dataesr/scientific-paragraphs-categorization huggingface dataset:
With larger sample sizes, a more accurate quantitative measure of selective pressure is possible. Here, we describe the detection of pLoF variants in a cohort of 125,748 individuals with whole-exome sequence data and 15,708 individuals with whole-genome sequence data, as part of the Genome Aggregation Database (gnomAD; https://gnomad. broadinstitute. org), the successor to the Exome Aggregation Consortium (ExAC). We develop a continuous measure of intolerance to pLoF variation, which places each gene on a spectrum of LoF intolerance. We validate this metric by comparing its distribution to several orthogonal indicators of constraint, including the incidence of structural variation and the essentiality of genes as measured using mouse gene knockout experiments and cellular inactivation assays.
Finally, we demonstrate that this metric improves the interpretation of genetic variants that influence rare disease and provides insight into common disease biology. These analyses provide, to our knowledge, the most comprehensive catalogue so far of the sensitivity of human genes to disruption. In a series of accompanying manuscripts, other complementary analyses of this dataset are described. Using an overlapping set of 14,237 whole genomes, the discovery and characterization of a wide variety of structural variants (large deletions, duplications, insertions, or other rearrangements of DNA) is reported  11. The value of pLoF variants for the discovery and validation of therapeutic drug targets is explored  12  , and a case study of the use of these variants from gnomAD and other large reference datasets is provided to validate the safety of inhibition of LRRK2-a candidate therapeutic target for Parkinson's disease  13.
By combining the gnomAD dataset with a large collection of RNA sequencing data from adult human tissues  14  , the value of tissue expression data in the interpretation of genetic variation across a range of human diseases is reported  15. Finally, the effect of two understudied classes of human variation-multi-nucleotide variants  16  and variants that create or disrupt open-reading frames in the 5′ untranslated region of human genes-is characterized and investigated  17.
A high-quality catalogue of variation We aggregated whole-exome sequencing data from 199,558 individuals and whole-genome sequencing data from 20,314 individuals. These data were obtained primarily from case-control studies of common adult-onset diseases, including cardiovascular disease, type 2 diabetes and psychiatric disorders. Each dataset, totalling more than 1. 3 and 1. 6 petabytes of raw sequencing data, respectively, was uniformly processed, joint variant calling was performed on each dataset using a standardized BWA-Picard-GATK pipeline  18  , and all data processing and analysis was performed using Hail  19. We performed stringent sample quality control (Extended Data Fig. 1 ), removing samples with lower sequencing quality by a variety of metrics, samples from second-degree or closer related individuals across both data types, samples with inadequate consent for the release of aggregate data, and samples from individuals known to have a severe childhood-onset disease as well as their first-degree relatives.
The final gnomAD release contains genetic variation from 125,748 exomes and 15,708 genomes from unique unrelated individuals with high-quality sequence data, spanning 6 global and 8 sub-continental ancestries (Fig. 1a, b ), which we have made publicly available at https://gnomad. broadinstitute. org. We also provide subsets of the gnomAD datasets, which exclude individuals who are cases in case-control studies, or who are cases of a few particular disease types such as cancer and neurological disorders, or who are also aggregated in the Bravo TOPMed variant browser (https://bravo. sph. umich. edu). Among these individuals, we discovered 17. 2 million and 261. 9 million variants in the exome and genome datasets, respectively; these variants were filtered using a custom random forest process (Supplementary Information) to 14.
9 million and 229. 9 million high-quality variants. Comparing our variant calls in two samples for which we had independent gold-standard variant calls, we found that our filtering achieves very high precision (more than 99% for single nucleotide variants (SNVs), over 98. 5% for indels in both exomes and genomes) and recall (over 90% for SNVs and more than 82% for indels for both exomes and genomes) at the single sample level (Extended Data Fig. 2 ). In addition, we leveraged data from 4,568 and 212 trios included in our exome and genome call-sets, respectively, to assess the quality of our rare variants. We found that our model retains over 97. 8% of the transmitted singletons (singletons in the unrelated individuals that are transmitted to an offspring) on chromosome 20 (which was not used for model training) (Extended Data Fig.
However, we only observe 52% of methylated CpG stop-gained variants, which illustrates the action of natural selection removing a substantial fraction of gene-disrupting variants from the population (Fig. 1c-h ). Across all mutational contexts, only 11. 5% and 3. 7% of the possible synonymous and stop-gained variants, respectively, are observed in the exome dataset, which indicates that current sample sizes remain far from capturing complete mutational saturation of the human exome (Extended Data Fig. 4 ).
Identifying loss-of-function variants Some LoF variants will result in embryonic lethality in humans in a heterozygous state, whereas others are benign even at homozygosity, with a wide spectrum of effects in between. Throughout this manuscript, we define pLoF variants to be those that introduce a premature stop (stop-gained), shift-reported transcriptional frame (frameshift), or alter the two essential splice-site nucleotides immediately to the left and right of each exon (splice) found in protein-coding transcripts, and ascertain their presence in the cohort of 125,748 individuals with exome sequence data. As these variants are enriched for annotation artefacts  1  , we developed the loss-of-function transcript effect estimator (LOFTEE) package, which applies stringent filtering criteria from first principles (such as removing terminal truncation variants, as well as rescued splice variants, that are predicted to escape nonsense-mediated decay) to pLoF variants annotated by the variant effect predictor (Extended Data Fig.
5a ). Despite not using frequency information, we find that this method disproportionately removes pLoF variants that are common in the population, which are known to be enriched for annotation errors 1 , while retaining rare, probable deleterious variations, as well as reported pathogenic variation (Fig. 2a ). LOFTEE distinguishes high-confidence pLoF variants from annotation artefacts, and identifies a set of putative splice variants outside the essential splice site. The filtering strategy of LOFTEE is conservative in the interest of increasing specificity, filtering some potentially functional variants that display a frequency spectrum consistent with that of missense variation (Fig. 2b ). Applying LOFTEE v1. 0, we discover 443,769 high-confidence pLoF variants, of which 413,097 fall on the canonical transcripts of 16,694 genes.
The number of pLoF variants per individual is consistent with previous reports  1  , and is highly dependent on the frequency filters chosen (Supplementary Table  17 ). Aggregating across variants, we created a gene-level pLoF frequency metric to estimate the proportion of haplotypes that contain an inactive   copy of each gene. We find that 1,555 genes have an aggregate pLoF frequency of at least 0. 1% across all individuals in the dataset (Extended Data Fig. 5c ), and 3,270 genes have an aggregate pLoF frequency of at least 0. 1% in any one population. Furthermore, we characterized the landscape of genic tolerance to homozygous inactivation, identifying 4,332 pLoF variants that are homozygous in at least one individual. Given the rarity of true homozygous LoF variants, we expected substantial enrichment of such variants for sequencing and annotation errors, and we subjected this set to additional filtering and deep manual curation before defining a set of 1,815 genes (2,636 high-confidence variants) that are likely to be tolerant to biallelic inactivation (Supplementary Data 7).
For this dataset, we computed a median of 17. 9 expected pLoF variants per gene (Fig. 2c ) and found that 72. 1% of genes have more than 10 pLoF variants (powered to be classified into the most constrained genes) (Supplementary Information) expected on the canonical transcript (Fig. 2d ), an increase from 13. 2% and 62. 8%, respectively, in ExAC. The smaller sample size in ExAC required a transformation of the observed and expected values for the number of pLoF variants in each gene into the pLI: this metric estimates the probability that a gene falls into the class of LoF-haploinsufficient genes (approximately 10% observed/expected variation) and is ideally used as a dichotomous metric (producing 3,230 genes with pLI > 0. 9). Here, our refined model and substantially increased sample size enabled us to directly assess the degree of intolerance to pLoF variation in each gene using the continuous metric of the observed/expected ratio and to estimate a confidence interval around the ratio.
As the smallest population in our exome dataset (African/African American) has only 8,128 individuals, our ability to detect constraint against pLoF variants for individual genes is limited. However, for well-powered genes (expected pLoF ≥ 10) (Supplementary Information), we observed a lower mean observed/expected ratio and LOEUF across genes among African/African American individuals, a population with a larger effective population size, compared with other populations (Extended Data Fig. 8d, e ), consistent with the increased efficiency of selection in populations with larger effective population sizes  25, 26.
Discussion In this paper and accompanying publications, we present the largest, to our knowledge, catalogue of harmonized variant data from any species so far, incorporating exome or genome sequence data from more than 140,000 humans. The gnomAD dataset of over 270 million variants is publicly available (https://gnomad. broadinstitute. org), and has already been widely used as a resource for estimates of allele frequency in the context of rare disease diagnosis (for a recent review, see Eilbeck et al. 32  ), improving power for disease gene discovery  [33] [34] [35]  , estimating genetic disease frequencies  36, 37  , and exploring the biological effect of genetic variation  38, 39. Here, we describe the application of this dataset to calculate a continuous metric that describes a spectrum of tolerance to pLoF variation for each protein-coding gene in the human genome.
Although the gnomAD dataset is of unprecedented scale, it has important limitations. At this sample size, we remain far from saturating all possible pLoF variants in the human exome; even at the most mutable sites in the genome (methylated CpG dinucleotides), we observe only half of all possible stop-gained variants. A substantial fraction of the remaining variants are likely to be heterozygous lethal, whereas others will exhibit an intermediate selection coefficient; much larger sample sizes (in the millions to hundreds of millions of individuals) will be required for comprehensive characterization of selection against all individual LoF variants in the human genome. Such future studies would also benefit substantially from increased ancestral diversity beyond the European-centric sampling of many current studies, which would provide opportunities to observe very rare and population-specific variation, as well as increase power to explore population differences in gene constraint.
In particular, current reference databases including gnomAD have a near-complete absence of representation from the Middle East, central and southeast Asia, Oceania, and the vast majority of the African continent  44  , and these gaps must be addressed if we are to fully understand the distribution and effect of human genetic variation. It is also important to understand the practical and evolutionary interpretation of pLoF constraint. In particular, it should be noted that these metrics primarily identify genes undergoing selection against heterozygous variation, rather than strong constraint against homozygous variation  45. In addition, the power of the LOEUF metric is affected by gene length, with approximately 30% of the coding genes in the genome still insufficiently powered for detection of constraint even at the scale of gnomAD (Fig.
As discussed in more detail in an accompanying manuscript  12  , careful attention must be paid to a variety of complicating factors when using pLoF constraint to assess candidates. More valuable information comes from directly exploring the phenotypic effect of LoF variants on carrier humans, both through 'forward genetics' approaches such as gene mapping to identify genes that cause Mendelian disease, as well as 'reverse genetics' approaches that leverage large collections of sequenced humans to find and clinically characterize individuals with disruptive mutations in specific genes. Although clinical data are currently available for only a small subset of gnomAD individuals, future efforts that integrate sequencing and deep phenotyping of large biobanks will provide valuable insight into the biological implications of partial disruption of specific genes.
This is illustrated in a companion manuscript that explores the clinical correlates of heterozygous pLoF variants in the LRRK2 gene, demonstrating that life-long partial inactivation of this gene is likely to be safe in humans  13. Such examples, and the sheer scale of pLoF discovery in this dataset, suggest the near-future feasibility and considerable value of a human 'knockout' project-a systematic attempt to discover the phenotypic consequences of functionally disruptive mutations, in either the heterozygous or homozygous state, for all human protein-coding genes. Such an approach will require cohorts of samples from millions of sequenced and deeply, consistently phenotyped individuals and, for the discovery of 'complete' knockouts, would benefit substantially from the targeted inclusion of large numbers of samples from populations that have either experienced strong demographic bottlenecks or high levels of recent parental relatedness (consanguinity)  12.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-020-2308-7. Extended Data Fig. 1  | Overview of the sample quality control workflow. a, Exome (square) and genome (circle) samples underwent quality control in the following stages: hard filtering (step 1), relatedness inference (step 2), ancestry inference (step 3), platform inference (step 4, for exomes only), and population-and platform-specific outlier filtering (step 5). See Supplementary Information for further details. Except for samples failing hard filters (dotted outline), all quality control analyses were applied to all samples, regardless of the presence or absence of other quality control flags (such as relatedness, lack of release permissions, or outlier status; red diagonal bar).
Sequencing platforms were inferred for exome samples based on principal component analysis of biallelic variant call rates over all known exome capture regions, and samples were assigned a cluster label (0-15, or unknown) using HDBSCAN. d, We performed platform-and population-specific outlier filtering for several quality-control metrics. The distribution of the number of deletions in samples from south Asian individuals across platforms is shown. Distributions (and accordingly, median and median absolute deviations) for these metrics varied widely both by population and sequencing platform (numbered on the y axis). Outliers (black dots) were defined as samples with values outside four median absolute deviations (shown by dotted vertical lines) from the median of a given metric. Extended Data Fig.
Extended Data Fig. 3  | Variant calling performance for rare variants. a-j, The x axes show the cumulative ranked percentile for our random forest (blue) model and, as a comparison, for the current state-of-the-art GATK variant quality score recalibration (orange). That is, the point at 10 shows the performance of the 10% best-scored data; the point at 50 shows the performance 50% best-scored data. a-d, The number of transmitted singletons (singletons in the unrelated individuals that are transmitted to an offspring) on chromosome 20 for exome SNVs (a) and indels (b), and genome SNVs (c) and indels (d). Chromosome 20 was not used for training our random forest model. We expect most of these to be real variants because we observe Mendelian transmission of an allele that was sequenced independently in a parent and child.
Article Extended Data Fig. 9  | Applications of constraint metrics to rare variant analysis of disease. a, Proportion of each LOEUF decile found in OMIM. b, Proportion of disease-associated genes discovered by whole-exome/ genome sequencing (WES/WGS) compared to conventional (typically linkage) methods, plotted by LOEUF decile. The former are more constrained (LOEUF 0. 674 versus 0. 806, two-sided t-test P = 1. 2 × 10 -16 ), which suggests that these techniques are more effective for picking up genes with a de novo mechanism of disease, compared to recessive genes identified by linkage methods. c, Similar to Fig. 5a , the rate ratio is defined by the rate of de novo variants (number per patient) in autism cases divided by the rate in controls. pLoF variants in the most constrained decile of the genome are approximately fourfold more likely to be found in cases compared to controls.
Acknowledgements We thank the many individuals whose sequence data are aggregated in gnomAD for their contributions to research, and the users of gnomAD for their collaborative feedback. We also thank  D. Altshuler  for contributions to the development of the gnomAD resource, and  A. Martin ,  E. Fauman ,  J. Bloom ,  D. King  and the  Hail team for discussions. The results published here are in part based on data: (1) generated by The  Cancer Genome Atlas (TCGA)  managed by the  NCI  and  NHGRI  (accession: phs000178. v10. p8);information about TCGA can be found at http://cancergenome. nih. gov;(2) generated by the  Genotype-Tissue Expression Project (GTEx)  managed by the  NIH Common Fund  and  NHGRI  (accession: phs000424. v7. p2); (3) generated by the  Exome Sequencing Project , managed by  NHLBI ; (4) generated by the  Alzheimer's Disease Sequencing Project (ADSP) , managed by the  NIA  and  NHGRI  (accession: phs000572.
v7. p4). K. J. K. was supported by  NIGMS   F32 GM115208. L. C. F. was supported by the  Swiss National Science Foundation  ( Advanced Postdoc. Mobility 177853 ). J. X. C. was supported by  NHGRI  and  NHLBI  grants  UM1 HG006493  and  U24 HG008956. Analysis of the Genome Aggregation Database was funded by  NIDDK   U54 DK105566 ,  NHGRI UM1 HG008900 ,  BioMarin Pharmaceutical Inc. , and  Sanofi Genzyme Inc. Development of LOFTEE was funded by  NIGMS   R01 GM104371. D. G. M, K. M. L, and M. E. T. were supported by  NICHD   HD081256. D. G. M. , R. L. C. and M. E. T. were supported by  NIMH   MH115957. The complete acknowledgments can be found in the Supplementary Information. We have complied with all relevant ethical regulations.
Code availability All code to perform quality control is provided at https://github. com/ broadinstitute/gnomad_qc, and the code to perform all analyses and regenerate all the figures in this manuscript is provided at https:// github. com/macarthur-lab/gnomad_lof. LOFTEE is available at https:// github. com/konradjk/loftee. All code and software to reproduce figures are available in a Docker image at konradjk/gnomad_lof_paper:0. 2.
Here, the factorization of the forward problem can be reused, using direct solvers such as Mumps  [4, 5] , to compute the gradient avoiding an additional matrix factorization (this is standard with the adjoint-state method for time-harmonic equations). However, we see that the local problems for the forward problem are not similar to the ones for the adjoint-state computation. Therefore, the code must be adapted with the appropriate operations (now using the matrix B e ), but it remains cheap thanks to the parallelizability of the local problems, as discussed in Subsection 3. 5. Compared to the adjoint-state method derived for the usual discretization (e. g. ,  [58, 8] ), the right-hand side is not simply made of the residuals (the difference between the observations and the simulations), but includes the discretization matrices A e and C e , see  (55).
Three-dimensional experiment In this section, we illustrate the performance 1  of the HDG discretization in the context of iterative reconstruction, and design a three-dimensional synthetic experiments of size 2 × 2 × 1 km 3. The surface is not flat, with a topography made of two large craters and smaller variations, it is illustrated Figure  3b. Firstly, it is necessary to accurately capture the topography to account for the reflections from the surface, therefore, we require a very fine mesh of the surface, that we illustrate in Figure  3a. In particular, we rely on the software mmg 2  to create meshes, and it allows to fix the surface cells while possibly coarsening or refining the deeper area. Namely, we use different meshes (to generate the data or for the iterations at high-frequency), but we have the guarantee that the surface remains the same.
Synthetic partial reflection-data We work with reflection data acquired from the surface only, with receivers positioned just underneath it to measure the pressure field. They are positioned to follow the topography in a lattice with about 75 m between each receivers, along the x and y directions. In total, we have 625 receivers. For the data, we consider a set of 100 point-sources (i. e. , delta-Dirac function for the right-hand side of (3)) that are independently excited. For each of the sources, the 625 receivers measure the resulting pressure field. The sources are also positioned to follow the topography, at the surface, in a lattice with about 190 m between them, for a total of 100 sources. This motivates the use of direct solvers which, as mentioned, allow for the fast resolution of all sources once the matrix is factorized.
Acknowledgments The authors would like to thank  Ha Pham  for thoughtful discussions. FF is funded by the  Austrian Science Fund (FWF)  under the Lise Meitner fellowship M 2791-N. OS is supported by the  FWF , with  SFB   F68 , project  F6807-N36  (Tomography with Uncertainties). The code used for the experiments, hawen, is developed by  FF  and is available at https: //ffaucher. gitlab. io/hawen-website/. The numerical experiments have been performed as part of the  GENCI  resource allocation project  AP010411013.
Schaphoff et al. (2018)  documented the core LPJmL structure, including equations and parameters from  Sitch et al. (2003)  and  Bondeau et al. (2007)  and all more recent code developments. Lastly,  Von Bloh et al. (2018)  extended the LPJmL model to cover the terrestrial nitrogen cycle by explicitly adding processes of soil nitrogen dynamics, plant uptake, photosynthesis, transpiration, and maintenance respiration to variable nitrogen concentrations in plant organs, and agricultural nitrogen management. The LPJmL model has been widely applied to research questions on the terrestrial carbon cycle, hydrology, and agricultural production  (Lutz et al. , 2019; Schaphoff et al. , 2018, and references therein). It has been tested in multimodel simulations of productivity and water fluxes  (Schewe et al.