Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies - PubMed (original) (raw)

Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

Qiong Yang et al. J Probab Stat. 2012.

Abstract

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical) or different types of components (e.g., some are continuous and others are categorical). We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.

PubMed Disclaimer

Figures

Figure 1

Figure 1

Causal diagram showing potential confounding mechanisms for the association between the genotype of a genetic marker G and the phenotype Y. The variable K denotes the intermediate phenotype, L the collection of known environmental and genetic risk factors, and U the unknown environmental and genetic risk factors such as population stratification and unknown genetic variants in linkage disequilibrium with G.

References

    1. International HapMap Consortium. Frazer KA, Ballinger DG, et al. A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449:851–861. - PMC - PubMed
    1. 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061–1073. - PMC - PubMed
    1. Hindorff LA, Junkins HA, Hall PN, Mehta JP, Manolio TA. A catalog of published genome-wide association studies. National Human Genome Research Institute; 2011. http://www.genome.gov/gwastudies/
    1. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–753. - PMC - PubMed
    1. Eichler EE, Flint J, Gibson G, et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nature Reviews Genetics. 2010;11(6):446–450. - PMC - PubMed

Grants and funding

LinkOut - more resources