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While pathogenic variants significantly increase disease risk in many genes, it is still challenging to estimate the clinical impact of rare missense variants more generally. Even in genes such as BRCA2 or PALB2 , large cohort studies find no significant association between breast cancer and rare germline missense variants collectively. Here we introduce REGatta, a method to improve the estimation of clinical risk in gene segments. We define gene regions using the density of pathogenic diagnostic reports, and then calculate the relative risk in each of these regions using 109,581 exome sequences from women in the UK Biobank. We apply this method in seven established breast cancer genes, and identify regions in each gene with statistically significant differences in breast cancer incidence for rare missense carriers. Even in genes with no significant difference at the gene level, this approach significantly separates rare missense variant carriers at higher or lower risk ( BRCA2 regional model OR=1.46 [1.12, 1.79], p=0.0036 vs. BRCA2 gene model OR=0.96 [0.85,1.07] p=0.4171). We find high concordance between these regional risk estimates and high-throughput functional assays of variant impact. We compare with existing methods and the use of protein domains (Pfam) as regions, and find REGatta better identifies individuals at elevated or reduced risk. These regions provide useful priors which can potentially be used to improve risk assessment and clinical management.

Deep mutational scanning assays enable the functional assessment of variants in high throughput. Phenotypic measurements from these assays are broadly concordant with clinical outcomes but are prone to noise at the individual variant level. We develop a framework to exploit related measurements within and across experimental assays to jointly estimate variant impact. Drawing from a large corpus of deep mutational scanning data, we collectively estimate the mean functional effect per AA residue position within each gene, normalize observed functional effects by substitution type, and make estimates for individual allelic variants with a pipeline called FUSE ( Fu nctional S ubstitution E stimation). FUSE improves the correlation of functional screening datasets covering the same variants, better separates estimated functional impacts for known pathogenic and benign variants (ClinVar BRCA1 , p=2.24×10 -51 ), and increases the number of variants for which predictions can be made (2,741 to 10,347) by inferring additional variant effects for substitutions not experimentally screened. For UK Biobank patients who carry a rare variant in TP53 , FUSE significantly improves the separation of patients who develop cancer syndromes from those without cancer (p=1.77×10 -6 ). These approaches promise to improve estimates of variant impact and broaden the utility of screening data generated from functional assays.

The adenosine analogue remdesivir has emerged as a front-line antiviral treatment for SARS-CoV-2, with preliminary evidence that it reduces the duration and severity of illness 1 .Prior clinical studies have identified adverse events 1,2 , and remdesivir has been shown to inhibit mitochondrial RNA polymerase in biochemical experiments 7 , yet little is known about the specific genetic pathways involved in cellular remdesivir metabolism and cytotoxicity. Through genome-wide CRISPR-Cas9 screening and RNA sequencing, we show that remdesivir treatment leads to a repression of mitochondrial respiratory activity, and we identify five genes whose loss significantly reduces remdesivir cytotoxicity. In particular, we show that loss of the mitochondrial nucleoside transporter SLC29A3 mitigates remdesivir toxicity without a commensurate decrease in SARS-CoV-2 antiviral potency and that the mitochondrial adenylate kinase AK2 is a remdesivir kinase required for remdesivir efficacy and toxicity. This work elucidates the cellular mechanisms of remdesivir metabolism and provides a candidate gene target to reduce remdesivir cytotoxicity., Competing Interests: Competing interests The authors declare competing interests: a patent application has been filed on this work.