ArticleCurrent genomics2007
A general quantitative genetic model for haplotyping a complex trait in humans.
Article in Current genomics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multi-marker linkage disequilibrium mapping of quantitative trait loci.Briefings in bioinformatics · 2017Article
- Genome-wide two-marker linkage disequilibrium mapping of quantitative trait loci.BMC genetics · 2014Article
- Modeling haplotype-haplotype interactions in case-control genetic association studies.Frontiers in genetics · 2012Article
- A statistical design for testing transgenerational genomic imprinting in natural human populations.PloS one · 2011Article
- Modeling genetic imprinting effects of DNA sequences with multilocus polymorphism data.Algorithms for molecular biology : AMB · 2009Article
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4 authors.
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Abstract
Uncertainty about linkage phases of multiple single nucleotide polymorphisms (SNPs) in heterozygous diploids challenges the identification of specific DNA sequence variants that encode a complex trait. A statistical technique implemented with the EM algorithm has been developed to infer the effects of SNP haplotypes from genotypic data by assuming that one haplotype (called the risk haplotype) performs differently from the rest (called the non-risk haplotype). This assumption simplifies the definition and estimation of genotypic values of diplotypes for a complex trait, but will reduce the power to detect the risk haplotype when non-risk haplotypes contain substantial diversity. In this article, we incorporate general quantitative genetic theory to specify the differentiation of different haplotypes in terms of their genetic control of a complex trait. A model selection procedure is deployed to test the best number and combination of risk haplotypes, thus providing a precise and powerful test of genetic determination in association studies. Our method is derived on the maximum likelihood theory and has been shown through simulation studies to be powerful for the characterization of the genetic architecture of complex quantitative traits.
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