Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10-6) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.
HGG advances. 2024 Jun 06 [Epub ahead of print]
Dorothy M Chen, Ruocheng Dong, Linda Kachuri, Thomas J Hoffmann, Yu Jiang, Sonja I Berndt, John P Shelley, Kerry R Schaffer, Mitchell J Machiela, Neal D Freedman, Wen-Yi Huang, Shengchao A Li, Hans Lilja, Amy C Justice, Ravi K Madduri, Alex A Rodriguez, Stephen K Van Den Eeden, Stephen J Chanock, Christopher A Haiman, David V Conti, Robert J Klein, Jonathan D Mosley, John S Witte, Rebecca E Graff
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA., Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA., Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA., Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA., Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA., Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA., Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA., Departments of Pathology and Laboratory Medicine, Surgery, Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Translational Medicine, Lund University, 21428 Malmö, Sweden., Argonne National Laboratory, Lemont, IL 60439, USA., Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA., Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA., Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA., Departments of Internal Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA., Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Departments of Biomedical Data Science and Genetics (by courtesy), Stanford University, Stanford, CA 94305, USA. Electronic address: ., Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA. Electronic address: .