Model-free linkage analysis with covariates confirms linkage of prostate cancer to chromosomes 1 and 4 |
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| Authors: | Goddard K A Witte J S Suarez B K Catalona W J Olson J M |
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| Affiliation: | Department of Epidemiology and Biostatistics, Rammelkamp Center for Research and Education, MetroHealth Campus, Case Western Reserve University, Cleveland, OH 44109, USA. katrina@darwin.cwru.edu |
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| Abstract: | As with many complex genetic diseases, genome scans for prostate cancer have given conflicting results, often failing to provide replication of previous findings. One factor contributing to the lack of consistency across studies is locus heterogeneity, which can weaken or even eliminate evidence for linkage that is present only in a subset of families. Currently, most analyses either fail to account for locus heterogeneity or attempt to account for it only by partitioning data sets into smaller and smaller portions. In the present study, we model locus heterogeneity among affected sib pairs with prostate cancer by including covariates in the linkage analysis that serve as surrogate measures of between-family linkage differences. The model is a modification of the Olson conditional logistic model for affected relative pairs. By including Gleason score, age at onset, male-to-male transmission, and/or number of affected first-degree family members as covariates, we detected linkage near three locations that were previously identified by linkage (1q24-25 HPC1; LOD score 3.25, P=.00012], 1q42.2-43 PCAP; LOD score 2.84, P=.0030], and 4q LOD score 2.80, P=.00038]), near the androgen-receptor locus on Xq12-13 (AR; LOD score 3.06, P=.00053), and at five new locations (LOD score > 2.5). Without covariates, only a few weak-to-moderate linkage signals were found, none of which replicate findings of previous genome scans. We conclude that covariate-based linkage analysis greatly improves the likelihood that linked regions will be found by incorporation of information about heterogeneity within the sample. |
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