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Patterns of amino acid conservation have served as a tool for understanding protein evolution (1). The same principles have also found broad application in human genomics, driven by the need to interpret the pathogenic potential of variants in patients (2). Here we performed a systematic comparative genomics analysis of human disease-causing missense variants. We found that an appreciable fraction of disease-causing alleles are fixed in the genomes of other species, suggesting a role for genomic context. We developed a model of genetic interactions that predicts most of these to be simple pairwise compensations. Functional testing of this model on two known human disease genes (3,4) revealed discrete cis amino acid residues that, although benign on their own, could rescue the human mutations in vivo. This approach was also applied to abinitiogene discovery to support the identification of a denovo disease driver in BTG2 that is subject to protective cis-modification in more than 50 species. Finally, on the basis of our data and models, we developed a computational tool to predict candidate residues subject to compensation. Taken together, our data highlight the importance of ds-genomic context as a contributor to protein evolution; they provide an insight into the complexity of allele effect on phenotype; and they are likely to assist methods for predicting allele pathogenicity (5,6)., Understanding the nature and prevalence of genetic interactions has the potential to elucidate the evolutionary forces that act on protein residues, protein complexes and, more broadly, genomes. Some studies have [...]

Following Cas9 cleavage, DNA repair without a donor template is generally considered stochastic, heterogeneous and impractical beyond gene disruption. Here, we show that template-free Cas9 editing is predictable and capable of precise repair to a predicted genotype, enabling correction of disease-associated mutations in humans. We constructed a library of 2,000 Cas9 guide RNAs paired with DNA target sites and trained inDelphi, a machine learning model that predicts genotypes and frequencies of 1- to 60-base-pair deletions and 1-base-pair insertions with high accuracy (r = 0.87) in five human and mouse cell lines. inDelphi predicts that 5-11% of Cas9 guide RNAs targeting the human genome are 'precise-50', yielding a single genotype comprising greater than or equal to 50% of all major editing products. We experimentally confirmed precise-50 insertions and deletions in 195 human disease-relevant alleles, including correction in primary patient-derived fibroblasts of pathogenic alleles to wild-type genotype for Hermansky-Pudlak syndrome and Menkes disease. This study establishes an approach for precise, template-free genome editing.The authors use a machine-learning algorithm to predict the spectrum of CRISPR-Cas9-nuclease-mediated DNA repair outcomes at human genomic target sites., Author(s): Max W. Shen [sup.1] [sup.2] , Mandana Arbab [sup.3] [sup.4] [sup.5] , Jonathan Y. Hsu [sup.6] [sup.7] , Daniel Worstell [sup.8] , Sannie J. Culbertson [sup.8] , Olga Krabbe [...]

In this Article, a data processing error affected Fig. 3e and Extended Data Table 2; these errors have been corrected online., Author(s): Max W. Shen [sup.1] [sup.2] , Mandana Arbab [sup.3] [sup.4] [sup.5] , Jonathan Y. Hsu [sup.6] [sup.7] , Daniel Worstell [sup.8] , Sannie J. Culbertson [sup.8] , Olga Krabbe [...]