title: Haplotypes vs single marker linkage disequilibrium tests: what do we gain?
abstract: The genetic dissection of complex diseases represents a formidable challenge for modern human genetics. Recently, it has been suggested that linkage disequilibrium (LD) based methods will be a powerful approach for delineating complex disease genes. Most proposed LD test statistics search for association between a single marker and a putative trait locus. However, the power of a single marker association test may suffer because LD information contained in flanking markers is ignored. Intuitively, haplotypes (which can be regarded as a collection of ordered markers) may be more powerful than individual, unorganised markers. In this study, we derive the analytical tools based on standard chi-square statistics to directly investigate and compare the power between multilocus haplotypes and single marker LD tests. More specifically, novel formulas are obtained in order to calculate expected haplotype frequencies of unlimited size. This study demonstrates that the use of haplotypes can significantly improve the power and robustness of mapping disease genes. Additionally, we detail how the power of haplotype based association tests are affected by important population genetic parameters such as the genetic distance between markers and disease locus, mode of disease inheritance, age of trait causing mutation, frequency of associated marker allele, and level of initial LD. Finally, published data from the Hereditary Hemochromatosis disease region is used to illustrate the utility of haplotypes.
authors: Human Genetics Center, University of Texas-Houston, Houston, Texas 77225, USA.
authors: J
authors: J
authors: Akey
authors: L
authors: L
authors: Jin
authors: M
authors: M
authors: Xiong
mesh_terms: D030342:Genetic Diseases, Inborn / Q000235:genetics; D005819:Genetic Markers; D005820:Genetic Testing; D005828:Genetics, Population; D006239:Haplotypes; D006432:Hemochromatosis; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D015233:Models, Statistical
chemical_list: D005819:Genetic Markers