A deep catalogue of protein-coding variation in 983,578 individuals

Rare coding variants that significantly impact function provide insights into the biology of a gene1-3. However, ascertaining their frequency requires large sample sizes4-8. Here, we present a catalogue of human protein-coding variation, derived from exome sequencing of 983,578 individuals across diverse populations. 23% of the Regeneron Genetics Center Million Exome data (RGC-ME) comes from non-European individuals of African, East Asian, Indigenous American, Middle Eastern, and South Asian ancestry. This catalogue includes over 10.4 million missense and 1.1 million predicted loss-of-function (pLOF) variants. We identify individuals with rare biallelic pLOF variants in 4,848 genes, 1,751 of which have not been previously reported. From precise quantitative estimates of selection against heterozygous loss-of-function, we identify 3,988 loss-of-function intolerant genes, including 86 that were previously assessed as tolerant and 1,153 lacking established disease annotation. We also define regions of missense depletion at high resolution. Notably, 1,482 genes have regions depleted of missense variants despite being tolerant to pLOF variants. Finally, we estimate that 3% of individuals have a clinically actionable genetic variant, and that 11,773 variants reported in ClinVar with unknown significance are likely to be deleterious cryptic splice sites. To facilitate variant interpretation and genetics-informed precision medicine, we make this important resource of coding variation from the RGC-ME accessible via a public variant allele frequency browser.

A large meta-analysis identifies genes associated with anterior uveitis

Anterior Uveitis (AU) is the inflammation of the anterior part of the eye, the iris and ciliary body and is strongly associated with HLA-B*27. We report AU exome sequencing results from eight independent cohorts consisting of 3,850 cases and 916,549 controls. We identify common genome-wide significant loci in HLA-B (OR = 3.37, p = 1.03e-196) and ERAP1 (OR = 0.86, p = 1.1e-08), and find IPMK (OR = 9.4, p = 4.42e-09) and IDO2 (OR = 3.61, p = 6.16e-08) as genome-wide significant genes based on the burden of rare coding variants. Dividing the cohort into HLA-B*27 positive and negative individuals, we find ERAP1 haplotype is strongly protective only for B*27-positive AU (OR = 0.73, p = 5.2e-10). Investigation of B*27-negative AU identifies a common signal near HLA-DPB1 (rs3117230, OR = 1.26, p = 2.7e-08), risk genes IPMK and IDO2, and several additional candidate risk genes, including ADGFR5, STXBP2, and ACHE. Taken together, we decipher the genetics underlying B*27-positive and -negative AU and identify rare and common genetic signals for both subtypes of disease.

A deep catalog of protein-coding variation in 985,830 individuals

Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.

Rare coding variants in CHRNB2 reduce the likelihood of smoking

Human genetic studies of smoking behavior have been thus far largely limited to common variants. Studying rare coding variants has the potential to identify drug targets. We performed an exome-wide association study of smoking phenotypes in up to 749,459 individuals and discovered a protective association in CHRNB2, encoding the β2 subunit of the α4β2 nicotine acetylcholine receptor. Rare predicted loss-of-function and likely deleterious missense variants in CHRNB2 in aggregate were associated with a 35% decreased odds for smoking heavily (odds ratio (OR) = 0.65, confidence interval (CI) = 0.56-0.76, P = 1.9 × 10^-8). An independent common variant association in the protective direction ( rs2072659 ; OR = 0.96; CI = 0.94-0.98; P = 5.3 × 10^-6) was also evident, suggesting an allelic series. Our findings in humans align with decades-old experimental observations in mice that β2 loss abolishes nicotine-mediated neuronal responses and attenuates nicotine self-administration. Our genetic discovery will inspire future drug designs targeting CHRNB2 in the brain for the treatment of nicotine addiction.

Epilepsy in a mouse model of GNB1 encephalopathy arises from altered potassium (GIRK) channel signaling and is alleviated by a GIRK inhibitor

De novo mutations in GNB1, encoding the Gβ1 subunit of G proteins, cause a neurodevelopmental disorder with global developmental delay and epilepsy, GNB1 encephalopathy. Here, we show that mice carrying a pathogenic mutation, K78R, recapitulate aspects of the disorder, including developmental delay and generalized seizures. Cultured mutant cortical neurons also display aberrant bursting activity on multi-electrode arrays. Strikingly, the antiepileptic drug ethosuximide (ETX) restores normal neuronal network behavior in vitro and suppresses spike-and-wave discharges (SWD) in vivo. ETX is a known blocker of T-type voltage-gated Ca2+ channels and G protein-coupled potassium (GIRK) channels. Accordingly, we present evidence that K78R results in a gain-of-function (GoF) effect by increasing the activation of GIRK channels in cultured neurons and a heterologous model (Xenopus oocytes)-an effect we show can be potently inhibited by ETX. This work implicates a GoF mechanism for GIRK channels in epilepsy, identifies a new mechanism of action for ETX in preventing seizures, and establishes this mouse model as a pre-clinical tool for translational research with predicative value for GNB1 encephalopathy.

The benefit of diagnostic whole genome sequencing in schizophrenia and other psychotic disorders

Schizophrenia has a multifactorial etiology, involving a polygenic architecture. The potential benefit of whole genome sequencing (WGS) in schizophrenia and other psychotic disorders is not well studied. We investigated the yield of clinical WGS analysis in 251 families with a proband diagnosed with schizophrenia (N = 190), schizoaffective disorder (N = 49), or other conditions involving psychosis (N = 48). Participants were recruited in Israel and USA, mainly of Jewish, Arab, and other European ancestries. Trio (parents and proband) WGS was performed for 228 families (90.8%); in the other families, WGS included parents and at least two affected siblings. In the secondary analyses, we evaluated the contribution of rare variant enrichment in particular gene sets, and calculated polygenic risk score (PRS) for schizophrenia. For the primary outcome, diagnostic rate was 6.4%; we found clinically significant, single nucleotide variants (SNVs) or small insertions or deletions (indels) in 14 probands (5.6%), and copy number variants (CNVs) in 2 (0.8%). Significant enrichment of rare loss-of-function variants was observed in a gene set of top schizophrenia candidate genes in affected individuals, compared with population controls (N = 6,840). The PRS for schizophrenia was significantly increased in the affected individuals group, compared to their unaffected relatives. Last, we were also able to provide pharmacogenomics information based on CYP2D6 genotype data for most participants, and determine their antipsychotic metabolizer status. In conclusion, our findings suggest that WGS may have a role in the setting of both research and genetic counseling for individuals with schizophrenia and other psychotic disorders and their families.

Focused goodness of fit tests for gene set analyses

Gene set-based signal detection analyses are used to detect an association between a trait and a set of genes by accumulating signals across the genes in the gene set. Since signal detection is concerned with identifying whether any of the genes in the gene set are non-null, a goodness-of-fit (GOF) test can be used to compare whether the observed distribution of gene-level tests within the gene set agrees with the theoretical null distribution. Here, we present a flexible gene set-based signal detection framework based on tail-focused GOF statistics. We show that the power of the various statistics in this framework depends critically on two parameters: the proportion of genes within the gene set that are non-null and the degree of separation between the null and alternative distributions of the gene-level tests. We give guidance on which statistic to choose for a given situation and implement the methods in a fast and user-friendly R package, wHC (https://github.com/mqzhanglab/wHC). Finally, we apply these methods to a whole exome sequencing study of amyotrophic lateral sclerosis.

ERAP1, ERAP2, and Two Copies of HLA-Aw19 Alleles Increase the Risk for Birdshot Chorioretinopathy in HLA-A29 Carriers

Purpose

Birdshot chorioretinopathy (BSCR) is strongly associated with HLA-A29. This study was designed to elucidate the genetic modifiers of BSCR in HLA-A29 carriers.

Methods

We sequenced the largest BSCR cohort to date, including 286 cases and 108 HLA-A29–positive controls to determine genome-wide common and rare variant associations. We further typed the HLA alleles of cases and 45,386 HLA-A29 controls of European ancestry to identify HLA alleles that associate with BSCR risk.

Results

Carrying a second allele that belongs to the HLA-Aw19 broad antigen family (including HLA-A29, -A30, -A31, and -A33) increases the risk for BSCR (odds ratio [OR] = 4.44; P = 2.2e-03). This result was validated by comparing allele frequencies to large HLA-A29-controlled cohorts (n = 45,386; OR > 2.5; P < 1.3e-06). We also confirm that ERAP1 and ERAP2 haplotypes modulate disease risk. A meta-analysis with an independent dataset confirmed that ERAP1 and ERAP2 haplotypes modulate the risk for disease at a genome-wide significant level: ERAP1-rs27432 (OR = 2.46; 95% confidence interval [CI], 1.85–3.26; P = 4.07e-10), an expression quantitative trait locus (eQTL) decreasing ERAP1 expression; and ERAP2-rs10044354 (OR = 1.95; 95% CI, 1.55–2.44; P = 6.2e-09), an eQTL increasing ERAP2 expression. Furthermore, ERAP2-rs2248374 that disrupts ERAP2 expression is protective (OR = 0.56; 95% CI, 0.45–0.70; P = 2.39e-07). BSCR risk is additively increased when combining ERAP1/ERAP2 risk genotypes with two copies of HLA-Aw19 alleles (OR = 13.53; 95% CI, 3.79–54.77; P = 1.17e-05).

Conclusions

The genetic factors increasing BSCR risk demonstrate a pattern of increased processing, as well as increased presentation of ERAP2-specific peptides. This suggests a mechanism in which exceeding a peptide presentation threshold activates the immune response in choroids of A29 carriers.

Incorporating external information to improve sparse signal detection in rare-variant gene-set-based analyses

Gene-set analyses are used to assess whether there is any evidence of association with disease among a set of biologically related genes. Such an analysis typically treats all genes within the sets similarly, even though there is substantial, external, information concerning the likely importance of each gene within each set. For example, for traits that are under purifying selection, we would expect genes showing extensive genic constraint to be more likely to be trait associated than unconstrained genes. Here we improve gene-set analyses by incorporating such external information into a higher-criticism-based signal detection analysis. We show that when this external information is predictive of whether a gene is associated with disease, our approach can lead to a significant increase in power. Further, our approach is particularly powerful when the signal is sparse, that is when only a small number of genes within the set are associated with the trait. We illustrate our approach with a gene-set analysis of amyotrophic lateral sclerosis (ALS) and implicate a number of gene-sets containing SOD1 and NEK1 as well as showing enrichment of small p values for gene-sets containing known ALS genes. We implement our approach in the R package wHC.

Exome-Based Rare-Variant Analyses in CKD

BACKGROUND

Studies have identified many common genetic associations that influence renal function and all-cause CKD, but these explain only a small fraction of variance in these traits. The contribution of rare variants has not been systematically examined.

METHODS

We performed exome sequencing of 3150 individuals, who collectively encompassed diverse CKD subtypes, and 9563 controls. To detect causal genes and evaluate the contribution of rare variants we used collapsing analysis, in which we compared the proportion of cases and controls carrying rare variants per gene.

RESULTS

The analyses captured five established monogenic causes of CKD: variants in PKD1, PKD2, and COL4A5 achieved study-wide significance, and we observed suggestive case enrichment for COL4A4 and COL4A3. Beyond known disease-associated genes, collapsing analyses incorporating regional variant intolerance identified suggestive dominant signals in CPT2 and several other candidate genes. Biallelic mutations in CPT2 cause carnitine palmitoyltransferase II deficiency, sometimes associated with rhabdomyolysis and acute renal injury. Genetic modifier analysis among cases with APOL1 risk genotypes identified a suggestive signal in AHDC1, implicated in Xia-Gibbs syndrome, which involves intellectual disability and other features. On the basis of the observed distribution of rare variants, we estimate that a two- to three-fold larger cohort would provide 80% power to implicate new genes for all-cause CKD.

CONCLUSIONS

This study demonstrates that rare-variant collapsing analyses can validate known genes and identify candidate genes and modifiers for kidney disease. In so doing, these findings provide a motivation for larger-scale investigation of rare-variant risk contributions across major clinical CKD categories.

A new approach for rare variation collapsing on functional protein domains implicates specific genic regions in ALS

Large-scale sequencing efforts in amyotrophic lateral sclerosis (ALS) have implicated novel genes using gene-based collapsing methods. However, pathogenic mutations may be concentrated in specific genic regions. To address this, we developed two collapsing strategies: One focuses rare variation collapsing on homology-based protein domains as the unit for collapsing, and the other is a gene-level approach that, unlike standard methods, leverages existing evidence of purifying selection against missense variation on said domains. The application of these two collapsing methods to 3093 ALS cases and 8186 controls of European ancestry, and also 3239 cases and 11,808 controls of diversified populations, pinpoints risk regions of ALS genes, including SOD1, NEK1, TARDBP, and FUS. While not clearly implicating novel ALS genes, the new analyses not only pinpoint risk regions in known genes but also highlight candidate genes as well.

meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays

Here we present an open-source R package 'meaRtools' that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL> = 3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.

Annotating pathogenic non-coding variants in genic regions

Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods ineffectively capture pathogenic non-coding variants in genic regions, resulting in overlooking synonymous and intronic variants when searching for disease risk. Here we present the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding variation that causes disease. High TraP scores single out extremely rare variants with lower minor allele frequencies than missense variants. TraP accurately distinguishes known pathogenic and benign variants in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, dismissing benign variants with exceptionally high specificity. TraP analysis of 843 exomes from epilepsy family trios identifies synonymous variants in known epilepsy genes, thus pinpointing risk factors of disease from non-coding sequence data. TraP outperforms leading methods in identifying non-coding variants that are pathogenic and is therefore a valuable tool for use in gene discovery and the interpretation of personal genomes.

While non-coding synonymous and intronic variants are often not under strong selective constraint, they can be pathogenic through affecting splicing or transcription. Here, the authors develop a score that uses sequence context alterations to predict pathogenicity of synonymous and non-coding genetic variants, and provide a web server of pre-computed scores.

Inhibition of microRNA 128 promotes excitability of cultured cortical neuronal networks

Cultured neuronal networks monitored with microelectrode arrays (MEAs) have been used widely to evaluate pharmaceutical compounds for potential neurotoxic effects. A newer application of MEAs has been in the development of in vitro models of neurological disease. Here, we directly evaluated the utility of MEAs to recapitulate in vivo phenotypes of mature microRNA-128 (miR-128) deficiency, which causes fatal seizures in mice. We show that inhibition of miR-128 results in significantly increased neuronal activity in cultured neuronal networks derived from primary mouse cortical neurons. These results support the utility of MEAs in developing in vitro models of neuroexcitability disorders, such as epilepsy, and further suggest that MEAs provide an effective tool for the rapid identification of microRNAs that promote seizures when dysregulated.

Enhancer methylation dynamics contribute to cancer plasticity and patient mortality

During development, enhancers play pivotal roles in regulating gene expression programs; however, their involvement in cancer progression has not been fully characterized. We performed an integrative analysis of DNA methylation, RNA-seq, and small RNA-seq profiles from thousands of patients, including 25 diverse primary malignances and seven body sites of metastatic melanoma. We found that enhancers are consistently the most differentially methylated regions (DMR) as cancer progresses from normal to primary tumors and then to metastases, compared to other genomic features. Remarkably, identification of enhancer DMRs (eDMRs) enabled classification of primary tumors according to physiological organ systems, and in metastasis eDMRs are the most correlated with patient outcome. To further understand the eDMR role in cancer progression, we developed a model to predict genes and microRNAs that are regulated by enhancer and not promotor methylation, which shows high accuracy with chromatin architecture methods and was experimentally validated. Interestingly, among all metastatic melanoma eDMRs, the most correlated with patient survival were eDMRs that "switched" their methylation patterns back and forth between normal, primary, and metastases and target cancer drivers, e.g., KIT We further demonstrated that eDMR target genes were modulated in melanoma by the bone metastasis microenvironment, suggesting that eDMRs respond to microenvironmental cues in metastatic niches. Our findings that aberrant methylation in cancer cells mostly affects enhancers, which contribute to tumor progression and cancer cell plasticity, will facilitate development of epigenetic anticancer approaches.

HP1 is involved in regulating the global impact of DNA methylation on alternative splicing

The global impact of DNA methylation on alternative splicing is largely unknown. Using a genome-wide approach in wild-type and methylation-deficient embryonic stem cells, we found that DNA methylation can either enhance or silence exon recognition and affects the splicing of more than 20% of alternative exons. These exons are characterized by distinct genetic and epigenetic signatures. Alternative splicing regulation of a subset of these exons can be explained by heterochromatin protein 1 (HP1), which silences or enhances exon recognition in a position-dependent manner. We constructed an experimental system using site-specific targeting of a methylated/unmethylated gene and demonstrate a direct causal relationship between DNA methylation and alternative splicing. HP1 regulates this gene's alternative splicing in a methylation-dependent manner by recruiting splicing factors to its methylated form. Our results demonstrate DNA methylation's significant global influence on mRNA splicing and identify a specific mechanism of splicing regulation mediated by HP1.

Differential GC content between exons and introns establishes distinct strategies of splice-site recognition

During evolution segments of homeothermic genomes underwent a GC content increase. Our analyses reveal that two exon-intron architectures have evolved from an ancestral state of low GC content exons flanked by short introns with a lower GC content. One group underwent a GC content elevation that abolished the differential exon-intron GC content, with introns remaining short. The other group retained the overall low GC content as well as the differential exon-intron GC content, and is associated with longer introns. We show that differential exon-intron GC content regulates exon inclusion level in this group, in which disease-associated mutations often lead to exon skipping. This group's exons also display higher nucleosome occupancy compared to flanking introns and exons of the other group, thus "marking" them for spliceosomal recognition. Collectively, our results reveal that differential exon-intron GC content is a previously unidentified determinant of exon selection and argue that the two GC content architectures reflect the two mechanisms by which splicing signals are recognized: exon definition and intron definition.