Rare Variant Association Studies Research Articles

Accurate detection of dilated cardiomyopathy onset through machine learning predictions from ECG data

Abstract Background Dilated cardiomyopathy (DCM) is a primary heart muscle disease characterised by left-ventricular dilatation and reduced contractility, often associated with arrhythmia and conduction disease. DCM often has a genetic component, putting family members of diagnosed patients at risk for the disease. Stratification tools to provide personalized screening advice are currently lacking. Artificial intelligence (AI) based analysis of the electrocardiogram (ECG) offers a potential solution to this challenge. Purpose Our study aims to develop and validate a novel AI-ECG model capable of prediction and early detection of DCM. Methods An AI-ECG model was developed on a United States (US) based secondary care centre dataset including 337,463 ECGs from 50,932 patients within 4 months of diagnostic echocardiography and 265,576 ECGs from 51,086 patients prior to the first diagnostic echo. The data was split 50/10/40% for training, validation, and testing, with each subset containing 9-12% DCM positive labels. The model architecture was based on a convolutional neural network with residual blocks with a modified final layer for a discrete-time survival model. The AI-ECG score was linked to changes in the median ECG within the test set to provide explainability. External validation was performed on 68,885 diagnostic ECGs (0.08% DCM positive) from the UK biobank (UKB) and a Chinese general hospital dataset with 317,854 diagnostic ECGs (2.2% DCM positive) and 76,936 predictive ECGs (1.7% DCM positive). We performed common and rare variant association studies for the AI ECG scores on quality-controlled UKB data. Results The AI-ECG score for predicted future DCM was associated with diverse ECG features (Fig. 1). In the US cohort test set, the model achieved high diagnostic accuracy with an AUC of 0.9 (95% CI 0.89-0.90) overall, and 0.89 (0.85-0.92) in a subset excluding ischemic or valvular heart disease. External datasets showed AUCs of 0.86 (0.81-0.91) in the UKB and 0.97 (96-0.97) in the Chinese dataset. The predictive performance of the AI-ECG score, adjusted for age and sex, closely approximated echocardiographic traits (LVEF and LVEDD, Fig. 2) with a concordance index (CI) of 0.79 (0.76-0.83) and 0.81 (0.77-0.85), respectively, at 10 years of follow up (FU). Combining AI-ECG scores with echocardiographic traits enhanced the CI to 0.84 (0.8-0.87). Similar findings were observed in the Chinese dataset, with a CI of 0.87 (0.85-0.89) for the AI-ECG score and 0.9 (0.88-0.92) for echocardiographic traits. Genetic analyses in UKB identified associations between the AI-ECG score and rare TTN variants as well as 25 common variant loci. Conclusion Our explainable AI ECG model for future DCM prediction demonstrates robust performance across datasets, providing comparable predictive accuracy to echocardiographic traits up to 10 years pre-diagnosis. Genetic associations support the model's validity and provide new tools for DCM genetic discovery.

Unsupervised electrocardiogram feature extraction using deep learning empowers discovery of genetic and phenotypic determinants of cardiac electrophysiology

Abstract Background Conventional analysis of the electrocardiogram (ECG) is based on the extraction of human-defined, visually recognisable features. However, these are not optimised to capture all the information content in the ECG. The variational autoencoder (VAE), a form of unsupervised machine learning, can address this shortcoming by computationally extracting comprehensive and interpretable new ECG features, called latent factors. These latent factors provide a low-dimensional representation of the ECG that maximises capture of data content. This approach could expand our understanding of electrophysiology by identifying novel genetic and phenotypic traits associations with the ECG. Purpose This study aims to uncover genetic determinants of VAE latent factors, comparing them to determinants of conventional, human-derived ECG parameters to gain novel insights into cardiac electrophysiology and related diseases. Methods Over one million median beat ECGs derived from a United States (US) based secondary care centre were used to train a machine learning VAE model, with external validation in the UK Biobank (UKB). We performed common and rare variant association studies for VAE latent factors and conventional ECG traits on quality-controlled UKB data. Associated genetic variants were compared to loci for conventional ECG parameters available in the UKB and literature. Novel GWAS associations were validated in a withheld subset of the UKB cohort. Additionally, we compared the associations of the VAE latent factors and conventional ECG traits with phenotypic traits, disease codes and echocardiographic traits. Results Utilising median beat ECGs, the VAE model identified 20 features, with partial correlations to traditional ECG traits. A genome-wide association study (GWAS) identified 105 associated genomic regions, compared with 62 regions identified with conventional ECG traits on the same dataset. Most discovered loci have been previously associated with ECG features in larger GWAS, providing a positive control validation. We also discovered and validated five genomic regions not previously linked to the ECG (mapped to TRIOBP, EFEMP1, NEDD9, ATP10A and P2RX1). We found rare variant associations for seven genes (NEK6, IL17RA, NME7, MYBPC3, CCT8, ADAMTS6 and SCN5A). The latent factors uncovered associations with 158 phenotypic traits (2093 phenotypes tested) and 147 disease codes (2074 disease phecodes tested) which were not identified by ECG traits. Out of 38 echocardiographic traits, 35 showed higher correlations with the latent factors, while six exhibited significant associations exclusively with the latent factors. Conclusion Our study demonstrates that VAE-derived latent factors outperform conventional ECG parameters in uncovering novel genetic and phenotypic associations. Our findings underscore the opportunity for optimised data-driven feature extraction to enhance our understanding of cardiac electrophysiology.

Assessment of ability of AlphaMissense to identify variants affecting susceptibility to common disease.

An important issue in the analysis of rare variant association studies is the ability to annotate nonsynonymous variants in terms of their likely importance as affecting protein function. To address this, AlphaMissense was recently released and was shown to have good performance using benchmarks based on variants causing severe disease and on functional assays. Here, we assess the performance of AlphaMissense across 18 genes which had previously demonstrated association between rare coding variants and hyperlipidaemia, hypertension or type 2 diabetes. The strength of evidence in favour of association, expressed as the signed log p value (SLP), was compared between AlphaMissense and 43 other annotation methods. The results demonstrated marked variability between genes regarding the extent to which nonsynonymous variants contributed to evidence for association and also between the performance of different methods of annotating the nonsynonymous variants. Although AlphaMissense produced the highest SLP on average across genes, it produced the maximum SLP for only 4 genes. For some genes, other methods produced a considerably higher SLP and there were examples of genes where AlphaMissense produced no evidence for association while another method performed well. The marked inconsistency across genes means that it is difficult to decide on an optimal method of analysis of sequence data. The fact that different methods perform well for different genes suggests that if one wished to use sequence data for individual risk prediction then gene-specific annotation methods should be used.

Bayesian Rare Variant Analysis Identifies Novel Schizophrenia Putative Risk Genes.

The genetics of schizophrenia is so complex that it involves both common variants and rare variants. Rare variant association studies of schizophrenia are challenging because statistical methods for rare variant analysis are under-powered due to the rarity of rare variants. The recent Schizophrenia Exome meta-analysis (SCHEMA) consortium, the largest consortium in this field to date, has successfully identified 10 schizophrenia risk genes from ultra-rare variants by burden test, while more risk genes remain to be discovered by more powerful rare variant association test methods. In this study, we use a recently developed Bayesian rare variant association method that is powerful for detecting sparse rare risk variants that implicates 88 new candidate risk genes associated with schizophrenia from the SCHEMA case-control sample. These newly identified genes are significantly enriched in autism risk genes and GO enrichment analysis indicates that new candidate risk genes are involved in mechanosensory behavior, regulation of cell size, neuron projection morphogenesis, and plasma-membrane-bounded cell projection morphogenesis, that may provide new insights on the etiology of schizophrenia.

Rare variations within the serine/arginine-rich splicing factor PtoRSZ21 modulate stomatal size to determine drought tolerance in Populus.

Rare variants contribute significantly to the 'missing heritability' of quantitative traits. The genome-wide characteristics of rare variants and their roles in environmental adaptation of woody plants remain unexplored. Utilizing genome-wide rare variant association study (RVAS), expression quantitative trait loci (eQTL) mapping, genetic transformation, and molecular experiments, we explored the impact of rare variants on stomatal morphology and drought adaptation in Populus. Through comparative analysis of five world-wide Populus species, we observed the influence of mutational bias and adaptive selection on the distribution of rare variants. RVAS identified 75 candidate genes correlated with stomatal size (SS)/stomatal density (SD), and a rare haplotype in the promoter of serine/arginine-rich splicing factor PtoRSZ21 emerged as the foremost association signal governing SS. As a positive regulator of drought tolerance, PtoRSZ21 can recruit the core splicing factor PtoU1-70K to regulate alternative splicing (AS) of PtoATG2b (autophagy-related 2). The rare haplotype PtoRSZ21hap2 weakens binding affinity to PtoMYB61, consequently affecting PtoRSZ21 expression and SS, ultimately resulting in differential distribution of Populus accessions in arid and humid climates. This study enhances the understanding of regulatory mechanisms that underlie AS induced by rare variants and might provide targets for drought-tolerant varieties breeding in Populus.

Rare-variant association study unveils the Achilles’ heel for HCC

In this issue of Cell Genomics, Wang, Liu, Zuo, Wang, etal.1 investigate rare variants in hepatocellular carcinoma (HCC) by performing the first rare-variant association study (RVAS) in a Chinese population cohort. It uncovers BRCAness phenotypes associated with the NRDE2-p.N377I variant, suggesting PARP inhibitors as a promising therapeutic approach for certain HCC patients.

NRDE2 deficiency impairs homologous recombination repair and sensitizes hepatocellular carcinoma to PARP inhibitors

To identify novel susceptibility genes for hepatocellular carcinoma (HCC), we performed a rare-variant association study in Chinese populations consisting of 2,750 cases and 4,153 controls. We identified four HCC-associated genes, including NRDE2, RANBP17, RTEL1, and STEAP3. Using NRDE2 (index rs199890497 [p.N377I], p= 1.19×10-9) as an exemplary candidate, we demonstrated that it promotes homologous recombination (HR) repair and suppresses HCC. Mechanistically, NRDE2 binds to the subunits of casein kinase 2 (CK2) and facilitates the assembly and activity of the CK2 holoenzyme. This NRDE2-mediated enhancement of CK2 activity increases the phosphorylation of MDC1 and then facilitates the HR repair. These functions are eliminated almost completely by the NRDE2-p.N377I variant, which sensitizes the HCC cells to poly(ADP-ribose) polymerase (PARP) inhibitors, especially when combined with chemotherapy. Collectively, our findings highlight the relevance of the rare variants to genetic susceptibility to HCC, which would be helpful for the precise treatment of this malignancy.

Genetic parameter estimation and molecular foundation of chicken beak shape

The bird beak is mainly functioned as feeding and attacking, and its shape has extremely important significance for survival and reproduction. In chickens, since beak shape could lead to some disadvantages including pecking and waste of feed, it is important to understand the inheritance of chicken beak shape. In the present study, we firstly established 4 indicators to describe the chicken beak shapes, including upper beak length (UL), lower beak length (LL), distance between upper and lower beak tips (DB) and upper beak curvature (BC). And then, we measured the 4 beak shape indicators as well as some production traits including body weight (BW), shank length (SL), egg weight (EW), eggshell strength (ES) of a layer breed, Rhode Island Red (RIR), in order to estimate genetic parameters of chicken beak shape. The heritabilities of UL and LL were 0.41 and 0.37, and the heritabilities of DB and BC were 0.22 and 0.21, indicating that beak shape was a highly or mediumly heritable. There were significant positive genetic and phenotypic correlations among UL, LL, and DB. And UL was positively correlated with body weight (BW18) and shank length (SL18) at 18 weeks of age in genetics, and DB was positively correlated with BC in terms of genetics and phenotype. We also found that layers of chicken cages played a role on beak shape, which could be attributed to the difference of lightness in different cage layers. By a genome-wide association study (GWAS) for the chicken UL, we identified 9 significant candidate genes associated with UL in RIR. For the variants with low minor allele frequencies (MAF <0.01) and outside of high linkage disequilibrium (LD) regions, we also conducted rare variant association studies (RVA) and GWAS to find the association between genotype and phenotype. We also analyzed transcriptomic data from multiple tissues of chicken embryos and revealed that all of the 9 genes were highly expressed in beak of chicken embryos, indicating their potential function for beak development. Our results provided the genetic foundation of chicken beak shape, which could help chicken breeding on beak related traits.

Inherited blood cancer predisposition through altered transcription elongation

Despite advances in defining diverse somatic mutations that cause myeloid malignancies, a significant heritable component for these cancers remains largely unexplained. Here, we perform rare variant association studies in a large population cohort to identify inherited predisposition genes for these blood cancers. CTR9, which encodes a key component of the PAF1 transcription elongation complex, is among the significant genes identified. The risk variants found in the cases cause loss of function and result in a ∼10-fold increased odds of acquiring a myeloid malignancy. Partial CTR9 loss of function expands human hematopoietic stem cells (HSCs) by increased super elongation complex-mediated transcriptional activity, which thereby increases the expression of key regulators of HSC self-renewal. By following up on insights from a human genetic study examining inherited predisposition to the myeloid malignancies, we define a previously unknown antagonistic interaction between the PAF1 and super elongation complexes. These insights could enable targeted approaches for blood cancer prevention.

Weighted Selection Probability to Prioritize Susceptible Rare Variants in Multi-Phenotype Association Studies with Application to a Soybean Genetic Data Set.

Rare variant association studies with multiple traits or diseases have drawn a lot of attention since association signals of rare variants can be boosted if more than one phenotype outcome is associated with the same rare variants. Most of the existing statistical methods to identify rare variants associated with multiple phenotypes are based on a group test, where a pre-specified genetic region is tested one at a time. However, these methods are not designed to locate susceptible rare variants within the genetic region. In this article, we propose new statistical methods to prioritize rare variants within a genetic region when a group test for the genetic region identifies a statistical association with multiple phenotypes. It computes the weighted selection probability (WSP) of individual rare variants and ranks them from largest to smallest according to their WSP. In simulation studies, we demonstrated that the proposed method outperforms other statistical methods in terms of true positive selection, when multiple phenotypes are correlated with each other. We also applied it to our soybean single nucleotide polymorphism (SNP) data with 13 highly correlated amino acids, where we identified some potentially susceptible rare variants in chromosome 19.

Variations in gender identity and sexual orientation of university students

Abstract Background Previous studies have shown that a small percentage of people in the general population have atypical gender identity and/or sexual orientation. Aim This study aimed to explore variations in gender identity and sexual orientation in university students and determine genetic factors associated with these variations. Methods Deviations from complete gender congruence and exclusive heterosexual orientation in 736 Japanese university students were quantitatively assessed with self-assessment questionnaires. Next, we conducted genetic tests for 80 participants who showed relatively low gender identity scores and/or atypical sexual orientation. These genetic tests consisted of repeat number analysis of the androgen receptor gene (AR) and a SKAT-O: an optimal unified sequence kernel association test, which is an exome-based rare variant association study. The results of the genetic tests were compared with the Japanese reference data and the results of our 637 control samples. Outcomes We calculated the gender identity and sexual orientation scores of all participants and analyzed the molecular data of 80 selected participants. Results The gender identity scores of 736 participants were broadly distributed: only ~15% of natal males and ~5% of natal females had the maximum score that corresponds to complete gender congruence. The sexual orientation scores also varied: ~80% of natal males and ~60% of natal females showed exclusive heterosexual orientation. We found no association between gender characteristics and AR repeat numbers. The SKAT-O showed that rare damaging variants of TDRP and 3 other genes were more common in the 80 participants than in the control group. Clinical Implications Our data support the view that gender is a phenotypic continuum rather than a binary trait. Strength and Limitations This study quantitatively assessed the gender characteristics of a large cohort of university students. Moreover, we conducted systematic screening for genetic factors associated with gender variations. The weaknesses of the study were the limited analytic power of the questionnaires, the relatively small sample for molecular analyses, and incomplete clinical information and relatively advanced ages of the control group. Conclusion This study revealed significant variations in gender identity and sexual orientation in university students, which may be partly associated with variants in TDRP or other genes.

234-LB: Cell-State Dependent Regulation of PPARg Signaling by Zbtb9 in Adipocytes

The discovery of new genes involved in adipocyte development and function can shed light on the pathophysiology of obesity and T2D and provide new therapeutic targets for these disorders. Towards this end, GWAS and rare variant association studies in the T2D Knowledge Portal revealed associations between ZBTB9 and T2D, BMI, and hip circumference. However, the function of ZBTB9 in organismal physiology was not known. We found that Zbtb9 plays a key role in adipocyte biology and differentiation via cell-state dependent regulation of PPARγ signaling. Zbtb9 directly interacts with the PPARγ/RXRα complex as shown by co-IP, and acts as a positive regulator of PPARγ transcriptional activity as shown by a PPARγ reporter assay, together establishing Zbtb9 as a new PPARγ cofactor. In line with these findings, endogenous PPARγ target gene expression was decreased in the context of Zbtb9 deficiency in adipocytes. Given the central role of PPARγ in adipogenesis, we also sought to investigate the role of Zbtb9 in this process. Surprisingly, loss of Zbtb9 in preadipocytes increased adipogenesis and the expression of adipogenic genes, including PPARγ. Transcriptome analysis prior to and during adipogenesis revealed enhanced expression of E2F target genes prior to the initiation of differentiation and later induction of adipogenic gene expression. Treatment of Zbtb9 deficient preadipocytes with an E2F1 inhibitor (HLM006474) blocked the effects of Zbtb9 deficiency on adipogenic gene expression and adipogenesis. Collectively, these results demonstrate that Zbtb9 inhibits adipogenesis as a negative regulator of PPARγ expression via altered E2F1 signaling. We further demonstrated that E2F1 activation by Zbtb9 is associated with increased phosphorylation of Rb, which is known to cause dissociation of the Rb/E2F complex, and activation of E2F. Our findings revealed cell-state dependent roles of Zbtb9 in adipocytes, identifying a new molecule that may be important in the pathogenesis and treatment of obesity and T2D. Disclosure X. Xu: None. D. Buchner: None. Funding National Institutes of Health (DK119305)

Rare genetic variants impact muscle strength

Muscle strength is highly heritable and predictive for multiple adverse health outcomes including mortality. Here, we present a rare protein-coding variant association study in 340,319 individuals for hand grip strength, a proxy measure of muscle strength. We show that the exome-wide burden of rare protein-truncating and damaging missense variants is associated with a reduction in hand grip strength. We identify six significant hand grip strength genes, KDM5B, OBSCN, GIGYF1, TTN, RB1CC1, and EIF3J. In the example of the titin (TTN) locus we demonstrate a convergence of rare with common variant association signals and uncover genetic relationships between reduced hand grip strength and disease. Finally, we identify shared mechanisms between brain and muscle function and uncover additive effects between rare and common genetic variation on muscle strength.

Whole exome sequencing and rare variant association study to identify genetic modifiers, KLF1 mutations, and a novel double mutation in Thai patients with hemoglobin E/beta-thalassemia

ABSTRACTObjectivesClinical manifestations of patients with Hemoglobin E/beta-thalassemia vary from mild to severe phenotypes despite exhibiting the same genotype. Studies have partially identified genetic modifiers. We aimed to study the association between rare variants in protein-coding regions and clinical severity in Thai patients.MethodsFrom April to November 2018, a case–control study was conducted based on clinical information and DNA samples collected from Thai patients with hemoglobin E/beta-thalassemia over the age of four years. Cases were patients with severe symptoms, while patients with mild symptoms acted as controls. Whole exome sequencing and rare variant association study were used to analyze the data.ResultsAll 338 unrelated patients were classified into 165 severe and 173 mild cases. Genotypes comprised 81.4% of hemoglobin E/beta-thalassemia, 2.7% of homozygous or compound heterozygous beta-thalassemia, and 0.3% of (δβ)0 thalassemia Hb E while 15.7% of samples were not classified as beta-thalassemia. A novel cis heterozygotes of IVS I-7 (A > T) and codon 26 (G > A) was identified. Six genes (COL4A3, DLK1, FAM186A, PZP, THPO, and TRIM51) showed the strongest associations with severity (observed p-values of <0.05; significance lost after correction for multiplicity). Among known modifiers, KLF1 variants were found in four mild patients and one severe patient.ConclusionNo rare variants were identified as contributors to the clinical heterogeneity of hemoglobin E/beta-thalassemia. KLF1 mutations are potential genetic modifiers. Studies to identify genetic factors are still important and helpful for predicting severity and developing targeted therapy.

Exome-based genome-wide screening of rare variants associated with the risk of polycystic ovary syndrome.

Genetic factors associated with the risk of polycystic ovary syndrome (PCOS) remain largely unknown. Here, we conducted an optimal sequence kernel association test (SKAT-O), an exome-based rare variant association study, to clarify whether rare variants in specific genes contribute to the development of PCOS. SKAT-O was performed using exome data of 44 Japanese patients with PCOS and 301 control women. We analyzed frequencies of rare probably damaging variants in the genome. Rare variants of GSTO2 were more commonly identified in the patient group than in the control group (6/44 vs. 1/301; Bonferroni-corrected p-value, 0.028), while the frequencies of variants in other genes were comparable between the two groups. The identified GSTO2 variants were predicted to affect the function, structure, stability, hydrophobicity, and/or the formation of intrinsically disordered regions of the protein. GSTO2 encodes a glutathione transferase that mediates the oxidative stress response and arsenic metabolism. Previously, common variants in GSTO2 and its paralog GSTO1 were associated with the risk of PCOS. The results indicate that there are no genes whose rare variants account for a large fraction of the etiology of PCOS, although rare damaging variants in GSTO2 may constitute a risk factor in some cases.

Whole-exome rare-variant analysis of Alzheimer's disease and related biomarker traits.

Despite increasing evidence of a role of rare genetic variation in the risk of Alzheimer's disease (AD), limited attention has been paid to its contribution to AD-related biomarker traits indicative of AD-relevant pathophysiological processes. We performed whole-exome gene-based rare-variant association studies (RVASs) of 17 AD-related traits on whole-exome sequencing (WES) data generated in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study (n=450) and whole-genome sequencing (WGS) data from ADNI (n=808). Mutation screening revealed a novel probably pathogenic mutation (PSEN1 p.Leu232Phe). Gene-based RVAS revealed the exome-wide significant contribution of rare coding variation in RBKS and OR7A10 to cognitive performance and protection against left hippocampal atrophy, respectively. The identification of these novel gene-trait associations offers new perspectives into the role of rare coding variation in the distinct pathophysiological processes culminating in AD, which may lead to identification of novel therapeutic and diagnostic targets.

Genetic Insights from the ADSP into Drug Discovery In Alzheimer’s Disease

AbstractBackgroundA major goal of the NAPA plan initiated in 2013 is the development of novel drugs for the treatment and prevention of Alzheimer’s disease (AD). It is widely recognized that drug targets with genetic support are more likely to successfully make it through the drug development pipeline than those without genetic support. The target may be a genetic risk factor for AD or within a genetically regulated network implicated by an AD genetic risk factor. Thus, one of the primary goals of the Alzheimer’s disease sequencing project (ADSP) is to identify and validate putative novel drug targets and to work with the community to advance testing of these candidates.MethodsSince 2013 the ADSP has performed genome wide association studies of both common and rare variants to identify novel AD risk genes in diverse human populations.ResultsGenomic analyses have demonstrated that these AD risk alleles are enriched with regulatory regions, in microglial enhancers and the PU.1 regulatory network. The ADSP has also made a major effort in including different ancestries into AD research and utilized the differences in AD risk between ancestries for major genetic risk factors (like ApoE) to identified potential therapeutic targets. A key challenge for common variants associated with AD is to link them to specific genes, and to understand the subsequent biological processes that lead to AD. This is a major focus of the Functional Genetics Consortium within the ADSP. Rare coding variants have directly implicated specific genes, which also provide support for the central role of microglial function in AD risk, e.g. Trem2, ABI3, PLCg2. The genetics of endophenotypes (e.g. sTrem2) and of protein quantitative trait loci offer alternative approaches to link genetics to biology.ConclusionsTogether, common and rare variants identify myeloid cell efferocytosis as a key disease risk hub. An alternative approach to drug discovery is the use of in silico approaches to match disease relevant signatures to existing drugs to enable drug repurposing. To date this approach has focused on transcriptomic signatures and implicates several biological pathways including the integrated stress response.

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes

Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.

Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots

For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results – using MRI-derived, BMI-independent measures of local adiposity – confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.

The impact of rare germline variants on human somatic mutation processes

Somatic mutations are an inevitable component of ageing and the most important cause of cancer. The rates and types of somatic mutation vary across individuals, but relatively few inherited influences on mutation processes are known. We perform a gene-based rare variant association study with diverse mutational processes, using human cancer genomes from over 11,000 individuals of European ancestry. By combining burden and variance tests, we identify 207 associations involving 15 somatic mutational phenotypes and 42 genes that replicated in an independent data set at a false discovery rate of 1%. We associate rare inherited deleterious variants in genes such as MSH3, EXO1, SETD2, and MTOR with two phenotypically different forms of DNA mismatch repair deficiency, and variants in genes such as EXO1, PAXIP1, RIF1, and WRN with deficiency in homologous recombination repair. In addition, we identify associations with other mutational processes, such as APEX1 with APOBEC-signature mutagenesis. Many of the genes interact with each other and with known mutator genes within cellular sub-networks. Considered collectively, damaging variants in the identified genes are prevalent in the population. We suggest that rare germline variation in diverse genes commonly impacts mutational processes in somatic cells.