Mapping Quantitative Trait Loci in Outbred Pedigrees (original) (raw)

Handbook of Statistical Genetics, 2004

Abstract

In this chapter, we present statistical methods for mapping quantitative trait loci (QTLs) in outbred or complex pedigrees. Such pedigrees exist primarily in livestock populations, also in human populations, and occasionally in experimental animal or plant populations. The main focus of this chapter is on linkage mapping, but methods for linkage disequilibrium (LD) and combined linkage/LD mapping are also outlined. The latter are very recent proposals and are at the time of writing less developed than linkage methods. We describe least-squares and maximum likelihood (ML) methods for estimating QTL effects, and variance components analysis by approximate (residual) ML for estimating QTL variance contributions. We describe Bayesian QTL mapping, its prior distributions and other distributional assumptions, its implementation via Markov chain Monte Carlo (MCMC) algorithms, its inferences, and contrast it with frequentist methodology. Genotype sampling algorithms using genotypic peeling, allelic peeling, or descent graphs are described. Genotype samplers are a critical component of MCMC algorithms implementing ML and Bayesian analyses for complex pedigrees. Lastly, fine-mapping methods including chromosome dissection and linkage disequilibrium mapping using current and historical recombinations, respectively, are outlined, and initial method developments combining linkage disequilibrium and linkage are presented. Keywords: quantitative trait loci; pedigree analysis; Bayesian inference; variance components; residual maximum likelihood; outbred population; linkage mapping; linkage disequilibrium; Markov chain monte carlo; peeling; descent graph

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