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Analysis of multiple phenotypes in genome-wide genetic mapping studies.

ABSTRACT: BACKGROUND: Complex traits may be defined by a range of different criteria. It would result in a loss of information to perform analyses simply on the basis of a final clinical dichotomized affected / unaffected variable. RESULTS: We assess the performance of four alternative approaches for the analysis of multiple phenotypes in genetic association studies. We describe the four methods in detail and discuss their relative theoretical merits and disadvantages. Using simulation we demonstrate that PCA provides the greatest power when applied to both correlated phenotypes and with large numbers of phenotypes. The multivariate approach had low type I error only with independent phenotypes or small numbers of phenotypes. In this study, our application of the four methods to schizophrenia data provides converging evidence of the relative performance of the methods. CONCLUSIONS: Via power analysis of simulated data and testing of experimental data, we conclude that PCA, creating one variable based on a linear combination of all the traits, performs optimally. We propose that our comparison will provide insight into the properties of the methods and help researchers to choose appropriate strategy in future experimental studies.

SUBMITTER: Suo C

PROVIDER: S-EPMC3655878 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Analysis of multiple phenotypes in genome-wide genetic mapping studies.

Suo Chen C Toulopoulou Timothea T Bramon Elvira E Walshe Muriel M Picchioni Marco M Murray Robin R Ott Jurg J

BMC bioinformatics 20130502

<h4>Background</h4>Complex traits may be defined by a range of different criteria. It would result in a loss of information to perform analyses simply on the basis of a final clinical dichotomized affected / unaffected variable.<h4>Results</h4>We assess the performance of four alternative approaches for the analysis of multiple phenotypes in genetic association studies. We describe the four methods in detail and discuss their relative theoretical merits and disadvantages. Using simulation we dem ...[more]

PMID: 23639181

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Project description:A goal of modern biology is to develop the genotype-phenotype (G→P) map, a predictive understanding of how genomic information generates trait variation that forms the basis of both natural and managed communities. As microbiome research advances, however, it has become clear that many of these traits are symbiotic extended phenotypes, being governed by genetic variation encoded not only by the host's own genome, but also by the genomes of myriad cryptic symbionts. Building a reliable G→P map therefore requires accounting for the multitude of interacting genes and even genomes involved in symbiosis. Here, we use naturally occurring genetic variation in 191 strains of the model microbial symbiont Sinorhizobium meliloti paired with two genotypes of the host Medicago truncatula in four genome-wide association studies (GWAS) to determine the genomic architecture of a key symbiotic extended phenotype-partner quality, or the fitness benefit conferred to a host by a particular symbiont genotype, within and across environmental contexts and host genotypes. We define three novel categories of loci in rhizobium genomes that must be accounted for if we want to build a reliable G→P map of partner quality; namely, (i) loci whose identities depend on the environment, (ii) those that depend on the host genotype with which rhizobia interact, and (iii) universal loci that are likely important in all or most environments. IMPORTANCE Given the rapid rise of research on how microbiomes can be harnessed to improve host health, understanding the contribution of microbial genetic variation to host phenotypic variation is pressing, and will better enable us to predict the evolution of (and select more precisely for) symbiotic extended phenotypes that impact host health. We uncover extensive context-dependency in both the identity and functions of symbiont loci that control host growth, which makes predicting the genes and pathways important for determining symbiotic outcomes under different conditions more challenging. Despite this context-dependency, we also resolve a core set of universal loci that are likely important in all or most environments, and thus, serve as excellent targets both for genetic engineering and future coevolutionary studies of symbiosis.

Dataset Information

Analysis of multiple phenotypes in genome-wide genetic mapping studies.

Publications

Analysis of multiple phenotypes in genome-wide genetic mapping studies.

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