The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic. Description Editor’s summary Single–amino acid changes in proteins sometimes have little effect but can often lead to problems in protein folding, activity, or stability. Only a small fraction of variants have been experimentally investigated, but there are vast amounts of biological sequence data that are suitable for use as training data for machine learning approaches. Cheng et al. developed AlphaMissense, a deep learning model that builds on the protein structure prediction tool AlphaFold2 (see the Perspective by Marsh and Teichmann). The model is trained on population frequency data and uses sequence and predicted structural context, all of which contribute to its performance. The authors evaluated the model against related methods using clinical databases not included in the training and demonstrated agreement with multiplexed assays of variant effect. Predictions for all single–amino acid substitutions in the human proteome are provided as a community resource. —Michael A. Funk AlphaFold fine-tuned on human and primate population variant frequency databases predicts variant pathogenicity. INTRODUCTION Genome sequencing has revealed extensive genetic variation in human populations. Missense variants are genetic variants that alter the amino acid sequence of proteins. Pathogenic missense variants disrupt protein function and reduce organismal fitness, while benign missense variants have limited effect. RATIONALE Classifying these variants is an important ongoing challenge in human genetics. Of more than 4 million observed missense variants, only an estimated 2% have been clinically classified as pathogenic or benign, while the vast majority of them are of unknown clinical significance. This limits the diagnosis of rare diseases, as well as the development or application of clinical treatments that target the underlying genetic cause. Machine learning approaches could close the variant interpretation gap by exploiting patterns in biological data to predict the pathogenicity of unannotated variants. Specifically, AlphaFold, which accurately predicts protein structure from protein sequence, may be used as a foundation to predict the pathogenicity of variants on proteins. RESULTS We developed AlphaMissense to leverage advances on multiple fronts: (i) unsupervised protein language modeling to learn amino acid distributions conditioned on sequence context; (ii) incorporating structural context by using an AlphaFold-derived system; and (iii) fine-tuning on weak labels from population frequency data, thereby avoiding bias from human-curated annotations. AlphaMissense achieves state-of-the-art missense pathogenicity predictions in clinical annotation, de novo disease variants, and experimental assay benchmarks without explicitly training on such data. As a resource to the community, we provide a database of predictions for all possible single amino acid substitutions in the human proteome. We classify 32% of all missense variants as likely pathogenic and 57% as likely benign using a cutoff yielding 90% precision on the ClinVar dataset, thereby providing a confident prediction for most human missense variants. We show how this resource can be used to accelerate research in multiple fields. Molecular biologists could use the database as a starting point for designing and interpreting experiments that probe saturating amino acid substitutions across the human proteome. Human geneticists could combine gene-level AlphaMissense predictions with population cohort–based approaches to quantify the functional significance of genes, especially for shorter human genes where cohort-based approaches lack statistical power. Finally, clinicians could benefit from the boost in coverage of confidently classified pathogenic variants when prioritizing de novo variants for rare disease diagnostics, and AlphaMissense predictions could inform studies of complex trait genetics that use annotations of rare, likely deleterious variants. CONCLUSION AlphaMissense predictions may illuminate the molecular effects of variants on protein function, contribute to the identification of pathogenic missense mutations and previously unknown disease-causing genes, and increase the diagnostic yield of rare genetic diseases. AlphaMissense will also foster further development of specialized protein variant effect predictors from structure prediction models. AlphaMissense pathogenicity prediction. AlphaMissense takes as input a missense variant and predicts its pathogenicity. We fine-tuned AlphaFold on human and primate variant population frequency data and calibrated the confidence on known disease variants. AlphaMissense predicts the probability of a missense variant being pathogenic and classifies it as either likely benign, likely pathogenic, or uncertain. We provide predictions for all possible human missense variants as a resource for the community.

Accurate proteome-wide missense variant effect prediction with AlphaMissense

Abstract The Open Targets Platform (https://platform.opentargets.org/) is an open source resource to systematically assist drug target identification and prioritisation using publicly available data. Since our last update, we have reimagined, redesigned, and rebuilt the Platform in order to streamline data integration and harmonisation, expand the ways in which users can explore the data, and improve the user experience. The gene–disease causal evidence has been enhanced and expanded to better capture disease causality across rare, common, and somatic diseases. For target and drug annotations, we have incorporated new features that help assess target safety and tractability, including genetic constraint, PROTACtability assessments, and AlphaFold structure predictions. We have also introduced new machine learning applications for knowledge extraction from the published literature, clinical trial information, and drug labels. The new technologies and frameworks introduced since the last update will ease the introduction of new features and the creation of separate instances of the Platform adapted to user requirements. Our new Community forum, expanded training materials, and outreach programme support our users in a range of use cases.

/pdf/the-next-generation-open-targets-platform-reimagined-j78tcm4m.pdf

The next-generation Open Targets Platform: reimagined, redesigned, rebuilt

Both common and rare genetic variants influence complex traits and common diseases. Genome-wide association studies have identified thousands of common-variant associations, and more recently, large-scale exome sequencing studies have identified rare-variant associations in hundreds of genes1-3. However, rare-variant genetic architecture is not well characterized, and the relationship between common-variant and rare-variant architecture is unclear4. Here we quantify the heritability explained by the gene-wise burden of rare coding variants across 22 common traits and diseases in 394,783 UK Biobank exomes5. Rare coding variants (allele frequency < 1 × 10-3) explain 1.3% (s.e. = 0.03%) of phenotypic variance on average-much less than common variants-and most burden heritability is explained by ultrarare loss-of-function variants (allele frequency < 1 × 10-5). Common and rare variants implicate the same cell types, with similar enrichments, and they have pleiotropic effects on the same pairs of traits, with similar genetic correlations. They partially colocalize at individual genes and loci, but not to the same extent: burden heritability is strongly concentrated in significant genes, while common-variant heritability is more polygenic, and burden heritability is also more strongly concentrated in constrained genes. Finally, we find that burden heritability for schizophrenia and bipolar disorder6,7 is approximately 2%. Our results indicate that rare coding variants will implicate a tractable number of large-effect genes, that common and rare associations are mechanistically convergent, and that rare coding variants will contribute only modestly to missing heritability and population risk stratification. 

Polygenic architecture of rare coding variation across 394,783 exomes

Abstract Phasing involves distinguishing the two parentally inherited copies of each chromosome into haplotypes. Here, we introduce SHAPEIT5, a new phasing method that quickly and accurately processes large sequencing datasets and applied it to UK Biobank (UKB) whole-genome and whole-exome sequencing data. We demonstrate that SHAPEIT5 phases rare variants with low switch error rates of below 5% for variants present in just 1 sample out of 100,000. Furthermore, we outline a method for phasing singletons, which, although less precise, constitutes an important step towards future developments. We then demonstrate that the use of UKB as a reference panel improves the accuracy of genotype imputation, which is even more pronounced when phased with SHAPEIT5 compared with other methods. Finally, we screen the UKB data for loss-of-function compound heterozygous events and identify 549 genes where both gene copies are knocked out. These genes complement current knowledge of gene essentiality in the human genome. 

/pdf/accurate-rare-variant-phasing-of-whole-genome-and-whole-1kre5s7m.pdf

Accurate rare variant phasing of whole-genome and whole-exome sequencing data in the UK Biobank

The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types and their positions within individual anatomical structures remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA sequencing with Slide-seq1,2-a recently developed spatial transcriptomics method with near-cellular resolution-across the entire mouse brain. Integration of these datasets revealed the cell type composition of each neuroanatomical structure. Cell type diversity was found to be remarkably high in the midbrain, hindbrain and hypothalamus, with most clusters requiring a combination of at least three discrete gene expression markers to uniquely define them. Using these data, we developed a framework for genetically accessing each cell type, comprehensively characterized neuropeptide and neurotransmitter signalling, elucidated region-specific specializations in activity-regulated gene expression and ascertained the heritability enrichment of neurological and psychiatric phenotypes. These data, available as an online resource ( www.BrainCellData.org ), should find diverse applications across neuroscience, including the construction of new genetic tools and the prioritization of specific cell types and circuits in the study of brain diseases. 

The molecular cytoarchitecture of the adult mouse brain

Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results. 

https://doi.org/10.1016/j.xgen.2022.100168

Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes

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Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes