ArticleGenetic epidemiology2012
Power comparison of admixture mapping and direct association analysis in genome-wide association studies.
Article in Genetic epidemiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Opportunities and challenges of local ancestry in genetic association analyses.American journal of human genetics · 2025Review
- Impact of cross-ancestry genetic architecture on GWASs in admixed populations.American journal of human genetics · 2023Article
- Admixture Mapping in African Americans Identifies New Risk Loci for HCV-Related Cirrhosis.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2023Article
- Impact of cross-ancestry genetic architecture on GWAS in admixed populations.bioRxiv : the preprint server for biology · 2023Article
- European genetic ancestry associated with risk of childhood ependymoma.Neuro-oncology · 2020Article
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- Genetics of cognitive trajectory in Brazilians: 15 years of follow-up from the Bambuí-Epigen Cohort Study of Aging.Scientific reports · 2019Article
- LEI: A Novel Allele Frequency-Based Feature Selection Method for Multi-ancestry Admixed Populations.Scientific reports · 2019Article
- Identifying Rare Variant Associations in Admixed Populations.Scientific reports · 2019Article
- Genome-wide Significance Thresholds for Admixture Mapping Studies.American journal of human genetics · 2019Article
- Ancient Ancestry Informative Markers for Identifying Fine-Scale Ancient Population Structure in Eurasians.Genes · 2018Article
- Detecting Multiethnic Rare Variants.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Genome-wide admixture and association study of subclinical atherosclerosis in the Women's Interagency HIV Study (WIHS).PloS one · 2017Article
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- Variants for HDL-C, LDL-C, and triglycerides identified from admixture mapping and fine-mapping analysis in African American families.Circulation. Cardiovascular genetics · 2015Article
- A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests.Human heredity · 2015Article
Corrections and comments
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2 authors.
Funding
Abstract
When dense markers are available, one can interrogate almost every common variant across the genome via imputation and single nucleotide polymorphism (SNP) test, which has become a routine in current genome-wide association studies (GWASs). As a complement, admixture mapping exploits the long-range linkage disequilibrium (LD) generated by admixture between genetically distinct ancestral populations. It is then questionable whether admixture mapping analysis is still necessary in detecting the disease associated variants in admixed populations. We argue that admixture mapping is able to reduce the burden of massive comparisons in GWASs; it therefore can be a powerful tool to locate the disease variants with substantial allele frequency differences between ancestral populations. In this report we studied a two-stage approach, where candidate regions are defined by conducting admixture mapping at stage 1, and single SNP association tests are followed at stage 2 within the candidate regions defined at stage 1. We first established the genome-wide significance levels corresponding to the criteria to define the candidate regions at stage 1 by simulations. We next compared the power of the two-stage approach with direct association analysis. Our simulations suggest that the two-stage approach can be more powerful than the standard genome-wide association analysis when the allele frequency difference of a causal variant in ancestral populations, is larger than 0.4. Our conclusion is consistent with a theoretical prediction by Risch and Tang ([2006] Am J Hum Genet 79:S254). Surprisingly, our study also suggests that power can be improved when we use less strict criteria to define the candidate regions at stage 1.
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