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Background Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants – missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs. Results On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions. Conclusions DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.

Background The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants. Methods To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles. Results We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33). Conclusions This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas.

Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease.

Any two unrelated individuals differ by about 10,000 single amino acid variants (SAVs). Do these impact molecular function? Experimental answers cannot answer comprehensively, while state-of-the-art prediction methods can. We predicted the functional impacts of SAVs within human and for variants between human and other species. Several surprising results stood out. Firstly, four methods (CADD, PolyPhen-2, SIFT, and SNAP2) agreed within 10 percentage points on the percentage of rare SAVs predicted with effect. However, they differed substantially for the common SAVs: SNAP2 predicted, on average, more effect for common than for rare SAVs. Given the large ExAC data sets sampling 60,706 individuals, the differences were extremely significant (p-value < 2.2e-16). We provided evidence that SNAP2 might be closer to reality for common SAVs than the other methods, due to its different focus in development. Secondly, we predicted significantly higher fractions of SAVs with effect between healthy individuals than between species; the difference increased for more distantly related species. The same trends were maintained for subsets of only housekeeping proteins and when moving from exomes of 1,000 to 60,000 individuals. SAVs frozen at speciation might maintain protein function, while many variants within a species might bring about crucial changes, for better or worse.

Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs) for particular proteins. Different experimental protocols proxy effect through a diversity of measures. We evaluated three early prediction methods trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2) along with a regression method optimized on DMS data (Envision). On a common subset of 32,981 SAVs, all methods capture some aspects of variant effects, albeit not the same. Early effect prediction methods correlated slightly more with measurements and better classified binary states (effect or neutral), while Envision predicted better the precise degree of effect. Most surprising was that a simple approach predicting residues conserved in families (found and aligned by PSI-BLAST) in many cases outperformed other methods. All methods predicted beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with several DMS measurements, experiments agreed more with each other than predictions with experiments. Our findings highlight challenges and opportunities of DMS for improving variant effect predictions.

Heritability in the immune tumor microenvironment (iTME) has been widely observed, yet remains largely uncharacterized and systematic approaches to discover germline genetic modifiers of the iTME still being established. Here, we developed the first machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWAS) for breast cancer (BrCa) incidence and outcome. A random forest model was trained on a positive set of immune-oncology (I-O) targets using BrCa and immune phenotypes from genetic perturbation studies, comparative genomics, Mendelian genetics, and colocalization with autoimmunity and inflammatory disease risk loci. Compared with random negative sets, an I-O target probability score was assigned to the 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Pathway analysis of the most probable I-O targets revealed significant enrichment in drivers of BrCa and immune biology, including the LSP1 locus associated with BrCa incidence and outcome. Quantitative cell type-specific immunofluorescent imaging of 1,109 BrCa patient biopsies revealed that LSP1 expression is restricted to tumor infiltrating leukocytes and correlated with BrCa patient outcome (HR = 1.73, p < 0.001). The human BrCa patient-based genomic and proteomic evidence, combined with phenotypic evidence that LSP1 is a negative regulator of leukocyte trafficking, prioritized LSP1 as a novel I-O target. Finally, a novel comparative mapping strategy using mouse genetic linkage revealed TLR1 as a plausible therapeutic candidate with strong genomic and phenotypic evidence. Collectively, these data demonstrate a robust and flexible analytical framework for functionally fine-mapping GWAS risk loci to identify the most translatable therapeutic targets for the associated disease.Competing Interest StatementB.R.G., E.K., S.W., J.R., Z.D., and M.J.F. are employees of AbbVie. All animal studies and histological analysis of human breast cancer specimens were conducted at the Medical College of Wisconsin (MCW), at which time M.J.F was a full-time faculty member of MCW. S.W.T., A.R.P., K.W., A.L., and H.R. are employees of MCW and have no financial relationship with AbbVie to disclose.