Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Admixture mapping analysis in the context of GWAS with GAW18 data.

ABSTRACT: Admixture mapping is a disease-mapping strategy to identify disease susceptibility variants in an admixed population that is a result of mating between 2 historically separated populations differing in allele frequencies and disease prevalence. With the increasing availability of high-density genotyping data generated in genome-wide association studies, it is of interest to investigate how to apply admixture mapping in the context of the genome-wide association studies and how to adjust for admixture in association tests. In this study, we first evaluated 3 different local ancestry inference methods, LAMP, LAMP-LD, and MULTIMIX. Then we applied admixture mapping analysis based on estimated local ancestry. Finally, we performed association tests with adjustment for local ancestry.

SUBMITTER: Chen M

PROVIDER: S-EPMC4143627 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Admixture mapping analysis in the context of GWAS with GAW18 data.

Chen Mengjie M Yang Can C Li Cong C Hou Lin L Chen Xiaowei X Zhao Hongyu H

BMC proceedings 20140617 Suppl 1

Admixture mapping is a disease-mapping strategy to identify disease susceptibility variants in an admixed population that is a result of mating between 2 historically separated populations differing in allele frequencies and disease prevalence. With the increasing availability of high-density genotyping data generated in genome-wide association studies, it is of interest to investigate how to apply admixture mapping in the context of the genome-wide association studies and how to adjust for admi ...[more]

PMID: 25519317

Similar Datasets

A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests.

Project description:To develop effective methods for GWAS in admixed populations such as African Americans.We show that, when testing the null hypothesis that the test SNP is not in background linkage disequilibrium with the causal variants, several existing methods cannot control well the family-wise error rate (FWER) in the strong sense in GWAS. These existing methods include association tests adjusting for global ancestry and joint association tests that combine statistics from admixture mapping tests and association tests that correct for local ancestry. Furthermore, we describe a generalized sequential Bonferroni (smooth-GSB) procedure for GWAS that incorporates smoothed weights calculated from admixture mapping tests into association tests that correct for local ancestry. We have applied the smooth-GSB procedure to analyses of GWAS data on American Africans from the Atherosclerosis Risk in Communities (ARIC) Study.Our simulation studies indicate that the smooth-GSB procedure not only control the FWER, but also improves statistical power compared with association tests correcting for local ancestry.The smooth-GSB procedure can result in a better performance than several existing methods for GWAS in admixed populations.

| S-EPMC4821476 | biostudies-literature

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx.

Project description:BackgroundPopulation structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the v8 release also includes up to 15% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx improves portability of this research across populations and further characterizes the impact of population structure on GWAS colocalization.ResultsHere, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in seven tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe 31 loci (0.02%) where a significant colocalization is called only with one eQTL ancestry adjustment method. Notably, both adjustments produce similar numbers of significant colocalizations within each of two different colocalization methods, COLOC and FINEMAP. Finally, we identify a small subset of eQTL-associated variants highly correlated with local ancestry, providing a resource to enhance functional follow-up.ConclusionsWe provide a local ancestry map for admixed individuals in the GTEx v8 release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of the results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.

| S-EPMC7488497 | biostudies-literature

Admixture mapping comes of age.

Project description:Admixture mapping is based on the hypothesis that differences in disease rates between populations are due in part to frequency differences in disease-causing genetic variants. In admixed populations, these genetic variants occur more often on chromosome segments inherited from the ancestral population with the higher disease variant frequency. A genome scan for disease association requires only enough markers to identify the ancestral chromosome segments; for recently admixed populations, such as African Americans, 1,500-2,500 ancestry-informative markers (AIMs) are sufficient. The method was proposed over 50 years ago, but the AIM panels and statistical methods required have only recently become available. Since the first admixture scan in 2005, the genetic bases for a range of diseases/traits have been identified by admixture mapping. Here, we provide a historical perspective, review AIM panels and software packages, and discuss recent successes and unexpected insights into human diseases that exhibit disparate rates across human populations.

| S-EPMC7454031 | biostudies-literature

Generalized admixture mapping for complex traits.

Project description:Admixture mapping is a popular tool to identify regions of the genome associated with traits in a recently admixed population. Existing methods have been developed primarily for identification of a single locus influencing a dichotomous trait within a case-control study design. We propose a generalized admixture mapping (GLEAM) approach, a flexible and powerful regression method for both quantitative and qualitative traits, which is able to test for association between the trait and local ancestries in multiple loci simultaneously and adjust for covariates. The new method is based on the generalized linear model and uses a quadratic normal moment prior to incorporate admixture prior information. Through simulation, we demonstrate that GLEAM achieves lower type I error rate and higher power than ANCESTRYMAP both for qualitative traits and more significantly for quantitative traits. We applied GLEAM to genome-wide SNP data from the Illumina African American panel derived from a cohort of black women participating in the Healthy Pregnancy, Healthy Baby study and identified a locus on chromosome 2 associated with the averaged maternal mean arterial pressure during 24 to 28 weeks of pregnancy.

| S-EPMC3704244 | biostudies-literature

Analysis of genomic admixture in Uyghur and its implication in mapping strategy.

Project description:The Uyghur (UIG) population, settled in Xinjiang, China, is a population presenting a typical admixture of Eastern and Western anthropometric traits. We dissected its genomic structure at population level, individual level, and chromosome level by using 20,177 SNPs spanning nearly the entire chromosome 21. Our results showed that UIG was formed by two-way admixture, with 60% European ancestry and 40% East Asian ancestry. Overall linkage disequilibrium (LD) in UIG was similar to that in its parental populations represented in East Asia and Europe with regard to common alleles, and UIG manifested elevation of LD only within 500 kb and at a level of 0.1 <r(2) < 0.8 when ancestry-informative markers (AIMs) were used. The size of chromosomal segments that were derived from East Asian and European ancestries averaged 2.4 cM and 4.1 cM, respectively. Both the magnitude of LD and fragmentary ancestral chromosome segments indicated a long history of Uyghur. Under the assumption of a hybrid isolation (HI) model, we estimated that the admixture event of UIG occurred about 126 [107 approximately 146] generations ago, or 2520 [2140 approximately 2920] years ago assuming 20 years per generation. In spite of the long history and short LD of Uyghur compared with recent admixture populations such as the African-American population, we suggest that mapping by admixture LD (MALD) is still applicable in the Uyghur population but approximately 10-fold AIMs are necessary for a whole-genome scan.

| S-EPMC2427216 | biostudies-literature

MAGMA: generalized gene-set analysis of GWAS data.

Project description:By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well.

| S-EPMC4401657 | biostudies-literature

Network-assisted analysis of primary Sjogren's syndrome GWAS data in Han Chinese.

Project description:Primary Sjögren's syndrome (pSS) is a complex autoimmune disorder. So far, genetic research in pSS has lagged far behind and the underlying biological mechanism is unclear. Further exploring existing genome-wide association study (GWAS) data is urgently expected to uncover disease-related gene combination patterns. Herein, we conducted a network-based analysis by integrating pSS GWAS in Han Chinese with a protein-protein interactions network to identify pSS candidate genes. After module detection and evaluation, 8 dense modules covering 40 genes were obtained for further functional annotation. Additional 31 MHC genes with significant gene-level P-values (sigMHC-gene) were also remained. The combined module genes and sigMHC-genes, a total of 71 genes, were denoted as pSS candidate genes. Of these pSS candidates, 14 genes had been reported to be associated with any of pSS, RA, and SLE, including STAT4, GTF2I, HLA-DPB1, HLA-DRB1, PTTG1, HLA-DQB1, MBL2, TAP2, CFLAR, NFKBIE, HLA-DRA, APOM, HLA-DQA2 and NOTCH4. This is the first report of the network-assisted analysis for pSS GWAS data to explore combined gene patterns associated with pSS. Our study suggests that network-assisted analysis is a useful approach to gaining further insights into the biology of associated genes and providing important clues for future research into pSS etiology.

| S-EPMC4685393 | biostudies-other

Admixture mapping of genetic variants for uterine fibroids.

Project description:Uterine leiomyoma (UL) are benign neoplasms arising from the smooth muscle cells of the uterus. One of the established risk factors for UL is African American ethnicity. Studies have consistently shown that African Americans have two to three times higher risk compared with that of non-Hispanic Whites. However, there is still no adequate explanation for the higher risk among African Americans. To investigate the genetic contribution to the observed difference between the African American and European American populations, we conducted an admixture scan in 525 eligible African American women participants to the NIEHS uterine fibroid study (NIEHS-UFS). In models with no stratification, we found multiple genomic regions showing significant and suggestive evidence of association, with chromosomal band 2q32.2 at rs256552 showing the highest score (Z-score=7.86, Bonferroni adjusted P-value=5.5 × 10(-12)) consistent with the suggestive evidence reported for this genomic region in the Black Women's Health Study. However, in models stratified by the body mass index (BMI) covariate, chromosome 1q42.2 was the sole genomic region that consistently showed suggestive associations across the BMI categories tested (Z-scores ?-3.96, Bonferroni adjusted P-values ?0.107). In age-stratified models, a significant association was observed in the older category (age >40) reaching a Z-score of 6.44 (Bonferroni-adjusted P-value=1.64 × 10(-7)) at rs256552. The mean percentage of European ancestry among cases was lower than that among controls in the NIEHS-UFS study. However, our study did not show a significant association between mean percentage of European ancestry and UL.

| S-EPMC4583808 | biostudies-literature

Genome-wide Significance Thresholds for Admixture Mapping Studies.

Project description:Admixture mapping studies have become more common in recent years, due in part to technological advances and growing international efforts to increase the diversity of genetic studies. However, many open questions remain about appropriate implementation of admixture mapping studies, including how best to control for multiple testing, particularly in the presence of population structure. In this study, we develop a theoretical framework to characterize the correlation of local ancestry and admixture mapping test statistics in admixed populations with contributions from any number of ancestral populations and arbitrary population structure. Based on this framework, we develop an analytical approach for obtaining genome-wide significance thresholds for admixture mapping studies. We validate our approach via analysis of simulated traits with real genotype data for 8,064 unrelated African American and 3,425 Hispanic/Latina women from the Women's Health Initiative SNP Health Association Resource (WHI SHARe). In an application to these WHI SHARe data, our approach yields genome-wide significant p value thresholds of 2.1 × 10-5 and 4.5 × 10-6 for admixture mapping studies in the African American and Hispanic/Latina cohorts, respectively. Compared to other commonly used multiple testing correction procedures, our method is fast, easy to implement (using our publicly available R package), and controls the family-wise error rate even in structured populations. Importantly, we note that the appropriate admixture mapping significance threshold depends on the number of ancestral populations, generations since admixture, and population structure of the sample; as a result, significance thresholds are not, in general, transferable across studies.

| S-EPMC6407497 | biostudies-literature

Local Ancestry Association, Admixture Mapping, and Ongoing Challenges.

Project description: Not available

| S-EPMC5407465 | biostudies-literature