Data Access Commitee for study Fine-Scale Genomic Analyses Of Admixed Individuals Reveal Unrecognized Genetic Ancestry Components In Argentina
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ABSTRACT:
PROVIDER: EGAC00001001634 | EGA |
REPOSITORIES: EGA
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Project description:Similarly to other populations across the Americas, Argentinean populations trace back their genetic ancestry into African, European and Native American ancestors, reflecting a complex demographic history with multiple migration and admixture events in pre- and post-colonial times. However, little is known about the sub-continental origins of these three main ancestries. We present new high-throughput genotyping data for 87 admixed individuals across Argentina. This data was combined to previously published data for admixed individuals in the region and then compared to different reference panels specifically built to perform population structure analyses at a sub-continental level. Concerning the Native American ancestry, we could identify four Native American components segregating in modern Argentinean populations. Three of them are also found in modern South American populations and are specifically represented in Central Andes, Central Chile/Patagonia, and Subtropical and Tropical Forests geographic areas. The fourth component might be specific to the Central Western region of Argentina, and it is not well represented in any genomic data from the literature. As for the European and African ancestries, we confirmed previous results about origins from Southern Europe, Western and Central Western Africa, and we provide evidences for the presence of Northern European and Eastern African ancestries.
| S-EPMC7365470 | biostudies-literature
Project description:Study the sub-continental origins of African, European and Native American genetic ancestries of Argentinean populations.
| EGAS00001004492 | EGA
Project description:For samples of admixed individuals, it is possible to test for both ancestry effects via admixture mapping and genotype effects via association mapping. Here, we describe a joint test called BMIX that combines admixture and association statistics at single markers. We first perform high-density admixture mapping using local ancestry. We then perform association mapping using stratified regression, wherein for each marker genotypes are stratified by local ancestry. In both stages, we use generalized linear models, providing the advantage that the joint test can be used with any phenotype distribution with an appropriate link function. To define the alternative densities for admixture mapping and association mapping, we describe a method based on autocorrelation to empirically estimate the testing burdens of admixture mapping and association mapping. We then describe a joint test that uses the posterior probabilities from admixture mapping as prior probabilities for association mapping, capitalizing on the reduced testing burden of admixture mapping relative to association mapping. By simulation, we show that BMIX is potentially orders-of-magnitude more powerful than the MIX score, which is currently the most powerful frequentist joint test. We illustrate the gain in power through analysis of fasting plasma glucose among 922 unrelated, non-diabetic, admixed African Americans from the Howard University Family Study. We detected loci at 1q24 and 6q26 as genome-wide significant via admixture mapping; both loci have been independently reported from linkage analysis. Using the association data, we resolved the 1q24 signal into two regions. One region, upstream of the gene FAM78B, contains three binding sites for the transcription factor PPARG and two binding sites for HNF1A, both previously implicated in the pathology of type 2 diabetes. The fact that both loci showed ancestry effects may provide novel insight into the genetic architecture of fasting plasma glucose in individuals of African ancestry.
| S-EPMC3245293 | biostudies-literature
Project description:Inferring the ancestry at each locus in the genome of recently admixed individuals (e.g., Latino Americans) plays a major role in medical and population genetic inferences, ranging from finding disease-risk loci, to inferring recombination rates, to mapping missing contigs in the human genome. Although many methods for local ancestry inference have been proposed, most are designed for use with genotyping arrays and fail to make use of the full spectrum of data available from sequencing. In addition, current haplotype-based approaches are very computationally demanding, requiring large computational time for moderately large sample sizes. Here we present new methods for local ancestry inference that leverage continent-specific variants (CSVs) to attain increased performance over existing approaches in sequenced admixed genomes. A key feature of our approach is that it incorporates the admixed genomes themselves jointly with public datasets, such as 1000 Genomes, to improve the accuracy of CSV calling. We use simulations to show that our approach attains accuracy similar to widely used computationally intensive haplotype-based approaches with large decreases in runtime. Most importantly, we show that our method recovers comparable local ancestries, as the 1000 Genomes consensus local ancestry calls in the real admixed individuals from the 1000 Genomes Project. We extend our approach to account for low-coverage sequencing and show that accurate local ancestry inference can be attained at low sequencing coverage. Finally, we generalize CSVs to sub-continental population-specific variants (sCSVs) and show that in some cases it is possible to determine the sub-continental ancestry for short chromosomal segments on the basis of sCSVs.
| S-EPMC3990492 | biostudies-literature
Project description:Studies of recombination and how it varies depend crucially on accurate recombination maps. We propose a new approach for constructing high-resolution maps of relative recombination rates based on the observation of ancestry switch points among admixed individuals. We show the utility of this approach using simulations and by applying it to SNP genotype data from a sample of 2,565 African Americans and 299 African Caribbeans and detecting several hundred thousand recombination events. Comparison of the inferred map with high-resolution maps from non-admixed populations provides evidence of fine-scale differentiation in recombination rates between populations. Overall, the admixed map is well predicted by the average proportion of admixture and the recombination rate estimates from the source populations. The exceptions to this are in areas surrounding known large chromosomal structural variants, specifically inversions. These results suggest that outside of structurally variable regions, admixture does not substantially disrupt the factors controlling recombination rates in humans.
| S-EPMC8582322 | biostudies-literature
Project description:Large-scale meta-analyses of genome-wide association studies (GWAS) have identified?>175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P?<?5?×?10-8) three novel loci associated with HDL-C near CD163-APOBEC1 (P?=?7.4?×?10-9), NCOA2 (P?=?1.6?×?10-8), and NID2-PTGDR (P?=?4.2?×?10-8), and one novel locus associated with TG near WDR11-FGFR2 (P?=?2.7?×?10-10). Conditional analyses identified a second signal near CD163-APOBEC1. We then combined results from the East Asian meta-analysis with association results from up to 187,365 European individuals from the Global Lipids Genetics Consortium in a trans-ancestry meta-analysis. This analysis identified (log10Bayes Factor??6.1) eight additional novel lipid loci. Among the twelve total loci identified, the index variants at eight loci have demonstrated at least nominal significance with other metabolic traits in prior studies, and two loci exhibited coincident eQTLs (P?<?1?×?10-5) in subcutaneous adipose tissue for BPTF and PDGFC. Taken together, these analyses identified multiple novel lipid loci, providing new potential therapeutic targets.
| S-EPMC6075203 | biostudies-literature
Project description:Recently, thanks to the increasing throughput of new technologies, we have begun to explore the full extent of alternative pre-mRNA splicing (AS) in the human transcriptome. This is unveiling a vast layer of complexity in isoform-level expression differences between individuals. We used previously published splicing sensitive microarray data from lymphoblastoid cell lines to conduct an in-depth analysis on splicing efficiency of known and predicted exons. By combining publicly available AS annotation with a novel algorithm designed to search for AS, we show that many real AS events can be detected within the usually unexploited, speculative majority of the array and at significance levels much below standard multiple-testing thresholds, demonstrating that the extent of cis-regulated differential splicing between individuals is potentially far greater than previously reported. Specifically, many genes show subtle but significant genetically controlled differences in splice-site usage. PCR validation shows that 42 out of 58 (72%) candidate gene regions undergo detectable AS, amounting to the largest scale validation of isoform eQTLs to date. Targeted sequencing revealed a likely causative SNP in most validated cases. In all 17 incidences where a SNP affected a splice-site region, in silico splice-site strength modeling correctly predicted the direction of the micro-array and PCR results. In 13 other cases, we identified likely causative SNPs disrupting predicted splicing enhancers. Using Fst and REHH analysis, we uncovered significant evidence that 2 putative causative SNPs have undergone recent positive selection. We verified the effect of five SNPs using in vivo minigene assays. This study shows that splicing differences between individuals, including quantitative differences in isoform ratios, are frequent in human populations and that causative SNPs can be identified using in silico predictions. Several cases affected disease-relevant genes and it is likely some of these differences are involved in phenotypic diversity and susceptibility to complex diseases.
| S-EPMC2780703 | biostudies-literature
Project description:We present a new haplotype-based approach for inferring local genetic ancestry of individuals in an admixed population. Most existing approaches for local ancestry estimation ignore the latent genetic relatedness between ancestral populations and treat them as independent. In this article, we exploit such information by building an inheritance model that describes both the ancestral populations and the admixed population jointly in a unified framework. Based on an assumption that the common hypothetical founder haplotypes give rise to both the ancestral and the admixed population haplotypes, we employ an infinite hidden Markov model to characterize each ancestral population and further extend it to generate the admixed population. Through an effective utilization of the population structural information under a principled nonparametric Bayesian framework, the resulting model is significantly less sensitive to the choice and the amount of training data for ancestral populations than state-of-the-art algorithms. We also improve the robustness under deviation from common modeling assumptions by incorporating population-specific scale parameters that allow variable recombination rates in different populations. Our method is applicable to an admixed population from an arbitrary number of ancestral populations and also performs competitively in terms of spurious ancestry proportions under a general multiway admixture assumption. We validate the proposed method by simulation under various admixing scenarios and present empirical analysis results from a worldwide-distributed dataset from the Human Genome Diversity Project.
| S-EPMC3416008 | biostudies-other
Project description:Admixed populations offer a unique opportunity for mapping diseases that have large disease allele frequency differences between ancestral populations. However, association analysis in such populations is challenging because population stratification may lead to association with loci unlinked to the disease locus.We show that local ancestry at a test single nucleotide polymorphism (SNP) may confound with the association signal and ignoring it can lead to spurious association. We demonstrate theoretically that adjustment for local ancestry at the test SNP is sufficient to remove the spurious association regardless of the mechanism of population stratification, whether due to local or global ancestry differences among study subjects; however, global ancestry adjustment procedures may not be effective. We further develop two novel association tests that adjust for local ancestry. Our first test is based on a conditional likelihood framework which models the distribution of the test SNP given disease status and flanking marker genotypes. A key advantage of this test lies in its ability to incorporate different directions of association in the ancestral populations. Our second test, which is computationally simpler, is based on logistic regression, with adjustment for local ancestry proportion. We conducted extensive simulations and found that the Type I error rates of our tests are under control; however, the global adjustment procedures yielded inflated Type I error rates when stratification is due to local ancestry difference.
| S-EPMC3042179 | biostudies-literature
Project description:Genome-wide association studies (GWAS) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWAS in admixed populations, such as the need to correctly adjust for population stratification to balance type I error with statistical power. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes we find that modeling HetLanc in its absence reduces statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs HetLanc is not large enough for GWAS to benefit from modeling heterogeneity.
| S-EPMC9900755 | biostudies-literature