The genetic structure of the indigenous hunter-gatherer peoples of southern Africa, the oldest known lineage of modern human, is important for understanding human diversity. Studies based on mitochondrial1 and small sets of nuclear markers2 have shown that these hunter-gatherers, known as Khoisan, San, or Bushmen, are genetically divergent from other humans1,3. However, until now, fully sequenced human genomes have been limited to recently diverged populations4–8. Here we present the complete genome sequences of an indigenous hunter-gatherer from the Kalahari Desert and a Bantu from southern Africa, as well as protein-coding regions from an additional three hunter-gatherers from disparate regions of the Kalahari. We characterize the extent of whole-genome and exome diversity among the five men, reporting 1.3 million novel DNA differences genome-wide, including 13,146 novel amino acid variants. In terms of nucleotide substitutions, the Bushmen seem to be, on average, more different from each other than, for example, a European and an Asian. Observed genomic differences between the hunter-gatherers and others may help to pinpoint genetic adaptations to an agricultural lifestyle. Adding the described variants to current databases will facilitate inclusion of southern Africans in medical research efforts, particularly when family and medical histories can be correlated with genome-wide data.
Rates of uterine leiomyomata (UL) are 2–3 times higher in African Americans than in European Americans. It is unclear whether inherited factors explain the ethnic disparity. To investigate the presence of risk alleles for UL that are highly differentiated in frequency between African Americans and European Americans, the authors conducted an admixture-based genome-wide scan of 2,453 UL cases confirmed by ultrasound or surgery in the Black Women's Health Study (1997–2009), a national prospective cohort study. Controls (n = 2,102) were women who did not report a UL diagnosis through 2009. Mean percentage of European ancestry was significantly lower among cases (20.00%) than among controls (21.63%; age-adjusted mean difference = −1.76%, 95% confidence interval: −2.40, −1.12; P < 0.0001), and the association was stronger in younger cases. Admixture analyses showed suggestive evidence of association at chromosomes 2, 4, and 10. The authors also genotyped a dense set of tag single nucleotide polymorphisms at different loci associated with UL in Japanese women but failed to replicate the associations. This suggests that genetic variation for UL differs in populations with and without African ancestry. The admixture findings further indicate that no single highly differentiated locus is responsible for the ethnic disparity in UL, raising the possibility that multiple variants jointly contribute to the higher incidence of UL in African Americans.
African Americans; African continental ancestry group; European continental ancestry group; female; genetics; leiomyoma; prospective studies; uterine neoplasms
To determine whether shared epitope (SE)–containing HLA–DRB1 alleles are associated with rheumatoid arthritis (RA) in African Americans and whether their presence is associated with higher degrees of global (genome-wide) genetic admixture from the European population.
In this multicenter cohort study, African Americans with early RA and matched control subjects were analyzed. In addition to measurement of serum anti–cyclic citrullinated peptide (anti-CCP) antibodies and HLA–DRB1 genotyping, a panel of >1,200 ancestry-informative markers was analyzed in patients with RA and control subjects, to estimate the proportion of European ancestry.
The frequency of SE-containing HLA–DRB1 alleles was 25.2% in African American patients with RA versus 13.6% in control subjects (P = 0.00005). Of 321 patients with RA, 42.1% had at least 1 SE-containing allele, compared with 25.3% of 166 control subjects (P = 0.0004). The mean estimated percent European ancestry was associated with SE-containing HLA–DRB1 alleles in African Americans, regardless of disease status (RA or control). As reported in RA patients of European ancestry, there was a significant association of the SE with the presence of the anti-CCP antibody: 86 (48.9%) of 176 patients with anti-CCP antibody–positive RA had at least 1 SE allele, compared with 36 (32.7%) of 110 patients with anti-CCP antibody–negative RA (P = 0.01, by chi-square test).
HLA–DRB1 alleles containing the SE are strongly associated with susceptibility to RA in African Americans. The absolute contribution is less than that reported in RA among populations of European ancestry, in which ~50–70% of patients have at least 1 SE allele. As in Europeans with RA, the SE association was strongest in the subset of African American patients with anti-CCP antibodies. The finding of a higher degree of European ancestry among African Americans with SE alleles suggests that a genetic risk factor for RA was introduced into the African American population through admixture, thus making these individuals more susceptible to subsequent environmental or unknown factors that trigger the disease.
Motivation: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature.
Results: We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants.
Availability: LTSOFT software is available online http://www.hsph.harvard.edu/faculty/alkes-price/software/
Supplementary information: Supplementary data are available at Bioinformatics online.
Important knowledge about the determinants of complex human phenotypes can be obtained from the estimation of heritability, the fraction of phenotypic variation in a population that is determined by genetic factors. Here, we make use of extensive phenotype data in Iceland, long-range phased genotypes, and a population-wide genealogical database to examine the heritability of 11 quantitative and 12 dichotomous phenotypes in a sample of 38,167 individuals. Most previous estimates of heritability are derived from family-based approaches such as twin studies, which may be biased upwards by epistatic interactions or shared environment. Our estimates of heritability, based on both closely and distantly related pairs of individuals, are significantly lower than those from previous studies. We examine phenotypic correlations across a range of relationships, from siblings to first cousins, and find that the excess phenotypic correlation in these related individuals is predominantly due to shared environment as opposed to dominance or epistasis. We also develop a new method to jointly estimate narrow-sense heritability and the heritability explained by genotyped SNPs. Unlike existing methods, this approach permits the use of information from both closely and distantly related pairs of individuals, thereby reducing the variance of estimates of heritability explained by genotyped SNPs while preventing upward bias. Our results show that common SNPs explain a larger proportion of the heritability than previously thought, with SNPs present on Illumina 300K genotyping arrays explaining more than half of the heritability for the 23 phenotypes examined in this study. Much of the remaining heritability is likely to be due to rare alleles that are not captured by standard genotyping arrays.
Phenotype is a function of a genome and its environment. Heritability is the fraction of variation in a phenotype determined by genetic factors in a population. Current methods to estimate heritability rely on the phenotypic correlations of closely related individuals and are potentially upwardly biased, due to the impact of epistasis and shared environment. We develop new methods to estimate heritability over both closely and distantly related individuals. By examining the phenotypic correlation among different types of related individuals such as siblings, half-siblings, and first cousins, we show that shared environment is the primary determinant of inflated estimates of heritability. For a large number of phenotypes, it is not known how much of the heritability is explained by SNPs included on current genotyping platforms. Existing methods to estimate this component of heritability are biased in the presence of related individuals. We develop a method that permits the inclusion of both closely and distantly related individuals when estimating heritability explained by genotyped SNPs and use it to make estimates for 23 medically relevant phenotypes. These estimates can be used to increase our understanding of the distribution and frequency of functionally relevant variants and thereby inform the design of future studies.
The recent explosion in available genetic data has led to significant advances in understanding the demographic histories of and relationships among human populations. It is still a challenge, however, to infer reliable parameter values for complicated models involving many populations. Here, we present MixMapper, an efficient, interactive method for constructing phylogenetic trees including admixture events using single nucleotide polymorphism (SNP) genotype data. MixMapper implements a novel two-phase approach to admixture inference using moment statistics, first building an unadmixed scaffold tree and then adding admixed populations by solving systems of equations that express allele frequency divergences in terms of mixture parameters. Importantly, all features of the model, including topology, sources of gene flow, branch lengths, and mixture proportions, are optimized automatically from the data and include estimates of statistical uncertainty. MixMapper also uses a new method to express branch lengths in easily interpretable drift units. We apply MixMapper to recently published data for Human Genome Diversity Cell Line Panel individuals genotyped on a SNP array designed especially for use in population genetics studies, obtaining confident results for 30 populations, 20 of them admixed. Notably, we confirm a signal of ancient admixture in European populations—including previously undetected admixture in Sardinians and Basques—involving a proportion of 20–40% ancient northern Eurasian ancestry.
admixture; human populations; genetic drift; moment statistics
We present a DNA library preparation method that has allowed us to reconstruct a high coverage (30X) genome sequence of a Denisovan, an extinct relative of Neandertals. The quality of this genome allows a direct estimation of Denisovan heterozygosity indicating that genetic diversity in these archaic hominins was extremely low. It also allows tentative dating of the specimen on the basis of “missing evolution” in its genome, detailed measurements of Denisovan and Neandertal admixture into present-day human populations, and the generation of a near-complete catalog of genetic changes that swept to high frequency in modern humans since their divergence from Denisovans.
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.
Mutations are the raw material of evolution, but have been difficult to study directly. We report the largest study of new mutations to date: 2,058 germline changes discovered by analyzing 85,289 Icelanders at 2,477 microsatellites. The paternal-to-maternal mutation rate ratio is 3.3, and the rate in fathers doubles from age 20 to 58 whereas there is no association with age in mothers. Longer microsatellite alleles are more mutagenic and tend to decrease in length, whereas the opposite is seen for shorter alleles. We use these empirical observations to build a model that we apply to individuals for whom we have both genome sequence and microsatellite data, allowing us to estimate key parameters of evolution without calibration to the fossil record. We infer that the sequence mutation rate is 1.4–2.3×10−8 per base pair per generation (90% credible interval), and that human-chimpanzee speciation occurred 3.7–6.6 million years ago.
Long-range migrations and the resulting admixtures between populations have been important forces shaping human genetic diversity. Most existing methods for detecting and reconstructing historical admixture events are based on allele frequency divergences or patterns of ancestry segments in chromosomes of admixed individuals. An emerging new approach harnesses the exponential decay of admixture-induced linkage disequilibrium (LD) as a function of genetic distance. Here, we comprehensively develop LD-based inference into a versatile tool for investigating admixture. We present a new weighted LD statistic that can be used to infer mixture proportions as well as dates with fewer constraints on reference populations than previous methods. We define an LD-based three-population test for admixture and identify scenarios in which it can detect admixture events that previous formal tests cannot. We further show that we can uncover phylogenetic relationships among populations by comparing weighted LD curves obtained using a suite of references. Finally, we describe several improvements to the computation and fitting of weighted LD curves that greatly increase the robustness and speed of the calculations. We implement all of these advances in a software package, ALDER, which we validate in simulations and apply to test for admixture among all populations from the Human Genome Diversity Project (HGDP), highlighting insights into the admixture history of Central African Pygmies, Sardinians, and Japanese.
admixture; linkage disequilibrium
The Roma people, living throughout Europe and West Asia, are a diverse population linked by the Romani language and culture. Previous linguistic and genetic studies have suggested that the Roma migrated into Europe from South Asia about 1,000–1,500 years ago. Genetic inferences about Roma history have mostly focused on the Y chromosome and mitochondrial DNA. To explore what additional information can be learned from genome-wide data, we analyzed data from six Roma groups that we genotyped at hundreds of thousands of single nucleotide polymorphisms (SNPs). We estimate that the Roma harbor about 80% West Eurasian ancestry–derived from a combination of European and South Asian sources–and that the date of admixture of South Asian and European ancestry was about 850 years before present. We provide evidence for Eastern Europe being a major source of European ancestry, and North-west India being a major source of the South Asian ancestry in the Roma. By computing allele sharing as a measure of linkage disequilibrium, we estimate that the migration of Roma out of the Indian subcontinent was accompanied by a severe founder event, which appears to have been followed by a major demographic expansion after the arrival in Europe.
Large data sets on human genetic variation have been collected recently, but their usefulness for learning about history and natural selection has been limited by biases in the ways polymorphisms were chosen. We report large subsets of SNPs from the International HapMap Project1,2 that allow us to overcome these biases and to provide accurate measurement of a quantity of crucial importance for understanding genetic variation: the allele frequency spectrum. Our analysis shows that East Asian and northern European ancestors shared the same population bottleneck expanding out of Africa but that both also experienced more recent genetic drift, which was greater in East Asians.
The Levant is a region in the Near East with an impressive record of continuous human existence and major cultural developments since the Paleolithic period. Genetic and archeological studies present solid evidence placing the Middle East and the Arabian Peninsula as the first stepping-stone outside Africa. There is, however, little understanding of demographic changes in the Middle East, particularly the Levant, after the first Out-of-Africa expansion and how the Levantine peoples relate genetically to each other and to their neighbors. In this study we analyze more than 500,000 genome-wide SNPs in 1,341 new samples from the Levant and compare them to samples from 48 populations worldwide. Our results show recent genetic stratifications in the Levant are driven by the religious affiliations of the populations within the region. Cultural changes within the last two millennia appear to have facilitated/maintained admixture between culturally similar populations from the Levant, Arabian Peninsula, and Africa. The same cultural changes seem to have resulted in genetic isolation of other groups by limiting admixture with culturally different neighboring populations. Consequently, Levant populations today fall into two main groups: one sharing more genetic characteristics with modern-day Europeans and Central Asians, and the other with closer genetic affinities to other Middle Easterners and Africans. Finally, we identify a putative Levantine ancestral component that diverged from other Middle Easterners ∼23,700–15,500 years ago during the last glacial period, and diverged from Europeans ∼15,900–9,100 years ago between the last glacial warming and the start of the Neolithic.
Population stratification caused by nonrandom mating between groups of the same species is often due to geographical distances leading to physical separation followed by genetic drift of allele frequencies in each group. In humans, population structures are also often driven by geographical barriers or distances; however, humans might also be structured by abstract factors such as culture, a consequence of their reasoning and self-awareness. Religion in particular, is one of the unusual conceptual factors that can drive human population structures. This study explores the Levant, a region flanked by the Middle East and Europe, where individual and population relationships are still strongly influenced by religion. We show that religious affiliation had a strong impact on the genomes of the Levantines. In particular, conversion of the region's populations to Islam appears to have introduced major rearrangements in populations' relations through admixture with culturally similar but geographically remote populations, leading to genetic similarities between remarkably distant populations like Jordanians, Moroccans, and Yemenis. Conversely, other populations, like Christians and Druze, became genetically isolated in the new cultural environment. We reconstructed the genetic structure of the Levantines and found that a pre-Islamic expansion Levant was more genetically similar to Europeans than to Middle Easterners.
Two African apes are the closest living relatives of humans: the chimpanzee (Pan troglodytes) and the bonobo (Pan paniscus). Although they are similar in many respects, bonobos and chimpanzees differ strikingly in key social and sexual behaviours1–4, and for some of these traits they show more similarity with humans than with each other. Here we report the sequencing and assembly of the bonobo genome to study its evolutionary relationship with the chimpanzee and human genomes. We find that more than three per cent of the human genome is more closely related to either the bonobo or the chimpanzee genome than these are to each other. These regions allow various aspects of the ancestry of the two ape species to be reconstructed. In addition, many of the regions that overlap genes may eventually help us understand the genetic basis of phenotypes that humans share with one of the two apes to the exclusion of the other.
Genome wide association studies (GWAS) have proven a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here we show that extremely low-coverage sequencing (0.1–0.5x) captures almost as much of the common (>5%) and low-frequency (1–5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r2 of 0.71 using off-target data (0.24x average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome sequencing datasets we show that association statistics obtained using ultra low-coverage sequencing data attain similar P-values at known associated variants as genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in ultra low-coverage sequencing can yield several times the effective sample size of SNP-array GWAS, and a commensurate increase in statistical power.
Comparisons of DNA sequences between Neandertals and present-day humans have shown that Neandertals share more genetic variants with non-Africans than with Africans. This could be due to interbreeding between Neandertals and modern humans when the two groups met subsequent to the emergence of modern humans outside Africa. However, it could also be due to population structure that antedates the origin of Neandertal ancestors in Africa. We measure the extent of linkage disequilibrium (LD) in the genomes of present-day Europeans and find that the last gene flow from Neandertals (or their relatives) into Europeans likely occurred 37,000–86,000 years before the present (BP), and most likely 47,000–65,000 years ago. This supports the recent interbreeding hypothesis and suggests that interbreeding may have occurred when modern humans carrying Upper Paleolithic technologies encountered Neandertals as they expanded out of Africa.
One of the key discoveries from the analysis of the Neandertal genome is that Neandertals share more genetic variants with non-Africans than with Africans. This observation is consistent with two hypotheses: interbreeding between Neandertals and modern humans after modern humans emerged out of Africa or population structure in the ancestors of Neandertals and modern humans. These hypotheses make different predictions about the date of last gene exchange between the ancestors of Neandertals and modern non-Africans. We estimate this date by measuring the extent of linkage disequilibrium (LD) in the genomes of present-day Europeans and find that the last gene flow from Neandertals into Europeans likely occurred 37,000–86,000 years before the present (BP), and most likely 47,000–65,000 years ago. This supports the recent interbreeding hypothesis and suggests that interbreeding occurred when modern humans carrying Upper Paleolithic technologies encountered Neandertals as they expanded out of Africa.
One enduring question in evolutionary biology is the extent of archaic admixture in the genomes of present-day populations. In this paper, we present a test for ancient admixture that exploits the asymmetry in the frequencies of the two nonconcordant gene trees in a three-population tree. This test was first applied to detect interbreeding between Neandertals and modern humans. We derive the analytic expectation of a test statistic, called the D statistic, which is sensitive to asymmetry under alternative demographic scenarios. We show that the D statistic is insensitive to some demographic assumptions such as ancestral population sizes and requires only the assumption that the ancestral populations were randomly mating. An important aspect of D statistics is that they can be used to detect archaic admixture even when no archaic sample is available. We explore the effect of sequencing error on the false-positive rate of the test for admixture, and we show how to estimate the proportion of archaic ancestry in the genomes of present-day populations. We also investigate a model of subdivision in ancestral populations that can result in D statistics that indicate recent admixture.
admixture; gene genealogies; lineage sorting
The risk of type 2 diabetes is approximately 2-fold higher in African Americans than in European Americans even after adjusting for known environmental risk factors, including socioeconomic status (SES), suggesting that genetic factors may explain some of this population difference in disease risk. However, relatively few genetic studies have examined this hypothesis in a large sample of African Americans with and without diabetes. Therefore, we performed an admixture analysis using 2,189 ancestry-informative markers in 7,021 African Americans (2,373 with type 2 diabetes and 4,648 without) from the Atherosclerosis Risk in Communities Study, the Jackson Heart Study, and the Multiethnic Cohort to 1) determine the association of type 2 diabetes and its related quantitative traits with African ancestry controlling for measures of SES and 2) identify genetic loci for type 2 diabetes through a genome-wide admixture mapping scan. The median percentage of African ancestry of diabetic participants was slightly greater than that of non-diabetic participants (study-adjusted difference = 1.6%, P<0.001). The odds ratio for diabetes comparing participants in the highest vs. lowest tertile of African ancestry was 1.33 (95% confidence interval 1.13–1.55), after adjustment for age, sex, study, body mass index (BMI), and SES. Admixture scans identified two potential loci for diabetes at 12p13.31 (LOD = 4.0) and 13q14.3 (Z score = 4.5, P = 6.6×10−6). In conclusion, genetic ancestry has a significant association with type 2 diabetes above and beyond its association with non-genetic risk factors for type 2 diabetes in African Americans, but no single gene with a major effect is sufficient to explain a large portion of the observed population difference in risk of diabetes. There undoubtedly is a complex interplay among specific genetic loci and non-genetic factors, which may both be associated with overall admixture, leading to the observed ethnic differences in diabetes risk.
Recombination, together with mutation, is the ultimate source of genetic variation in populations. We leverage the recent mixture of people of African and European ancestry in the Americas to build a genetic map measuring the probability of crossing-over at each position in the genome, based on about 2.1 million crossovers in 30,000 unrelated African Americans. At intervals of more than three megabases it is nearly identical to a map built in Europeans. At finer scales it differs significantly, and we identify about 2,500 recombination hotspots that are active in people of West African ancestry but nearly inactive in Europeans. The probability of a crossover at these hotspots is almost fully controlled by the alleles an individual carries at PRDM9 (P<10−245). We identify a 17 base pair DNA sequence motif that is enriched in these hotspots, and is an excellent match to the predicted binding target of African-enriched alleles of PRDM9.
Previous genetic studies have suggested a history of sub-Saharan African gene flow into some West Eurasian populations after the initial dispersal out of Africa that occurred at least 45,000 years ago. However, there has been no accurate characterization of the proportion of mixture, or of its date. We analyze genome-wide polymorphism data from about 40 West Eurasian groups to show that almost all Southern Europeans have inherited 1%–3% African ancestry with an average mixture date of around 55 generations ago, consistent with North African gene flow at the end of the Roman Empire and subsequent Arab migrations. Levantine groups harbor 4%–15% African ancestry with an average mixture date of about 32 generations ago, consistent with close political, economic, and cultural links with Egypt in the late middle ages. We also detect 3%–5% sub-Saharan African ancestry in all eight of the diverse Jewish populations that we analyzed. For the Jewish admixture, we obtain an average estimated date of about 72 generations. This may reflect descent of these groups from a common ancestral population that already had some African ancestry prior to the Jewish Diasporas.
Southern Europeans and Middle Eastern populations are known to have inherited a small percentage of their genetic material from recent sub-Saharan African migrations, but there has been no estimate of the exact proportion of this gene flow, or of its date. Here, we apply genomic methods to show that the proportion of African ancestry in many Southern European groups is 1%–3%, in Middle Eastern groups is 4%–15%, and in Jewish groups is 3%–5%. To estimate the dates when the mixture occurred, we develop a novel method that estimates the size of chromosomal segments of distinct ancestry in individuals of mixed ancestry. We verify using computer simulations that the method produces useful estimates of population mixture dates up to 300 generations in the past. By applying the method to West Eurasians, we show that the dates in Southern Europeans are consistent with events during the Roman Empire and subsequent Arab migrations. The dates in the Jewish groups are older, consistent with events in classical or biblical times that may have occurred in the shared history of Jewish populations.
Obesity is an important cause of morbidity and mortality worldwide. In the U.S., the prevalence of obesity is higher in African Americans than whites, even after adjustment for socioeconomic status. This leads to the hypothesis that differences in genetic background may contribute to racial/ethnic differences in obesity-related traits. We tested this hypothesis by conducting a genome-wide admixture mapping scan using 1,350 ancestry-informative SNPs in 3,531 self-identified blacks from the Atherosclerosis Risk in Communities (ARIC) study. We used these markers to estimate the overall proportions of European ancestry (PEA) for each individual and then scanned for the association between PEA and obesity-related traits (both continuous and dichotomous) at each locus. The median (interquartile range) PEA was 0.151 (0.115). PEA was inversely correlated with continuous body mass index (BMI), weight, and subscapular skinfold thickness, even after adjusting for socioeconomic factors. In contrast, PEA was positively correlated with BMI-adjusted waist circumference. Using admixture mapping on dichotomized traits, we identified a locus on 2p23.3 to be suggestively associated with BMI (locus-specific LOD = 4.11) and weight (locus-specific LOD = 4.07). After adjusting for global PEA, each additional copy of a European ancestral allele at the 2p23.3 peak was associated with a BMI decrease of ∼0.92 kg/m2 (p = 2.9 × 10-5). Further mapping in this region on chromosome 2 may be able to uncover causative variants underlying obesity, which may offer insights into the control of energy homeostasis.
Coronary heart disease (CHD) is the leading cause of mortality in African Americans. To identify common genetic polymorphisms associated with CHD and its risk factors (LDL- and HDL-cholesterol (LDL-C and HDL-C), hypertension, smoking, and type-2 diabetes) in individuals of African ancestry, we performed a genome-wide association study (GWAS) in 8,090 African Americans from five population-based cohorts. We replicated 17 loci previously associated with CHD or its risk factors in Caucasians. For five of these regions (CHD: CDKN2A/CDKN2B; HDL-C: FADS1-3, PLTP, LPL, and ABCA1), we could leverage the distinct linkage disequilibrium (LD) patterns in African Americans to identify DNA polymorphisms more strongly associated with the phenotypes than the previously reported index SNPs found in Caucasian populations. We also developed a new approach for association testing in admixed populations that uses allelic and local ancestry variation. Using this method, we discovered several loci that would have been missed using the basic allelic and global ancestry information only. Our conclusions suggest that no major loci uniquely explain the high prevalence of CHD in African Americans. Our project has developed resources and methods that address both admixture- and SNP-association to maximize power for genetic discovery in even larger African-American consortia.
To date, most large-scale genome-wide association studies (GWAS) carried out to identify risk factors for complex human diseases and traits have focused on population of European ancestry. It is currently unknown whether the same loci associated with complex diseases and traits in Caucasians will replicate in population of African ancestry. Here, we conducted a large GWAS to identify common DNA polymorphisms associated with coronary heart disease (CHD) and its risk factors (type-2 diabetes, hypertension, smoking status, and LDL- and HDL-cholesterol) in 8,090 African Americans as part of the NHLBI Candidate gene Association Resource (CARe) Project. We replicated 17 associations previously reported in Caucasians, suggesting that the same loci carry common DNA sequence variants associated with CHD and its risk factors in Caucasians and African Americans. At five of these 17 loci, we used the different patterns of linkage disequilibrium between populations of European and African ancestry to identify DNA sequence variants more strongly associated with phenotypes than the index SNPs found in Caucasians, suggesting smaller genomic intervals to search for causal alleles. We also used the CARe data to develop new statistical methods to perform association studies in admixed populations. The CARe Project data represent an extraordinary resource to expand our understanding of the genetics of complex diseases and traits in non-European-derived populations.
Lipoprotein(a) (Lp(a)) is an important causal cardiovascular risk factor, with serum Lp(a) levels predicting atherosclerotic heart disease and genetic determinants of Lp(a) levels showing association with myocardial infarction. Lp(a) levels vary widely between populations, with African-derived populations having nearly 2-fold higher Lp(a) levels than European Americans. We investigated the genetic basis of this difference in 4464 African Americans from the Jackson Heart Study (JHS) using a panel of up to 1447 ancestry informative markers, allowing us to accurately estimate the African ancestry proportion of each individual at each position in the genome. In an unbiased genome-wide admixture scan for frequency-differentiated genetic determinants of Lp(a) level, we found a convincing peak (LOD = 13.6) at 6q25.3, which spans the LPA locus. Dense fine-mapping of the LPA locus identified a number of strongly associated, common biallelic SNPs, a subset of which can account for up to 7% of the variation in Lp(a) level, as well as >70% of the African-European population differences in Lp(a) level. We replicated the association of the most strongly associated SNP, rs9457951 (p = 6×10−22, 27% change in Lp(a) per allele, ∼5% of Lp(a) variance explained in JHS), in 1,726 African Americans from the Dallas Heart Study and found an even stronger association after adjustment for the kringle(IV) repeat copy number. Despite the strong association with Lp(a) levels, we find no association of any LPA SNP with incident coronary heart disease in 3,225 African Americans from the Atherosclerosis Risk in Communities Study.