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Isaac Newton Institute for Mathematical Sciences

Mathematical and Statistical Aspects of Molecular Biology

On family-based association tests: a nonparametric test for repeatedly measured quantitative traits confounded by unknown environmental effects, poly-genetic factors and other unknown covariates.

Authors: Christoph Lange (Harvard School of Public Health, Boston), Alex J. MacGregor (Twin Research & Genetic Epidemiology Unit, London), Nan M. Laird (Harvard School of Public Health, Boston)

Abstract

We propose a new family-based association test, FBAT-PC, for repeatedly measured quantitative traits. It is designed for situations where there may be partially or completely unknown confounding factors or covariates which cannot be adequately modelled. Using generalized principal component analysis, FBAT-PC amplifies the genetic effects of each measurement by constructing a new overall phenotype with maximal heritability. Analytically and in simulation studies, we compare FBAT-PC with standard methodology and assess its power. Applications of FBAT-PC to an osteopetrosis study show the practical relevance of FBAT-PC.