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Background: Family history of prostate cancer (PCa) is a well-known risk factor, and both common and rare genetic variants are associated with the disease. Objective(s): To detect new genetic variants associated with PCa, capitalizing on the role of family history and more aggressive PCa. Design, setting, and participants: A two-stage design was used. In stage one, whole-exome sequencing was used to identify potential risk alleles among affected men with a strong family history of disease or with more aggressive disease (491 cases and 429 controls). Aggressive disease was based on a sum of scores for Gleason score, node status, metastasis, tumor stage, prostate-specific antigen at diagnosis, systemic recurrence, and time to PCa death. Genes identified in stage one were screened in stage two using a custom-capture design in an independent set of 2917 cases and 1899 controls. Outcome measurements and statistical analysis: Frequencies of genetic variants (singly or jointly in a gene) were compared between cases and controls. Results and limitations: Eleven genes previously reported to be associated with PCa were detected (ATM, BRCA2, HOXB13, FAM111A, EMSY, HNF1B, KLK3, MSMB, PCAT1, PRSS3, and TERT), as well as an additional 10 novel genes (PABPC1, QK1, FAM114A1, MUC6, MYCBP2, RAPGEF4, RNASEH2B, ULK4, XPO7, and THAP3). Of these 10 novel genes, all but PABPC1 and ULK4 were primarily associated with the risk of aggressive PCa. Conclusion(s): Our approach demonstrates the advantage of gene sequencing in the search for genetic variants associated with PCa and the benefits of sampling patients with a strong family history of disease or an aggressive form of disease. Patient Summary: Multiple genes are associated with prostate cancer (PCa) among men with a strong family history of this disease or among men with an aggressive form of PCa. Family history of prostate cancer (PCa) is a well-known risk factor. By capitalizing on affected men with a strong family history of disease

BACKGROUND: Family history of prostate cancer (PCa) is a well-known risk factor, and both common and rare genetic variants are associated with the disease. OBJECTIVE: To detect new genetic variants associated with PCa, capitalizing on the role of family history and more aggressive PCa. DESIGN, SETTING, AND PARTICIPANTS: A two-stage design was used. In stage one, whole-exome sequencing was used to identify potential risk alleles among affected men with a strong family history of disease or with more aggressive disease (491 cases and 429 controls). Aggressive disease was based on a sum of scores for Gleason score, node status, metastasis, tumor stage, prostate-specific antigen at diagnosis, systemic recurrence, and time to PCa death. Genes identified in stage one were screened in stage two using a custom-capture design in an independent set of 2917 cases and 1899 controls. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Frequencies of genetic variants (singly or jointly in a gene) were compared between cases and controls. RESULTS AND LIMITATIONS: Eleven genes previously reported to be associated with PCa were detected (ATM, BRCA2, HOXB13, FAM111A, EMSY, HNF1B, KLK3, MSMB, PCAT1, PRSS3, and TERT), as well as an additional 10 novel genes (PABPC1, QK1, FAM114A1, MUC6, MYCBP2, RAPGEF4, RNASEH2B, ULK4, XPO7, and THAP3). Of these 10 novel genes, all but PABPC1 and ULK4 were primarily associated with the risk of aggressive PCa. CONCLUSIONS: Our approach demonstrates the advantage of gene sequencing in the search for genetic variants associated with PCa and the benefits of sampling patients with a strong family history of disease or an aggressive form of disease. PATIENT SUMMARY: Multiple genes are associated with prostate cancer (PCa) among men with a strong family history of this disease or among men with an aggressive form of PCa.

The authors regret not all contributing authors correctly acknowledged funding. Jonathan Peter’s IMARI-Africa project (AFRISCAR) is part of the EDCTP2 programme supported by the European Union (grant number TMA2017SF-1981 ). The authors would like to apologise for any inconvenience caused.

Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) are potentially life-threatening, immune-mediated adverse reactions characterized by widespread erythema, epidermal necrosis, and detachment of skin and mucosa. Efforts to grow and develop functional international collaborations and a multidisciplinary interactive network focusing on SJS/TEN as an uncommon but high burden disease will be necessary to improve efforts in prevention, early diagnosis and improved acute and long-term management. SJS/TEN 2019: From Science to Translation was a 1.5-day scientific program held April 26-27, 2019, in Vancouver, Canada. The meeting successfully engaged clinicians, researchers, and patients and conducted many productive discussions on research and patient care needs.

Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare-variant analysis using component gene-level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family-wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case-control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open-source R code for public use to facilitate easy application of our methods to existing rare-variant analysis results.

Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work.