Person:

Price, Alkes

The Icelandic population has been sampled in many disease association studies, providing a strong motivation to understand the structure of this population and its ramifications for disease gene mapping. Previous work using 40 microsatellites showed that the Icelandic population is relatively homogeneous, but exhibits subtle population structure that can bias disease association statistics. Here, we show that regional geographic ancestries of individuals from Iceland can be distinguished using 292,289 autosomal single-nucleotide polymorphisms (SNPs). We further show that subpopulation differences are due to genetic drift since the settlement of Iceland 1100 years ago, and not to varying contributions from different ancestral populations. A consequence of the recent origin of Icelandic population structure is that allele frequency differences follow a null distribution devoid of outliers, so that the risk of false positive associations due to stratification is minimal. Our results highlight an important distinction between population differences attributable to recent drift and those arising from more ancient divergence, which has implications both for association studies and for efforts to detect natural selection using population differentiation.

Identifying the ancestry of chromosomal segments of distinct ancestry has a wide range of applications from disease mapping to learning about history. Most methods require the use of unlinked markers; but, using all markers from genome-wide scanning arrays, it should in principle be possible to infer the ancestry of even very small segments with exquisite accuracy. We describe a method, HAPMIX, which employs an explicit population genetic model to perform such local ancestry inference based on fine-scale variation data. We show that HAPMIX outperforms other methods, and we explore its utility for inferring ancestry, learning about ancestral populations, and inferring dates of admixture. We validate the method empirically by applying it to populations that have experienced recent and ancient admixture: 935 African Americans from the United States and 29 Mozabites from North Africa. HAPMIX will be of particular utility for mapping disease genes in recently admixed populations, as its accurate estimates of local ancestry permit admixture and case-control association signals to be combined, enabling more powerful tests of association than with either signal alone.

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a “cosmopolitan” tagging approach to capture the genetic diversity across ∼2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general “phase change” phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like (F_{ST})) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure.

The “thrifty genotype” hypothesis proposes that the high prevalence of type 2 diabetes (T2D) in Native Americans and admixed Latin Americans has a genetic basis and reflects an evolutionary adaptation to a past low calorie/high exercise lifestyle. However, identification of the gene variants underpinning this hypothesis remains elusive. Here we assessed the role of Native American ancestry, socioeconomic status (SES) and 21 candidate gene loci in susceptibility to T2D in a sample of 876 T2D cases and 399 controls from Antioquia (Colombia). Although mean Native American ancestry is significantly higher in T2D cases than in controls (32% v 29%), this difference is confounded by the correlation of ancestry with SES, which is a stronger predictor of disease status. Nominally significant association (P<0.05) was observed for markers in: TCF7L2, RBMS1, CDKAL1, ZNF239, KCNQ1 and TCF1 and a significant bias (P<0.05) towards OR>1 was observed for markers selected from previous T2D genome-wide association studies, consistent with a role for Old World variants in susceptibility to T2D in Latin Americans. No association was found to the only known Native American-specific gene variant previously associated with T2D in a Mexican sample (rs9282541 in ABCA1). An admixture mapping scan with 1,536 ancestry informative markers (AIMs) did not identify genome regions with significant deviation of ancestry in Antioquia. Exclusion analysis indicates that this scan rules out ∼95% of the genome as harboring loci with ancestry risk ratios >1.22 (at P < 0.05).

The genetic basis of gene expression variation has long been studied with the aim to understand the landscape of regulatory variants, but also more recently to assist in the interpretation and elucidation of disease signals. To date, many studies have looked in specific tissues and population-based samples, but there has been limited assessment of the degree of inter-population variability in regulatory variation. We analyzed genome-wide gene expression in lymphoblastoid cell lines from a total of 726 individuals from 8 global populations from the HapMap3 project and correlated gene expression levels with HapMap3 SNPs located in (cis) to the genes. We describe the influence of ancestry on gene expression levels within and between these diverse human populations and uncover a non-negligible impact on global patterns of gene expression. We further dissect the specific functional pathways differentiated between populations. We also identify 5,691 expression quantitative trait loci (eQTLs) after controlling for both non-genetic factors and population admixture and observe that half of the (cis)-eQTLs are replicated in one or more of the populations. We highlight patterns of eQTL-sharing between populations, which are partially determined by population genetic relatedness, and discover significant sharing of eQTL effects between Asians, European-admixed, and African subpopulations. Specifically, we observe that both the effect size and the direction of effect for eQTLs are highly conserved across populations. We observe an increasing proximity of eQTLs toward the transcription start site as sharing of eQTLs among populations increases, highlighting that variants close to TSS have stronger effects and therefore are more likely to be detected across a wider panel of populations. Together these results offer a unique picture and resource of the degree of differentiation among human populations in functional regulatory variation and provide an estimate for the transferability of complex trait variants across populations.

Ancient population structure shaping contemporary genetic variation has been recently appreciated and has important implications regarding our understanding of the structure of modern human genomes. We identified a ∼36-kb DNA segment in the human genome that displays an ancient substructure. The variation at this locus exists primarily as two highly divergent haplogroups. One of these haplogroups (the NE1 haplogroup) aligns with the Neandertal haplotype and contains a 4.6-kb deletion polymorphism in perfect linkage disequilibrium with 12 single nucleotide polymorphisms (SNPs) across diverse populations. The other haplogroup, which does not contain the 4.6-kb deletion, aligns with the chimpanzee haplotype and is likely ancestral. Africans have higher overall pairwise differences with the Neandertal haplotype than Eurasians do for this NE1 locus (p<10−15). Moreover, the nucleotide diversity at this locus is higher in Eurasians than in Africans. These results mimic signatures of recent Neandertal admixture contributing to this locus. However, an in-depth assessment of the variation in this region across multiple populations reveals that African NE1 haplotypes, albeit rare, harbor more sequence variation than NE1 haplotypes found in Europeans, indicating an ancient African origin of this haplogroup and refuting recent Neandertal admixture. Population genetic analyses of the SNPs within each of these haplogroups, along with genome-wide comparisons revealed significant FST (p = 0.00003) and positive Tajima's D (p = 0.00285) statistics, pointing to non-neutral evolution of this locus. The NE1 locus harbors no protein-coding genes, but contains transcribed sequences as well as sequences with putative regulatory function based on bioinformatic predictions and in vitro experiments. We postulate that the variation observed at this locus predates Human–Neandertal divergence and is evolving under balancing selection, especially among European populations.

The peopling of the Americas has been the subject of extensive genetic, archaeological and linguistic research; however, central questions remain unresolved1–5. One contentious issue is whether the settlement occurred via a single6–8 or multiple streams of migration from Siberia9–15. The pattern of dispersals within the Americas is also poorly understood. To address these questions at higher resolution than was previously possible, we assembled data from 52 Native American and 17 Siberian groups genotyped at 364,470 single nucleotide polymorphisms. We show that Native Americans descend from at least three streams of Asian gene flow. Most descend entirely from a single ancestral population that we call “First American”. However, speakers of Eskimo-Aleut languages from the Arctic inherit almost half their ancestry from a second stream of Asian gene flow, and the Na-Dene-speaking Chipewyan from Canada inherit roughly one-tenth of their ancestry from a third stream. We show that the initial peopling followed a southward expansion facilitated by the coast, with sequential population splits and little gene flow after divergence, especially in South America. A major exception is in Chibchan-speakers on both sides of the Panama Isthmus, who have ancestry from both North and South America.

Variation in gene expression is a fundamental aspect of human phenotypic variation. Several recent studies have analyzed gene expression levels in populations of different continental ancestry and reported population differences at a large number of genes. However, these differences could largely be due to non-genetic (e.g., environmental) effects. Here, we analyze gene expression levels in African American cell lines, which differ from previously analyzed cell lines in that individuals from this population inherit variable proportions of two continental ancestries. We first relate gene expression levels in individual African Americans to their genome-wide proportion of European ancestry. The results provide strong evidence of a genetic contribution to expression differences between European and African populations, validating previous findings. Second, we infer local ancestry (0, 1, or 2 European chromosomes) at each location in the genome and investigate the effects of ancestry proximal to the expressed gene (cis) versus ancestry elsewhere in the genome (trans). Both effects are highly significant, and we estimate that 12±3% of all heritable variation in human gene expression is due to cis variants.

Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.