Searching for disease susceptibility variants in structured populations
1Department of Statistics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890, USA. roeder@stat.cmu.edu
Genomics
|June 4, 2008
Summary
Genome-wide association studies (GWAS) need careful control selection. This review covers methods to match control samples to case samples using genetic ancestry from SNP arrays, improving study accuracy.
Area of Science:
- Genetics
- Population Genetics
- Bioinformatics
Background:
- Genome-wide association studies (GWAS) are increasingly common for diverse phenotypes.
- Many GWAS lack control samples matched for genetic ancestry.
- Publicly available control databases, often genotyped on SNP arrays, present a valuable resource.
Purpose of the Study:
- To review methods for modeling genetic ancestry from SNP array data.
- To describe strategies for selecting appropriate control samples from databases.
- To address the challenge of effectively coupling case and control databases in GWAS.
Main Methods:
- Review of existing computational and statistical methods for genetic ancestry inference.
- Description of techniques for assessing population structure from single nucleotide polymorphism (SNP) array data.
- Analysis of strategies for matching control cohorts to case cohorts based on inferred ancestry.
Main Results:
- Several methods exist for modeling genetic ancestry using SNP array data.
- Effective strategies can be employed to select genetically similar control samples.
- Proper handling of population structure is crucial to avoid spurious associations in GWAS.
Conclusions:
- Matching control samples to case samples by genetic ancestry is essential for robust GWAS.
- Leveraging control databases requires careful consideration of population structure.
- Accurate genetic ancestry modeling improves the reliability of GWAS findings.
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