As whole genome sequence becomes a routine component of gene discovery studies in humans, we will have an exhaustive catalog of genetic variation and the challenge becomes understanding the phenotypic consequences of these variants. Statistical genetic methods and analytical approaches that are concerned with optimizing phenotypes for gene discovery for complex traits offer two general categories of advantages. They may increase power to localize genes of interest and also aid in interpreting associations between genetic variants and disease outcomes by suggesting potential mechanisms and pathways through which genes may affect outcomes. Such phenotype optimization approaches include use of allied phenotypes such as symptoms or ages of onset to reduce genetic heterogeneity within a set of cases, study of quantitative risk factors or endophenotypes, joint analyses of related phenotypes, and derivation of new phenotypes designed to extract independent measures underlying the correlations among a set of related phenotypes through approaches such as principal components. New opportunities are also presented by technological advances that permit efficient collection of hundreds or thousands of phenotypes on an individual, including phenotypes more proximal to the level of gene action such as levels of gene expression, microRNAs, or metabolic and proteomic profiles.
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