Linkage disequilibrium interval mapping of quantitative trait loci
1 Unité de Biométrie et Intelligence Artificielle, Institut National de la Recherche Agronomique, BP 52627, 31326 Castanet-Tolosan Cedex, France
2 Laboratoire de Statistiques et Probabilités, Université Paul Sabatier, 118 route de Narbonne, 31400 Toulouse, France
3 Laboratoire de Génétique Cellulaire, Institut National de la Recherche Agronomique, BP 52627, 31326 Castanet-Tolosan Cedex, France
4 Station d'Amélioration Génétique des Animaux, Institut National de la Recherche Agronomique, BP 52627, 31326 Castanet-Tolosan Cedex, France
BMC Genomics 2006, 7:54 doi:10.1186/1471-2164-7-54Published: 16 March 2006
For many years gene mapping studies have been performed through linkage analyses based on pedigree data. Recently, linkage disequilibrium methods based on unrelated individuals have been advocated as powerful tools to refine estimates of gene location. Many strategies have been proposed to deal with simply inherited disease traits. However, locating quantitative trait loci is statistically more challenging and considerable research is needed to provide robust and computationally efficient methods.
Under a three-locus Wright-Fisher model, we derived approximate expressions for the expected haplotype frequencies in a population. We considered haplotypes comprising one trait locus and two flanking markers. Using these theoretical expressions, we built a likelihood-maximization method, called HAPim, for estimating the location of a quantitative trait locus. For each postulated position, the method only requires information from the two flanking markers. Over a wide range of simulation scenarios it was found to be more accurate than a two-marker composite likelihood method. It also performed as well as identity by descent methods, whilst being valuable in a wider range of populations.
Our method makes efficient use of marker information, and can be valuable for fine mapping purposes. Its performance is increased if multiallelic markers are available. Several improvements can be developed to account for more complex evolution scenarios or provide robust confidence intervals for the location estimates.