Rare Coding Variants Associated with Electrocardiographic Intervals Identify Monogenic Arrhythmia Susceptibility Genes: A Multi-ancestry Analysis

Background - Alterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between ECG intervals and rare genetic variation at a population level are poorly understood. Methods - Using a discovery sample of 29,000 individuals with whole-genome sequencing from TOPMed and replication in nearly 100,000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured ECG traits (RR, P-wave, PR, and QRS intervals and corrected QT interval [QTc]). Results - We found that rare variants associated with population-based ECG intervals identify established monogenic SCD genes ( KCNQ1 , KCNH2 , SCN5A ), a controversial monogenic SCD gene ( KCNE1 ), and novel genes ( PAM , MFGE8 ) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of first-degree atrioventricular block ( P =8.4x10 -5 ). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked QTc prolongation ( P =4x10 -25 ), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal ECG intervals. Conclusions - Our findings indicate that large-scale high-depth sequence data and ECG analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked ECG interval prolongation.

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Abstract 14629: Rare Variants for Electrocardiographic Traits Identify Arrhythmia Susceptibility Genes

Circulation ◽

10.1161/circ.142.suppl_3.14629 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Sean J Jurgens ◽

Seung Hoan Choi ◽

Christopher M Haggerty ◽

Amelia W Hall ◽

Jennifer Halford ◽

...

Keyword(s):

Rare Variants ◽

Sequence Data ◽

Low Frequency ◽

Population Based ◽

P Wave ◽

Cardiac Conduction ◽

Incomplete Penetrance ◽

Loss Of Function ◽

Discovery Sample ◽

Pathogenic Variants

Introduction: Electrocardiogram (ECG) intervals are quantitative and heritable endophenotypes for arrhythmias and sudden cardiac death (SCD). Studying rare sequence variation related to ECG intervals may help identify the genetic underpinnings of cardiac conduction and SCD. Methods: Using a discovery sample of 29,000 individuals with whole-genome sequences from TOPMed and a replication sample of about 100,000 individuals with whole-exome sequence data from the UK Biobank and MyCode, we examined associations between low-frequency (MAF<1%) and rare (MAF<0.1%) coding variants with 5 routinely ascertained ECG intervals (RR, P-wave, PR, QRS, and QTc intervals). We further assessed pathogenic variants in identified genes using ClinVar. Results: In low-frequency single variant analysis, we observed associations for PR interval in PAM ( P =2x10 -7 ) and MFGE8 ( P =5x10 -8 ). In gene-based tests, we identified rare coding variation associated with marked effects in established SCD genes KCNQ1, KCNH2, SCN5A and KCNE1 . For example, loss-of-function or pathogenic variants in KCNQ1 and KCNH2 were carried in 0.2% of individuals, were associated with 29 ms longer QTc intervals ( P =2x10 -82 ) and conferred up to 23-fold increased odds of marked QTc prolongation ( P =4x10 -25 ). Nevertheless, over 75% of carriers had normal QTc intervals. Similarly, loss-of-function or pathogenic variants in SCN5A , carried by 0.1% of individuals, conferred marked PR prolongation (31 ms), yet less than 30% of carriers had first-degree atrioventricular block. Discussion: This study demonstrates the value of studying ECGs in large sequenced biobanks for identifying rare variants predisposing to cardiac arrhythmias. Results define the frequency of pathogenic variation in SCD genes in the population and document incomplete penetrance of such variation. Our findings may serve as a benchmark for future population-based analyses aimed at discovering clinically actionable variants and genes.

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Genome-wide rare variant analysis for thousands of phenotypes in 54,000 exomes

10.1101/692368 ◽

2019 ◽

Cited By ~ 2

Author(s):

Elizabeth T. Cirulli ◽

Simon White ◽

Robert W. Read ◽

Gai Elhanan ◽

William J Metcalf ◽

...

Keyword(s):

Rare Variant ◽

Large Scale ◽

Rare Variants ◽

Sequence Data ◽

Statistical Significance ◽

European Ancestry ◽

Hair Color ◽

Common Variants ◽

Loss Of Function ◽

Test Statistic

Defining the effects that rare variants can have on human phenotypes is essential to advancing our understanding of human health and disease. Large-scale human genetic analyses have thus far focused on common variants, but the development of large cohorts of deeply phenotyped individuals with exome sequence data has now made comprehensive analyses of rare variants possible. We analyzed the effects of rare (MAF<0.1%) variants on 3,166 phenotypes in 40,468 exome-sequenced individuals from the UK Biobank and performed replication as well as meta-analyses with 1,067 phenotypes in 13,470 members of the Healthy Nevada Project (HNP) cohort who underwent Exome+ sequencing at Helix. Our analyses of non-benign coding and loss of function (LoF) variants identified 78 gene-based associations that passed our statistical significance threshold (p<5×10-9). These are associations in which carrying any rare coding or LoF variant in the gene is associated with an enrichment for a specific phenotype, as opposed to GWAS-based associations of strictly single variants. Importantly, our results do not suffer from the test statistic inflation that is often seen with rare variant analyses of biobank-scale data because of our rare variant-tailored methodology, which includes a step that optimizes the carrier frequency threshold for each phenotype based on prevalence. Of the 47 discovery associations whose phenotypes were represented in the replication cohort, 98% showed effects in the expected direction, and 45% attained formal replication significance (p<0.001). Six additional significant associations were identified in our meta-analysis of both cohorts. Among the results, we confirm known associations of PCSK9 and APOB variation with LDL levels; we extend knowledge of variation in the TYRP1 gene, previously associated with blonde hair color only in Solomon Islanders to blonde hair color in individuals of European ancestry; we show that PAPPA, a gene in which common variants had previously associated with height via GWAS, contains rare variants that decrease height; and we make the novel discovery that STAB1 variation is associated with blood flow in the brain. Our results are available for download and interactive browsing in an app (https://ukb.research.helix.com). This comprehensive analysis of the effects of rare variants on human phenotypes marks one of the first steps in the next big phase of human genetics, where large, deeply phenotyped cohorts with next generation sequence data will elucidate the effects of rare variants.

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Quantifying the unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects

10.1101/030841 ◽

2015 ◽

Cited By ~ 1

Author(s):

James Zou ◽

Gregory Valiant ◽

Paul Valiant ◽

Konrad Karczewski ◽

Siu On Chan ◽

...

Keyword(s):

Frequency Distribution ◽

Statistical Power ◽

Large Scale ◽

Rare Variants ◽

Human Populations ◽

Loss Of Function ◽

Protein Coding ◽

Missense Variants ◽

Healthy Humans ◽

Coding Variants

As new proposals aim to sequence ever larger collection of humans, it is critical to have a quantitative framework to evaluate the statistical power of these projects. We developed a new algorithm, UnseenEst, and applied it to the exomes of 60,706 individuals to estimate the frequency distribution of all protein-coding variants, including rare variants that have not been observed yet in the current cohorts. Our results quantified the number of new variants that we expect to identify as sequencing cohorts reach hundreds of thousands of individuals. With 500K individuals, we find that we expect to capture 7.5% of all possible loss-of-function variants and 12% of all possible missense variants. We also estimate that 2,900 genes have loss-of-function frequency of less than 0.00001 in healthy humans, consistent with very strong intolerance to gene inactivation.

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Rare variants in KDR, encoding VEGF Receptor 2, are associated with tetralogy of Fallot

Genetics in Medicine ◽

10.1038/s41436-021-01212-y ◽

2021 ◽

Author(s):

Doris Škorić-Milosavljević ◽

Najim Lahrouchi ◽

Fernanda M. Bosada ◽

Gregor Dombrowsky ◽

Simon G. Williams ◽

...

Keyword(s):

Exome Sequencing ◽

Tetralogy Of Fallot ◽

Large Scale ◽

Rare Variants ◽

Growth Factor Receptor ◽

Vegf Receptor ◽

Loss Of Function ◽

Endothelial Growth Factor

Abstract Purpose Rare genetic variants in KDR, encoding the vascular endothelial growth factor receptor 2 (VEGFR2), have been reported in patients with tetralogy of Fallot (TOF). However, their role in disease causality and pathogenesis remains unclear. Methods We conducted exome sequencing in a familial case of TOF and large-scale genetic studies, including burden testing, in >1,500 patients with TOF. We studied gene-targeted mice and conducted cell-based assays to explore the role of KDR genetic variation in the etiology of TOF. Results Exome sequencing in a family with two siblings affected by TOF revealed biallelic missense variants in KDR. Studies in knock-in mice and in HEK 293T cells identified embryonic lethality for one variant when occurring in the homozygous state, and a significantly reduced VEGFR2 phosphorylation for both variants. Rare variant burden analysis conducted in a set of 1,569 patients of European descent with TOF identified a 46-fold enrichment of protein-truncating variants (PTVs) in TOF cases compared to controls (P = 7 × 10-11). Conclusion Rare KDR variants, in particular PTVs, strongly associate with TOF, likely in the setting of different inheritance patterns. Supported by genetic and in vivo and in vitro functional analysis, we propose loss-of-function of VEGFR2 as one of the mechanisms involved in the pathogenesis of TOF.

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Sequencing of over 100,000 individuals identifies multiple genes and rare variants associated with Crohns disease susceptibility

10.1101/2021.06.15.21258641 ◽

2021 ◽

Author(s):

Aleksejs Sazonovs ◽

Christine R Stevens ◽

Guhan R Venkataraman ◽

Kai Yuan ◽

Brandon Avila ◽

...

Keyword(s):

Rare Variants ◽

Disease Risk ◽

Sequence Data ◽

Association Studies ◽

Genome Wide Association Studies ◽

Crohns Disease ◽

Biological Targets ◽

Genome Wide ◽

Coding Variants ◽

First Time

Genome-wide association studies (GWAS) have identified hundreds of loci associated with Crohns disease (CD); however, as with all complex diseases, deriving pathogenic mechanisms from these non-coding GWAS discoveries has been challenging. To complement GWAS and better define actionable biological targets, we analysed sequence data from more than 30,000 CD cases and 80,000 population controls. We observe rare coding variants in established CD susceptibility genes as well as ten genes where coding variation directly implicates the gene in disease risk for the first time.

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Common schizophrenia alleles are enriched in mutation-intolerant genes and maintained by background selection

10.1101/068593 ◽

2016 ◽

Cited By ~ 21

Author(s):

Antonio F Pardiñas ◽

Peter Holmans ◽

Andrew J Pocklington ◽

Valentina Escott-Price ◽

Stephan Ripke ◽

...

Keyword(s):

Large Scale ◽

Genome Wide Association Study ◽

Rare Variants ◽

Meta Analysis ◽

Single Gene ◽

Poor Quality ◽

Background Selection ◽

Loss Of Function ◽

Risk Variants ◽

Genomic Studies

Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide such insight. We report the largest single cohort genome-wide association study of schizophrenia (11,260 cases and 24,542 controls) and through meta-analysis with existing data we identify 50 novel GWAS loci. Using gene-wide association statistics we implicate an additional set of 22 novel associations that map onto a single gene. We show for the first time that the common variant association signal is highly enriched among genes that are intolerant to loss of function mutations and that variants in these genes persist in the population despite the low fecundity associated with the disorder through the process of background selection. Associations point to novel areas of biology (e.g. metabotropic GABA-B signalling and acetyl cholinesterase), reinforce those implicated in earlier GWAS studies (e.g. calcium channel function), converge with earlier rare variants studies (e.g. NRXN1, GABAergic signalling), identify novel overlaps with autism (e.g. RBFOX1, FOXP1, FOXG1), and support early controversial candidate gene hypotheses (e.g. ERBB4 implicating neuregulin signalling). We also demonstrate the involvement of six independent central nervous system functional gene sets in schizophrenia pathophysiology. These findings provide novel insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation intolerant genes and suggest a mechanism by which common risk variants are maintained in the population.

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Population-scale analysis of common and rare genetic variation associated with hearing loss in adults

10.1101/2021.09.27.21264091 ◽

2021 ◽

Author(s):

Kavita Praveen ◽

Lee Dobbyn ◽

Lauren Gurski ◽

Ariane H. Ayer ◽

Jeffrey Staples ◽

...

Keyword(s):

Hearing Loss ◽

Exome Sequencing ◽

Large Scale ◽

Meta Analysis ◽

Adult Population ◽

Loss Of Function ◽

Common Coding ◽

A Genome ◽

Increased Risk ◽

Coding Variants

ABSTRACTUnderstanding the genetic underpinnings of disabling hearing loss, which affects ∼466 million people worldwide, can provide avenues for new therapeutic target development. We performed a genome-wide association meta-analysis of hearing loss with 125,749 cases and 469,497 controls across five cohorts, including UK Biobank, Geisinger DiscovEHR, the Malmö Diet and Cancer Study, Mount Sinai’s BioMe Personalized Medicine Cohort, and FinnGen. We identified 53 loci affecting hearing loss risk, 15 of which are novel, including common coding variants in COL9A3 and TMPRSS3. Through exome-sequencing of 108,415 cases and 329,581 controls from the same cohorts, we identified hearing loss associations with burden of rare coding variants in FSCN2 (odds ratio [OR] = 1.14, P = 1.9 × 10−15) and burden of predicted loss-of-function variants in KLHDC7B (OR = 2.14, P = 5.2 × 10−30). We also observed single-variant and gene-burden associations with 11 genes known to cause Mendelian forms of hearing loss, including an increased risk in heterozygous carriers of mutations in the autosomal recessive hearing loss genes GJB2 (Gly12fs; OR = 1.21, P = 4.2 × 10−11) and SLC26A5 (gene burden; OR = 1.96, P = 2.8 × 10−17). Our results suggest that loss of KLHDC7B function increases risk for hearing loss, and show that Mendelian hearing loss genes contribute to the burden of hearing loss in the adult population, suggesting a shared etiology between common and rare forms of hearing loss. This work illustrates the potential of large-scale exome sequencing to elucidate the genetic architecture of common traits in which risk is modulated by both common and rare variation.

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Predicting RNA splicing from DNA sequence using Pangolin

10.1101/2021.07.06.451243 ◽

2021 ◽

Author(s):

Tony Zeng ◽

Yang I Li

Keyword(s):

Deep Learning ◽

Dna Sequence ◽

Rna Splicing ◽

Rare Variants ◽

Sequence Data ◽

Learning Approaches ◽

Loss Of Function ◽

Pathogenic Variants ◽

Uncertain Significance ◽

The Impact

Recent progress in deep learning approaches have greatly improved the prediction of RNA splicing from DNA sequence. Here, we present Pangolin, a deep learning model to predict splice site strength in multiple tissues that has been trained on RNA splicing and sequence data from four species. Pangolin outperforms state of the art methods for predicting RNA splicing on a variety of prediction tasks. We use Pangolin to study the impact of genetic variants on RNA splicing, including lineage-specific variants and rare variants of uncertain significance. Pangolin predicts loss-of-function mutations with high accuracy and recall, particularly for mutations that are not missense or nonsense (AUPRC = 0.93), demonstrating remarkable potential for identifying pathogenic variants.

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Abstract P054: Rare Variants in and Near IRS1 are Associated with Fasting Insulin in CHARGE-S Cohorts

Circulation ◽

10.1161/circ.127.suppl_12.ap054 ◽

2013 ◽

Vol 127 (suppl_12) ◽

Author(s):

Belinda K Cornes ◽

Jennifer Brody ◽

Alanna C Morrison ◽

David Siscovick ◽

James B Meigs ◽

...

Keyword(s):

Large Scale ◽

Rare Variants ◽

Meta Analysis ◽

Weighted Sum ◽

Fasting Insulin ◽

Loss Of Function ◽

Gene Encoding ◽

Annotation Information ◽

Heart Study ◽

Meta Analyses

Introduction: Common variants in the gene encoding insulin receptor substrate 1 ( IRS1 ) and nearby on 2q36.3 have been associated with levels of fasting insulin (FI). We hypothesized that a greater burden of rare variants in these regions is associated with higher FI. Methods: CHARGE-S sequenced (average coverage >60x) the IRS1 and 2q36.6 regions (totaling 185 kb) in 3,539 individuals on the SOLiD platform. FI information among non-diabetics was available in 3 studies: Framingham Heart Study ( N =811), Cardiovascular Heart Study ( N =967) and Atherosclerosis Risk in Communities Study ( N =1761). We analyzed rare variants (MAF < 1%) using a weighted sum test, similar to Madsen-Browning (powerful to detect an association if effects of casual rare variants are in the same direction), and the SKAT test (preferred method if variant effects are in opposite directions). Meta-analyses of weighted rare variants results used the inverse-variance method while SKAT results used a similar approach. For multi-variant tests, the threshold for significance was considered to be α = 0.05. Coding annotation predictions were obtained from the dbNSFP database which includes functional predictions from SIFT, MutationTaster, Polyphen-2, Phylo-P and LRT. Non-coding annotation information (protein binding regions, transcription factor binding sites, DNase hypersensitivity sites, conservation scores) was obtained from ENCODE and ORegAnno databases. From these annotations, we grouped different types of variants together (possible loss of function; possibly regulatory) in order to determine specific variants contributing most to the effect. Results: Sequencing found 4,534 variants in two regions, 86.7% of which were rare and novel, not seen in 1000 genomes or dbSNP. Approximately 20% of variants had annotation information available; of these, 34 variants were possibly damaging. We found suggestive association with FI ( p =0.03) for all rare variants in the meta-analysis of weighted-sum tests at 2q36.3 but not at IRS1 . At IRS1 (but not at 2q36.3), SKAT meta-analysis tests showed evidence for all rare variants associated with FI ( p =0.03). SKAT tests restricted to N =365 possibly damaging variants at IRS1 suggested an association with FI in coding ( p =0.06) and in non-coding ( p =0.02) variants. Conclusion: Large scale deep sequencing in the IRS1 and 2q36.3 regions found very large numbers of new, rare variants. Multi-variant tests suggest that rare variation in these regions influence FI levels, with individuals with more and rarer variants having higher FI. Further investigation is warranted to address why weighted sum and SKAT tests provide different levels of evidence for association in the two regions. Also, conditional analyses will test whether new rare variants at IRS1 or 2q36 explain observed GWAS associations.

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Genetic Determinants of Electrocardiographic P-Wave Duration and Relation to Atrial Fibrillation

Circulation Genomic and Precision Medicine ◽

10.1161/circgen.119.002874 ◽

2020 ◽

Vol 13 (5) ◽

pp. 387-395

Author(s):

Lu-Chen Weng ◽

Amelia Weber Hall ◽

Seung Hoan Choi ◽

Sean J. Jurgens ◽

Jeffrey Haessler ◽

...

Keyword(s):

Atrial Fibrillation ◽

Rare Variants ◽

Association Studies ◽

Low Frequency ◽

P Wave ◽

Cardiac Conduction ◽

Genome Wide Association Studies ◽

Genetic Loci ◽

Wave Duration ◽

P Wave Duration

Background: The P-wave duration (PWD) is an electrocardiographic measurement that represents cardiac conduction in the atria. Shortened or prolonged PWD is associated with atrial fibrillation (AF). We used exome-chip data to examine the associations between common and rare variants with PWD. Methods: Fifteen studies comprising 64 440 individuals (56 943 European, 5681 African, 1186 Hispanic, 630 Asian) and ≈230 000 variants were used to examine associations with maximum PWD across the 12-lead ECG. Meta-analyses summarized association results for common variants; gene-based burden and sequence kernel association tests examined low-frequency variant-PWD associations. Additionally, we examined the associations between PWD loci and AF using previous AF genome-wide association studies. Results: We identified 21 common and low-frequency genetic loci (14 novel) associated with maximum PWD, including several AF loci ( TTN , CAND2 , SCN10A , PITX2 , CAV1 , SYNPO2L , SOX5 , TBX5, MYH6, RPL3L ). The top variants at known sarcomere genes ( TTN, MYH6 ) were associated with longer PWD and increased AF risk. However, top variants at other loci (eg, PITX2 and SCN10A ) were associated with longer PWD but lower AF risk. Conclusions: Our results highlight multiple novel genetic loci associated with PWD, and underscore the shared mechanisms of atrial conduction and AF. Prolonged PWD may be an endophenotype for several different genetic mechanisms of AF.

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