An efficient, robust and unified method for mapping complex traits (III): combined linkage/linkage-disequilibrium analysis.
Extending the method for linkage analysis [Zhao et al., 1998a: Am. J. Med. Genet. 77:366-383; 1998b: Am. J. Med. Genet. 79:49-61], this article describes a method for the linkage-disequilibrium analysis, and for combining linkage and linkage-disequilibrium analyses. As highly dense markers are increasingly used in genome scans, one or more markers are not only linked with the disease genes if they exist, but also likely in linkage-disequilibrium with those putative genes. Hence, linkage-disequilibrium analysis potentially offers additional information about positions of putative disease genes. Combining both linkage and linkage-disequilibrium signals, this approach is able to improve positional signals. As before, the proposed method is a model-based approach, but semiparametric via the estimating equation technique. Under the assumptions of penetrance and allele frequency, this method efficiently estimates recombination fractions for linkage analysis and odds ratios for linkage-disequilibrium analysis. As described in two previous papers, this method is relatively more robust than the lod score methods, since it requires weaker assumption than conditional independence. While the estimated recombination fractions are used for inference as part of linkage analysis, the estimated odds ratios are used for linkage-disequilibrium inference and combined linkage, and linkage-disequilibrium parameters can be used to test combined linkage/linkage-disequilibrium analysis. This approach has been implemented, named gSCAN, and its compiled version is available for trial on request via the web site (http:/lynx.fhcrc.org/qge). We applied this new approach to affected sib-pair data collected for the genome scan to localize type 1 diabetes genes. Under an assumed autosomal dominant gene model, the linkage analysis confirms an earlier suggestion of one major gene around D6S281. Interestingly, the linkage-disequilibrium analysis suggests several additional signals around D6S250, GATA30, D6S311, D6S441, D6S442, D6S415, D6S411, D6S305, and a290xh9. The linkage analysis, on the other hand, suggests a signal around D6S281, while providing supporting evidence for several other marker loci. However, the combined analysis did not provide strong support for any of the findings, implying that linkage and linkage-disequilibrium findings are not consistent.
Zhao
L P
LP
Quantitative Genetic Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. lzhao@fhcrc.org
Quiaoit
F
F
Aragaki
C
C
Hsu
L
L
D000483:Alleles; D002874:Chromosome Mapping / Q000379:methods; D002896:Chromosomes, Human, Pair 6; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics*; D005260:Female; D005787:Gene Frequency; D008040:Genetic Linkage; D005819:Genetic Markers; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium; D008297:Male; D008433:Mathematics; D008957:Models, Genetic; D010375:Pedigree; D019655:Quantitative Trait, Heritable
D005819:Genetic Markers