The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
New approaches to population stratification in genome-wide association studies
Open accessAbstract
Genome-wide association (GWA) studies are an effective approach for identifying genetic variants associated with disease risk. GWA studies can be confounded by population stratification — systematic ancestry differences between cases and controls — which has previously been addressed by methods that infer genetic ancestry. Those methods perform well in data sets in which population structure is the only kind of structure present but are inadequate in data sets that also contain family structure or cryptic relatedness. Here, we review recent progress on methods that correct for stratification while accounting for these additional complexities. systematic ancestry differences between cases and controls — which has previously been addressed by methods that infer genetic ancestry. Those methods perform well in data sets in which population structure is the only kind of structure present but are inadequate in data sets that also contain family structure or cryptic relatedness. Here, we review recent progress on methods that correct for stratification while accounting for these additional complexities.
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Principal components analysis corrects for stratification in genome-wide association studies
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Background
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Genome-wide efficient mixed-model analysis for association studies
Zhou, X., & Stephens, M. (2012) · Nature Genetics · doi:10.1038/ng.2310 · Cited by 3,627
“For the derivatives of the log-likelihood and log-restricted likelihood functions (5)–(8), we need to evaluate two types of quantities: trace terms (trace(H−1G), trace(H−1GH−1G), trace(PxG) and trace(PxGPxG)), and vector-matrix-vector (5)”
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Efficient Bayesian mixed-model analysis increases association power in large cohorts
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Self-citationComputationally efficient whole-genome regression for quantitative and binary traits
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Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
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Metadata
- Authors
- Price, A. L., Zaitlen, N. A., Reich, D., & Patterson, N.
- Venue
- Nature Reviews Genetics
- Published
- 2010-06-15
- Volume
- 11
- Issue
- 7
- Pages
- 459-463
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- OpenAlex
- W2009588715
- Cited by
- 1,205
- References
- 17
- FWCI
- 5.59
- Type
- journal article
- Language
- en
- Open access
- green
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