Evidence for 28 genetic disorders discovered by combining healthcare and research data.

De novo mutations in protein-coding genes are a well-established cause of developmental disorders1. However, genes known to be associated with developmental disorders account for only a minority of the observed excess of such de novo mutations1,2. Here, to identify previously undescribed genes associated with developmental disorders, we integrate healthcare and research exome-sequence data from 31,058 parent-offspring trios of individuals with developmental disorders, and develop a simulation-based statistical test to identify gene-specific enrichment of de novo mutations. We identified 285 genes that were significantly associated with developmental disorders, including 28 that had not previously been robustly associated with developmental disorders. Although we detected more genes associated with developmental disorders, much of the excess of de novo mutations in protein-coding genes remains unaccounted for. Modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of genes associated with developmental disorders.

Exome-wide assessment of the functional impact and pathogenicity of multinucleotide mutations.

Approximately 2% of de novo single-nucleotide variants (SNVs) appear as part of clustered mutations that create multinucleotide variants (MNVs). MNVs are an important source of genomic variability as they are more likely to alter an encoded protein than a SNV, which has important implications in disease as well as evolution. Previous studies of MNVs have focused on their mutational origins and have not systematically evaluated their functional impact and contribution to disease. We identified 69,940 MNVs and 91 de novo MNVs in 6688 exome-sequenced parent-offspring trios from the Deciphering Developmental Disorders Study comprising families with severe developmental disorders. We replicated the previously described MNV mutational signatures associated with DNA polymerase zeta, an error-prone translesion polymerase, and the APOBEC family of DNA deaminases. We estimate the simultaneous MNV germline mutation rate to be 1.78 × 10-10 mutations per base pair per generation. We found that most MNVs within a single codon create a missense change that could not have been created by a SNV. MNV-induced missense changes were, on average, more physicochemically divergent, were more depleted in highly constrained genes (pLI ≥ 0.9), and were under stronger purifying selection compared with SNV-induced missense changes. We found that de novo MNVs were significantly enriched in genes previously associated with developmental disorders in affected children. This shows that MNVs can be more damaging than SNVs even when both induce missense changes, and are an important variant type to consider in relation to human disease.

Predicting Splicing from Primary Sequence with Deep Learning.

The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%-11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.

Author Correction: Predicting the clinical impact of human mutation with deep neural networks.

In the version of this article originally published, the name of author Serafim Batzoglou was misspelled. The error has been corrected in the HTML and PDF versions of the article.

Pathogenicity and selective constraint on variation near splice sites.

Mutations that perturb normal pre-mRNA splicing are significant contributors to human disease. We used exome sequencing data from 7833 probands with developmental disorders (DDs) and their unaffected parents, as well as more than 60,000 aggregated exomes from the Exome Aggregation Consortium, to investigate selection around the splice sites and quantify the contribution of splicing mutations to DDs. Patterns of purifying selection, a deficit of variants in highly constrained genes in healthy subjects, and excess de novo mutations in patients highlighted particular positions within and around the consensus splice site of greater functional relevance. By using mutational burden analyses in this large cohort of proband-parent trios, we could estimate in an unbiased manner the relative contributions of mutations at canonical dinucleotides (73%) and flanking noncanonical positions (27%), and calculate the positive predictive value of pathogenicity for different classes of mutations. We identified 18 patients with likely diagnostic de novo mutations in dominant DD-associated genes at noncanonical positions in splice sites. We estimate 35%-40% of pathogenic variants in noncanonical splice site positions are missing from public databases.

Quantifying the contribution of recessive coding variation to developmental disorders.

We estimated the genome-wide contribution of recessive coding variation in 6040 families from the Deciphering Developmental Disorders study. The proportion of cases attributable to recessive coding variants was 3.6% in patients of European ancestry, compared with 50% explained by de novo coding mutations. It was higher (31%) in patients with Pakistani ancestry, owing to elevated autozygosity. Half of this recessive burden is attributable to known genes. We identified two genes not previously associated with recessive developmental disorders, KDM5B and EIF3F, and functionally validated them with mouse and cellular models. Our results suggest that recessive coding variants account for a small fraction of currently undiagnosed nonconsanguineous individuals, and that the role of noncoding variants, incomplete penetrance, and polygenic mechanisms need further exploration.

Predicting the clinical impact of human mutation with deep neural networks.

Millions of human genomes and exomes have been sequenced, but their clinical applications remain limited due to the difficulty of distinguishing disease-causing mutations from benign genetic variation. Here we demonstrate that common missense variants in other primate species are largely clinically benign in human, enabling pathogenic mutations to be systematically identified by the process of elimination. Using hundreds of thousands of common variants from population sequencing of six non-human primate species, we train a deep neural network that identifies pathogenic mutations in rare disease patients with 88% accuracy and enables the discovery of 14 new candidate genes in intellectual disability at genome-wide significance. Cataloging common variation from additional primate species would improve interpretation for millions of variants of uncertain significance, further advancing the clinical utility of human genome sequencing.

De novo mutations in regulatory elements in neurodevelopmental disorders.

We previously estimated that 42% of patients with severe developmental disorders carry pathogenic de novo mutations in coding sequences. The role of de novo mutations in regulatory elements affecting genes associated with developmental disorders, or other genes, has been essentially unexplored. We identified de novo mutations in three classes of putative regulatory elements in almost 8,000 patients with developmental disorders. Here we show that de novo mutations in highly evolutionarily conserved fetal brain-active elements are significantly and specifically enriched in neurodevelopmental disorders. We identified a significant twofold enrichment of recurrently mutated elements. We estimate that, genome-wide, 1-3% of patients without a diagnostic coding variant carry pathogenic de novo mutations in fetal brain-active regulatory elements and that only 0.15% of all possible mutations within highly conserved fetal brain-active elements cause neurodevelopmental disorders with a dominant mechanism. Our findings represent a robust estimate of the contribution of de novo mutations in regulatory elements to this genetically heterogeneous set of disorders, and emphasize the importance of combining functional and evolutionary evidence to identify regulatory causes of genetic disorders.

Making new genetic diagnoses with old data: iterative reanalysis and reporting from genome-wide data in 1,133 families with developmental disorders.

PURPOSE: Given the rapid pace of discovery in rare disease genomics, it is likely that improvements in diagnostic yield can be made by systematically reanalyzing previously generated genomic sequence data in light of new knowledge. METHODS: We tested this hypothesis in the United Kingdom-wide Deciphering Developmental Disorders study, where in 2014 we reported a diagnostic yield of 27% through whole-exome sequencing of 1,133 children with severe developmental disorders and their parents. We reanalyzed existing data using improved variant calling methodologies, novel variant detection algorithms, updated variant annotation, evidence-based filtering strategies, and newly discovered disease-associated genes. RESULTS: We are now able to diagnose an additional 182 individuals, taking our overall diagnostic yield to 454/1,133 (40%), and another 43 (4%) have a finding of uncertain clinical significance. The majority of these new diagnoses are due to novel developmental disorder-associated genes discovered since our original publication. CONCLUSION: This study highlights the importance of coupling large-scale research with clinical practice, and of discussing the possibility of iterative reanalysis and recontact with patients and health professionals at an early stage. We estimate that implementing parent-offspring whole-exome sequencing as a first-line diagnostic test for developmental disorders would diagnose >50% of patients.

Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data.

BACKGROUND: Human genome sequencing has transformed our understanding of genomic variation and its relevance to health and disease, and is now starting to enter clinical practice for the diagnosis of rare diseases. The question of whether and how some categories of genomic findings should be shared with individual research participants is currently a topic of international debate, and development of robust analytical workflows to identify and communicate clinically relevant variants is paramount. METHODS: The Deciphering Developmental Disorders (DDD) study has developed a UK-wide patient recruitment network involving over 180 clinicians across all 24 regional genetics services, and has performed genome-wide microarray and whole exome sequencing on children with undiagnosed developmental disorders and their parents. After data analysis, pertinent genomic variants were returned to individual research participants via their local clinical genetics team. FINDINGS: Around 80,000 genomic variants were identified from exome sequencing and microarray analysis in each individual, of which on average 400 were rare and predicted to be protein altering. By focusing only on de novo and segregating variants in known developmental disorder genes, we achieved a diagnostic yield of 27% among 1133 previously investigated yet undiagnosed children with developmental disorders, whilst minimising incidental findings. In families with developmentally normal parents, whole exome sequencing of the child and both parents resulted in a 10-fold reduction in the number of potential causal variants that needed clinical evaluation compared to sequencing only the child. Most diagnostic variants identified in known genes were novel and not present in current databases of known disease variation. INTERPRETATION: Implementation of a robust translational genomics workflow is achievable within a large-scale rare disease research study to allow feedback of potentially diagnostic findings to clinicians and research participants. Systematic recording of relevant clinical data, curation of a gene-phenotype knowledge base, and development of clinical decision support software are needed in addition to automated exclusion of almost all variants, which is crucial for scalable prioritisation and review of possible diagnostic variants. However, the resource requirements of development and maintenance of a clinical reporting system within a research setting are substantial. FUNDING: Health Innovation Challenge Fund, a parallel funding partnership between the Wellcome Trust and the UK Department of Health.

A Mendelian trait for olfactory sensitivity affects odor experience and food selection.

Humans vary in acuity to many odors [1-4], with variation within olfactory receptor (OR) genes contributing to these differences [5-9]. How such variation also affects odor experience and food selection remains uncertain [10], given that such effects occur for taste [11-15]. Here we investigate ß-ionone, which shows extreme sensitivity differences [4, 16, 17]. ß-ionone is a key aroma in foods and beverages [18-21] and is added to products in order to give a pleasant floral note [22, 23]. Genome-wide and in vitro assays demonstrate rs6591536 as the causal variant for ß-ionone odor sensitivity. rs6591536 encodes a N183D substitution in the second extracellular loop of OR5A1 and explains >96% of the observed phenotypic variation, resembling a monogenic Mendelian trait. Individuals carrying genotypes for ß-ionone sensitivity can more easily differentiate between food and beverage stimuli with and without added ß-ionone. Sensitive individuals typically describe ß-ionone in foods and beverages as "fragrant" and "floral," whereas less-sensitive individuals describe these stimuli differently. rs6591536 genotype also influences emotional associations and explains differences in food and product choices. These studies demonstrate that an OR variant that influences olfactory sensitivity can affect how people experience and respond to foods, beverages, and other products.

Identification of regions associated with variation in sensitivity to food-related odors in the human genome.

Humans vary in their ability to smell numerous odors [1-3], including those associated with food [4-6]. Odor sensitivity is heritable [7-11], with examples linking genetic variation for sensitivity to specific odors typically located near olfactory receptor (OR) genes [12-16]. However, with thousands of aromas and few deorphaned ORs [17, 18], there has been little progress toward linking variation at OR loci to odor sensitivity [19, 20]. We hypothesized that OR genes contain the variation that explains much of the differences in sensitivity for odors, paralleling the genetics of taste [21, 22], which affect the flavor experience of foods [23-25]. We employed a genome-wide association approach for ten food-related odors and identified genetic associations to sensitivity for 2-heptanone (p = 5.1 × 10(-8)), isobutyraldehyde (p = 6.4 × 10(-10)), ß-damascenone (p = 1.6 × 10(-7)), and ß-ionone (p = 1.4 × 10(-31)). Each locus is located in/near distinct clusters of OR genes. These findings increase the number of olfactory sensitivity loci to nine and demonstrate the importance of OR-associated variation in sensory acuity for food-related odors. Analysis of genotype frequencies across human populations implies that variation in sensitivity for these odors is widespread. Furthermore, each participant possessed one of many possible combinations of sensitivities for these odors, supporting the notion that everyone experiences their own unique "flavor world."

Genetic variation in the odorant receptor OR2J3 is associated with the ability to detect the "grassy" smelling odor, cis-3-hexen-1-ol.

The ability to detect many odors varies among individuals; however, the contribution of genotype to this variation has been assessed for relatively few compounds. We have identified a genetic basis for the ability to detect the flavor compound cis-3-hexen-1-ol. This compound is typically described as "green grassy" or the smell of "cut grass," with variation in the ability to detect it linked to single nucleotide polymorphisms (SNPs) in a region on human chromosome 6 containing 25 odorant receptor genes. We have sequenced the coding regions of all 25 receptors across an ethnically mixed population of 52 individuals and identified 147 sequence variants. We tested these for association with cis-3-hexen-1-ol detection thresholds and found 3 strongly associated SNPs, including one found in a functional odorant receptor (rs28757581 in OR2J3). In vitro assays of 13 odorant receptors from the region identified 3 receptors that could respond to cis-3-hexen-1-ol, including OR2J3. This gene contained 5 predicted haplotypes across the 52 individuals. We tested all 5 haplotypes in vitro and several amino acid substitutions on their own, such as rs28757581 (T113A). Two amino acid substitutions, T113A and R226Q, impaired the ability of OR2J3 to respond to cis-3-hexen-1-ol, and together these two substitutions effectively abolished the response to the compound. The haplotype of OR2J3 containing both T113A and R226Q explains 26.4% of the variation in cis-3-hexen-1-ol detection in our study cohort. Further research is required to examine whether OR2J3 haplotypes explain variation in perceived flavor experience and the consumption of foods containing cis-3-hexen-1-ol.