Phenotype and genetic analysis of data collected within the first year of NeuroDev

Genetic association studies have made significant contributions to our understanding of the etiology of neurodevelopmental disorders (NDDs). However, these studies rarely focused on the African continent. The NeuroDev Project aims to address this diversity gap through detailed phenotypic and genetic characterization of children with NDDs from Kenya and South Africa. We present results from NeuroDev’s first year of data collection, including phenotype data from 206 cases and clinical genetic analyses of 99 parent-child trios. Most cases met criteria for global developmental delay/intellectual disability (GDD/ID, 80.3%). Approximately half of the children with GDD/ID also met criteria for autism. Analysis of exome-sequencing data identified a pathogenic or likely pathogenic variant in 13 (17%) of the 75 cases from South Africa and 9 (38%) of the 24 cases from Kenya. Data from the trio pilot are publicly available, and the NeuroDev Project will continue to develop resources for the global genetics community

Health and population effects of rare gene knockouts in adult humans with related parents.

Examining complete gene knockouts within a viable organism can inform on gene function. We sequenced the exomes of 3222 British adults of Pakistani heritage with high parental relatedness, discovering 1111 rare-variant homozygous genotypes with predicted loss of function (knockouts) in 781 genes. We observed 13.7% fewer homozygous knockout genotypes than we expected, implying an average load of 1.6 recessive-lethal-equivalent loss-of-function (LOF) variants per adult. When genetic data were linked to the individuals' lifelong health records, we observed no significant relationship between gene knockouts and clinical consultation or prescription rate. In this data set, we identified a healthy PRDM9-knockout mother and performed phased genome sequencing on her, her child, and control individuals. Our results show that meiotic recombination sites are localized away from PRDM9-dependent hotspots. Thus, natural LOF variants inform on essential genetic loci and demonstrate PRDM9 redundancy in humans.The study was funded by the Wellcome Trust (WT102627 and WT098051), Barts Charity (845/1796), Medical Research Council (MR/M009017/1). This paper presents independent research funded by the National Institute for Health Research (NIHR) under its Collaboration for Applied Health Research and Care (CLAHRC) for Yorkshire and Humber. Core support for Born in Bradford is also provided by the Wellcome Trust (WT101597). V.N. was supported by the Wellcome Trust PhD Studentship (WT099769). D.G.M. and K.K. were supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM104371. E.R.M. is funded by NIHR Cambridge Biomedical Research Centre. H.H. is supported by awards to establish the Farr Institute of Health Informatics Research, London, from the Medical Research Council, Arthritis Research UK, British Heart Foundation, Cancer Research UK, Chief Scientist Office, Economic and Social Research Council, Engineering and Physical Sciences Research Council, NIHR, National Institute for Social Care and Health Research, and Wellcome Trust.This is the author accepted manuscript. The final version is available from the American Association for the Advancement of Science via https://doi.org/10.1126/science.aac862

Rare penetrant mutations confer severe risk of common diseases

[INTRODUCTION] Genome-wide association studies (GWASs) have identified thousands of common genetic variants that are predictive of common disease susceptibility, but these variants individually have mild effects on disease owing to the effects of natural selection. By contrast, rare genetic variants can have large effects on common disease risk, but their use in genetic risk prediction has been limited to date owing to the difficulty of distinguishing pathogenic from benign variants and estimating the magnitude of their effects.[RATIONALE] PrimateAI-3D is a three-dimensional convolutional neural network for missense variant–effect prediction, which was trained with common genetic variants from the population sequencing of 233 primate species. By applying this method to estimate the pathogenicity of rare coding variants in 454,712 UK Biobank individuals, we aimed to improve rare-variant association tests and genetic risk prediction for common diseases and complex traits.[RESULTS] We performed rare-variant burden tests for 90 well-powered, clinically relevant phenotypes in the UK Biobank exome dataset. Stratifying missense variants with PrimateAI-3D greatly improved gene discovery, revealing 73% more significant gene-phenotype associations (false discovery rate <0.05) compared with not using PrimateAI-3D. When benchmarked against prior studies, gene-phenotype pairs identified with our method were better supported by orthogonal genetic evidence from GWAS and genes from related Mendelian disorders. In addition, PrimateAI-3D scores showed the strongest correlation among existing variant interpretation algorithms for predicting the quantitative effects of rare variants on continuous clinical phenotypes. Having validated our method for finding gene-phenotype relationships, we next constructed a rare-variant polygenic risk score (PRS) model by combining the rare-variant genes for each phenotype, weighting variants by their PrimateAI-3D prediction score and the direction and effect size of each associated gene. For comparison, we constructed common-variant PRS models and evaluated the performance of the two models for genetic risk prediction in a withheld-test subset of the cohort. Although common variants better explained overall population variance, rare-variant PRSs had more power at the ends of the distribution to identify individuals at the greatest risk for disease, and thus may be more relevant for population genetic screening and risk management. By contrast to common-variant PRS models derived from European populations that show poor generalization to non-Europeans, rare-variant PRSs were substantially more portable to different cohorts and ancestry groups that were not seen during model training. Moreover, because they incorporate orthogonal information from nonoverlapping sets of variants, we combined rare- and common-variant PRS models into a unified model and observed further improvement in genetic risk prediction for common diseases. To understand the extent by which rare-variant PRSs can be expected to improve with increases in discovery cohort size, we repeated our analyses in down-sampled subsets of the UK Biobank cohort. We found that the number of genes contributing to the rare-variant PRS increased linearly, with no signs of plateauing at a half-million exomes. Newly discovered rare-variant genes were strongly enriched at GWAS loci, forming allelic series with effect sizes that were ~10-fold larger on average than the respective common GWAS variant. Among well-powered GWAS loci that could be unambiguously assigned to a single gene, the majority showed subthreshold signal on the rare-variant burden test, indicating that rare penetrant variants exist at a large fraction of GWAS loci and can be incorporated into the rare-variant PRS with further advances in cohort size and variant effect prediction.[CONCLUSION] Understanding the impact of rare variants in common diseases is of prime interest for both precision medicine and the discovery of drug targets. By leveraging advances in variant effect prediction, we have demonstrated major improvements in rare-variant burden testing and genetic risk prediction. Notably, we observed that nearly all individuals carried at least one rare penetrant variant for the phenotypes we examined, demonstrating the utility of personal genome sequencing for otherwise healthy individuals in the general population.T.M.B. is supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 864203), PID2021-126004NB-100 (MICIIN/FEDER, UE) and Secretaria d’Universitats i Recerca, and CERCA Programme del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2021 SGR 00177).Peer reviewe

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes

The landscape of tolerated genetic variation in humans and primates.

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases

A genomic mutational constraint map using variation in 76,156 human genomes

The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders 1–4, but attempts to assess constraint for non-protein-coding regions have proved more difficult. Here we aggregate, process and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD)—the largest public open-access human genome allele frequency reference dataset—and use it to build a genomic constraint map for the whole genome (genomic non-coding constraint of haploinsufficient variation (Gnocchi)). We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation. As expected, the average constraint for protein-coding sequences is stronger than that for non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants that are implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, which in turn suggests that non-coding constraint can aid the identification of constrained genes that are as yet unrecognized by current gene constraint metrics. We demonstrate that this genome-wide constraint map improves the identification and interpretation of functional human genetic variation.</p

Strategies to uplift novel Mendelian gene discovery for improved clinical outcomes

Rare genetic disorders, while individually rare, are collectively common. They represent some of the most severe disorders affecting patients worldwide with significant morbidity and mortality. Over the last decade, advances in genomic methods have significantly uplifted diagnostic rates for patients and facilitated novel and targeted therapies. However, many patients with rare genetic disorders still remain undiagnosed as the genetic etiology of only a proportion of Mendelian conditions has been discovered to date. This article explores existing strategies to identify novel Mendelian genes and how these discoveries impact clinical care and therapeutics. We discuss the importance of data sharing, phenotype-driven approaches, patient-led approaches, utilization of large-scale genomic sequencing projects, constraint-based methods, integration of multi-omics data, and gene-to-patient methods. We further consider the health economic advantages of novel gene discovery and speculate on potential future methods for improved clinical outcomes.</p