Benign prostatic hyperplasia (BPH) results in a significant public health burden due to the morbidity caused by the disease and many of the available remedies. As much as 70% of men over 70 will develop BPH. Few studies have been conducted to discover the genetic determinants of BPH risk. Understanding the biological basis for this condition may provide necessary insight for development of novel pharmaceutical therapies or risk prediction. We have evaluated SNP-based heritability of BPH in two cohorts and conducted a genome-wide association study (GWAS) of BPH risk using 2,656 cases and 7,763 controls identified from the Electronic Medical Records and Genomics (eMERGE) network. SNP-based heritability estimates suggest that roughly 60% of the phenotypic variation in BPH is accounted for by genetic factors. We used logistic regression to model BPH risk as a function of principal components of ancestry, age, and imputed genotype data, with meta-analysis performed using METAL. The top result was on chromosome 22 in SYN3 at rs2710383 (p-value = 4.6 × 10-7; Odds Ratio = 0.69, 95% confidence interval = 0.55-0.83). Other suggestive signals were near genes GLGC, UNCA13, SORCS1 and between BTBD3 and SPTLC3. We also evaluated genetically-predicted gene expression in prostate tissue. The most significant result was with increasing predicted expression of ETV4 (chr17; p-value = 0.0015). Overexpression of this gene has been associated with poor prognosis in prostate cancer. In conclusion, although there were no genome-wide significant variants identified for BPH susceptibility, we present evidence supporting the heritability of this phenotype, have identified suggestive signals, and evaluated the association between BPH and genetically-predicted gene expression in prostate.
Scientific reports. 2019 Apr 15*** epublish ***
Jacklyn N Hellwege, Sarah Stallings, Eric S Torstenson, Robert Carroll, Kenneth M Borthwick, Murray H Brilliant, David Crosslin, Adam Gordon, George Hripcsak, Gail P Jarvik, James G Linneman, Parimala Devi, Peggy L Peissig, Patrick A M Sleiman, Hakon Hakonarson, Marylyn D Ritchie, Shefali Setia Verma, Ning Shang, Josh C Denny, Dan M Roden, Digna R Velez Edwards, Todd L Edwards
Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Division of Geriatric Medicine, Meharry-Vanderbilt Alliance, Nashville, TN, USA., Department of Biomedical Informatics Vanderbilt University, Nashville, TN, USA., Hood Center for Health Research, Geisinger Health System, Danville, PA, USA., Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI, USA., Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA, USA., Division of Medical Genetics, University of Washington, Seattle, WA, USA., Department of Biomedical Informatics, Columbia University, New York, NY, USA., Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA., Office of Research Computing and Analytics/Marshfield Clinic Research Institute, Marshfield, WI, USA., Center for Computational and Biomedical Informatics, Marshfield Clinic Research Institute, Marshfield, WI, USA., Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA., Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA., Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA. ., Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. .