Genetic Ancestry Inference for Pharmacogenomics.

Genetic ancestry inference can be used to stratify patient cohorts and to model pharmacogenomic variation within and between populations. We provide a detailed guide to genetic ancestry inference using genome-wide genetic variant datasets, with an emphasis on two widely used techniques: principal components analysis (PCA) and ADMIXTURE analysis. PCA can be used for patient stratification and categorical ancestry inference, whereas ADMIXTURE is used to characterize genetic ancestry as a continuous variable. Visualization methods are critical for the interpretation of genetic ancestry inference methods, and we provide instructions for how the results of PCA and ADMIXTURE can be effectively visualized.

Equitable Expanded Carrier Screening Needs Indigenous Clinical and Population Genomic Data.

Expanded carrier screening (ECS) for recessive monogenic diseases requires prior knowledge of genomic variation, including DNA variants that cause disease. The composition of pathogenic variants differs greatly among human populations, but historically, research about monogenic diseases has focused mainly on people with European ancestry. By comparison, less is known about pathogenic DNA variants in people from other parts of the world. Consequently, inclusion of currently underrepresented Indigenous and other minority population groups in genomic research is essential to enable equitable outcomes in ECS and other areas of genomic medicine. Here, we discuss this issue in relation to the implementation of ECS in Australia, which is currently being evaluated as part of the national Government's Genomics Health Futures Mission. We argue that significant effort is required to build an evidence base and genomic reference data so that ECS can bring significant clinical benefit for many Aboriginal and/or Torres Strait Islander Australians. These efforts are essential steps to achieving the Australian Government's objectives and its commitment "to leveraging the benefits of genomics in the health system for all Australians." They require culturally safe, community-led research and community involvement embedded within national health and medical genomics programs to ensure that new knowledge is integrated into medicine and health services in ways that address the specific and articulated cultural and health needs of Indigenous people. Until this occurs, people who do not have European ancestry are at risk of being, in relative terms, further disadvantaged.

Indigenous Genomic Databases: Pragmatic Considerations and Cultural Contexts.

The potential to grow genomic knowledge and harness the subsequent clinical benefits has escalated the building of background variant databases (BVDs) for genetic diagnosis across the globe. Alongside the upsurge of this precision medicine, potential benefits have been highlighted for both rare genetic conditions and other diagnoses. However, with the ever-present "genomic divide," Indigenous peoples globally have valid concerns as they endure comparatively greater health disparities but stand to benefit the least from these novel scientific discoveries and progress in healthcare. The paucity of Indigenous healthcare providers and researchers in these fields contributes to this genomic divide both in access to, and availability of culturally safe, relevant and respectful healthcare using this genetic knowledge. The vital quest to provide equitable clinical research, and provision and use of genomic services and technologies provides a strong rationale for building BVDs for Indigenous peoples. Such tools would ground their representation and participation in accompanying genomic health research and benefit acquisition. We describe two, independent but highly similar initiatives-the "Silent Genomes" in Canada and the "Aotearoa Variome" in New Zealand-as exemplars that have had to address the aforementioned issues and work to create Indigenous BVDs with these populations. Taking into account the baseline inequities in genomic medicine for Indigenous populations and the ongoing challenges of implementing genomic research with Indigenous communities, we provide a rationale for multiple changes required that will assure communities represented in BVDs, as well as Indigenous researchers, that their participation will maximize benefits and minimize risk.

The Y-chromosome of the Soliga, an ancient forest-dwelling tribe of South India.

A previous autosomal STR study provided evidence of a connection between the ancient Soliga tribe at the southern tip of the Indian subcontinent and Australian aboriginal populations, possibly reflecting an eastbound coastal migration circa (15 Kya). The Soliga are considered to be among India's earliest inhabitants. In this investigation, we focus on the Y chromosomal characteristics shared between the Soliga population and other Indian tribes as well as western Eurasia and Sub-Saharan Africa groups. Some noteworthy findings of this present analysis include the following: The three most frequent haplogroups detected in the Soliga population are F*, H1 and J2. F*, the oldest (43 to 63 Kya), has a significant frequency bias in favor of Indian tribes versus castes. This observation coupled with the fact that Y-STR haplotypes shared with sub-Saharan African populations are found only in F* males of the Soliga, Irula and Kurumba may indicate a unique genetic connection between these Indian tribes and sub-Saharan Africans. In addition, our study suggests that haplogroup H is confined mostly to South Asia and immediate neighbors and the H1 network may indicate minimal sharing of Y-STR haplotypes among South Asian collections, tribal and otherwise. Also, J2, brought into India by Neolithic farmers, is present at a significantly higher frequency in caste versus tribal communities. This last observation may reflect the marginalization of Indian tribes to isolated regions not ideal for agriculture.

A panel of 32 AIMs suitable for population stratification correction and global ancestry estimation in Mexican mestizos.

BACKGROUND: Association studies are useful to unravel the genetic basis of common human diseases. However, the presence of undetected population structure can lead to both false positive results and failures to detect genuine associations. Even when most of the approaches to deal with population stratification require genome-wide data, the use of a well-selected panel of ancestry informative markers (AIMs) may appropriately correct for population stratification. Few panels of AIMs have been developed for Latino populations and most contain a high number of markers (> 100 AIMs). For some association studies such as candidate gene approaches, it may be unfeasible to genotype a numerous set of markers to avoid false positive results. In such cases, methods that use fewer AIMs may be appropriate. RESULTS: We validated an accurate and cost-effective panel of AIMs, for use in population stratification correction of association studies and global ancestry estimation in Mexicans, as well as in populations having large proportions of both European and Native American ancestries. Based on genome-wide data from 1953 Mexican individuals, we performed a PCA and SNP weights were calculated to select subsets of unlinked AIMs within percentiles 0.10 and 0.90, ensuring that all chromosomes were represented. Correlations between PC1 calculated using genome-wide data versus each subset of AIMs (16, 32, 48 and 64) were r2 = 0.923, 0.959, 0.972 and 0.978, respectively. When evaluating PCs performance as population stratification adjustment covariates, no correlation was found between P values obtained from uncorrected and genome-wide corrected association analyses (r2 = 0.141), highlighting that population stratification correction is compulsory for association analyses in admixed populations. In contrast, high correlations were found when adjusting for both PC1 and PC2 for either subset of AIMs (r2 > 0.900). After multiple validations, including an independent sample, we selected a minimal panel of 32 AIMs, which are highly informative of the major ancestral components of Mexican mestizos, namely European and Native American ancestries. Finally, the correlation between the global ancestry proportions calculated using genome-wide data and our panel of 32 AIMs was r2 = 0.972. CONCLUSIONS: Our panel of 32 AIMs accurately estimated global ancestry and corrected for population stratification in association studies in Mexican individuals.

Social-group identity and population substructure in admixed populations in New Mexico and Latin America.

We examined the relationship between continental-level genetic ancestry and racial and ethnic identity in an admixed population in New Mexico with the goal of increasing our understanding of how racial and ethnic identity influence genetic substructure in admixed populations. Our sample consists of 98 New Mexicans who self-identified as Hispanic or Latino (NM-HL) and who further categorized themselves by race and ethnic subgroup membership. The genetic data consist of 270 newly-published autosomal microsatellites from the NM-HL sample and previously published data from 57 globally distributed populations, including 13 admixed samples from Central and South America. For these data, we 1) summarized the major axes of genetic variation using principal component analyses, 2) performed tests of Hardy Weinberg equilibrium, 3) compared empirical genetic ancestry distributions to those predicted under a model of admixture that lacked substructure, 4) tested the hypotheses that individuals in each sample had 100%, 0%, and the sample-mean percentage of African, European, and Native American ancestry. We found that most NM-HL identify themselves and their parents as belonging to one of two groups, conforming to a region-specific narrative that distinguishes recent immigrants from Mexico from individuals whose families have resided in New Mexico for generations and who emphasize their Spanish heritage. The "Spanish" group had significantly lower Native American ancestry and higher European ancestry than the "Mexican" group. Positive FIS values, PCA plots, and heterogeneous ancestry distributions suggest that most Central and South America admixed samples also contain substructure, and that this substructure may be related to variation in social identity. Genetic substructure appears to be common in admixed populations in the Americas and may confound attempts to identify disease-causing genes and to understand the social causes of variation in health outcomes and social inequality.

Exploring public genomics data for population pharmacogenomics.

Racial and ethnic differences in drug responses are now well studied and documented. Pharmacogenomics research seeks to unravel the genetic underpinnings of inter-individual variability with the aim of tailored-made theranostics and therapeutics. Taking into account the differential expression of pharmacogenes coding for key metabolic enzymes and transporters that affect drug pharmacokinetics and pharmacodynamics, we advise that data interpretation and analysis need to occur in light of geographical ancestry, if implications for drug development and global health are to be considered. Herein, we exploit ePGA, a web-based electronic Pharmacogenomics Assistant and publicly available genetic data from the 1000 Genomes Project to explore genotype to phenotype associations among the 1000 Genomes Project populations.

Polymorphic Alu Insertion/Deletion in Different Caste and Tribal Populations from South India.

Seven human-specific Alu markers were studied in 574 unrelated individuals from 10 endogamous groups and 2 hill tribes of Tamil Nadu and Kerala states. DNA was isolated, amplified by PCR-SSP, and subjected to agarose gel electrophoresis, and genotypes were assigned for various Alu loci. Average heterozygosity among caste populations was in the range of 0.292-0.468. Among tribes, the average heterozygosity was higher for Paliyan (0.3759) than for Kani (0.2915). Frequency differences were prominent in all loci studied except Alu CD4. For Alu CD4, the frequency was 0.0363 in Yadavas, a traditional pastoral and herd maintaining population, and 0.2439 in Narikuravars, a nomadic gypsy population. The overall genetic difference (Gst) of 12 populations (castes and tribes) studied was 3.6%, which corresponds to the Gst values of 3.6% recorded earlier for Western Asian populations. Thus, our study confirms the genetic similarities between West Asian populations and South Indian castes and tribes and supported the large scale coastal migrations from Africa into India through West Asia. However, the average genetic difference (Gst) of Kani and Paliyan tribes with other South Indian tribes studied earlier was 8.3%. The average Gst of combined South and North Indian Tribes (CSNIT) was 9.5%. Neighbor joining tree constructed showed close proximity of Kani and Paliyan tribal groups to the other two South Indian tribes, Toda and Irula of Nilgiri hills studied earlier. Further, the analysis revealed the affinities among populations and confirmed the presence of North and South India specific lineages. Our findings have documented the highly diverse (micro differentiated) nature of South Indian tribes, predominantly due to isolation, than the endogamous population groups of South India. Thus, our study firmly established the genetic relationship of South Indian castes and tribes and supported the proposed large scale ancestral migrations from Africa, particularly into South India through West Asian corridor.

Genomics in research and health care with Aboriginal and Torres Strait Islander peoples.

Genomics is increasingly becoming an integral component of health research and clinical care. The perceived difficulties associated with genetic research involving Aboriginal and Torres Strait Islander people mean that they have largely been excluded as research participants. This limits the applicability of research findings for Aboriginal and Torres Strait Islander patients. Emergent use of genomic technologies and personalised medicine therefore risk contributing to an increase in existing health disparities unless urgent action is taken. To allow the potential benefits of genomics to be more equitably distributed, and minimise potential harms, we recommend five actions: (1) ensure diversity of participants by implementing appropriate protocols at the study design stage; (2) target diseases that disproportionately affect disadvantaged groups; (3) prioritise capacity building to promote Indigenous leadership across research professions; (4) develop resources for consenting patients or participants from different cultural and linguistic backgrounds; and (5) integrate awareness of issues relating to Indigenous people into the governance structures, formal reviews, data collection protocols and analytical pipelines of health services and research projects.

MAPK genes interact with diet and lifestyle factors to alter risk of breast cancer: the Breast Cancer Health Disparities Study.

Mitogen-activated protein kinases (MAPK) are integration points for multiple biochemical signals. We evaluated 13 MAPK genes with breast cancer risk and determined if diet and lifestyle factors mediated risk. Data from 3 population-based case-control studies conducted in Southwestern United States, California, and Mexico included 4183 controls and 3592 cases. Percent Indigenous American (IA) ancestry was determined from 104 ancestry informative markers. The adaptive rank truncated product (ARTP) was used to determine the significance of each gene and the pathway with breast cancer risk, by menopausal status, genetic ancestry level, and estrogen receptor (ER)/progesterone receptor (PR) strata. MAP3K9 was associated with breast cancer overall (P(ARTP) = 0.02) with strongest association among women with the highest IA ancestry (P(ARTP) = 0.04). Several SNPs in MAP3K9 were associated with ER+/PR+ tumors and interacted with dietary oxidative balance score (DOBS), dietary folate, body mass index (BMI), alcohol consumption, cigarette smoking, and a history of diabetes. DUSP4 and MAPK8 interacted with calories to alter breast cancer risk; MAPK1 interacted with DOBS, dietary fiber, folate, and BMI; MAP3K2 interacted with dietary fat; and MAPK14 interacted with dietary folate and BMI. The patterns of association across diet and lifestyle factors with similar biological properties for the same SNPs within genes provide support for associations.

Human population studies and the World Health Organization.

This essay draws attention to the role of the WHO in shaping research agendas in the biomedical sciences in the postwar era. It considers in particular the genetic studies of human populations that were pursued under the aegis of the WHO from the late 1950s to 1970s. The study provides insights into how human and medical genetics entered the agenda of the WHO. At the same time, the population studies become a focus for tracking changing notions of international relations, cooperation, and development and their impact on research in biology and medicine in the post-World War I era. After a brief discussion of the early history of the WHO and its position in Cold War politics, the essay considers the WHO program in radiation protection and heredity and how the genetic study of "vanishing" human populations and a world-wide genetic study of newborns fitted this broader agenda. It then considers in more detail the kind of support offered by the WHO for these projects. The essay highlights the role of single individuals in taking advantage of WHO support for pushing their research agendas while establishing a trend towards cooperative international projects in biology.

Blood groups and human groups: collecting and calibrating genetic data after World War Two.

Arthur Mourant's The Distribution of the Human Blood Groups (1954) was an "indispensable" reference book on the "anthropology of blood groups" containing a vast collection of human genetic data. It was based on the results of blood-grouping tests carried out on half-a-million people and drew together studies on diverse populations around the world: from rural communities, to religious exiles, to volunteer transfusion donors. This paper pieces together sequential stages in the production of a small fraction of the blood-group data in Mourant's book, to examine how he and his colleagues made genetic data from people. Using sources from several collecting projects, I follow how blood was encountered, how it was inscribed, and how it was turned into a laboratory resource. I trace Mourant's analytical and representational strategies to make blood groups both credibly 'genetic' and understood as relevant to human ancestry, race and history. In this story, 'populations' were not simply given, but were produced through public health, colonial and post-colonial institutions, and by the labour and expertise of subjects, assistants and mediators. Genetic data were not self-evidently 'biological', but were shaped by existing historical and geographical identities, by political relationships, and by notions of kinship and belonging.

Chromosome surveys of human populations: between epidemiology and anthropology.

It is commonly held that after 1945 human genetics turned medical and focussed on the individual rather than on the study of human populations that had become discredited. However, a closer look at the research practices at the time quickly reveals that human population studies, using old and new tools, prospered in this period. The essay focuses on the rise of chromosome analysis as a new tool for the study of human populations. It reviews a broad array of population studies ranging from newborn screening programmes to studies of isolated or 'primitive' people. Throughout, it highlights the continuing role of concerns and opportunities raised by the propagation of atomic energy for civilian and military uses, the collection of large data bases and computers, and the role of international organisations like the World Health Organisation and the International Biological Programme in shaping research agendas and carving out a space for human heredity in the postwar era.

Back to BaySICS: a user-friendly program for Bayesian Statistical Inference from Coalescent Simulations.

Inference of population demographic history has vastly improved in recent years due to a number of technological and theoretical advances including the use of ancient DNA. Approximate Bayesian computation (ABC) stands among the most promising methods due to its simple theoretical fundament and exceptional flexibility. However, limited availability of user-friendly programs that perform ABC analysis renders it difficult to implement, and hence programming skills are frequently required. In addition, there is limited availability of programs able to deal with heterochronous data. Here we present the software BaySICS: Bayesian Statistical Inference of Coalescent Simulations. BaySICS provides an integrated and user-friendly platform that performs ABC analyses by means of coalescent simulations from DNA sequence data. It estimates historical demographic population parameters and performs hypothesis testing by means of Bayes factors obtained from model comparisons. Although providing specific features that improve inference from datasets with heterochronous data, BaySICS also has several capabilities making it a suitable tool for analysing contemporary genetic datasets. Those capabilities include joint analysis of independent tables, a graphical interface and the implementation of Markov-chain Monte Carlo without likelihoods.

[Diabetes mellitus and metabolic syndrome in Siberia and in the Far East].

In Siberia and in the Far East region overall incidence of diabetes mellitus is somewhat lower than in the European part of the country, though these indices reduplicate on average in 10-15 years. The prevalence of both diabetes (both types) and metabolic syndrome among indigenous mongoloid population is lower than among Caucasian.

Bridging the divide between genomic science and indigenous peoples.

The new science of genomics endeavors to chart the genomes of individuals around the world, with the dual goals of understanding the role genetic factors play in human health and solving problems of disease and disability. From the perspective of indigenous peoples and developing countries, the promises and perils of genomic science appear against a backdrop of global health disparity and political vulnerability. These conditions pose a dilemma for many communities when attempting to decide about participating in genomic research or any other biomedical research. Genomic research offers the possibility of improved technologies for managing the acute and chronic diseases that plague their members. Yet, the history of particularly biomedical research among people in indigenous and developing nations offers salient examples of unethical practice, misuse of data, and failed promises. This dilemma creates risks for communities who decide either to participate or not to participate in genomic science research. Some argue that the history of poor scientific practice justifies refusal to join genomic research projects. Others argue that disease poses such great threats to the well-being of people in indigenous communities and developing nations that not participating in genomic research risks irrevocable harm. Thus, some communities particularly among indigenous peoples have declined to participate as subjects in genomic research. At the same time, some communities have begun developing new guidelines, procedures, and practices for engaging with the scientific community that offer opportunities to bridge the gap between genomic science and indigenous and/or developing communities. Four new approaches warrant special attention and further support: consulting with local communities; negotiating the complexities of consent; training members of local communities in science and health care; and training scientists to work with indigenous communities. Implicit is a new definition of "rigorous scientific research," one that includes both community development and scientific progress as legitimate objectives of genomic research. Innovative translational research is needed to develop practical, mutually acceptable methods for crossing the divide between genomic researchers and indigenous communities. This may mean the difference between success and failure in genomic science, and in improving health for all peoples.

Principal-component analysis for assessment of population stratification in mitochondrial medical genetics.

Although inherited mitochondrial genetic variation can cause human disease, no validated methods exist for control of confounding due to mitochondrial population stratification (PS). We sought to identify a reliable method for PS assessment in mitochondrial medical genetics. We analyzed mitochondrial SNP data from 1513 European American individuals concomitantly genotyped with the use of a previously validated panel of 144 mitochondrial markers as well as the Affymetrix 6.0 (n = 432), Illumina 610-Quad (n = 458), or Illumina 660 (n = 623) platforms. Additional analyses were performed in 938 participants in the Human Genome Diversity Panel (HGDP) (Illumina 650). We compared the following methods for controlling for PS: haplogroup-stratified analyses, mitochondrial principal-component analysis (PCA), and combined autosomal-mitochondrial PCA. We computed mitochondrial genomic inflation factors (mtGIFs) and test statistics for simulated case-control and continuous phenotypes (10,000 simulations each) with varying degrees of correlation with mitochondrial ancestry. Results were then compared across adjustment methods. We also calculated power for discovery of true associations under each method, using a simulation approach. Mitochondrial PCA recapitulated haplogroup information, but haplogroup-stratified analyses were inferior to mitochondrial PCA in controlling for PS. Correlation between nuclear and mitochondrial principal components (PCs) was very limited. Adjustment for nuclear PCs had no effect on mitochondrial analysis of simulated phenotypes. Mitochondrial PCA performed with the use of data from commercially available genome-wide arrays correlated strongly with PCA performed with the use of an exhaustive mitochondrial marker panel. Finally, we demonstrate, through simulation, no loss in power for detection of true associations with the use of mitochondrial PCA.

Identification of population substructure among Jews using STR markers and dependence on reference populations included.

BACKGROUND: Detecting population substructure is a critical issue for association studies of health behaviors and other traits. Whether inherent in the population or an artifact of marker choice, determining aspects of a population's genetic history as potential sources of substructure can aid in design of future genetic studies. Jewish populations, among which association studies are often conducted, have a known history of migrations. As a necessary step in understanding population structure to conduct valid association studies of health behaviors among Israeli Jews, we investigated genetic signatures of this history and quantified substructure to facilitate future investigations of these phenotypes in this population. RESULTS: Using 32 autosomal STR markers and the program STRUCTURE, we differentiated between Ashkenazi (AJ, N = 135) and non-Ashkenazi (NAJ, N = 226) Jewish populations in the form of Northern and Southern geographic genetic components (AJ north 73%, south 23%, NAJ north 33%, south 60%). The ability to detect substructure within these closely related populations using a small STR panel was contingent on including additional samples representing major continental populations in the analyses. CONCLUSIONS: Although clustering programs such as STRUCTURE are designed to assign proportions of ancestry to individuals without reference population information, when Jewish samples were analyzed in the absence of proxy parental populations, substructure within Jews was not detected. Generally, for samples with a given grandparental country of birth, STRUCTURE assignment values to Northern, Southern, African and Asian clusters agreed with mitochondrial DNA and Y-chromosomal data from previous studies as well as historical records of migration and intermarriage.