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Author(s): Nicola Whiffin [sup.1] [sup.2] [sup.3] , Irina M. Armean [sup.3] [sup.4] , Aaron Kleinman [sup.5] , Jamie L. Marshall [sup.3] , Eric V. Minikel [sup.3] , Julia K. Goodrich [...], 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.sup.1,2. Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson's disease.sup.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.sup.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).sup.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.sup.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. 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.

Author(s): Konrad J. Karczewski [sup.1] [sup.2] , Laurent C. Francioli [sup.1] [sup.2] , Grace Tiao [sup.1] [sup.2] , Beryl B. Cummings [sup.1] [sup.2] [sup.3] , Jessica Alföldi [sup.1] [sup.2] , [...], 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.sup.1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases. A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.

Author(s): Beryl B. Cummings [sup.1] [sup.2] [sup.3] , Konrad J. Karczewski [sup.1] [sup.2] , Jack A. Kosmicki [sup.1] [sup.2] [sup.4] , Eleanor G. Seaby [sup.1] [sup.2] [sup.5] , Nicholas A. [...], The acceleration of DNA sequencing in samples from patients and population studies has resulted in extensive catalogues of human genetic variation, but the interpretation of rare genetic variants remains problematic. A notable example of this challenge is the existence of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals. Here, by manual curation of putative loss-of-function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD).sup.1, we show that one explanation for this paradox involves alternative splicing of mRNA, which allows exons of a gene to be expressed at varying levels across different cell types. Currently, no existing annotation tool systematically incorporates information about exon expression into the interpretation of variants. We develop a transcript-level annotation metric known as the 'proportion expressed across transcripts', which quantifies isoform expression for variants. We calculate this metric using 11,706 tissue samples from the Genotype Tissue Expression (GTEx) project.sup.2 and show that it can differentiate between weakly and highly evolutionarily conserved exons, a proxy for functional importance. We demonstrate that expression-based annotation selectively filters 22.8% of falsely annotated pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of high-confidence pathogenic variants in the same genes. Finally, we apply our expression filter to the analysis of de novo variants in patients with autism spectrum disorder and intellectual disability or developmental disorders to show that pLoF variants in weakly expressed regions have similar effect sizes to those of synonymous variants, whereas pLoF variants in highly expressed exons are most strongly enriched among cases. Our annotation is fast, flexible and generalizable, making it possible for any variant file to be annotated with any isoform expression dataset, and will be valuable for the genetic diagnosis of rare diseases, the analysis of rare variant burden in complex disorders, and the curation and prioritization of variants in recall-by-genotype studies. A novel variant annotation metric that quantifies the level of expression of genetic variants across tissues is validated in the Genome Aggregation Database (gnomAD) and is shown to improve rare variant interpretation.

Author(s): Ryan L. Collins [sup.1] [sup.2] [sup.3] , Harrison Brand [sup.1] [sup.2] [sup.4] , Konrad J. Karczewski [sup.1] [sup.5] , Xuefang Zhao [sup.1] [sup.2] [sup.4] , Jessica Alföldi [sup.1] [sup.5] [...], Structural variants (SVs) rearrange large segments of DNA.sup.1 and can have profound consequences in evolution and human disease.sup.2,3. 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).sup.4 have become integral in the interpretation of single-nucleotide variants (SNVs).sup.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 dosage.sup.6. 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 findings.sup.7. This SV resource is freely distributed via the gnomAD browser.sup.8 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.

Author(s): Eric Vallabh Minikel [sup.1] [sup.2] [sup.3] [sup.4] [sup.5] [sup.6] [sup.7] [sup.8] , Konrad J. Karczewski [sup.1] [sup.4] , Hilary C. Martin [sup.9] , Beryl B. Cummings [sup.1] [sup.4] [sup.5] [...], Naturally occurring human genetic variants that are predicted to inactivate protein-coding genes provide an in vivo model of human gene inactivation that complements knockout studies in cells and model organisms. Here we report three key findings regarding the assessment of candidate drug targets using human loss-of-function variants. First, even essential genes, in which loss-of-function variants are not tolerated, can be highly successful as targets of inhibitory drugs. Second, in most genes, loss-of-function variants are sufficiently rare that genotype-based ascertainment of homozygous or compound heterozygous 'knockout' humans will await sample sizes that are approximately 1,000 times those presently available, unless recruitment focuses on consanguineous individuals. Third, automated variant annotation and filtering are powerful, but manual curation remains crucial for removing artefacts, and is a prerequisite for recall-by-genotype efforts. Our results provide a roadmap for human knockout studies and should guide the interpretation of loss-of-function variants in drug development. Analysis of predicted loss-of-function variants from 125,748 human exomes and 15,708 whole genomes in the Genome Aggregation Database (gnomAD) provides a roadmap for human 'knockout' studies and a guide for future research into disease biology and drug-target selection.

Author(s): Konrad J. Karczewski [sup.1] [sup.2] , Laurent C. Francioli [sup.1] [sup.2] , Grace Tiao [sup.1] [sup.2] , Beryl B. Cummings [sup.1] [sup.2] [sup.3] , Jessica Alföldi [sup.1] [sup.2] , [...], A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03174-8.

Author(s): Eric Vallabh Minikel [sup.1] [sup.2] [sup.3] [sup.4] [sup.5] [sup.6] [sup.7] [sup.8] , Konrad J. Karczewski [sup.1] [sup.4] , Hilary C. Martin [sup.9] , Beryl B. Cummings [sup.1] [sup.4] [sup.5] [...], A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03177-5

Author(s): Beryl B. Cummings [sup.1] [sup.2] [sup.3], Konrad J. Karczewski [sup.1] [sup.2], Jack A. Kosmicki [sup.1] [sup.2] [sup.4], Eleanor G. Seaby [sup.1] [sup.2] [sup.5], Nicholas A. Watts [sup.1] [sup.2], Moriel [...], A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03175-7

Author(s): Ryan L. Collins [sup.1] [sup.2] [sup.3] , Harrison Brand [sup.1] [sup.2] [sup.4] , Konrad J. Karczewski [sup.1] [sup.5] , Xuefang Zhao [sup.1] [sup.2] [sup.4] , Jessica Alföldi [sup.1] [sup.5] [...], A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03176-6.

Author(s): Nicola Whiffin [sup.1] [sup.2] [sup.3], Irina M. Armean [sup.3] [sup.4], Aaron Kleinman [sup.5], Jamie L. Marshall [sup.3], Eric V. Minikel [sup.3], Julia K. Goodrich [sup.3] [sup.4], Nicholas M. Quaife [...], A Correction to this paper has been published: https://doi.org/10.1038/s41591-020-01185-6.