has to have two copies to be affected.

"Identifying these animals via Genemark and removing them from the herd as calves will save in lost production and the rearing cost for these animals.

"We estimate this could be worth up to $10 million in lost production each year across the national herd," he says.

"Even having a few of these cows in a herd could equate to significant loss in productivity and profitability."

He says that LIC's continued investment in gene discovery, with the Genemark testing platform, will allow farmers to identify genetic variations in animals as calves, remove them from the herd, and allow them to focus on the rest of their replacements.

Each of the variants exist within the national herd at differing frequencies and all have varying effects on an animal's production.

From spring 2021, all farmers using LIC's Genemark services will automotically be notified at no additional charge if any of their calves are affected by any of the variations.

"We are pleased our combined investment into science and technology has come together to deliver a simple and convenient service for farmers that is likely to save millions in lost production."

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Cow production improved by genetic research and tech - Rural News Group

Ancient people brought the plague to Siberia by about 4,400 years ago, which may have led to collapses in the population there, a new genetic analysis suggests.

That preliminary finding raises the possibility that plague-induced die offs influenced the genetic structure of northeast Asians who trekked to North America starting perhaps 5,500 years ago. If the result holds up, it, along with other newly uncovered insights into human population dynamics in the region, would unveil a more complex ancestry among those ancient travelers than has usually been assumed.

A team led by evolutionary geneticists Glah Merve Kilin and Anders Gtherstrm, both of Stockholm University, extracted DNA from the remains of 40 human skeletons previously excavated in parts of eastern Siberia. Among those samples, DNA from Yersinia pestis, the bacterium that causes plague, was found in two ancient Siberians, the researchers report January 6 in Science Advances. One person lived around 4,400 years ago. The other dated to roughly 3,800 years ago.

Its unclear how the plague bacterium first reached Siberia or whether it caused widespread infections and death, Gtherstrm says. But he and his colleagues found that genetic diversity in their ancient samples of human DNA declined sharply from around 4,700 to 4,400 years ago, possibly the result of population collapse.

Headlines and summaries of the latest Science News articles, delivered to your inbox

The new data coincide with evidence reported in June 2020 in Cell of Y. pestis DNA in two ancient individuals from eastern Siberias Lake Baikal region, dating to around 4,500 years ago.

The plague may well have reached Siberia by roughly 4,500 years ago, at a time when Y. pestis infected people inhabiting other parts of Eurasia (SN: 10/22/15), says evolutionary geneticist Hendrik Poinar of McMaster University in Hamilton, Canada who did not participate in the new study.

But its possible that the ancient Siberians were infected with a version of Y. pestis that wasnt virulent. If so, the bacterium wouldnt have killed enough people to alter the genetic structure of Siberians. Genetic data from only two individuals provides too little evidence to confirm that they possessed a virulent strain of Y. pestis, Poinar says.

The genetic findings do provide a glimpse of a series of previously unknown ancient population shifts in that region. Ancient individuals included in the new research dated from around 16,900 years ago, shortly after the last Ice Age peaked, to 550 years ago. The researchers compared those ancient Siberians DNA to DNA from present-day humans in different parts of the world and to previous samples of ancient human DNA mainly from Europe, Asia and North America. The analyses showed that despite Siberias harsh climate, groups near Lake Baikal and regions further east mixed with various populations in and outside of Siberia from the Late Stone Age up to medieval times.

The two plague-carrying Siberians, in particular, came from regions that had experienced major population transformations during much of the sampled time period, the researchers say. Those events could have included migrations of plague-carrying people from outside Siberia. For instance, the 4,400-year-old skeleton was found just west of Lake Baikal, a region that witnessed the emergence of several distinct genetic groups with roots mainly further to the west and southwest of Lake Baikal between around 8,980 and 560 years ago.

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Plague may have caused die-offs of ancient Siberians - Science News

The pandemic coronavirus SARS-CoV-2 (shown above) mayunder certain conditionsintegrate its genetic material into human cells, confounding COVID-19 diagnostic tests.

By Jon CohenDec. 16, 2020 , 6:30 PM

Sciences COVID-19 reporting is supported by the Pulitzer Center and the Heising-Simons Foundation.

People who recover from COVID-19 sometimes later test positive for SARS-CoV-2, suggesting their immune systems could not ward off a second attack by the coronavirus or that they have a lingering infection. A study now hints at a different explanation in which the virus hides in an unexpected place. The work, only reported in a preprint, suggests the pandemic pathogen takes a page from HIV and other retroviruses and integrates its genetic codebut, importantly, just parts of itinto peoples chromosomes. The phenomenon, if true and frequent, could have profound implications that range from false signals of active infection to misleading results from COVID-19 treatment studies.

The current study only showed this integration in a lab dish, although it also cites published sequence data from humans infected with SARS-CoV-2that suggest it has happened. The authors emphasize that their results dont imply that SARS-CoV-2 establishes permanent genetic residence in human cells to keep pumping out new copies, as HIV does.

Other scientists are divided about the importance of the new work and its relevance to human health, and some are harshly critical. There are open questions that well have to address, saysmolecular biologist Rudolf Jaenisch of the Massachusetts Institute of Technology (MIT), who led the work.

Yet a few veteran retrovirologists are fascinated. This is a very interesting molecular analysis and speculation with supportive data provided, says Robert Gallo, who heads the Institute of Human Virology and looked at the newly posted preprint at Sciences request. I do not think it is a complete story to be certain but as is, I like it and my guess is it will be right.

All viruses insert their genetic material into the cells they infect, but it generally remains separate from the cells own DNA. Jaenischs team, intrigued by reports of people testing positive for SARS-CoV-2 after recovering, wondered whether these puzzling results reflected something of an artifact from the polymerase chain reaction (PCR) assay, which detects specific virus sequences in biological samples such as nasal swabs, even if they are fragmented and cant produce new viruses. Why do we have this positivity, which is now seen all over the place, long after the active infection has disappeared? says Jaenisch, who collaborated with the lab of MITs Richard Young.

To test whether SARS-CoV-2s RNA genome could integrate into the DNA of our chromosomes, the researchers added the gene for reverse transcriptase (RT), an enzyme that converts RNA into DNA, to human cells and cultured the engineered cells with SARS-CoV-2. In one experiment, the researchers used an RT gene from HIV. They also provided RT using human DNA sequences known as LINE-1 elements, which are remnants of ancient retroviral infections and make up about 17% of the human genome. Cells making either form of the enzyme led to some chunks of SARS-CoV-2 RNA being converted to DNA and integrated into human chromosomes, the team reports in their preprint, posted on bioRxiv on 13 December.

If the LINE-1 sequences naturally make RT in human cells, SARS-CoV-2 integration might happen in people who have COVID-19. This could occur in people coinfected with SARS-CoV-2 and HIV, too. Either situation may explain PCR detecting lingering traces of coronavirus genetic material in people who no longer have a true infection. And it could confuse studies of COVID-19 treatments that rely on PCR tests to indirectly measure changes in the amount of infectious SARS-CoV-2 in the body.

David Baltimore, a virologist at the California Institute of Technology who won the Nobel Prize for his role in discovering RT, describes the new work as impressive and the findings as unexpected but he notes that Jaenisch and colleagues only show that fragments of SARS-CoV-2s genome integrate. Because it is all pieces of the coronaviral genome, it cant lead to infectious RNA or DNA and therefore it is probably biologically a dead end, Baltimore says. It is also not clear if, in people, the cells that harbor the reverse transcripts stay around for a long time or they die. The work raises a lot of interesting questions.

Virologist Melanie Ott, who studies HIV at the Gladstone Institute of Virology and Immunology, says the findings are pretty provocative but need thorough follow-up and confirmation. I have no doubt that reverse transcription can happen in vitro with optimized conditions, Ott says. But she notes that SARS-CoV-2 RNA replication takes place in specialized compartments in the cytoplasm. Whether it happens in infected cells and leads to significant integration in the cell nucleus is another question.

Retrovirologist John Coffin of Tufts University calls the new work believable, noting that solid evidence shows that LINE-1 RT can allow viral material to integrate in people, but hes not yet convinced. The evidence of SARS-CoV-2 sequences in people, Coffin says, should be more solid, and the in vitro experiments conducted by Jaenischs team lack controls he would have liked to have seen. All in all, I doubt that the phenomenon has much biological relevance, despite the authors speculation, Coffin says.

Zandrea Ambrose, a retrovirologist at the University of Pittsburgh, adds that this kind of integration would be extremely rare if it does indeed happen. She notes that LINE-1 elements in the human genome rarely are active. It is not clear what the activity would be in different primary cell types that are infected by SARS-CoV-2, she says.

One particularly harsh Twitter critic, a postdoctoral researcher in a lab that specializes in retroviruses, went so far as to call the preprints conclusions a strong, dangerous, and largely unsupported claim. Jaenisch emphasizes that the paper clearly states the integration the authors think happens could not lead to the production of infectious SARS-CoV-2. Lets assume that we can really resolve these criticisms fully, which Im trying to do, Jaenisch says. This might be something not to worry about.

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The coronavirus may sometimes slip its genetic material into human chromosomesbut what does that mean? - Science Magazine

OKLAHOMA CITY (KFOR) Behind one Oklahoma 8-year-olds infectious smile is a fighter.

Im smaller than most people, said Madison Cain.

Madison was born smaller than most babies, too, at 5 lbs. 9 oz.

She was teeny tiny, she calls herself a little itty-bitty baby,said Madisons mom, Melissa Cain.

For Madisons first year, Melissa says there werent many issues.

Around 15 months or so she quit growing in length, she quit gaining weight, and so that began our journey to figure out what was going on, said Melissa.

The Tulsa residents had no idea what this journey would entail.

By age two, Madison was diagnosed with hip dysplasia and cataracts.

She got those initial diagnoses treated, but still wasnt growing.

Then we really started thinking this isnt all adding up she doesnt grow, she has the hip thing, she has cataracts, there has to be something, said Melissa.

The family started genetic testing, while Madisons symptoms persisted.

Still low energy not growing well, said Melissa. She couldnt keep up with her peers, you know running and things werent the same we were doing all kinds of things and just not a lot of answers.

The Cains spent hours researching, and even more time at the doctors office, but it was years of dead ends.

No energy, sleeping 16 hours a day barely making it through school, not gaining any weight, said Melissa.She was 5 and weighed about 25-28 pounds, but she is the most easy going, not stressed out, tough child.

Madisons strength paid off.

A break-through finally coming in 2019.

The genetics doctor called and said here this is what it is, theres one published paper, with a patient with this. Its not her, so well just put it in a database and see if anything ever hits, said Melissa.

But as a nurse practitioner herself, Melissa sat down and read the article.

She realized it was written by doctors, just down the turnpike, at the Oklahoma Medical Research Foundation.

This is a new disease and were the first ones that discovered it, said Dr. Lijun Xia,Member and Chair, Cardiovascular Biology Research Program at OMRF.

Madison has rare gene mutation to the MBTPS1 gene.

Madison, inherited a wrong copy from her mother and the father so, therefore even though she has two copies of the gene both are wrong both have mutation, said Dr. Xia.

The mutation, resulted in a condition called Spondyloepiphyseal Dysplasia, Kondo-Fu type, or SEDKF for short.

The condition named after two of Oklahomas scientists.

The disorder hinders Madisons bone growth and development.

This is a very rare genetic disease,said Dr. Xia.

There are only two known cases in the state, Madisons and another girl named Sydney in Yukon, who was the first diagnosed.

Since publishing the article, OMRF now knows of about eight cases worldwide.

We have one contact us from Germany, one from Brazil, and theres also one from San Francisco, said Dr. Xia.

Doctors think that could be because many patients are misdiagnosed.

The mutation can also affect every patient differently.

However, theres hope on the horizon.

Researchers have come up with a possible treatment but need 50 patients for a clinical trial.

Now theyre searching for cases across the country.

Of course, I wish that we had the answer plus enough patients to do a trial and see if the treatment would work and Im hopeful that we can get there before her bones stop growing, said Cain.

The protein used for treatment has already been approved by the FDA to treat a different disease.

Researchers have tested the treatment on mice successfully.

For Madison, this treatment could mean everything.

It could change our life and change her life for the rest of her life, said Cain. We never thought weve get a Madison, but theres no one like Madison.

For more information visit the OMRF website.

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Oklahoma researchers looking for additional patients across the US with rare genetic mutation - KFOR Oklahoma City