Human QTL linkage mapping

Abstract

Human quantitative trait locus (QTL) linkage mapping, although based on classical statistical genetic methods that have been around for many years, has been employed for genome-wide screening for only the last 10-15 years. In this time, there have been many success stories, ranging from QTLs that have been replicated in independent studies to those for which one or more genes underlying the linkage peak have been identified to a few with specific functional variants that have been confirmed in in vitro laboratory assays. Despite these successes, there is a general perception that linkage approaches do not work for complex traits, possibly because many human QTL linkage studies have been limited in sample size and have not employed the family configurations that maximize the power to detect linkage. We predict that human QTL linkage studies will continue to be productive for the next several years, particularly in combination with RNA expression level traits that are showing evidence of regulatory QTLs of large effect sizes and in combination with high-density genome-wide SNP panels. These SNP panels are being used to identify QTLs previously localized by linkage and linkage results are being used to place informative priors on genome-wide association studies. The methods are old the results are newThe basic statistical genetic methods that are used today for human quantitative trait locus (QTL) linkage mapping are extensions of classic models that were available twenty years ago at the second International Conference on Quantitative Genetics (ICQG2). The methods currently used for human QTL linkage mapping derive from classical variance component methods(1) and from the sibling pair regression methods proposed by Haseman and Elston (2). These methods are based on the simple intuition that relatives who are more alike phenotypically should be more likely to share alleles at the QTL (and at nearby linked markers) than relatives who are phenotypically discordant. Identity by descent (IBD) allele sharing is used to quantify the proportion of alleles a relative pair shares at a particular location in the genome that are copies of the same ancestral chromosome. In the simplest case of a sample of independent sibling pairs, the original Haseman-Elston linkage method proposed that the squared difference in the siblings' phenotypes can be regressed on IBD with evidence of linkage coming from a negative regression slope indicating that siblings who are relatively more discordant share fewer alleles at a marker than siblings who are relatively more phenotypically concordant. The variance component (1,3) and the newer revised Haseman-Elston methods (4,5) are somewhat more complex, allowing for the non-independence of multiple relative pairs from the same pedigree and incorporating polygenic background effects, effects of measured

Fluoride’s Effects on the Formation of Teeth and Bones, and the Influence of Genetics

“…QTL linkage studies in mice differ from QTL mapping in humans because many human QTL linkage studies are limited in sample size and do not have the family pedigrees that maximize the power to detect linkage (Almasy and Blangero, 2009). High-throughput genotyping and advanced computational analyses have led to the application of genome-wide association studies (GWAS) as a tool for mapping human disease genes (Hindorff et al, 2009).…”

Power of Variance Component Linkage Analysis to Identify Quantitative Trait Locus in Chickens

“…Therefore, results of linkage can be used as informative priors on the identification of genes responsible for the economically important phenotypic variations. In addition, this two step approach can reduce the multiple testing problems in the genome-wide association studies by the focused positional candidate gene analysis in the promising genome regions identified by linkage analysis (Almasy and Blangero, 2009). The appropriate use of VC linkage analysis can be facilitatedby projecting realistic scenarios of the performance of the method with certain data structure and size (Williams and Blangero, 1999).…”

The Maximum-Likelihood-Binomial method revisited: a robust approach for model-free linkage analysis of quantitative traits in large sibships

“…Their popularity has increased with the interest of the medical community in studying quantitative endophenotypes and the discovery of regulatory QTLs with a large effect on RNA levels [Almasy and Blangero, 2009]. In complex quantitative traits, linkage analysis methods based on identity-by-descent (IBD) allele sharing are commonly used, as they require no assumptions to be made concerning the genetic model underlying the trait.…”