The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.

We report a genome-wide association study (GWAS) of cutaneous squamous cell carcinoma (SCC) conducted among non-Hispanic white (NHW) members of the Kaiser Permanente Northern California (KPNC) health care system. The study includes a genome-wide screen of 61,457 members (6,891 cases and 54,566 controls) genotyped on the Affymetrix Axiom European array and a replication phase involving an independent set of 6,410 additional members (810 cases and 5600 controls). Combined analysis of screening and replication phases identified ten loci containing single-nucleotide polymorphisms (SNPs) with P-values < 5×10-8. Six loci contain genes in the pigmentation pathway; SNPs at these loci appear to modulate SCC risk independently of the pigmentation phenotypes. Another locus contains HLA class II genes studied in relation to elevated SCC risk following immunosuppression. SNPs at the remaining three loci include an intronic SNP in FOXP1 at locus 3p13, an intergenic SNP at 3q28 near TP63, and an intergenic SNP at 9p22 near BNC2. These findings provide insights into the genetic factors accounting for inherited SCC susceptibility.

Purpose: Limb Girdle Muscular Dystrophies (LGMD) are a genetically heterogeneous category of autosomal inherited muscle diseases. Many genes causing LGMD have been identified, and clinical trials are beginning for treatment of some genetic subtypes. However, even with the gene-level mechanisms known, it is still difficult to get a reliable and generalizable prevalence estimation for each subtype due to the limited amount of epidemiology data and the low incidence of LGMDs. Methods: Taking advantage of recently published whole exome and genome sequencing data from the general population, we used a Bayesian method to develop a reliable disease prevalence estimator. Results: This method was applied to nine recessive LGMD subtypes. The estimated disease prevalence calculated by this method were largely comparable to published estimates from epidemiological studies, however highlighted instances of possible under-diagnosis for LGMD2B and 2L. Conclusion: The increasing size of aggregated population variant databases will allow for robust and reproducible prevalence estimates of recessive disease, which is critical for the strategic design and prioritization of clinical trials..

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