Genome-wide association studies have uncovered thousands of common variants associated with human disease, but the contribution of rare variation to common disease remains relatively unexplored. The UK Biobank (UKB) contains detailed phenotypic data linked to medical records for approximately 500,000 participants, offering an unprecedented opportunity to evaluate the impact of rare variation on a broad collection of traits1,2. Here, we studied the relationships between rare protein-coding variants and 17,361 binary and 1,419 quantitative phenotypes using exome sequencing data from 269,171 UKB participants of European ancestry. Gene-based collapsing analyses revealed 1,703 statistically significant gene-phenotype associations for binary traits, with a median odds ratio of 12.4. Furthermore, 83% of these associations were undetectable via single variant association tests, emphasizing the power of gene-based collapsing analysis in the setting of high allelic heterogeneity. Gene-phenotype associations were also significantly enriched for loss-of-function-mediated traits and approved drug targets. Finally, we performed ancestry-specific and pan-ancestry collapsing analyses using exome sequencing data from 11,933 UKB participants of African, East Asian, or South Asian ancestry. Together, our results highlight a significant contribution of rare variants to common disease. Summary statistics are publicly available through an interactive portal (http://azphewas.com/).

Purpose Despite the recognized clinical value of exome-based diagnostics, methods for comprehensive genomic interpretation remain immature. Diagnoses are based on known or presumed pathogenic variants in genes already associated with a similar phenotype. Here, we extend this paradigm by evaluating novel bioinformatics approaches to aid identification of new gene–disease associations. Methods We analyzed 119 trios to identify both diagnostic genotypes in known genes and candidate genotypes in novel genes. We considered qualifying genotypes based on their population frequency and in silico predicted effects, and characterized the patterns of genotypes enriched across this collection of patients. Results We obtained a genetic diagnosis for 29 (24%) of our patients. We showed that patients carried an excess of damaging de novo mutations in intolerant genes, particularly those shown to be essential in mice (P = 3.4 × 10−8). This enrichment is only partially explained by mutations found in known disease-causing genes. Conclusion This work indicates that the application of appropriate bioinformatics analyses to clinical sequence data can also help implicate novel disease genes and suggest expanded phenotypes for known disease genes. These analyses further suggest that some cases resolved by whole-exome sequencing will have direct therapeutic implications.

Sequencing-based studies have identified novel risk genes associated with severe epilepsies and revealed an excess of rare deleterious variation in less-severe forms of epilepsy. To identify the shared and distinct ultra-rare genetic risk factors for different types of epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,436 controls of European ancestry. We focused on three phenotypic groups: severe developmental and epileptic encephalopathies (DEEs), genetic generalized epilepsy (GGE), and non-acquired focal epilepsy (NAFE). We observed that compared to controls, individuals with any type of epilepsy carried an excess of ultra-rare, deleterious variants in constrained genes and in genes previously associated with epilepsy; we saw the strongest enrichment in individuals with DEEs and the least strong in individuals with NAFE. Moreover, we found that inhibitory GABA A receptor genes were enriched for missense variants across all three classes of epilepsy, whereas no enrichment was seen in excitatory receptor genes. The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEEs and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the lead associations; such genes included CACNA1G, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study, the largest epilepsy WES study to date, confirms a convergence in the genetics of severe and less-severe epilepsies associated with ultra-rare coding variation, and it highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology.

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.

Objective:To elucidate the functional consequences of epileptic encephalopathy–causing de novo mutations in DNM1 (A177P, K206N, G359A), which encodes a large mechanochemical GTPase essential for neuronal synaptic vesicle endocytosis.Methods:HeLa and COS-7 cells transfected with wild-type and mutant DNM1 constructs were used for transferrin assays, high-content imaging, colocalization studies, Western blotting, and electron microscopy (EM). EM was also conducted on the brain sections of mice harboring a middle-domain Dnm1 mutation (Dnm1Ftfl).Results:We demonstrate that the expression of each mutant protein decreased endocytosis activity in a dominant-negative manner. One of the G-domain mutations, K206N, decreased protein levels. The G359A mutation, which occurs in the middle domain, disrupted higher-order DNM1 oligomerization. EM of mutant DNM1-transfected HeLa cells and of the Dnm1Ftfl mouse brain revealed vesicle defects, indicating that the mutations likely interfere with DNM1's vesicle scission activity.Conclusion:Together, these data suggest that the dysfunction of vesicle scission during synaptic vesicle endocytosis can lead to serious early-onset epilepsies.