Identification of tag single-nucleotide polymorphisms in regions with varying linkage disequilibrium

We compared seven different tagging single-nucleotide polymorphism (SNP) programs in 10 regions with varied amounts of linkage disequilibrium (LD) and physical distance. We used the Collaborative Studies on the Genetics of Alcoholism dataset, part of the Genetic Analysis Workshop 14. We show that in regions with moderate to strong LD these programs are relatively consistent, despite different parameters and methods. In addition, we compared the selected SNPs in a multipoint linkage analysis for one region with strong LD. As the number of selected SNPs increased, the LOD score, mean information content, and type I error also increased

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

Genome-wide linkage analysis of 1,233 prostate cancer pedigrees from the International Consortium for prostate cancer Genetics using novel sumLINK and sumLOD analyses

Prostate cancer is generally believed to have a strong inherited component, but the search for susceptibility genes has been hindered by the effects of genetic heterogeneity. The recently developed sumLINK and sumLOD statistics are powerful tools for linkage analysis in the presence of heterogeneity

Two-locus genome-wide linkage scan for prostate cancer susceptibility genes with an interaction effect

Prostate cancer represents a significant worldwide public health burden. Epidemiological and genetic epidemiological studies have consistently provided data supporting the existence of inherited prostate cancer susceptibility genes. Segregation analyses of prostate cancer suggest that a multigene model may best explain familial clustering of this disease. Therefore, modeling gene–gene interactions in linkage analysis may improve the power to detect chromosomal regions harboring these disease susceptibility genes. In this study, we systematically screened for prostate cancer linkage by modeling two-locus gene–gene interactions for all possible pairs of loci across the genome in 426 prostate cancer families from Johns Hopkins Hospital, University of Michigan, University of Umeå, and University of Tampere. We found suggestive evidence for an epistatic interaction for six sets of loci (target chromosome-wide/reference marker-specific P ≤0.0001). Evidence for these interactions was found in two independent subsets from within the 426 families. While the validity of these results requires confirmation from independent studies and the identification of the specific genes underlying this linkage evidence, our approach of systematically assessing gene–gene interactions across the entire genome represents a promising alternative approach for gene identification for prostate cancer.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47598/1/439_2005_Article_99.pd

Autosomal dominant inheritance of prostate cancer: A confirmatory study

Objectives. To confirm, in a study of a large, independent cohort of families with prostate cancer, the findings of three segregation analyses that have suggested the existence of an inherited form of prostate cancer with an autosomal dominant inheritance mode. Methods. Between January 1991 and December 1993, 1199 pedigrees were ascertained through single, unrelated, prostate cancer probands who presented for radical prostatectomy at the Division of Urologic Surgery, Washington University Medical Center in St. Louis, Missouri. Maximum likelihood segregation analysis was used to test specifically for mendelian inheritance of prostate cancer. Results. Segregation analyses revealed that the familial aggregation of prostate cancer can be best explained by the autosomal dominant inheritance of a rare (q = 0.0037) high-risk allele. According to the best-fitting autosomal dominant model, 97% of all carriers will be affected by 85 years of age compared with 10% of noncarriers. Furthermore, the autosomal dominant model predicts that the high-risk allele accounts for a large proportion (65%) of all patients diagnosed with prostate cancer before 56 years of age. However, of all prostate cancer cases, a relatively small proportion is inherited (8% by 85 years old). Conclusions. These results are in agreement with earlier reports of segregation analyses of prostate cancer and strengthen the evidence that prostate cancer is inherited in a mendelian fashion within a subset of families. Copyright © 2001 Elsevier Science Inc

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