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 disorders1-4, 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.

CHARR efficiently estimates contamination from DNA sequencing data.

DNA sample contamination is a major issue in clinical and research applications of whole-genome and -exome sequencing. Even modest levels of contamination can substantially affect the overall quality of variant calls and lead to widespread genotyping errors. Currently, popular tools for estimating the contamination level use short-read data (BAM/CRAM files), which are expensive to store and manipulate and often not retained or shared widely. We propose a metric to estimate DNA sample contamination from variant-level whole-genome and -exome sequence data called CHARR, contamination from homozygous alternate reference reads, which leverages the infiltration of reference reads within homozygous alternate variant calls. CHARR uses a small proportion of variant-level genotype information and thus can be computed from single-sample gVCFs or callsets in VCF or BCF formats, as well as efficiently stored variant calls in Hail VariantDataset format. Our results demonstrate that CHARR accurately recapitulates results from existing tools with substantially reduced costs, improving the accuracy and efficiency of downstream analyses of ultra-large whole-genome and exome sequencing datasets.

Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies.

Short-read genome sequencing (GS) holds the promise of becoming the primary diagnostic approach for the assessment of autism spectrum disorder (ASD) and fetal structural anomalies (FSAs). However, few studies have comprehensively evaluated its performance against current standard-of-care diagnostic tests: karyotype, chromosomal microarray (CMA), and exome sequencing (ES). To assess the clinical utility of GS, we compared its diagnostic yield against these three tests in 1,612 quartet families including an individual with ASD and in 295 prenatal families. Our GS analytic framework identified a diagnostic variant in 7.8% of ASD probands, almost 2-fold more than CMA (4.3%) and 3-fold more than ES (2.7%). However, when we systematically captured copy-number variants (CNVs) from the exome data, the diagnostic yield of ES (7.4%) was brought much closer to, but did not surpass, GS. Similarly, we estimated that GS could achieve an overall diagnostic yield of 46.1% in unselected FSAs, representing a 17.2% increased yield over karyotype, 14.1% over CMA, and 4.1% over ES with CNV calling or 36.1% increase without CNV discovery. Overall, GS provided an added diagnostic yield of 0.4% and 0.8% beyond the combination of all three standard-of-care tests in ASD and FSAs, respectively. This corresponded to nine GS unique diagnostic variants, including sequence variants in exons not captured by ES, structural variants (SVs) inaccessible to existing standard-of-care tests, and SVs where the resolution of GS changed variant classification. Overall, this large-scale evaluation demonstrated that GS significantly outperforms each individual standard-of-care test while also outperforming the combination of all three tests, thus warranting consideration as the first-tier diagnostic approach for the assessment of ASD and FSAs.

GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data.

Copy number variants (CNVs) are major contributors to genetic diversity and disease. While standardized methods, such as the genome analysis toolkit (GATK), exist for detecting short variants, technical challenges have confounded uniform large-scale CNV analyses from whole-exome sequencing (WES) data. Given the profound impact of rare and de novo coding CNVs on genome organization and human disease, we developed GATK-gCNV, a flexible algorithm to discover rare CNVs from sequencing read-depth information, complete with open-source distribution via GATK. We benchmarked GATK-gCNV in 7,962 exomes from individuals in quartet families with matched genome sequencing and microarray data, finding up to 95% recall of rare coding CNVs at a resolution of more than two exons. We used GATK-gCNV to generate a reference catalog of rare coding CNVs in WES data from 197,306 individuals in the UK Biobank, and observed strong correlations between per-gene CNV rates 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 WES data, with broad applications.

CHARR efficiently estimates contamination from DNA sequencing data.

DNA sample contamination is a major issue in clinical and research applications of whole genome and exome sequencing. Even modest levels of contamination can substantially affect the overall quality of variant calls and lead to widespread genotyping errors. Currently, popular tools for estimating the contamination level use short-read data (BAM/CRAM files), which are expensive to store and manipulate and often not retained or shared widely. We propose a new metric to estimate DNA sample contamination from variant-level whole genome and exome sequence data, CHARR, Contamination from Homozygous Alternate Reference Reads, which leverages the infiltration of reference reads within homozygous alternate variant calls. CHARR uses a small proportion of variant-level genotype information and thus can be computed from single-sample gVCFs or callsets in VCF or BCF formats, as well as efficiently stored variant calls in Hail VDS format. Our results demonstrate that CHARR accurately recapitulates results from existing tools with substantially reduced costs, improving the accuracy and efficiency of downstream analyses of ultra-large whole genome and exome sequencing datasets.

Rare coding variation provides insight into the genetic architecture and phenotypic context of autism.

Some individuals with autism spectrum disorder (ASD) carry functional mutations rarely observed in the general population. We explored the genes disrupted by these variants from joint analysis of protein-truncating variants (PTVs), missense variants and copy number variants (CNVs) in a cohort of 63,237 individuals. We discovered 72 genes associated with ASD at false discovery rate (FDR) ≤ 0.001 (185 at FDR ≤ 0.05). De novo PTVs, damaging missense variants and CNVs represented 57.5%, 21.1% and 8.44% of association evidence, while CNVs conferred greatest relative risk. Meta-analysis with cohorts ascertained for developmental delay (DD) (n = 91,605) yielded 373 genes associated with ASD/DD at FDR ≤ 0.001 (664 at FDR ≤ 0.05), some of which differed in relative frequency of mutation between ASD and DD cohorts. The DD-associated genes were enriched in transcriptomes of progenitor and immature neuronal cells, whereas genes showing stronger evidence in ASD were more enriched in maturing neurons and overlapped with schizophrenia-associated genes, emphasizing that these neuropsychiatric disorders may share common pathways to risk.

Mitochondrial DNA variation across 56,434 individuals in gnomAD.

Genomic databases of allele frequency are extremely helpful for evaluating clinical variants of unknown significance; however, until now, databases such as the Genome Aggregation Database (gnomAD) have focused on nuclear DNA and have ignored the mitochondrial genome (mtDNA). Here, we present a pipeline to call mtDNA variants that addresses three technical challenges: (1) detecting homoplasmic and heteroplasmic variants, present, respectively, in all or a fraction of mtDNA molecules; (2) circular mtDNA genome; and (3) misalignment of nuclear sequences of mitochondrial origin (NUMTs). We observed that mtDNA copy number per cell varied across gnomAD cohorts and influenced the fraction of NUMT-derived false-positive variant calls, which can account for the majority of putative heteroplasmies. To avoid false positives, we excluded contaminated samples, cell lines, and samples prone to NUMT misalignment due to few mtDNA copies. Furthermore, we report variants with heteroplasmy ≥10%. We applied this pipeline to 56,434 whole-genome sequences in the gnomAD v3.1 database that includes individuals of European (58%), African (25%), Latino (10%), and Asian (5%) ancestry. Our gnomAD v3.1 release contains population frequencies for 10,850 unique mtDNA variants at more than half of all mtDNA bases. Importantly, we report frequencies within each nuclear ancestral population and mitochondrial haplogroup. Homoplasmic variants account for most variant calls (98%) and unique variants (85%). We observed that 1/250 individuals carry a pathogenic mtDNA variant with heteroplasmy above 10%. These mtDNA population allele frequencies are freely accessible and will aid in diagnostic interpretation and research studies.

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

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.

Development, formulation, and cellular mechanism of a lipophilic copper chelator for the treatment of Wilson's disease.

Copper homeostasis is finely regulated in human to avoid any detrimental impact of free intracellular copper ions. Upon copper accumulation, biliary excretion is triggered in liver thanks to trafficking of the ATP7B copper transporter to bile canaliculi. However, in Wilson's disease this protein is mutated leading to copper accumulation. Current therapy uses Cu chelators acting extracellularly and requiring a life-long treatment with side effects. Herein, a new Cu(I) pro-chelator was encapsulated in long-term stable nanostructured lipid carriers. Cellular assays revealed that the pro-chelator protects hepatocytes against Cu-induced cell death. Besides, the cellular stresses induced by moderate copper concentrations, including protein unfolding, are counteracted by the pro-chelator. These data showed the pro-chelator efficiency to deliver intracellularly an active chelator that copes with copper stress and surpasses current and under development chelators. Although its biological activity is more mitigated, the pro-chelator nanolipid formulation led to promising results. This innovative approach is of outmost importance in the quest of better treatments for Wilson's disease.

Lectin recognition and hepatocyte endocytosis of GalNAc-decorated nanostructured lipid carriers.

Liver is the main organ for metabolism but is also subject to various pathologies, from viral, genetic, cancer or metabolic origin. There is thus a crucial need to develop efficient liver-targeted drug delivery strategies. Asialoglycoprotein receptor (ASGPR) is a C-type lectin expressed in the hepatocyte plasma membrane that efficiently endocytoses glycoproteins exposing galactose (Gal) or N-acetylgalactosamine (GalNAc). Its targeting has been successfully used to drive the uptake of small molecules decorated with three or four GalNAc, thanks to an optimisation of their spatial arrangement. Herein, we assessed the biological properties of highly stable nanostructured lipid carriers (NLC) made of FDA-approved ingredients and formulated with increasing amounts of GalNAc. Cellular studies showed that a high density of GalNAc was required to favour hepatocyte internalisation via the ASGPR pathway. Interaction studies using surface plasmon resonance and the macrophage galactose-lectin as GalNAc-recognising lectin confirmed the need of high GalNAc density for specific recognition of these NLC. This work is the first step for the development of efficient nanocarriers for prolonged liver delivery of active compounds.

Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes.

Multi-nucleotide variants (MNVs), defined as two or more nearby variants existing on the same haplotype in an individual, are a clinically and biologically important class of genetic variation. However, existing tools typically do not accurately classify MNVs, and understanding of their mutational origins remains limited. Here, we systematically survey MNVs in 125,748 whole exomes and 15,708 whole genomes from the Genome Aggregation Database (gnomAD). We identify 1,792,248 MNVs across the genome with constituent variants falling within 2 bp distance of one another, including 18,756 variants with a novel combined effect on protein sequence. Finally, we estimate the relative impact of known mutational mechanisms - CpG deamination, replication error by polymerase zeta, and polymerase slippage at repeat junctions - on the generation of MNVs. Our results demonstrate the value of haplotype-aware variant annotation, and refine our understanding of genome-wide mutational mechanisms of MNVs.

A structural variation reference for medical and population genetics.

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.

The mutational constraint spectrum quantified from variation in 141,456 humans.

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 sizes1. 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.

Lean and deep models for more accurate filtering of SNP and INDEL variant calls.

SUMMARY: We investigate convolutional neural networks (CNNs) for filtering small genomic variants in short-read DNA sequence data. Errors created during sequencing and library preparation make variant calling a difficult task. Encoding the reference genome and aligned reads covering sites of genetic variation as numeric tensors allows us to leverage CNNs for variant filtration. Convolutions over these tensors learn to detect motifs useful for classifying variants. Variant filtering models are trained to classify variants as artifacts or real variation. Visualizing the learned weights of the CNN confirmed it detects familiar DNA motifs known to correlate with real variation, like homopolymers and short tandem repeats (STR). After confirmation of the biological plausibility of the learned features we compared our model to current state-of-the-art filtration methods like Gaussian Mixture Models, Random Forests and CNNs designed for image classification, like DeepVariant. We demonstrate improvements in both sensitivity and precision. The tensor encoding was carefully tailored for processing genomic data, respecting the qualitative differences in structure between DNA and natural images. Ablation tests quantitatively measured the benefits of our tensor encoding strategy. Bayesian hyper-parameter optimization confirmed our notion that architectures designed with DNA data in mind outperform off-the-shelf image classification models. Our cross-generalization analysis identified idiosyncrasies in truth resources pointing to the need for new methods to construct genomic truth data. Our results show that models trained on heterogenous data types and diverse truth resources generalize well to new datasets, negating the need to train separate models for each data type. AVAILABILITY AND IMPLEMENTATION: This work is available in the Genome Analysis Toolkit (GATK) with the tool name CNNScoreVariants (https://github.com/broadinstitute/gatk). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.