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An important application of modern genomics is diagnosing genetic disorders. We use the largest publicly available exome sequence database to show that this key clinical service can currently be performed much more effectively in individuals of European genetic ancestry.

Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate intolerance to variation, functional genomic annotations and primary genomic sequence to build JARVIS: a comprehensive deep learning model to prioritize non-coding regions, outperforming other human lineage-specific scores. Despite being agnostic to evolutionary conservation, JARVIS performs comparably or outperforms conservation-based scores in classifying pathogenic single-nucleotide and structural variants. In constructing JARVIS, we introduce the genome-wide residual variation intolerance score (gwRVIS), applying a sliding-window approach to whole genome sequencing data from 62,784 individuals. gwRVIS distinguishes Mendelian disease genes from more tolerant CCDS regions and highlights ultra-conserved non-coding elements as the most intolerant regions in the human genome. Both JARVIS and gwRVIS capture previously inaccessible human-lineage constraint information and will enhance our understanding of the non-coding genome. Intolerance to variation is a strong indicator of disease relevance for coding regions of the human genome. Here, the authors present JARVIS, a deep learning method integrating intolerance to variation in non-coding regions and sequence-specific annotations to infer non-coding variant pathogenicity.

Genomics now has an increasingly important role in neurology clinics. Regarding the epilepsies, innovations centred around technology, analytics, and collaboration have led to remarkable progress in gene discovery and have revealed the diverse array of genetic mechanisms and neurobiological pathways that contribute to these disorders. The new genomic era can present a challenge to clinicians, who now find themselves asked to interpret and apply genetic data to their daily management of patients with epilepsy. Navigation of this new era will require genetic literacy and familiarity with research advances in epilepsy genetics. Genetic epilepsy diagnoses now directly affect clinical care, and their importance will only increase as new targeted treatments continue to emerge. At the same time, new genetic insights challenge us to move from a deterministic view of genetic changes to a more nuanced appreciation of genetic risk within complex neurobiological systems that give rise to epilepsy.

Identification of chromosomal aneuploidies and copy number variants that are associated with fetal structural anomalies has substantial value. Although whole-exome sequencing (WES) has been applied to case series of a few selected prenatal cases, its value in routine clinical settings has not been prospectively assessed in a large unselected cohort of fetuses with structural anomalies. We therefore aimed to determine the incremental diagnostic yield (ie, the added value) of WES following uninformative results of standard investigations with karyotype testing and chromosomal microarray in an unselected cohort of sequential pregnancies showing fetal structural anomalies. In this prospective cohort study, the parents of fetuses who were found to have a structural anomaly in a prenatal ultrasound were screened for possible participation in the study. These participants were predominantly identified in or were referred to the Columbia University Carmen and John Thain Center for Prenatal Pediatrics (New York, NY, USA). Fetuses with confirmed aneuploidy or a causal pathogenic copy number variant were excluded from WES analyses. By use of WES of the fetuses and parents (parent–fetus trios), we identified genetic variants that indicated an underlying cause (diagnostic genetic variants) and genetic variants that met the criteria of bioinformatic signatures that had previously been described to be significantly enriched among diagnostic genetic variants. Between April 24, 2015, and April 19, 2017, 517 sequentially identified pregnant women found to have fetuses with a structural anomaly were screened for their eligibility for inclusion in our study. 71 (14%) couples declined testing, 87 (17%) trios were missing at least one DNA sample (from either parent or the fetus), 69 (13%) trios had a clinically relevant abnormal karyotype or chromosomal microarray finding, 51 (10%) couples did not consent to WES or withdrew consent, and five (1%) samples were not of good enough quality for analysis. DNA samples from 234 (45%) eligible trios were therefore used for analysis of the primary outcome. By use of trio sequence data, we identified diagnostic genetic variants in 24 (10%) families. Mutations with bioinformatic signatures that were indicative of pathogenicity but with insufficient evidence to be considered diagnostic were also evaluated; 46 (20%) of the 234 fetuses assessed were found to have such signatures. Our analysis of WES data in a prospective cohort of unselected fetuses with structural anomalies shows the value added by WES following the use of routine genetic tests. Our findings suggest that, in cases of fetal anomalies in which assessment with karyotype testing and chromosomal microarray fail to determine the underlying cause of a structural anomaly, WES can add clinically relevant information that could assist current management of a pregnancy. The unique challenges of WES-based prenatal diagnostics require analysis by a multidisciplinary team of perinatal practitioners and laboratory specialists. Institute for Genomic Medicine (Columbia University Irving Medical Center).

A central challenge in interpreting personal genomes is determining which mutations most likely influence disease. Although progress has been made in scoring the functional impact of individual mutations, the characteristics of the genes in which those mutations are found remain largely unexplored. For example, genes known to carry few common functional variants in healthy individuals may be judged more likely to cause certain kinds of disease than genes known to carry many such variants. Until now, however, it has not been possible to develop a quantitative assessment of how well genes tolerate functional genetic variation on a genome-wide scale. Here we describe an effort that uses sequence data from 6503 whole exome sequences made available by the NHLBI Exome Sequencing Project (ESP). Specifically, we develop an intolerance scoring system that assesses whether genes have relatively more or less functional genetic variation than expected based on the apparently neutral variation found in the gene. To illustrate the utility of this intolerance score, we show that genes responsible for Mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease, but with striking variation in intolerance among genes causing different classes of genetic disease. We conclude by showing that use of an intolerance ranking system can aid in interpreting personal genomes and identifying pathogenic mutations.

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored 1 , 2 . FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P  < 2.6 × 10 –11 ) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants. Genome-wide association studies of individuals from an isolated population (data from the Finnish biobank study FinnGen) and consequent meta-analyses facilitate the identification of previously unknown coding variant associations for both rare and common diseases.

The first phase of genome-wide association studies (GWAS) assessed the role of common variation in human disease. Advances optimizing and economizing high-throughput sequencing have enabled a second phase of association studies that assess the contribution of rare variation to complex disease in all protein-coding genes. Unlike the early microarray-based studies, sequencing-based studies catalogue the full range of genetic variation, including the evolutionarily youngest forms. Although the experience with common variants helped establish relevant standards for genome-wide studies, the analysis of rare variation introduces several challenges that require novel analysis approaches.

An expanded version of a human-genome database called gnomAD, containing 76,156 whole-genome sequences, has enabled investigation of how variants in non-protein-coding regions of the genome affect health. Updated gnomAD contains 76,156 whole genomes from diverse ancestries.

Ranking human genes based on their tolerance to functional genetic variation can greatly facilitate patient genome interpretation. It is well established, however, that different parts of proteins can have different functions, suggesting that it will ultimately be more informative to focus attention on functionally distinct portions of genes. Here we evaluate the intolerance of genic sub-regions using two biological sub-region classifications. We show that the intolerance scores of these sub-regions significantly correlate with reported pathogenic mutations. This observation extends the utility of intolerance scores to indicating where pathogenic mutations are mostly likely to fall within genes.

Clonal hematopoiesis (CH), the clonal expansion of a blood stem cell and its progeny driven by somatic driver mutations, affects over a third of people, yet remains poorly understood. Here we analyze genetic data from 200,453 UK Biobank participants to map the landscape of inherited predisposition to CH, increasing the number of germline associations with CH in European-ancestry populations from 4 to 14. Genes at new loci implicate DNA damage repair ( PARP1 , ATM , CHEK2 ), hematopoietic stem cell migration/homing ( CD164 ) and myeloid oncogenesis ( SETBP1 ). Several associations were CH-subtype-specific including variants at TCL1A and CD164 that had opposite associations with DNMT3A - versus TET2 -mutant CH, the two most common CH subtypes, proposing key roles for these two loci in CH development. Mendelian randomization analyses showed that smoking and longer leukocyte telomere length are causal risk factors for CH and that genetic predisposition to CH increases risks of myeloproliferative neoplasia, nonhematological malignancies, atrial fibrillation and blood epigenetic ageing. Analysis of whole-exome sequencing data from 200,453 UK Biobank participants identifies loci associated with clonal hematopoiesis and highlights causal links between clonal hematopoiesis and other traits.