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Accurate haplotype inference for multiple linked single-nucleotide polymorphisms using sibship data. Genetics 2006 Sep;174(1):499-509

Date

06/20/2006

Pubmed ID

16783022

Pubmed Central ID

PMC1569787

DOI

10.1534/genetics.105.054213

Scopus ID

2-s2.0-33748917422   7 Citations

Abstract

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 HW

Author

Pengyuan Liu PhD Adjunct Professor in the Physiology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Algorithms
Apolipoproteins 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
jenkins-FCD Prod-484 8aa07fc50b7f6d102f3dda2f4c7056ff84294d1d