ArticleAmerican journal of human genetics2008
A unified association analysis approach for family and unrelated samples correcting for stratification.
Article in American journal of human genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.
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Who cites it
86 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study.Human molecular genetics · 2011Pooled it
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- Exploration of shared genetic susceptibility loci between type 1 diabetes and rheumatoid arthritis in the Pakistani population.BMC research notes · 2019Article
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- A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS).Statistical applications in genetics and molecular biology · 2018Article
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26 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
There are two common designs for association mapping of complex diseases: case-control and family-based designs. A case-control sample is more powerful to detect genetic effects than a family-based sample that contains the same numbers of affected and unaffected persons, although additional markers may be required to control for spurious association. When family and unrelated samples are available, statistical analyses are often performed in the family and unrelated samples separately, conditioning on parental information for the former, thus resulting in reduced power. In this report, we propose a unified approach that can incorporate both family and case-control samples and, provided the additional markers are available, at the same time corrects for population stratification. We apply the principal components of a marker matrix to adjust for the effect of population stratification. This unified approach makes it unnecessary to perform a conditional analysis of the family data and is more powerful than the separate analyses of unrelated and family samples, or a meta-analysis performed by combining the results of the usual separate analyses. This property is demonstrated in both a variety of simulation models and empirical data. The proposed approach can be equally applied to the analysis of both qualitative and quantitative traits.
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