Advantages and pitfalls in the application of mixed-model association methods
Top Cited Papers
- 29 January 2014
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Genetics
- Vol. 46 (2), 100-106
- https://doi.org/10.1038/ng.2876
Abstract
Alkes Price, Peter Visscher and colleagues provide recommendations on the application of mixed-linear-model association methods across a range of study designs. 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.Keywords
This publication has 45 references indexed in Scilit:
- Host–microbe interactions have shaped the genetic architecture of inflammatory bowel diseaseNature, 2012
- Genome-wide efficient mixed-model analysis for association studiesNature Genetics, 2012
- Link Functions in Multi‐Locus Genetic Models: Implications for Testing, Prediction, and InterpretationGenetic Epidemiology, 2012
- Differential confounding of rare and common variants in spatially structured populationsNature Genetics, 2012
- Genomic inflation factors under polygenic inheritanceEuropean Journal of Human Genetics, 2011
- Estimating Missing Heritability for Disease from Genome-wide Association StudiesAmerican Journal of Human Genetics, 2011
- GCTA: A Tool for Genome-wide Complex Trait AnalysisAmerican Journal of Human Genetics, 2010
- Hundreds of variants clustered in genomic loci and biological pathways affect human heightNature, 2010
- Principal components analysis corrects for stratification in genome-wide association studiesNature Genetics, 2006
- A unified mixed-model method for association mapping that accounts for multiple levels of relatednessNature Genetics, 2005