Variance Component Models for Mapping QTLs

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DESCRIPTION (provided by applicant):Spectacular advances in knowledge about the human genome, and parallel advances in marker technology, have out paced the complementary development of methods in statistical genetics for complex traits. In response to this situation, the current proposal seeks support of our work on quantitative trait loci (QTL) mapping to accommodate data structures and models that are of practical importance to the design and analysis of modern genetic studies. Our revised renewal application is to support a collaborative effort from the Institute for Behavioral Genetics (University of Colorado), the Institute of Psychiatry (University of London), the Wellcome Trust Centre of Human Genetics (University of Oxford), and the Center for Statistical Genetics (University of Michigan). Our goal in the continuation of this productive collaboration is to further extend the methodology of variance components analysis to accommodate more general data structures and models that are of practical importance to the design and analysis of modern genetic studies, and to integrate these into a comprehensive software package. Specifically we will: 1) further develop methods for linkage and association analysis of selected samples that are not only robust and unbiased but also sufficiently efficient for application to general pedigrees; 2) develop models for epistasis and gene-environment (GE) interaction using optimal selection procedures for the detection of these interactive effects; 3) extend the Fulker et al (1999) model that partitions genotypic effects into between-sibships and within-sibship components from single locus allelic associations to multilocus haplotype associations; 4) develop variance-components methods that are able to accommodate genotyping errors; 5) refine a method for multipoint IBD calculations by use of regression (Fulker et al, 1995) to combine exact single-marker IBD information obtained from our software package, MERLIN; and 6) extend MERLIN into a comprehensive variance components analysis package for combined, multi-point linkage and association analysis of multivariate discrete and continuous data, for both selected and random samples.