Bruce M. Cohen

Abstract 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 sizes 1 . 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.

Abstract Structural variants (SVs) rearrange large segments of DNA 1 and can have profound consequences in evolution and human disease 2,3 . 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 dosage 6 . 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 findings 7 . This SV resource is freely distributed via the gnomAD browser 8 and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

Rare coding variation has historically provided the most direct connections between gene function and disease pathogenesis. By meta-analysing the whole exomes of 24,248 schizophrenia cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in 10 genes as conferring substantial risk for schizophrenia (odds ratios of 3–50, P < 2.14 × 10−6) and 32 genes at a false discovery rate of <5%. These genes have the greatest expression in central nervous system neurons and have diverse molecular functions that include the formation, structure and function of the synapse. The associations of the NMDA (N-methyl-d-aspartate) receptor subunit GRIN2A and AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptor subunit GRIA3 provide support for dysfunction of the glutamatergic system as a mechanistic hypothesis in the pathogenesis of schizophrenia. We observe an overlap of rare variant risk among schizophrenia, autism spectrum disorders1, epilepsy and severe neurodevelopmental disorders2, although different mutation types are implicated in some shared genes. Most genes described here, however, are not implicated in neurodevelopment. We demonstrate that genes prioritized from common variant analyses of schizophrenia are enriched in rare variant risk3, suggesting that common and rare genetic risk factors converge at least partially on the same underlying pathogenic biological processes. Even after excluding significantly associated genes, schizophrenia cases still carry a substantial excess of URVs, which indicates that more risk genes await discovery using this approach. Whole-exome sequencing identifies ten risk genes for schizophrenia implicated by rare protein-coding variants, a subset of which overlap with risk genes in other neurodevelopmental disorders.

BACKGROUND: Youths with bipolar disorder are ideal for studying illness pathophysiology given their early presentation, lack of extended treatment, and high genetic loading. Adult bipolar disorder MRI studies have focused increasingly on limbic structures and the thalamus because of their role in mood and cognition. On the basis of adult studies, the authors hypothesized a priori that youths with bipolar disorder would have amygdalar, hippocampal, and thalamic volume abnormalities. METHOD: Forty-three youths 6-16 years of age with DSM-IV bipolar disorder (23 male, 20 female) and 20 healthy comparison subjects (12 male, eight female) similar in age and sex underwent structured and clinical interviews, neurological examination, and cognitive testing. Differences in limbic and thalamic brain volumes, on the logarithmic scale, were tested using a two-way (diagnosis and sex) univariate analysis of variance, with total cerebral volume and age controlled. RESULTS: The subjects with bipolar disorder had smaller hippocampal volumes. Further analysis revealed that this effect was driven predominantly by the female bipolar disorder subjects. In addition, both male and female youths with bipolar disorder had significantly smaller cerebral volumes. No significant hemispheric effects were seen. CONCLUSIONS: These findings support the hypothesis that the limbic system, in particular the hippocampus, may be involved in the pathophysiology of pediatric bipolar disorder. While this report may represent the largest MRI study of pediatric bipolar disorder to date, more work is needed to confirm these findings and to determine if they are unique to pediatric bipolar disorder.