Practical barriers and ethical challenges in genetic data sharing.

The underlying ethos of dbGaP is that access to these data by secondary data analysts facilitates advancement of science. NIH has required that genome-wide association study data be deposited in the Database of Genotypes and Phenotypes (dbGaP) since 2003. In 2013, a proposed updated policy extended this requirement to next-generation sequencing data.

Suggestive evidence of linkage identified at chromosomes 12q24 and 2p16 in African American prostate cancer families from Louisiana.

Background: In the United States, incidence of prostate cancer in African American men is more than twice than that of any other race. Thus far, numerous disease susceptibility loci have been identified for this cancer but definite locus-specific information is not yet established due to the tremendous amount of genetic and disease heterogeneity; additionally, despite high prevalence of prostate cancer amongst African American men, this population has been under represented in genetic studies of prostate cancer.

Methods: In order to identify the susceptible locus (loci) for prostate cancer in African Americans, we have performed linkage analyses on members of 15 large high-risk families.

Identification of a novel germline missense mutation of the androgen receptor in African American men with familial prostate cancer.

Race, family history and age are the unequivocally accepted risk factors for prostate cancer (PCa). Androgen receptor (AR)-dependent signaling is an important element in prostate carcinogenesis and its progression to metastatic disease. We examined the possibility of genomic changes in the AR in association with familial PCa in African Americans who have a higher incidence and mortality rate and a clinically more aggressive disease presentation than Caucasians.

Allele frequency misspecification: effect on power and Type I error of model-dependent linkage analysis of quantitative traits under random ascertainment.

Background: Studies of model-based linkage analysis show that trait or marker model misspecification leads to decreasing power or increasing Type I error rate. An increase in Type I error rate is seen when marker related parameters (e.g.