Powerful regression-based quantitative-trait linkage analysis of general pedigrees.
We present a new method of quantitative-trait linkage analysis that combines the simplicity and robustness of regression-based methods and the generality and greater power of variance-components models. The new method is based on a regression of estimated identity-by-descent (IBD) sharing between relative pairs on the squared sums and squared differences of trait values of the relative pairs. The method is applicable to pedigrees of arbitrary structure and to pedigrees selected on the basis of trait value, provided that population parameters of the trait distribution can be correctly specified. Ambiguous IBD sharing (due to incomplete marker information) can be accommodated in the method by appropriate specification of the variance-covariance matrix of IBD sharing between relative pairs. We have implemented this regression-based method and have performed simulation studies to assess, under a range of conditions, estimation accuracy, type I error rate, and power. For normally distributed traits and in large samples, the method is found to give the correct type I error rate and an unbiased estimate of the proportion of trait variance accounted for by the additive effects of the locus-although, in cases where asymptotic theory is doubtful, significance levels should be checked by simulations. In large sibships, the new method is slightly more powerful than variance-components models. The proposed method provides a practical and powerful tool for the linkage analysis of quantitative traits.
Sham
Pak C
PC
SGDP Research Centre, Institute of Psychiatry, King's College, Denmark Hill, London SE5 8AF, United Kingdom. p.sham@iop.kcl.ac.uk
Purcell
Shaun
S
Cherny
Stacey S
SS
Abecasis
Gonçalo R
GR
D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D010375:Pedigree; D019655:Quantitative Trait, Heritable; D012044:Regression Analysis; D012985:Software Design; D018709:Statistics, Nonparametric