Premium
Estimating haplotype relative risks in complex disease from unphased SNPs data in families using a likelihood adjusted for ascertainment
Author(s) -
Carayol J.,
Philippi A.,
Tores F.
Publication year - 2006
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20178
Subject(s) - haplotype , haplotype estimation , inference , expectation–maximization algorithm , statistics , econometrics , single nucleotide polymorphism , computer science , biology , genetics , maximum likelihood , genotype , mathematics , gene , artificial intelligence
The understanding of complex diseases and insights to improve their medical management may be achieved through the deduction of how specific haplotypes may play a joint effect to change relative risk information. In this paper we describe an ascertainment adjusted likelihood‐based method to estimate haplotype relative risks using pooled family data coming from association and/or linkage studies that were used to identify specific haplotypes. Haplotype‐based analysis tends to require a large amount of parameters to capture all the information that leads to efficiency problems. An adaptation of the Stochastic Expectation Maximization algorithm is used for haplotypes inference from genotypic data and to reduce the number of nuisance parameters for risk estimation. Using different simulations, we show that this method provides unbiased relative risk estimates even in case of departure from Hardy‐Weinberg equilibrium. Genet. Epidemiol . 2006. © 2006 Wiley‐Liss, Inc.