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Predicted loss of function (pLoF) variants are highly deleterious and play an important role in disease biology, but many of these variants may not actually result in loss-of-function. Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines’s PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 autosomal recessive disease-genes from the Genome Aggregation Database (gnomAD, v2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in low per-base expression (pext) score regions, or the presence of cryptic splice rescues. Variants predicted to be potential artifacts or to evade LoF were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of LoF evading variants assessed, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines, and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.

Human mitochondria contain a high copy number, maternally transmitted genome (mtDNA) that encodes 13 proteins required for oxidative phosphorylation. Heteroplasmy arises when multiple mtDNA variants co-exist in an individual and can exhibit complex dynamics in disease and in aging. As all proteins involved in mtDNA replication and maintenance are nuclear-encoded, heteroplasmy levels can, in principle, be under nuclear genetic control, however this has never been shown in humans. Here, we develop algorithms to quantify mtDNA copy number (mtCN) and heteroplasmy levels using blood-derived whole genome sequences from 274,832 individuals of diverse ancestry and perform GWAS to identify nuclear loci controlling these traits. After careful correction for blood cell composition, we observe that mtCN declines linearly with age and is associated with 92 independent nuclear genetic loci. We find that nearly every individual carries heteroplasmic variants that obey two key patterns: (1) heteroplasmic single nucleotide variants are somatic mutations that accumulate sharply after age 70, while (2) heteroplasmic indels are maternally transmitted as mtDNA mixtures with resulting levels influenced by 42 independent nuclear loci involved in mtDNA replication, maintenance, and novel pathways. These nuclear loci do not appear to act by mtDNA mutagenesis, but rather, likely act by conferring a replicative advantage to specific mtDNA molecules. As an illustrative example, the most common heteroplasmy we identify is a length variant carried by >50% of humans at position m.302 within a G-quadruplex known to serve as a replication switch. We find that this heteroplasmic variant exertscis-acting genetic control over mtDNA abundance and is itself undertrans-acting genetic control of nuclear loci encoding protein components of this regulatory switch. Our study showcases how nuclear haplotype can privilege the replication of specific mtDNA molecules to shape mtCN and heteroplasmy dynamics in the human population.

Broad yet detailed data collected in biobanks captures variation reflective of human health and behavior, but insights are hard to extract given their complexity and scale. In the largest factor analysis to date, we distill hundreds of medical record codes, physical assays, and survey items from UK Biobank into 35 understandable latent constructs. The identified factors recapitulate known disease classifications, highlight the relevance of psychiatric constructs, improve measurement of health-related behavior, and disentangle elements of socioeconomic status. We demonstrate the power of this principled data reduction approach to clarify genetic signal, enhance discovery, and identify associations between underlying phenotypic structure and health outcomes such as mortality. We emphasize the importance of considering the interwoven nature of the human phenome when evaluating large-scale patterns relevant to public health.

SUMMARYCopy number variants (CNVs) are major contributors to genetic diversity and disease. To date, exome sequencing (ES) has been generated for millions of individuals in international biobanks, human disease studies, and clinical diagnostic screening. While standardized methods exist for detecting short variants (single nucleotide and insertion/deletion variants) using tools such as the Genome Analysis ToolKit (GATK), technical challenges have confounded similarly uniform large-scale CNV analyses from ES data. Given the profound impact of rare and de novo coding CNVs on genome organization and human disease, the lack of widely-adopted and robustly benchmarked rare CNV discovery tools has presented a barrier to routine exome-wide assessment of this critical class of variation. Here, we introduce GATK-gCNV, a flexible algorithm to discover rare CNVs from genome sequencing read-depth information, which we distribute as an open-source tool packaged in GATK. GATK-gCNV uses a probabilistic model and inference framework that accounts for technical biases while simultaneously predicting CNVs, which enables self-consistency between technical read-depth normalization and variant calling. We benchmarked GATK-gCNV in 7,962 exomes from individuals in quartet families with matched genome sequencing and microarray data. These analyses demonstrated 97% recall of rare (≤1% site frequency) coding CNVs detected by microarrays and 95% recall of rare coding CNVs discovered by genome sequencing at a resolution of more than two exons. We applied GATK-gCNV to generate a reference catalog of rare coding CNVs in 197,306 individuals with ES from the UK Biobank. We observed strong correlations between CNV rates per gene and measures of mutational constraint, as well as rare CNV associations with multiple traits. In summary, GATK-gCNV is a tunable approach for sensitive and specific CNV discovery in ES, which can easily be applied across trait association and clinical screening.

Both common and rare genetic variants influence complex traits and common diseases. Genome-wide association studies have discovered 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- and rare-variant architecture is unclear4. Here, we quantify the heritability explained by gene-wise burden of rare coding variants and compare the genetic architecture of common and rare variation across 22 common traits and diseases in 400,000 UK Biobank exomes5. Rare coding variants (AF = 1e-6 - 1e-3) explain 1.3% (SE = 0.03%) of phenotypic variance on average – much less than common variants – and most burden heritability is explained by ultra-rare loss-of-function variants (AF = 1e-6 - 1e-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 a limited number of significant genes (median: 6 genes explaining 19% of h2), while common-variant heritability is much more polygenic. Burden heritability is also more strongly concentrated in constrained genes (median enrichment: 4.5x vs. 2.1x for common variants), indicating that negative selection affects common- and rare-variant architecture differently. Finally, we find that burden heritability for schizophrenia and bipolar disorder6,7 is approximately 2%. Our results show that there are a tractable number of large-effect genes to discover by studying rare variants, that common and rare associations are mechanistically convergent, and that rare coding variants will contribute only modestly to missing heritability and population risk stratification.

Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African–European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants. Tractor is a statistical framework that facilitates the inclusion of admixed individuals in association studies by leveraging local ancestry. Tractor generates accurate ancestry-specific effect-size estimates and improves the resolution of association signals.

Rare coding variation has historically provided the most direct connections between gene function and disease pathogenesis. By meta-analysing the whole exomes of 24,248 schizophrenia cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in 10 genes as conferring substantial risk for schizophrenia (odds ratios of 3–50, P < 2.14 × 10−6) and 32 genes at a false discovery rate of 1, epilepsy and severe neurodevelopmental disorders2, although different mutation types are implicated in some shared genes. Most genes described here, however, are not implicated in neurodevelopment. We demonstrate that genes prioritized from common variant analyses of schizophrenia are enriched in rare variant risk3, suggesting that common and rare genetic risk factors converge at least partially on the same underlying pathogenic biological processes. Even after excluding significantly associated genes, schizophrenia cases still carry a substantial excess of URVs, which indicates that more risk genes await discovery using this approach.

Large-scale next-generation sequencing datasets have been transformative for informing clinical variant interpretation and as reference panels for statistical and population genetic efforts. While such resources are often treated as ground truth, we find that in widely used reference datasets such as the Genome Aggregation Database (gnomAD), some variants pass gold standard filters yet are systematically different in their genotype calls across genotype discovery approaches. The inclusion of such discordant sites in study designs involving multiple genotype discovery strategies could bias results and lead to false-positive hits in association studies due to technological artifacts rather than a true relationship to the phenotype. Here, we describe this phenomenon of discordant genotype calls across genotype discovery approaches, characterize the error mode of wrong calls, provide a blacklist of discordant sites identified in gnomAD that should be treated with caution in analyses, and present a metric and machine learning classifier trained on gnomAD data to identify likely discordant variants in other datasets. We find that different genotype discovery approaches have different sets of variants at which this problem occurs but that there are characteristic variant features that can be used to predict discordant behavior. Discordant sites are largely shared across ancestry groups, though different populations are powered for discovery of different variants. We find that the most common error mode is that of a variant being heterozygous for one approach and homozygous for the other, with heterozygous in the genomes and homozygous reference in the exomes making up the majority of miscalls.

Inherited bone marrow failure (IBMF) syndromes are rare blood disorders characterized by hematopoietic cell dysfunction and predisposition to hematologic malignancies. Despite advances in the understanding of molecular pathogenesis of these heterogeneous diseases, genetic variant interpretation, genotype–phenotype correlation, and outcome prognostication remain difficult. As new IBMF and other myelodysplastic syndrome (MDS) predisposition genes continue to be discovered (frequently in small kindred studies), there is an increasing need for a systematic framework to evaluate penetrance and prevalence of mutations in genes associated with IBMF phenotypes. To address this need, we analyzed population-based genomic data from >125 000 individuals in the Genome Aggregation Database for loss-of-function (LoF) variants in 100 genes associated with IBMF. LoF variants in genes associated with IBMF/MDS were present in 0.426% of individuals. Heterozygous LoF variants in genes in which haploinsufficiency is associated with IBMF/MDS were identified in 0.422% of the population; homozygous LoF variants associated with autosomal recessive IBMF/MDS diseases were identified in only .004% of the cohort. Using age distribution of LoF variants and 2 measures of mutational constraint, LOEUF (“loss-of-function observed/expected upper bound fraction”) and pLI (“probability of being loss-of-function intolerance”), we evaluated the pathogenicity, tolerance, and age-related penetrance of LoF mutations in specific genes associated with IBMF syndromes. This analysis led to insights into rare IBMF diseases, including syndromes associated with DHX34, MDM4, RAD51, SRP54, and WIPF1. Our results provide an important population-based framework for the interpretation of LoF variant pathogenicity in rare and emerging IBMF syndromes.

Understanding the influence of genetics on human disease is among the primary goals for biology and medicine. To this end, the direct study of natural human genetic variation has provided valuable insights into human physiology and disease as well as into the origins and migrations of humans. In this review, we discuss the foundations of population genetics, which provide a crucial context to the study of human genes and traits. In particular, genome-wide association studies and similar methods have revealed thousands of genetic loci associated with diseases and traits, providing invaluable information into the biology of these traits. Simultaneously, as the study of rare genetic variation has expanded, so-called human knockouts have elucidated the function of human genes and the therapeutic potential of targeting them.

Despite their widespread use in research, there has not yet been a systematic genomic analysis of human embryonic stem cell (hESC) lines at a single-nucleotide resolution. We therefore performed whole-genome sequencing (WGS) of 143 hESC lines and annotated their single-nucleotide and structural genetic variants. We found that while a substantial fraction of hESC lines contained large deleterious structural variants, finer-scale structural and single-nucleotide variants (SNVs) that are ascertainable only through WGS analyses were present in hESC genomes and human blood-derived genomes at similar frequencies. Moreover, WGS allowed us to identify SNVs associated with cancer and other diseases that could alter cellular phenotypes and compromise the safety of hESC-derived cellular products transplanted into humans. As a resource to enable reproducible hESC research and safer translation, we provide a user-friendly WGS data portal and a data-driven scheme for cell line maintenance and selection.

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene–phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene–phenotype associations.

Identifying Mendelian diseases with recessive inheritance is challenging as the majority of cases are caused by compound heterozygous genotypes which require sequencing data in families to definitively identify. Bottleneck events, such as in the Finnish population, enrich specific homozygous variants to higher frequencies and thus facilitate identification of disease associations through easily recognized homozygous genotypes. Here, we study homozygous and heterozygous effects of 82,516 coding variants on 2,444 disease endpoints using nationwide electronic health record (EHR) data of 176,899 Finns. We find known and novel associations to homozygous genotypes across a broad spectrum of phenotypes such as retinal dystrophy, adult-onset cataract and female infertility (13/20 of which would have been missed by the traditional additive GWAS model). With these results, and supporting simulations, we demonstrate the added benefit of homozygous scans in GWAS. We further use these results to explore inheritance patterns of known Mendelian variants. We find many Mendelian variants whose inheritance cannot be adequately described with the traditional definition of dominant or recessive. In particular, we find disease risk in heterozygous carriers of variants known to cause disease with recessive inheritance, as well as for variants labeled benign in ClinVar. Our results demonstrate how biobank efforts, particularly in founder populations, can broaden our understanding of the impact of genetic variants.

Despite the great success of genome-wide association studies (GWAS) in identifying genetic loci significantly associated with diseases, the vast majority of causal variants underlying disease-associated loci have not been identified1–3. To create an atlas of causal variants, we performed and integrated fine-mapping across 148 complex traits in three large-scale biobanks (BioBank Japan4,5, FinnGen6, and UK Biobank7,8; total n = 811,261), resulting in 4,518 variant-trait pairs with high posterior probability (> 0.9) of causality. Of these, we found 285 high-confidence variant-trait pairs replicated across multiple populations, and we characterized multiple contributors to the surprising lack of overlap among fine-mapping results from different biobanks. By studying the bottlenecked Finnish and Japanese populations, we identified 21 and 26 putative causal coding variants with extreme allele frequency enrichment (> 10-fold) in these two populations, respectively. Aggregating data across populations enabled identification of 1,492 unique fine-mapped coding variants and 176 genes in which multiple independent coding variants influence the same trait (i.e., with an allelic series of coding variants). Our results demonstrate that fine-mapping in diverse populations enables novel insights into the biology of complex traits by pinpointing high-confidence causal variants for further characterization.

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes

MotivationLoss-of-Function (LoF) variants in human genes are important due to their impact on clinical phenotypes and frequent occurrence in the genomes of healthy individuals. Current approaches predict high-confidence LoF variants without identifying the specific genes or the number of copies they affect. Moreover, there is a lack of methods for detecting knockout genes caused by compound heterozygous (CH) LoF variants.ResultsWe have developed the Loss-of-Function ToolKit (LoFTK), which allows efficient and automated prediction of LoF variants from both genotyped and sequenced genomes. LoFTK enables the identification of genes that are inactive in one or two copies and provides summary statistics for downstream analyses. LoFTK can identify CH LoF variants, which result in LoF genes with two copies lost. Using data from parents and offspring we show that 96% of CH LoF genes predicted by LoFTK in the offspring have the respective alleles donated by each parent.Availability and implementationLoFTK is an open source software and is freely available to non-commercial users at https://github.com/CirculatoryHealth/LoFTKContactj.vansetten@umcutrecht.nlSupplementary informationSupplementary data are available at Bioinformatics online.

UK Biobank has released the whole-exome sequencing (WES) data for 200,000 participants, but the best practices remain unclear for rare variant tests, and an existing approach, SAIGE-GENE, can have inflated type I error rates with high computation cost. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency compared to SAIGE-GENE. In the analysis of UKBB WES data of 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene-phenotype associations. In addition, we showed that incorporating multiple MAF cutoffs and functional annotations can help identify novel gene-phenotype associations and SAIGE-GENE+ can facilitate this.

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene-phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene-phenotype associations.

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types., Graphical abstract, D’Antonio et al. characterize associations between GWAS signals for COVID-19 disease and eQTLs in 69 human tissues to identify causal variants and their underlying molecular mechanisms. They show that diverse symptoms and disease severity of COVID-19 are associated with variants affecting gene expression in a wide variety of tissues.

Most age-related human diseases are accompanied by a decline in cellular organelle integrity, including impaired lysosomal proteostasis and defective mitochondrial oxidative phosphorylation. An open question, however, is the degree to which inherited variation in or near genes encoding each organelle contributes to age-related disease pathogenesis. Here, we evaluate if genetic loci encoding organelle proteomes confer greater-than-expected age-related disease risk. As mitochondrial dysfunction is a ‘hallmark’ of aging, we begin by assessing nuclear and mitochondrial DNA loci near genes encoding the mitochondrial proteome and surprisingly observe a lack of enrichment across 24 age-related traits. Within nine other organelles, we find no enrichment with one exception: the nucleus, where enrichment emanates from nuclear transcription factors. In agreement, we find that genes encoding several organelles tend to be ‘haplosufficient,’ while we observe strong purifying selection against heterozygous protein-truncating variants impacting the nucleus. Our work identifies common variation near transcription factors as having outsize influence on age-related trait risk, motivating future efforts to determine if and how this inherited variation then contributes to observed age-related organelle deterioration., eLife digest Getting older increases our risk of experiencing a wide range of diseases, such as diabetes, heart disease and neurodegenerative disease. The genetic variations that we inherit from our parents play a major role in predicting this risk. However, the biological networks involved in this process are extremely complex and remain challenging to decipher. Prior studies have suggested that specialised structures inside our body’s cells, called organelles, may have an important role to play in aging. Organelles represent self-contained biological factories inside each cell, designed to perform specific tasks. Examples include the nucleus, which harbours most of the cell’s genetic material, and mitochondria, which help provide cells with energy. Organelles tend to deteriorate and become dysfunctional with age, and mitochondria in particular are badly affected by the ageing process. A decline in organelle activity has been thought to explain ageing and the development of age-related diseases. However, this has never been systematically tested on a large scale at the inherited genetic level. Gupta et al. assessed whether common inherited genetic variation in genes associated with ten different organelles could affect the risk of age-related disease, using a database of DNA samples from more than 300,000 individuals. They considered 24 diseases and traits that become more common with advanced age. Gupta et al. discovered that inherited variants in or near genes associated with the nucleus were consistently linked to age-related disease risks. Most of this signal arose from genes encoding the nuclear transcription factors, proteins that help to control the rate at which genes are expressed. However, variants in genes associated with other organelles, including mitochondria, did not appear to be linked to age-related diseases. This research suggests that inherited variation in transcription factors in the nucleus could act as genetic levers that increase the risk of common, age-related diseases. It also suggests that common genetic variation in other cellular organelles may not be as heavily involved in the development of such diseases. Such insights into the cellular structures and biological pathways involved in ageing and age-related disease also establish new targets for drugs to prevent or treat disease.

Correction to: Nature Communications https://doi.org/10.1038/s41467-019-10717-9, published online 27 May 2020.The original version of this Article omitted from the Genome Aggregation Database consortium the member Marquis P. Vawter, from the Department of Psychiatry & Human Behavior, University of California Irvine, Irvine, CA, USA. Additionally, the following was added to the Author Contributions: ‘All authors listed under The Genome Aggregation Database Consortium contributed to the generation of the primary data incorporated into the gnomAD resource’. This has been corrected in both the PDF and HTML versions of the Article.

SUMMARYRare deletions and duplications of genomic segments, collectively known as rare copy number variants (rCNVs), contribute to a broad spectrum of human diseases. To date, most disease-association studies of rCNVs have focused on recognized genomic disorders or on the impact of haploinsufficiency caused by deletions. By comparison, our understanding of duplications in disease remains rudimentary as very few individual genes are known to be triplosensitive (i.e., duplication intolerant). In this study, we meta-analyzed rCNVs from 753,994 individuals across 30 primarily neurological disease phenotypes to create a genome-wide catalog of rCNV association statistics across disorders. We discovered 114 rCNV-disease associations at 52 distinct loci surpassing genome-wide significance (P=3.72×10−6), 42% of which involve duplications. Using Bayesian fine-mapping methods, we further prioritized 38 novel triplosensitive disease genes (e.g., GMEB2 in brain abnormalities), including three known haploinsufficient genes that we now reveal as bidirectionally dosage sensitive (e.g., ANKRD11 in growth abnormalities). By integrating our results with prior literature, we found that disease-associated rCNV segments were enriched for genes constrained against damaging coding variation and identified likely dominant driver genes for about one-third (32%) of rCNV segments based on de novo mutations from exome sequencing studies of developmental disorders. However, while the presence of constrained driver genes was a common feature of many pathogenic large rCNVs across disorders, most of the rCNVs showing genome-wide significant association were incompletely penetrant (mean odds ratio=11.6) and we also identified two examples of noncoding disease-associated rCNVs (e.g., intronic CADM2 deletions in behavioral disorders). Finally, we developed a statistical model to predict dosage sensitivity for all genes, which defined 3,006 haploinsufficient and 295 triplosensitive genes where the effect sizes of rCNVs were comparable to deletions of genes constrained against truncating mutations. These dosage sensitivity scores classified disease genes across molecular mechanisms, prioritized pathogenic de novo rCNVs in children with autism, and revealed features that distinguished haploinsufficient and triplosensitive genes, such as insulation from other genes and local cis-regulatory complexity. Collectively, the cross-disorder rCNV maps and metrics derived in this study provide the most comprehensive assessment of dosage sensitive genomic segments and genes in disease to date and set the foundation for future studies of dosage sensitivity throughout the human genome.

Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplopTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.

Otosclerosis is one of the most common causes of conductive hearing loss, affecting 0.3% of the population. It typically presents in adulthood and half of the patients have a positive family history. The pathophysiology of otosclerosis is poorly understood and treatment options are limited. A previous genome-wide association study (GWAS) identified a single association locus in an intronic region of RELN. Here, we report a meta-analysis of GWAS studies of otosclerosis in three population-based biobanks comprising 2,413 cases and 762,382 controls. We identify 15 novel risk loci (p < 5*10−8) and replicate the regions of RELN and two previously reported candidate genes (TGFB1 and MEPE). Implicated genes in many loci are essential for bone remodelling or mineralization. Otosclerosis is genetically correlated with height and fracture risk, and the association loci overlap with severe skeletal disorders. Our results highlight TGFβ1 signalling for follow-up mechanistic studies.

There has not yet been a systematic analysis of hESC whole genomes at a single nucleotide resolution. We therefore performed whole genome sequencing (WGS) of 143 hESC lines and annotated their single nucleotide and structural genetic variants. We found that while a substantial fraction of hESC lines contained large deleterious structural variants, finer scale structural and single nucleotide variants (SNVs) that are ascertainable only through WGS analyses were present in hESCs genomes and human blood-derived genomes at similar frequencies. However, WGS did identify SNVs associated with cancer or other diseases that will likely alter cellular phenotypes and may compromise the safety of hESC-derived cellular products transplanted into humans. As a resource to enable reproducible hESC research and safer translation, we provide a user-friendly WGS data portal and a data-driven scheme for cell line maintenance and selection.GRAPHICAL ABSTRACTIN BRIEFMerkle and Ghosh et al. describe insights from the whole genome sequences of commonly used human embryonic stem cell (hESC) lines. Analyses of these sequences show that while hESC genomes had more large structural variants than humans do from genetic inheritance, hESCs did not have an observable excess of finer-scale variants. However, many hESC lines contained rare loss-of-function variants and combinations of common variants that may profoundly shape their biological phenotypes. Thus, genome sequencing data can be valuable to those selecting cell lines for a given biological or clinical application, and the sequences and analysis reported here should facilitate such choices.HIGHLIGHTSOne third of hESCs we analysed are siblings, and almost all are of European ancestryLarge structural variants are common in hESCs, but finer-scale variation is similar to that human populationsMany strong-effect loss-of-function mutations and cancer-associated mutations are present in specific hESC linesWe provide user-friendly resources for rational hESC line selection based on genome sequence

Analysis of large genomic datasets, including gnomAD, reveals that partial LRRK2 loss of function is not strongly associated with diseases, serving as an example of how human genetics can be leveraged for target validation in drug discovery. Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes(1,2). Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson's disease(3,4), suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. While preclinical studies in model organisms have raised some on-target toxicity concerns(5-8), the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here, we systematically analyze pLoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)(9), 49,960 exome-sequenced individuals from the UK Biobank and over 4 million participants in the 23andMe genotyped dataset. After stringent variant curation, we identify 1,455 individuals with high-confidence pLoF variants in LRRK2. Experimental validation of three variants, combined with previous work(10), confirmed reduced protein levels in 82.5% of our cohort. We show that heterozygous pLoF variants in LRRK2 reduce LRRK2 protein levels but that these are not strongly associated with any specific phenotype or disease state. Our results demonstrate the value of large-scale genomic databases and phenotyping of human loss-of-function carriers for target validation in drug discovery.

Structural variants (SVs) rearrange large segments of DNA1 and can have profound consequences in evolution and human disease2,3. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD)4 have become integral in the interpretation of single-nucleotide variants (SNVs)5. However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25–29% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage6. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9% of samples, and estimate that 0.13% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings7. This SV resource is freely distributed via the gnomAD browser8 and will have broad utility in population genetics, disease-association studies, and diagnostic screening., A large empirical assessment of sequence-resolved structural variants from 14,891 genomes across diverse global populations in the Genome Aggregation Database (gnomAD) provides a reference map for disease-association studies, population genetics, and diagnostic screening.