Accurate haplotype inference for multiple linked single-nucleotide polymorphisms using sibship data. Genetics 2006 Sep;174(1):499-509
Date
06/20/2006Pubmed ID
16783022Pubmed Central ID
PMC1569787DOI
10.1534/genetics.105.054213Scopus ID
2-s2.0-33748917422 (requires institutional sign-in at Scopus site) 8 CitationsAbstract
Sibships are commonly used in genetic dissection of complex diseases, particularly for late-onset diseases. Haplotype-based association studies have been advocated as powerful tools for fine mapping and positional cloning of complex disease genes. Existing methods for haplotype inference using data from relatives were originally developed for pedigree data. In this study, we proposed a new statistical method for haplotype inference for multiple tightly linked single-nucleotide polymorphisms (SNPs), which is tailored for extensively accumulated sibship data. This new method was implemented via an expectation-maximization (EM) algorithm without the usual assumption of linkage equilibrium among markers. Our EM algorithm does not incur extra computational burden for haplotype inference using sibship data when compared with using unrelated parental data. Furthermore, its computational efficiency is not affected by increasing sibship size. We examined the robustness and statistical performance of our new method in simulated data created from an empirical haplotype data set of human growth hormone gene 1. The utility of our method was illustrated with an application to the analyses of haplotypes of three candidate genes for osteoporosis.
Author List
Liu PY, Lu Y, Deng HWMESH terms used to index this publication - Major topics in bold
AlgorithmsApolipoproteins E
Computer Simulation
Data Interpretation, Statistical
Gene Frequency
Genetics, Population
Haplotypes
Humans
Likelihood Functions
Models, Genetic
Polymorphism, Single Nucleotide
Receptor, Parathyroid Hormone, Type 1
Receptors, Calcitriol
Sample Size
Software
