Reprioritizing Genetic Associations in Hit Regions Using LASSO-Based Resample Model Averaging

Department of Genetics, and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7265, USA.
Genetic Epidemiology (Impact Factor: 2.6). 07/2012; 36(5):451-62. DOI: 10.1002/gepi.21639
Source: PubMed


Significance testing one SNP at a time has proven useful for identifying genomic regions that harbor variants affecting human disease. But after an initial genome scan has identified a "hit region" of association, single-locus approaches can falter. Local linkage disequilibrium (LD) can make both the number of underlying true signals and their identities ambiguous. Simultaneous modeling of multiple loci should help. However, it is typically applied ad hoc: conditioning on the top SNPs, with limited exploration of the model space and no assessment of how sensitive model choice was to sampling variability. Formal alternatives exist but are seldom used. Bayesian variable selection is coherent but requires specifying a full joint model, including priors on parameters and the model space. Penalized regression methods (e.g., LASSO) appear promising but require calibration, and, once calibrated, lead to a choice of SNPs that can be misleadingly decisive. We present a general method for characterizing uncertainty in model choice that is tailored to reprioritizing SNPs within a hit region under strong LD. Our method, LASSO local automatic regularization resample model averaging (LLARRMA), combines LASSO shrinkage with resample model averaging and multiple imputation, estimating for each SNP the probability that it would be included in a multi-SNP model in alternative realizations of the data. We apply LLARRMA to simulations based on case-control genome-wide association studies data, and find that when there are several causal loci and strong LD, LLARRMA identifies a set of candidates that is enriched for true signals relative to single locus analysis and to the recently proposed method of Stability Selection.

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    • "With this in mind, it has been noted that (a) it is important to account for correlation between variables to obtain useful ranking; (b) we should increasingly be able to take advantage of the information gathered through other studies; and (c) Bayesian models provide a principled approach to guide variable prioritization. To adequately select among variables in the same locus, researchers have resorted to model selection approaches (Valdar et al. 2012) or approximations of the joint distribution of univariate test statistics (Faye et al. 2013; Hormozdiari et al. 2014). Prior information on variant annotation has been incorporated in models for eQTL (Veyrieras et al. 2008) and more recently for general traits (Pickrell 2014; Kichaev et al. 2014; Chung et al. 2014), and annotation programs increasingly attempt to include information on identified genetic loci (Wang et al. 2010). "
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