Genome-wide linkage of 77 families from the African American Hereditary Prostate Cancer study (AAHPC).

BACKGROUND: The African American Hereditary Prostate Cancer (AAHPC) Study was designed to recruit families with early-onset disease fulfilling criteria of >or=4 affected. METHODS: We present a approximately 10 cM genome-wide linkage (GWL) analysis on 77 families including 254 affected and 274 unaffected genotyped. RESULTS: Linkage analysis revealed three chromosomal regions with GENEHUNTER multipoint HLOD scores >or=1.3 for all 77 families at 11q22, 17p11, and Xq21. One family yielded genome-wide significant evidence of linkage (LOD = 3.5) to the 17p11 region with seven other families >or=2.3 in this region. Twenty-nine families with no-male-to-male (MM) transmission gave a peak HLOD of 1.62 (alpha = 0.33) at the Xq21 locus. Two novel peaks >or=0.91 for the 16 families with '>6 affected' occurred at 2p21 and 22q12. CONCLUSIONS: These chromosomal regions in the genome warrant further follow-up based on the hypothesis of multiple susceptibility genes with modest effects, or several major genes segregating in small subsets of families.

Importance sampling method of correction for multiple testing in affected sib-pair linkage analysis.

Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions.