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Am. J. Hum. Genet. 76:934949, 2005 Joint Modeling of Linkage and Association: Identifying SNPs Responsible
 

Summary: Am. J. Hum. Genet. 76:934­949, 2005
934
Joint Modeling of Linkage and Association: Identifying SNPs Responsible
for a Linkage Signal
Mingyao Li, Michael Boehnke, and Gonc¸alo R. Abecasis
Department of Biostatistics, School of Public Health, and Center for Statistical Genetics, University of Michigan, Ann Arbor
Once genetic linkage has been identified for a complex disease, the next step is often association analysis, in which
single-nucleotide polymorphisms (SNPs) within the linkage region are genotyped and tested for association with
the disease. If a SNP shows evidence of association, it is useful to know whether the linkage result can be explained,
in part or in full, by the candidate SNP. We propose a novel approach that quantifies the degree of linkage
disequilibrium (LD) between the candidate SNP and the putative disease locus through joint modeling of linkage
and association. We describe a simple likelihood of the marker data conditional on the trait data for a sample of
affected sib pairs, with disease penetrances and disease-SNP haplotype frequencies as parameters. We estimate model
parameters by maximum likelihood and propose two likelihood-ratio tests to characterize the relationship of the
candidate SNP and the disease locus. The first test assesses whether the candidate SNP and the disease locus are
in linkage equilibrium so that the SNP plays no causal role in the linkage signal. The second test assesses whether
the candidate SNP and the disease locus are in complete LD so that the SNP or a marker in complete LD with it
may account fully for the linkage signal. Our method also yields a genetic model that includes parameter estimates
for disease-SNP haplotype frequencies and the degree of disease-SNP LD. Our method provides a new tool for
detecting linkage and association and can be extended to study designs that include unaffected family members.

  

Source: Abecasis, Goncalo - Department of Biostatistics, University of Michigan

 

Collections: Biology and Medicine; Mathematics