Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

Evaluating Approaches for Constructing Polygenic Risk Scores for Prostate Cancer in Men of African and European Ancestry.

Genome-wide polygenic risk scores (GW-PRS) have been reported to have better predictive ability than PRS based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer risk variants from multi-ancestry GWAS and fine-mapping studies (PRS 269 ). GW-PRS models were trained using a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls used to develop the multi-ancestry PRS 269 . Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California/Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI=0.635-0.677) in African and 0.844 (95% CI=0.840-0.848) in European ancestry men and corresponding prostate cancer OR of 1.83 (95% CI=1.67-2.00) and 2.19 (95% CI=2.14-2.25), respectively, for each SD unit increase in the GW-PRS. However, compared to the GW-PRS, in African and European ancestry men, the PRS 269 had larger or similar AUCs (AUC=0.679, 95% CI=0.659-0.700 and AUC=0.845, 95% CI=0.841-0.849, respectively) and comparable prostate cancer OR (OR=2.05, 95% CI=1.87-2.26 and OR=2.21, 95% CI=2.16-2.26, respectively). Findings were similar in the validation data. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the multi-ancestry PRS 269 constructed with fine-mapping.

Evolutionary analysis of enzymes using Chisel.

MOTIVATION: Availability of large volumes of genomic and enzymatic data for taxonomically and phenotypically diverse organisms allows for exploration of the adaptive mechanisms that led to diversification of enzymatic functions. We present Chisel, a computational framework and a pipeline for an automated, high-resolution analysis of evolutionary variations of enzymes. Chisel allows automatic as well as interactive identification, and characterization of enzymatic sequences. Such knowledge can be utilized for comparative genomics, microbial diagnostics, metabolic engineering, drug design and analysis of metagenomes. RESULTS: Chisel is a comprehensive resource that contains 8575 clusters and subsequent computational models specific for 939 distinct enzymatic functions and, when data is sufficient, their taxonomic variations. Application of Chisel to identification of enzymatic sequences in newly sequenced genomes, analysis of organism-specific metabolic networks, 'binning' of metagenomes and other biological problems are presented. We also provide a thorough analysis of Chisel performance with other similar resources and manual annotations on Shewanella oneidensis MR1 genome.