Advantages and pitfalls in the application of mixed-model association methods

Yang, Jian, Zaitlen, Noah A., Goddard, Michael E., Visscher, Peter M. and Price, Alkes L. (2014) Advantages and pitfalls in the application of mixed-model association methods. Nature Genetics, 46 2: 100-106. doi:10.1038/ng.2876


Author Yang, Jian
Zaitlen, Noah A.
Goddard, Michael E.
Visscher, Peter M.
Price, Alkes L.
Title Advantages and pitfalls in the application of mixed-model association methods
Journal name Nature Genetics   Check publisher's open access policy
ISSN 1061-4036
1546-1718
Publication date 2014-02
Year available 2014
Sub-type Article (original research)
DOI 10.1038/ng.2876
Open Access Status
Volume 46
Issue 2
Start page 100
End page 106
Total pages 7
Publisher Nature Publishing Group
Language eng
Formatted abstract
Mixed linear models are emerging as a method of choice for conducting genetic association studies in humans and other organisms. The advantages of the mixed-linear-model association (MLMA) method include the prevention of false positive associations due to population or relatedness structure and an increase in power obtained through the application of a correction that is specific to this structure. An underappreciated point is that MLMA can also increase power in studies without sample structure by implicitly conditioning on associated loci other than the candidate locus. Numerous variations on the standard MLMA approach have recently been published, with a focus on reducing computational cost. These advances provide researchers applying MLMA methods with many options to choose from, but we caution that MLMA methods are still subject to potential pitfalls. Here we describe and quantify the advantages and pitfalls of MLMA methods as a function of study design and provide recommendations for the application of these methods in practical settings.
Keyword Genome-Wide Association
Structured Populations
Common Snps
Multiple-Sclerosis
Variants
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2015 Collection
UQ Diamantina Institute Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 74 times in Thomson Reuters Web of Science Article | Citations
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