Evaluation of approaches to identify associated SNPs that explain the linkage evidence in nuclear families with affected siblings.
Linkage analysis is often followed by association mapping to localize disease variants. In this paper, we evaluate approaches to determine how much of the observed linkage evidence, namely the identity-by-descent (IBD) sharing at the linkage peak, is explained by associated SNPs. We study several methods: Homozygote Sharing Tests (HST), Genotype Identity-by-Descent Sharing Test (GIST), and a permutation approach. We also propose a new approach, HSTMLB, combining HST and the Maximum Likelihood Binomial (MLB) linkage statistic. These methods can identify SNPs partially explaining the linkage peak, but only HST and HSTMLB can identify SNPs that do not fully explain the linkage evidence and be applied to multiple-SNPs. We contrast these methods with the association tests implemented in the software LAMP. In our simulations, GIST is more powerful at finding SNPs that partially explain the linkage peak, while HST and HSTMLB are equally powerful at identifying SNPs that do not fully explain the linkage peak. When applied to the North American Rheumatoid Arthritis Consortium data, HST and HSTMLB identify marker pairs that may fully explain the linkage peak on chromosome 6. In conclusion, HST and HSTMLB provide simple and flexible tools to identify SNPs that explain the IBD sharing at the linkage peak.
Chen
Ming-Huei
MH
Department of Neurology and Framingham Heart Study, Boston University, Boston, Mass., USA. mhchen@bu.edu
Van Eerdewegh
Paul
P
Vincent
Quentin B
QB
Alcais
Alexandre
A
Abel
Laurent
L
Dupuis
Josée
J
D056726:Genetic Association Studies / Q000379:methods*; D006720:Homozygote; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D009679:Nuclear Family; D020641:Polymorphism, Single Nucleotide; D035781:Siblings; D012984:Software