Source:http://linkedlifedata.com/resource/pubmed/id/20470424
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
2010-6-28
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| pubmed:abstractText |
Genome-wide association (GWA) studies that use population-based association approaches may identify spurious associations in the presence of population admixture. In this paper, we propose a novel three-stage approach that is computationally efficient and robust to population admixture and more powerful than the family-based association test (FBAT) for GWA studies with family data.We propose a three-stage approach for GWA studies with family data. The first stage is to perform linear regression ignoring phenotypic correlations among family members. SNPs with a first stage p-value below a liberal cut-off (e.g. 0.1) are then analyzed in the second stage that employs a linear mixed effects (LME) model that accounts for within family correlations. Next, SNPs that reach genome-wide significance (e.g. 10-6 for 34,625 genotyped SNPs in this paper) are analyzed in the third stage using FBAT, with correction of multiple testing only for SNPs that enter the third stage. Simulations are performed to evaluate type I error and power of the proposed method compared to LME adjusting for 10 principal components (PC) of the genotype data. We also apply the three-stage approach to the GWA analyses of uric acid in Framingham Heart Study's SNP Health Association Resource (SHARe) project.
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| pubmed:grant | |
| pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-10782012,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-11329691,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-11793695,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-12454799,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-12689793,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-14502464,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-16451707,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-16614226,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-16862161,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-17701906,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-17924333,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-18834626,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-20040588,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-7607457,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-8447318,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20470424-9545414
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:issn |
1471-2156
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| pubmed:author | |
| pubmed:issnType |
Electronic
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| pubmed:volume |
11
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
40
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| pubmed:dateRevised |
2010-9-30
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| pubmed:meshHeading | |
| pubmed:year |
2010
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| pubmed:articleTitle |
A three-stage approach for genome-wide association studies with family data for quantitative traits.
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| pubmed:affiliation |
Department of Neurology and Framingham Heart Study, Boston University School of Medicine, MA, USA.
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| pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't,
Evaluation Studies,
Research Support, N.I.H., Extramural
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