An efficient, robust, and unified method for mapping complex traits (II): multipoint linkage analysis.
Extending the method for two-point linkage analysis [Zhao et al., 1998: Am J Med Genet 77:366-383], this paper introduces a semiparametric method for multipoint linkage analysis, expected to gain efficiency by using multiple markers simultaneously. Overcoming the longstanding statistical and computational challenge to the parametric approaches (or lod score methods) for multipoint linkage analysis, this semiparametric approach, based on the estimating equation technique, yields statistically efficient and yet robust estimates and enjoys the computational efficiency in processing multiple markers from large pedigrees. Its computational burden increases linearly with the sizes of pedigrees and with the number of marker loci. To illustrate this semiparametric method, we apply it to marker data gathered for the Breast Cancer Consortium. The result supports the earlier finding of the positive linkage with BRCA1 and has also shown that the multipoint linkage analysis has an improved power. In addition, we have applied this method to analyze genome scanning data that have been used to localize genes responsible for type 1 diabetes. In support of the earlier findings, the genome scanning detects the linkage signals on chromosome 6 but does not support the earlier suggestions of two major genes in that genome segment. Through sensitivity analysis, it appears that the results are robust to misspecification of penetrance and allele frequency.
Zhao
L P
LP
Quantitative Genetic Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA. lzhao@fhcrc.org
Quiaoit
F
F
Aragaki
C
C
Hsu
L
L
D001331:Automation; D001943:Breast Neoplasms / Q000235:genetics; D002874:Chromosome Mapping / Q000379:methods*; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D005260:Female; D008040:Genetic Linkage; D006801:Humans; D008957:Models, Genetic