An integrated phenomic approach to multivariate allelic association.
- Authors
- Medland, Sarah Elizabeth; Neale, Michael Churton
- Year
- 2010
- Journal
- European journal of human genetics : EJHG
- PMID
- 19707246
- DOI
- 10.1038/ejhg.2009.133
- PMCID
- PMC2807471
The increased feasibility of genome-wide association has resulted in association becoming the primary method used to localize genetic variants that cause phenotypic variation. Much attention has been focused on the vast multiple testing problems arising from analyzing large numbers of single nucleotide polymorphisms. However, the inflation of experiment-wise type I error rates through testing numerous phenotypes has received less attention. Multivariate analyses can be used to detect both pleiotropic effects that influence a latent common factor, and monotropic effects that operate at a variable-specific levels, whilst controlling for non-independence between phenotypes. In this study, we present a maximum likelihood approach, which combines both latent and variable-specific tests and which may be used with either individual or family data. Simulation results indicate that in the presence of factor-level association, the combined multivariate (CMV) analysis approach performs well with a minimal loss of power as compared with a univariate analysis of a factor or sum score (SS). As the deviation between the pattern of allelic effects and the factor loadings increases, the power of univariate analyses of both factor and SSs decreases dramatically, whereas the power of the CMV approach is maintained. We show the utility of the approach by examining the association between dopamine receptor D2 TaqIA and the initiation of marijuana, tranquilizers and stimulants in data from the Add Health Study. Perl scripts that takes ped and dat files as input and produces Mx scripts and data for running the CMV approach can be downloaded from www.vipbg.vcu.edu/~sarahme/WriteMx.
(a) Power to detect association (defined as the proportion of tests significant at an Ξ± of 0.05) under multivariate backgrounds a, b and c, for association models 2β6 (described in Table 2). In each case power is shown for the Combined multivariate approach (CMV) and for univariate analyses of the sum score (SS), Regression factor score (RFS) and Bartlett factor score (BFS). (b) Power to detect association under multivariate backgrounds d, e and f, for association models 2β6. (c) Power to detect association under multivariate backgrounds g, h and i, for association models 2β6.
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