Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank

Exome sequencing identifies protein-coding variants associated with loneliness and social isolation

BACKGROUND

Loneliness and social isolation are serious yet underappreciated public health problems, with their genetic underpinnings remaining largely unknown. We aimed to explore the role of protein-coding variants in the manifestation of loneliness and social isolation.

METHODS

We conducted the first exome-wide association analysis on loneliness and social isolation, utilizing 336,115 participants of white-British ancestry for loneliness and 346,115 for social isolation. Sensitivity analyses were performed to validate the genetic findings. We estimated the genetic burden heritability of loneliness and social isolation and provided biological insights into them.

RESULTS

We identified six novel risk genes (ANKRD12, RIPOR2, PTEN, ARL8B, NF1, and PIMREG) associated with loneliness and two (EDARADD and GIGYF1) with social isolation through analysis of rare coding variants. Brain-wide association analysis uncovered 47 associations between identified genes and brain structure phenotypes, many of which are critical for social processing and interaction. Phenome-wide association analysis established significant links between these genes and phenotypes across five categories, mostly blood biomarkers and cognitive measures.

LIMITATIONS

The measurements of loneliness and social isolation in UK Biobank are brief for these multi-layer social factors, some relevant aspects may be missed.

CONCLUSIONS

Our study revealed 13 risk genes associated with loneliness and 6 with social isolation, with the majority being novel discoveries. These findings advance our understanding of the genetic basis of these two traits. The study provides a foundation for future studies aimed at exploring the functional mechanisms of these genes and their potential implications for public health interventions targeting loneliness and social isolation.

Population-Scale Studies of Protein S Abnormalities and Thrombosis

Importance

Clinical decision-making in thrombotic disorders is impeded by long-standing uncertainty regarding the magnitude of venous and arterial thrombosis risk associated with low protein S. Population-scale multiomic datasets offer an unprecedented opportunity to answer questions regarding the epidemiology and clinical impacts of protein S deficiency.

Objective

To evaluate the risk associated with protein S deficiency across multiple thrombosis phenotypes.

Design, Setting, and Participants

Cross-sectional study using longitudinal population cohorts derived from the UK Biobank (n = 426 436) and the US National Institutes of Health All of Us (n = 204 006) biorepositories. UK Biobank participants were enrolled in 2006-2010 (last follow-up, May 19, 2020) and underwent whole exome sequencing, with a subset (n = 44 431) having protein S levels measured by high-throughput plasma proteomics. Recruitment for All of Us began in 2017 and is ongoing, with participants receiving germline whole genome sequencing. Both cohorts include individual-level data on demographics, laboratory measurements, and clinical outcomes.

Exposure

Presence of rare germline genetic variants in PROS1, segmented by functional impact score (FIS), an in silico prediction of the probability that a genetic variant will disrupt protein activity.

Main Outcomes and Measures

Firth logistic regression and linear regression modeling were used to evaluate the thrombosis risk associated with low plasma protein S levels and PROS1 variants across a range of FIS ratings.

Results

The UK Biobank cohort was 54.3% female, with a median age of 58.3 (IQR, 50.5-63.7) years at enrollment. Most participants (95.6%) were of European ancestry, and 18 011 had experienced a venous thromboembolism (VTE). In this population cohort, heterozygosity for the highest-risk PROS1 variants with an FIS of 1.0 (nonsense, frameshift, and essential splice site disruptions) was rare (adjusted prevalence, 0.0091% in the UK and 0.0178% in the US) and associated with markedly increased risk of VTE (odds ratio [OR], 14.01; 95% CI, 6.98-27.14; P = 9.09 × 10-11). Plasma proteomics (n = 44 431) demonstrated that carriers of these variants had total protein S levels that were 48.0% of normal (P = .02 compared with noncarriers). In contrast, less damaging missense variants (FIS ≥0.7) occurred more commonly (adjusted prevalence, 0.22% in the UK and 0.20% in the US) and were associated with marginally reduced plasma protein S concentrations and a smaller point estimate for VTE risk (OR, 1.977; 95% CI, 1.552-2.483; P = 1.95 × 10-7). Associations between PROS1 and VTE at both FIS cutoffs were independently validated in the All of Us cohort with similar effect sizes. No association was detected between the presence of coding PROS1 variants and 3 forms of arterial thrombosis: myocardial infarction, peripheral artery disease, and noncardioembolic ischemic stroke. The presence of PROS1 variants correlated poorly with low plasma protein S levels, and protein S deficiency was significantly associated with VTE and peripheral artery disease regardless of PROS1 variant carrier status. The elevated risk of VTE associated with germline loss of function in PROS1 was evident in Kaplan-Meier survival analysis and appeared to persist throughout life (log-rank P = .0005).

Conclusions and Relevance

True inherited loss of function in PROS1 is rare but represents a stronger risk factor for VTE in the general population than previously understood. Acquired, environmental, or trans-acting genetic factors are more likely to cause circulating protein S deficiency than coding variation in PROS1, and low plasma protein S is associated with VTE.

Rare variant association studies: Significance, methods, and applications in chronic pain studies

Rare genetic variants, characterized by their low frequency in a population, have emerged as essential components in the study of complex disease genetics. The biology of rare variants underscores their significance, as they can exert profound effects on phenotypic variation and disease susceptibility. Recent advancements in sequencing technologies have yielded the availability of large-scale sequencing data such as the UK Biobank whole-exome sequencing (WES) cohort empowered researchers to conduct rare variant association studies (RVASs). This review paper discusses the significance of rare variants, available methodologies, and applications. We provide an overview of RVASs, emphasizing their relevance in unraveling the genetic architecture of complex diseases with special focus on chronic pain and Arthritis. Additionally, we discuss the strengths and limitations of various rare variant association testing methods, outlining a typical pipeline for conducting rare variant association. This pipeline encompasses crucial steps such as quality control of WES data, rare variant annotation, and association testing. It serves as a comprehensive guide for researchers in the field of chronic pain diseases interested in rare variant association studies in large-scale sequencing datasets like the UK Biobank WES cohort. Lastly, we discuss how the identified variants can be further investigated through detailed experimental studies in animal models to elucidate their functional impact and underlying mechanisms.

Exome sequencing identifies novel genes associated with cerebellar volume and microstructure

Proteins encoded by exons are critical for cellular functions, and mutations in these genes often result in significant phenotypic effects. The cerebellum is linked to various heritable human disease phenotypes, yet genome-wide association studies have struggled to capture the effects of rare variants on cerebellar traits. This study conducts a large-scale exome association analysis using data from approximately 35,000 UK Biobank participants, examining seven cerebellar traits, including total cerebellar volume and white matter microstructure. We identify 90 genes associated with cerebellar traits, 60 of which were previously unreported in genome-wide association studies. Notable findings include the discovery of genes like PRKRA and TTK, as well as RASGRP3, linked to cerebellar volume and white matter microstructure. Gene enrichment analysis reveals associations with non-coding RNA processing, cognitive function, neurodegenerative diseases, and mental disorders, suggesting shared biological mechanisms between cerebellar phenotypes and neuropsychiatric diseases.

Biobanking with genetics shapes precision medicine and global health

Precision medicine provides patients with access to personally tailored treatments based on individual-level data. However, developing personalized therapies requires analyses with substantial statistical power to map genetic and epidemiologic associations that ultimately create models informing clinical decisions. As one solution, biobanks have emerged as large-scale, longitudinal cohort studies with long-term storage of biological specimens and health information, including electronic health records and participant survey responses. By providing access to individual-level data for genotype-phenotype mapping efforts, pharmacogenomic studies, polygenic risk score assessments and rare variant analyses, biobanks support ongoing and future precision medicine research. Notably, due in part to the geographical enrichment of biobanks in Western Europe and North America, European ancestries have become disproportionately over-represented in precision medicine research. Herein, we provide a genetics-focused review of biobanks from around the world that are in pursuit of supporting precision medicine. We discuss the limitations of their designs, ongoing efforts to diversify genomics research and strategies to maximize the benefits of research leveraging biobanks for all.

Evaluation of polygenic scores for hypertrophic cardiomyopathy in the general population and across clinical settings

Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality, with pathogenic variants found in about a third of cases. Large-scale genome-wide association studies (GWAS) demonstrate that common genetic variation contributes to HCM risk. Here we derive polygenic scores (PGS) from HCM GWAS and genetically correlated traits and test their performance in the UK Biobank, 100,000 Genomes Project, and clinical cohorts. We show that higher PGS significantly increases the risk of HCM in the general population, particularly among pathogenic variant carriers, where HCM penetrance differs 10-fold between those in the highest and lowest PGS quintiles. Among relatives of HCM probands, PGS stratifies risks of developing HCM and adverse outcomes. Finally, among HCM cases, PGS strongly predicts the risk of adverse outcomes and death. These findings support the broad utility of PGS across clinical settings, enabling tailored screening and surveillance and stratification of risk of adverse outcomes.

Large-scale exome sequencing identified 18 novel genes for neuroticism in 394,005 UK-based individuals

Existing genetic studies of neuroticism have been largely limited to common variants. Here we performed a large-scale exome analysis of white British individuals from UK Biobank, revealing the role of coding variants in neuroticism. For rare variants, collapsing analysis uncovered 14 neuroticism-associated genes. Among these, 12 (PTPRE, BCL10, TRIM32, ANKRD12, ADGRB2, MON2, HIF1A, ITGB2, STK39, CAPNS2, OGFOD1 and KDM4B) were novel, and the remaining (MADD and TRPC4AP) showed convergent evidence with common variants. Heritability of rare coding variants was estimated to be up to 7.3% for neuroticism. For common variants, we identified 78 significant associations, implicating 6 unreported genes. We subsequently replicated these variants using meta-analysis across other four ancestries from UK Biobank and summary data from 23andMe sample. Furthermore, these variants had widespread impacts on neuropsychiatric disorders, cognitive abilities and brain structure. Our findings deepen the understanding of neuroticism's genetic architecture and provide potential targets for future mechanistic research.

Investigating the contribution of coding variants in alcohol use disorder using whole-exome sequencing across ancestries

BACKGROUND

Alcohol use disorder (AUD) is a leading cause of death and disability worldwide. There has been substantial progress in identifying genetic variants underlying AUD. However, whole-exome sequencing (WES) studies of AUD are hampered by the lack of available samples.

METHODS

We analyzed WES data of 4,530 samples from the Yale-Penn cohort and 469,835 samples from the UK Biobank (UKB), which represents an unprecedented resource for exploring the contribution of coding variants in AUD. After quality controls, 2,039 European-ancestry (EUR: 1,420 cases) and 1,750 African-ancestry samples (AFR: 1,142 cases) from Yale-Penn, and 415,617 EUR samples (12,861‬ cases), 6,142 AFR samples (130 cases) and 4,607 South Asian (SAS) samples (130 cases) from UKB were included in the analyses.

RESULTS

We confirmed the well-known functional variant rs1229984 in ADH1B (P=4.88×10-31) and several other variants in ADH1C. Gene-based collapsing tests considering the high allelic heterogeneity revealed the previously unreported genes, CNST (P=1.19×10-6) attributable to rare variants with allele frequency < 0.001, and IFIT5 (P=3.74×10-6) driven by the burden of both common and rare loss-of-function and missense variants.

CONCLUSIONS

This study extends our understanding of the genetic architecture of AUD, by providing insights into the contribution of rare coding variants, separately and convergently with common variants in AUD.

Enriched phenotypes in rare variant carriers suggest pathogenic mechanisms in rare disease patients

BACKGROUND

The mechanistic pathways that give rise to the extreme symptoms exhibited by rare disease patients are complex, heterogeneous, and difficult to discern. Understanding these mechanisms is critical for developing treatments that address the underlying causes of diseases rather than merely the presenting symptoms. Moreover, the same dysfunctional series of interrelated symptoms implicated in rare recessive diseases may also lead to milder and potentially preventable symptoms in carriers in the general population. Seizures are a common and extreme phenotype that can result from diverse and often elusive pathways in patients with ultrarare or undiagnosed disorders.

METHODS

In this pilot study, we present an approach to understand the underlying pathways leading to seizures in patients from the Undiagnosed Diseases Network (UDN) by analyzing aggregated genotype and phenotype data from the UK Biobank (UKB). Specifically, we look for enriched phenotypes across UKB participants who harbor rare variants in the same gene known or suspected to be causally implicated in a UDN patient's recessively manifesting disorder. Analyzing these milder but related associated phenotypes in UKB participants can provide insight into the disease-causing mechanisms at play in rare disease UDN patients.

RESULTS

We present six vignettes of undiagnosed patients experiencing seizures as part of their recessive genetic condition. For each patient, we analyze a gene of interest: MPO, P2RX7, SQSTM1, COL27A1, PIGQ, or CACNA2D2, and find relevant symptoms associated with UKB participants. We discuss the potential mechanisms by which the digestive, skeletal, circulatory, and immune system abnormalities found in the UKB patients may contribute to the severe presentations exhibited by UDN patients. We find that in our set of rare disease patients, seizures may result from diverse, multi-step pathways that involve multiple body systems.

CONCLUSIONS

Analyses of large-scale population cohorts such as the UKB can be a critical tool to further our understanding of rare diseases in general. Continued research in this area could lead to more precise diagnostics and personalized treatment strategies for patients with rare and undiagnosed conditions.

Exome sequencing identifies genes for socioeconomic status in 350,770 individuals

Socioeconomic status (SES) is a critical factor in determining health outcomes and is influenced by genetic and environmental factors. However, our understanding of the genetic structure of SES remains incomplete. Here, we conducted a large-scale exome study of SES markers (household income, occupational status, educational attainment, and social deprivation) in 350,770 individuals. For rare coding variants, we identified 56 significant associations by gene-based collapsing tests, unveiling 7 additional SES-associated genes (NRN1, CCDC36, RHOB, EP400, NCAM1, TPTEP2-CSNK1E, and LINC02881). Exome-wide single common variant analysis revealed nine lead single-nucleotide polymorphisms (SNPs) associated with household income and 34 lead SNPs associated with EduYears, replicating previous GWAS findings. The gene-environment correlations had a substantial impact on the genetic associations with SES, as indicated by the significantly increased P values in several associations after controlling for geographic regions. Furthermore, we observed the pleiotropic effects of SES-associated genetic factors on a wide range of health outcomes, such as cognitive function, psychosocial status, and diabetes. This study highlights the contribution of coding variants to SES and their associations with health phenotypes.

The impact of common and rare genetic variants on bradyarrhythmia development

To broaden our understanding of bradyarrhythmias and conduction disease, we performed common variant genome-wide association analyses in up to 1.3 million individuals and rare variant burden testing in 460,000 individuals for sinus node dysfunction (SND), distal conduction disease (DCD) and pacemaker (PM) implantation. We identified 13, 31 and 21 common variant loci for SND, DCD and PM, respectively. Four well-known loci (SCN5A/SCN10A, CCDC141, TBX20 and CAMK2D) were shared for SND and DCD, while others were more specific for SND or DCD. SND and DCD showed a moderate genetic correlation (rg = 0.63). Cardiomyocyte-expressed genes were enriched for contributions to DCD heritability. Rare-variant analyses implicated LMNA for all bradyarrhythmia phenotypes, SMAD6 and SCN5A for DCD and TTN, MYBPC3 and SCN5A for PM. These results show that variation in multiple genetic pathways (for example, ion channel function, cardiac developmental programs, sarcomeric structure and cellular homeostasis) appear critical to the development of bradyarrhythmias.

Loss of function in protein Z (PROZ) is associated with increased risk of ischemic stroke in the UK Biobank

BACKGROUND

The vitamin K-dependent coagulation factor protein Z (PZ), encoded by the PROZ gene, is canonically considered to have anticoagulant effects through negative regulation of factor Xa. Paradoxically, higher circulating PZ concentrations have repeatedly been associated with an elevated risk of acute ischemic stroke.

OBJECTIVES

We performed a large-scale genetic association study to examine the relationship between germline genetic variants in PROZ and the risk of ischemic stroke.

METHODS

Using whole-exome sequencing and clinical data for 416 711 participants in the UK Biobank (UKB), we identified individuals with rare (minor allele frequency ≤0.1%) putatively function-altering variants in PROZ. Using Firth's logistic regression and controlling for known stroke risk factors, we evaluated the association between variant carrier status and noncardioembolic ischemic stroke (NCEIS). Additionally, we evaluated differences in the plasma levels of 1472 proteins between PROZ variant carriers and noncarriers in a subset of 48 893 UKB participants.

RESULTS

After accounting for missing data, qualifying variants in PROZ were identified in 414 UKB participants (99.0% heterozygous). Variant carriers had a significantly increased risk of NCEIS (odds ratio, 2.34; 95% CI, 1.15-4.13; P = .02) but not of venous thromboembolism, myocardial infarction, or peripheral artery disease. Plasma proteomics analysis revealed that PROZ variant carriers had significantly elevated levels of 2 proteins related to the response to cerebral ischemia, peroxiredoxins 1 and 6 (PRDX1: fold change, 1.83; P = 1.3 × 10-5; PRDX6: fold change, 1.78; P = 9.6 × 10-10).

CONCLUSION

Lifelong exposure to decreased PZ levels confers a significantly increased risk of NCEIS, consistent with the role of PZ as an anticoagulant factor.

Analysis of rare coding variants in 470,000 exome-sequenced subjects characterises contributions to risk of type 2 diabetes

AIMS

To follow up results from an earlier study using an extended sample of 470,000 exome-sequenced subjects to identify genes associated with type 2 diabetes (T2D) and to characterise the distribution of rare variants in these genes.

MATERIALS AND METHODS

Exome sequence data for 470,000 UK Biobank participants was analysed using a combined phenotype for T2D obtained from diagnostic and prescription data. Gene-wise weighted burden analysis of rare coding variants in the new cohort of 270,000 samples was carried out for the 32 genes previously significant with uncorrected p < 0.001 along with 7 other genes previously implicated in T2D. Follow-up studies of GCK, GIGYF1, HNF1A and HNF4A used the full sample of 470,000 to investigate the effects of different categories of variant.

RESULTS

No novel genes were identified as exome wide significant. Rare loss of function (LOF) variants in GCK exerted a very large effect on T2D risk but more common (though still very rare) nonsynonymous variants classified as probably damaging by PolyPhen on average approximately doubled risk. Rare variants in the other three genes also had large effects on risk.

CONCLUSIONS

In spite of the very large sample size, no novel genes are implicated. Coding variants with an identifiable effect are collectively too rare be generally useful for guiding treatment choices for most patients. The finding that some nonsynonymous variants in GCK affect T2D risk is novel but not unexpected and does not have obvious practical implications. This research has been conducted using the UK Biobank Resource.

Underestimated risk of secondary complications in pathogenic and glucose-elevating GCK variant carriers with type 2 diabetes

BACKGROUND

Natural HbA1c levels in GCK Maturity-onset diabetes of the young (GCK-MODY) patients often sit above the diagnostic threshold for type 2 diabetes (T2D). Treatments to lower HbA1c levels show reduced effectiveness in these individuals, yet in case studies to date, GCK-MODY patients often evade secondary T2D complications. Given these deviations, genetic screening of GCK may be clinically useful, but population studies are needed to more broadly understand T2D-related complications in GCK variant carriers.

METHODS

To identify GCK variant carriers at the population level, we used both ACMG/AMP variant interpretation for GCK-MODY pathogenicity and a state-of-the-art variant interpretation strategy based on functional and statistical evidence to predict glucose elevations. Presence of pathogenic and glucose-elevating GCK variants was assessed in two cohorts (n~535,000). We identified 442 individuals with GCK variants predicted to increase glucose (~1/1200), with 150 (34%) of these individuals harboring variants reaching a pathogenic interpretation.

RESULTS

In a retrospective analysis, we show that in addition to elevated HbA1c, pathogenic variant carriers are 10x as likely, and all other glucose-elevating GCK variant carriers are 3x as likely, to receive a T2D diagnosis compared to non-GCK carriers. Surprisingly, carriers of pathogenic and glucose-elevating GCK variants with T2D develop T2D-related complications at rates more than double that of individuals without T2D, comparable to non-GCK individuals with T2D.

CONCLUSIONS

This population-level assessment shows secondary complications in individuals with pathogenic and glucose-elevating GCK variants and T2D and suggests that genotyping for these variants should be considered in a precision medicine approach for T2D treatment and prevention.

Mapping rare protein-coding variants on multi-organ imaging traits

Human organ structure and function are important endophenotypes for clinical outcomes. Genome-wide association studies (GWAS) have identified numerous common variants associated with phenotypes derived from magnetic resonance imaging (MRI) of the brain and body. However, the role of rare protein-coding variations affecting organ size and function is largely unknown. Here we present an exome-wide association study that evaluates 596 multi-organ MRI traits across over 50,000 individuals from the UK Biobank. We identified 107 variant-level associations and 224 gene-based burden associations (67 unique gene-trait pairs) across all MRI modalities, including PTEN with total brain volume, TTN with regional peak circumferential strain in the heart left ventricle, and TNFRSF13B with spleen volume. The singleton burden model and AlphaMissense annotations contributed 8 unique gene-trait pairs including the association between an approved drug target gene of KCNA5 and brain functional activity. The identified rare coding signals elucidate some shared genetic regulation across organs, prioritize previously identified GWAS loci, and are enriched for drug targets. Overall, we demonstrate how rare variants enhance our understanding of genetic effects on human organ morphology and function and their connections to complex diseases.

Promise and Peril of a Genotype-First Approach to Mendelian Cardiovascular Disease

Precision medicine, which among other aspects includes an individual's genomic data in diagnosis and management, has become the standard-of-care for Mendelian cardiovascular disease (CVD). However, early identification and management of asymptomatic patients with potentially lethal and manageable Mendelian CVD through screening, which is the promise of precision health, remains an unsolved challenge. The reduced costs of genomic sequencing have enabled the creation of biobanks containing in-depth genetic and health information, which have facilitated the understanding of genetic variation, penetrance, and expressivity, moving us closer to the genotype-first screening of asymptomatic individuals for Mendelian CVD. This approach could transform health care by diagnostic refinement and facilitating prevention or therapeutic interventions. Yet, potential benefits must be weighed against the potential risks, which include evolving variant pathogenicity assertion or identification of variants with low disease penetrance; costly, stressful, and inappropriate diagnostic evaluations; negative psychological impact; disqualification for employment or of competitive sports; and denial of insurance. Furthermore, the natural history of Mendelian CVD is often unpredictable, making identification of those who will benefit from preventive measures a priority. Currently, there is insufficient evidence that population-based genetic screening for Mendelian CVD can reduce adverse outcomes at a reasonable cost to an extent that outweighs the harms of true-positive and false-positive results. Besides technical, clinical, and financial burdens, ethical and legal aspects pose unprecedented challenges. This review highlights key developments in the field of genotype-first approaches to Mendelian CVD and summarizes challenges with potential solutions that can pave the way for implementing this approach for clinical care.

Rare RNF213 variants is related to early-onset intracranial atherosclerosis: A Chinese community-based study

BACKGROUND

The relationship between rare variants in Ring finger protein 213 (RNF213) and intracranial atherosclerosis (ICAS) remained unelucidated. Using whole-exome sequencing (WES) and high-resolution magnetic resonance imaging (HR-MRI), this study aimed at investigating the association between rare RNF213 variants and ICAS within a Chinese community-dwelling population.

METHODS

The present study included 821 participants from Shunyi cohort. Genetic data of rare RNF213 variants were acquired by WES and were categorized by functional domains. Intracranial and extracranial atherosclerosis were assessed by brain HR-MRI and carotid ultrasound, respectively. Logistic regression and generalized linear regression were applied to evaluate the effects of rare RNF213 variants on atherosclerosis. Stratification by age were conducted with 50 years old set as the cutoff value.

RESULTS

Ninety-five participants were identified as carriers of rare RNF213 variants. Carotid plaques were observed in 367 (44.7 %) participants, while ICAS was identified in 306 (37.3 %). Rare variants of RNF213 was not associated with ECAS. Employing HR-MRI, both the presence of rare variants (β = 0.150, P = 0.025) and numerical count of variants (β = 0.182, P = 0.003) were significantly correlated with ICAS within the group of age ≤50 years. Both variant existence (β = 0.154, P = 0.014) and variant count (β = 0.188, P = 0.003) were significantly associated with plaques in middle cerebral arteries within younger subgroup, rather than basilar arteries. Furthermore, a significant association was observed between variants that located outside the N-arm domain and ICAS in the younger subgroup (OR = 2.522, P = 0.030). Statistical results remained robust after adjusted for age, gender, and cardiovascular risk factors.

CONCLUSIONS

Rare variants of RNF213 is associated with age-related ICAS in general Chinese population, highlighting the potential role of RNF213 as a genetic contributor to early-onset ICAS.

Secondary ACMG and non-ACMG genetic findings in a multiethnic cohort of 16,713 pediatric participants

PURPOSE

Clinical next-generation sequencing is an effective approach for identifying pathogenic sequence variants that are medically actionable for participants and families but are not associated with the participant's primary diagnosis. These variants are called secondary findings (SFs). According to the literature, there is no report of the types and frequencies of SFs in a large pediatric cohort that includes substantial African-American participants. We sought to investigate the types (including American College of Medical Genetics and Genomics [ACMG] and non-ACMG-recommended gene lists), frequencies, and rates of SFs, as well as the effects of SF disclosure on the participants and families of a large pediatric cohort at the Center for Applied Genomics at The Children's Hospital of Philadelphia.

METHODS

We systematically identified pathogenic (P) and likely pathogenic (LP) variants in established disease-causing genes, adhering to ACMG v3.2 secondary finding guidelines and beyond. For non-ACMG SFs, akin to incidental findings in clinical settings, we utilized a set of criteria focusing on pediatric onset, high penetrance, moderate to severe phenotypes, and the clinical actionability of the variants. This criteria-based approach was applied rather than using a fixed gene list to ensure that the variants identified are likely to affect participant health significantly. To identify and categorize these variants, we used a clinical-grade variant classification standard per ACMG/AMP recommendations; additionally, we conducted a detailed literature search to ensure a comprehensive exploration of potential SFs relevant to pediatric participants.

RESULTS

We report a distinctive distribution of 1464 P/LP SF variants in 16,713 participants. There were 427 unique variants in ACMG genes and 265 in non-ACMG genes. The most frequently mutated genes among the ACMG and non-ACMG gene lists were TTR(41.6%) and CHEK2 (7.16%), respectively. Overall, variants of possible medical importance were found in 8.76% of participants in both ACMG (5.81%) and non-ACMG (2.95%) genes.

CONCLUSION

Our study revealed that 8.76% of a large, multiethnic pediatric cohort carried actionable secondary genetic findings, with 5.81% in ACMG genes and 2.95% in non-ACMG genes. These findings emphasize the importance of including diverse populations in genetic research to ensure that all groups benefit from early identification of disease risks. Our results provide a foundation for expanding the ACMG gene list and improving clinical care through early interventions.

NK2R control of energy expenditure and feeding to treat metabolic diseases

The combination of decreasing food intake and increasing energy expenditure represents a powerful strategy for counteracting cardiometabolic diseases such as obesity and type 2 diabetes1. Yet current pharmacological approaches require conjugation of multiple receptor agonists to achieve both effects2-4, and so far, no safe energy-expending option has reached the clinic. Here we show that activation of neurokinin 2 receptor (NK2R) is sufficient to suppress appetite centrally and increase energy expenditure peripherally. We focused on NK2R after revealing its genetic links to obesity and glucose control. However, therapeutically exploiting NK2R signalling has previously been unattainable because its endogenous ligand, neurokinin A, is short-lived and lacks receptor specificity5,6. Therefore, we developed selective, long-acting NK2R agonists with potential for once-weekly administration in humans. In mice, these agonists elicit weight loss by inducing energy expenditure and non-aversive appetite suppression that circumvents canonical leptin signalling. Additionally, a hyperinsulinaemic-euglycaemic clamp reveals that NK2R agonism acutely enhances insulin sensitization. In diabetic, obese macaques, NK2R activation significantly decreases body weight, blood glucose, triglycerides and cholesterol, and ameliorates insulin resistance. These findings identify a single receptor target that leverages both energy-expending and appetite-suppressing programmes to improve energy homeostasis and reverse cardiometabolic dysfunction across species.

Damaging mutations in liver X receptor-α are hepatotoxic and implicate cholesterol sensing in liver health

Liver X receptor-α (LXRα) regulates cellular cholesterol abundance and potently activates hepatic lipogenesis. Here we show that at least 1 in 450 people in the UK Biobank carry functionally impaired mutations in LXRα, which is associated with biochemical evidence of hepatic dysfunction. On a western diet, male and female mice homozygous for a dominant negative mutation in LXRα have elevated liver cholesterol, diffuse cholesterol crystal accumulation and develop severe hepatitis and fibrosis, despite reduced liver triglyceride and no steatosis. This phenotype does not occur on low-cholesterol diets and can be prevented by hepatocyte-specific overexpression of LXRα. LXRα knockout mice exhibit a milder phenotype with regional variation in cholesterol crystal deposition and inflammation inversely correlating with steatosis. In summary, LXRα is necessary for the maintenance of hepatocyte health, likely due to regulation of cellular cholesterol content. The inverse association between steatosis and both inflammation and cholesterol crystallization may represent a protective action of hepatic lipogenesis in the context of excess hepatic cholesterol.

Whole exome sequencing analyses identified novel genes for Alzheimer's disease and related dementia

INTRODUCTION

The heritability of Alzheimer's disease (AD) is estimated to be 58%-79%. However, known genes can only partially explain the heritability.

METHODS

Here, we conducted gene-based exome-wide association study (ExWAS) of rare variants and single-variant ExWAS of common variants, utilizing data of 54,569 clinically diagnosed/proxy AD and related dementia (ADRD) and 295,421 controls from the UK Biobank.

RESULTS

Gene-based ExWAS identified 11 genes predicting a higher ADRD risk, including five novel ones, namely FRMD8, DDX1, DNMT3L, MORC1, and TGM2, along with six previously reported ones, SORL1, GRN, PSEN1, ABCA7, GBA, and ADAM10. Single-variant ExWAS identified two ADRD-associated novel genes, SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-β process pathways, microglia, and brain regions like hippocampus. The druggability evidence suggests that DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets.

DISCUSSION

Our study contributes to the current body of evidence on the genetic etiology of ADRD.

HIGHLIGHTS

Gene-based analyses of rare variants identified five novel genes for Alzheimer's disease and related dementia (ADRD), including FRMD8, DDX1, DNMT3L, MORC1, and TGM2. Single-variant analyses of common variants identified two novel genes for ADRD, including SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-β process pathways, microglia, and brain regions like hippocampus. DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets.

Integration of variant annotations using deep set networks boosts rare variant association testing

Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.

Rare coding variant analysis for human diseases across biobanks and ancestries

Large-scale sequencing has enabled unparalleled opportunities to investigate the role of rare coding variation in human phenotypic variability. Here, we present a pan-ancestry analysis of sequencing data from three large biobanks, including the All of Us research program. Using mixed-effects models, we performed gene-based rare variant testing for 601 diseases across 748,879 individuals, including 155,236 with ancestry dissimilar to European. We identified 363 significant associations, which highlighted core genes for the human disease phenome and identified potential novel associations, including UBR3 for cardiometabolic disease and YLPM1 for psychiatric disease. Pan-ancestry burden testing represented an inclusive and useful approach for discovery in diverse datasets, although we also highlight the importance of ancestry-specific sensitivity analyses in this setting. Finally, we found that effect sizes for rare protein-disrupting variants were concordant between samples similar to European ancestry and other genetic ancestries (βDeming = 0.7-1.0). Our results have implications for multi-ancestry and cross-biobank approaches in sequencing association studies for human disease.

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising the vast majority of human genetic variations, are likely to have more deleterious impact in the context of human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

Large-scale whole-exome sequencing analyses identified protein-coding variants associated with immune-mediated diseases in 350,770 adults

The genetic contribution of protein-coding variants to immune-mediated diseases (IMDs) remains underexplored. Through whole exome sequencing of 40 IMDs in 350,770 UK Biobank participants, we identified 162 unique genes in 35 IMDs, among which 124 were novel genes. Several genes, including FLG which is associated with atopic dermatitis and asthma, showed converging evidence from both rare and common variants. 91 genes exerted significant effects on longitudinal outcomes (interquartile range of Hazard Ratio: 1.12-5.89). Mendelian randomization identified five causal genes, of which four were approved drug targets (CDSN, DDR1, LTA, and IL18BP). Proteomic analysis indicated that mutations associated with specific IMDs might also affect protein expression in other IMDs. For example, DXO (celiac disease-related gene) and PSMB9 (alopecia areata-related gene) could modulate CDSN (autoimmune hypothyroidism-, psoriasis-, asthma-, and Graves' disease-related gene) expression. Identified genes predominantly impact immune and biochemical processes, and can be clustered into pathways of immune-related, urate metabolism, and antigen processing. Our findings identified protein-coding variants which are the key to IMDs pathogenesis and provided new insights into tailored innovative therapies.

Whole exome sequencing analysis identifies genes for alcohol consumption

Alcohol consumption is a heritable behavior seriously endangers human health. However, genetic studies on alcohol consumption primarily focuses on common variants, while insights from rare coding variants are lacking. Here we leverage whole exome sequencing data across 304,119 white British individuals from UK Biobank to identify protein-coding variants associated with alcohol consumption. Twenty-five variants are associated with alcohol consumption through single variant analysis and thirteen genes through gene-based analysis, ten of which have not been reported previously. Notably, the two unreported alcohol consumption-related genes GIGYF1 and ANKRD12 show enrichment in brain function-related pathways including glial cell differentiation and are strongly expressed in the cerebellum. Phenome-wide association analyses reveal that alcohol consumption-related genes are associated with brain white matter integrity and risk of digestive and neuropsychiatric diseases. In summary, this study enhances the comprehension of the genetic architecture of alcohol consumption and implies biological mechanisms underlying alcohol-related adverse outcomes.

Whole-exome sequencing identifies protein-coding variants associated with brain iron in 29,828 individuals

Iron plays a fundamental role in multiple brain disorders. However, the genetic underpinnings of brain iron and its implications for these disorders are still lacking. Here, we conduct an exome-wide association analysis of brain iron, measured by quantitative susceptibility mapping technique, across 26 brain regions among 26,789 UK Biobank participants. We find 36 genes linked to brain iron, with 29 not being previously reported, and 16 of them can be replicated in an independent dataset with 3,039 subjects. Many of these genes are involved in iron transport and homeostasis, such as FTH1 and MLX. Several genes, while not previously connected to brain iron, are associated with iron-related brain disorders like Parkinson's (STAB1, KCNA10), Alzheimer's (SHANK1), and depression (GFAP). Mendelian randomization analysis reveals six causal relationships from regional brain iron to brain disorders, such as from the hippocampus to depression and from the substantia nigra to Parkinson's. These insights advance our understanding of the genetic architecture of brain iron and offer potential therapeutic targets for brain disorders.

Benchmarking computational variant effect predictors by their ability to infer human traits

BACKGROUND

Computational variant effect predictors offer a scalable and increasingly reliable means of interpreting human genetic variation, but concerns of circularity and bias have limited previous methods for evaluating and comparing predictors. Population-level cohorts of genotyped and phenotyped participants that have not been used in predictor training can facilitate an unbiased benchmarking of available methods. Using a curated set of human gene-trait associations with a reported rare-variant burden association, we evaluate the correlations of 24 computational variant effect predictors with associated human traits in the UK Biobank and All of Us cohorts.

RESULTS

AlphaMissense outperformed all other predictors in inferring human traits based on rare missense variants in UK Biobank and All of Us participants. The overall rankings of computational variant effect predictors in these two cohorts showed a significant positive correlation.

CONCLUSION

We describe a method to assess computational variant effect predictors that sidesteps the limitations of previous evaluations. This approach is generalizable to future predictors and could continue to inform predictor choice for personal and clinical genetics.

Exome sequencing identifies novel genetic variants associated with varicose veins

BACKGROUND

Varicose veins (VV) are one of the common human diseases, but the role of genetics in its development is not fully understood.

METHODS

We conducted an exome-wide association study of VV using whole-exome sequencing data from the UK Biobank, and focused on common and rare variants using single-variant association analysis and gene-level collapsing analysis.

FINDINGS

A total of 13,823,269 autosomal genetic variants were obtained after quality control. We identified 36 VV-related independent common variants mapping to 34 genes by single-variant analysis and three rare variant genes (PIEZO1, ECE1, FBLN7) by collapsing analysis, and most associations between genes and VV were replicated in FinnGen. PIEZO1 was the closest gene associated with VV (P = 5.05 × 10-31), and it was found to reach exome-wide significance in both single-variant and collapsing analyses. Two novel rare variant genes (ECE1 and METTL21A) associated with VV were identified, of which METTL21A was associated only with females. The pleiotropic effects of VV-related genes suggested that body size, inflammation, and pulmonary function are strongly associated with the development of VV.

CONCLUSIONS

Our findings highlight the importance of causal genes for VV and provide new directions for treatment.

Atrial fibrillation: age at diagnosis, incident cardiovascular events, and mortality

BACKGROUND AND AIMS

Patients with atrial fibrillation (AF) are at increased risks of cardiovascular diseases and mortality, but risks according to age at diagnosis have not been reported. This study investigated age-specific risks of outcomes among patients with AF and the background population.

METHODS

This nationwide population-based cohort study included patients with AF and controls without outcomes by the application of exposure density matching on the basis of sex, year of birth, and index date. The absolute risks and hazard rates were stratified by age groups and assessed using competing risk survival analyses and Cox regression models, respectively. The expected differences in residual life years among participants were estimated.

RESULTS

The study included 216 579 AF patients from year 2000 to 2020 and 866 316 controls. The mean follow-up time was 7.9 years. Comparing AF patients with matched controls, the hazard ratios among individuals ≤50 years was 8.90 [95% confidence interval (CI), 7.17-11.0] for cardiomyopathy, 8.64 (95% CI, 7.74-9.64) for heart failure, 2.18 (95% CI, 1.89-2.52) for ischaemic stroke, and 2.74 (95% CI, 2.53-2.96) for mortality. The expected average loss of life years among individuals ≤50 years was 9.2 years (95% CI, 9.0-9.3) years. The estimates decreased with older age.

CONCLUSIONS

The findings show that earlier diagnosis of AF is associated with a higher hazard ratio of subsequent myocardial disease and shorter life expectancy. Further studies are needed to determine causality and whether AF could be used as a risk marker among particularly younger patients.

Whole-genome sequencing identifies variants in ANK1, LRRN1, HAS1, and other genes and regulatory regions for stroke in type 1 diabetes

Individuals with type 1 diabetes (T1D) carry a markedly increased risk of stroke, with distinct clinical and neuroimaging characteristics as compared to those without diabetes. Using whole-exome or whole-genome sequencing of 1,051 individuals with T1D, we aimed to find rare and low-frequency genomic variants associated with stroke in T1D. We analysed the genome comprehensively with single-variant analyses, gene aggregate analyses, and aggregate analyses on genomic windows, enhancers and promoters. In addition, we attempted replication in T1D using a genome-wide association study (N = 3,945) and direct genotyping (N = 3,263), and in the general population from the large-scale population-wide FinnGen project and UK Biobank summary statistics. We identified a rare missense variant on SREBF1 exome-wide significantly associated with stroke (rs114001633, p.Pro227Leu, p-value = 7.30 × 10-8), which replicated for hemorrhagic stroke in T1D. Using gene aggregate analysis, we identified exome-wide significant genes: ANK1 and LRRN1 displayed replication evidence in T1D, and LRRN1, HAS1 and UACA in the general population (UK Biobank). Furthermore, we performed sliding-window analyses and identified 14 genome-wide significant windows for stroke on 4q33-34.1, of which two replicated in T1D, and a suggestive genomic window on LINC01500, which replicated in T1D. Finally, we identified a suggestively stroke-associated TRPM2-AS promoter (p-value = 5.78 × 10-6) with borderline significant replication in T1D, which we validated with an in vitro cell-based assay. Due to the rarity of the identified genetic variants, future replication of the genomic regions represented here is required with sequencing of individuals with T1D. Nevertheless, we here report the first genome-wide analysis on stroke in individuals with diabetes.

The Importance of Molecular Genetic Testing for Precision Diagnostics, Management, and Genetic Counseling in MODY Patients

Maturity-onset diabetes of the young (MODY) is part of the heterogeneous group of monogenic diabetes (MD) characterized by the non-immune dysfunction of pancreatic β-cells. The diagnosis of MODY still remains a challenge for clinicians, with many cases being misdiagnosed as type 1 or type 2 diabetes mellitus (T1DM/T2DM), and over 80% of cases remaining undiagnosed. With the introduction of modern technologies, important progress has been made in deciphering the molecular mechanisms and heterogeneous etiology of MD, including MODY. The aim of our study was to identify genetic variants associated with MODY in a group of patients with early-onset diabetes/prediabetes in whom a form of MD was clinically suspected. Genetic testing, based on next-generation sequencing (NGS) technology, was carried out either in a targeted manner, using gene panels for monogenic diabetes, or by analyzing the entire exome (whole-exome sequencing). GKC-MODY 2 was the most frequently detected variant, but rare forms of KCNJ11-MODY 13, specifically, HNF4A-MODY 1, were also identified. We have emphasized the importance of genetic testing for early diagnosis, MODY subtype differentiation, and genetic counseling. We presented the genotype-phenotype correlations, especially related to the clinical evolution and personalized therapy, also emphasizing the particularities of each patient in the family context.

Thrombosis risk in single- and double-heterozygous carriers of factor V Leiden and prothrombin G20210A in FinnGen and the UK Biobank

ABSTRACT

The factor V Leiden (FVL; rs6025) and prothrombin G20210A (PTGM; rs1799963) polymorphisms are 2 of the most well-studied genetic risk factors for venous thromboembolism (VTE). However, double heterozygosity (DH) for FVL and PTGM remains poorly understood, with previous studies showing marked disagreement regarding thrombosis risk conferred by the DH genotype. Using multidimensional data from the UK Biobank (UKB) and FinnGen biorepositories, we evaluated the clinical impact of DH carrier status across 937 939 individuals. We found that 662 participants (0.07%) were DH carriers. After adjustment for age, sex, and ancestry, DH individuals experienced a markedly elevated risk of VTE compared with wild-type individuals (odds ratio [OR] = 5.24; 95% confidence interval [CI], 4.01-6.84; P = 4.8 × 10-34), which approximated the risk conferred by FVL homozygosity. A secondary analysis restricted to UKB participants (N = 445 144) found that effect size estimates for the DH genotype remained largely unchanged (OR = 4.53; 95% CI, 3.42-5.90; P < 1 × 10-16) after adjustment for commonly cited VTE risk factors, such as body mass index, blood type, and markers of inflammation. In contrast, the DH genotype was not associated with a significantly higher risk of any arterial thrombosis phenotype, including stroke, myocardial infarction, and peripheral artery disease. In summary, we leveraged population-scale genomic data sets to conduct, to our knowledge, the largest study to date on the DH genotype and were able to establish far more precise effect size estimates than previously possible. Our findings indicate that the DH genotype may occur as frequently as FVL homozygosity and may confer a similarly increased risk of VTE.

Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults

While numerous genomic loci have been identified for neuropsychiatric conditions, the contribution of protein-coding variants has yet to be determined. Here we conducted a large-scale whole-exome-sequencing study to interrogate the impact of protein-coding variants on 46 neuropsychiatric diseases and 23 traits in 350,770 adults from the UK Biobank. Twenty new genes were associated with neuropsychiatric diseases through coding variants, among which 16 genes had impacts on the longitudinal risks of diseases. Thirty new genes were associated with neuropsychiatric traits, with SYNGAP1 showing pleiotropic effects across cognitive function domains. Pairwise estimation of genetic correlations at the coding-variant level highlighted shared genetic associations among pairs of neurodegenerative diseases and mental disorders. Lastly, a comprehensive multi-omics analysis suggested that alterations in brain structures, blood proteins and inflammation potentially contribute to the gene-phenotype linkages. Overall, our findings characterized a compendium of protein-coding variants for future research on the biology and therapeutics of neuropsychiatric phenotypes.

Genetic variants in DBC1, SIRT1, UCP2 and ADRB2 as potential biomarkers for severe obesity and metabolic complications

Introduction

Obesity is a multifactorial disease associated with the development of many comorbidities. This disease is associated with several metabolic alterations; however, it has been shown that some individuals with obesity do not exhibit metabolic syndrome. Adipose tissue neutralizes the detrimental effects of circulating fatty acids, ectopic deposition, and inflammation, among others, through its esterification into neutral lipids that are stored in the adipocyte. However, when the adipocyte is overloaded, i.e., its expansion capacity is exceeded, this protection is lost, resulting in fatty acid toxicity with ectopic fat accumulation in peripheral tissues and inflammation. In this line, this study aimed to investigate whether polymorphisms in genes that control adipose tissue fat storage capacity are potential biomarkers for severe obesity susceptibility and also metabolic complications.

Methods

This study enrolled 305 individuals with severe obesity (cases, BMI≥35 kg/m2) and 196 individuals with normal weight (controls, 18.5≤BMI≤24.9 kg/m2). Demographic, anthropometric, biochemical, and blood pressure variables were collected from the participants. Plasma levels of leptin, resistin, MCP1, and PAI1 were measured by Bio-Plex 200 Multiplexing Analyzer System. Genomic DNA was extracted and variants in DBC1 (rs17060940), SIRT1 (rs7895833 and rs1467568), UCP2 (rs660339), PPARG (rs1801282) and ADRB2 (rs1042713 and rs1042714) genes were genotyped by PCR allelic discrimination using TaqMan® assays.

Results

Our findings indicated that SIRT1 rs7895833 polymorphism was a risk factor for severe obesity development in the overdominant model. SIRT1 rs1467568 and UCP2 rs660339 were associated with anthropometric traits. SIRT1 rs1467568 G allele was related to lower medians of body adipose index and hip circumference, while the UCP2 rs660339 AA genotype was associate with increased body mass index. Additionally, DBC1 rs17060940 influenced glycated hemoglobin. Regarding metabolic alterations, 27% of individuals with obesity presented balanced metabolic status in our cohort. Furthermore, SIRT1 rs1467568 AG genotype increased 2.5 times the risk of developing metabolic alterations. No statistically significant results were observed with Peroxisome Proliferator-Activated Receptor Gama and ADRB2 polymorphisms.

Discussion/Conclusion

This study revealed that SIRT1 rs7895833 and rs1467568 are potential biomarkers for severe obesity susceptibility and the development of unbalanced metabolic status in obesity, respectively. UCP2 rs660339 and DBC1 rs17060940 also showed a significant role in obesity related-traits.

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in diseased patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

TAD boundary deletion causes PITX2-related cardiac electrical and structural defects

While 3D chromatin organization in topologically associating domains (TADs) and loops mediating regulatory element-promoter interactions is crucial for tissue-specific gene regulation, the extent of their involvement in human Mendelian disease is largely unknown. Here, we identify 7 families presenting a new cardiac entity associated with a heterozygous deletion of 2 CTCF binding sites on 4q25, inducing TAD fusion and chromatin conformation remodeling. The CTCF binding sites are located in a gene desert at 1 Mb from the Paired-like homeodomain transcription factor 2 gene (PITX2). By introducing the ortholog of the human deletion in the mouse genome, we recapitulate the patient phenotype and characterize an opposite dysregulation of PITX2 expression in the sinoatrial node (ectopic activation) and ventricle (reduction), respectively. Chromatin conformation assay performed in human induced pluripotent stem cell-derived cardiomyocytes harboring the minimal deletion identified in family#1 reveals a conformation remodeling and fusion of TADs. We conclude that TAD remodeling mediated by deletion of CTCF binding sites causes a new autosomal dominant Mendelian cardiac disorder.

Phenotypic overlap between rare disease patients and variant carriers in a large population cohort informs biological mechanisms

The biological mechanisms giving rise to the extreme symptoms exhibited by rare disease patients are complex, heterogenous, and difficult to discern. Understanding these mechanisms is critical for developing treatments that address the underlying causes of diseases rather than merely the presenting symptoms. Moreover, the same dysfunctional biological mechanisms implicated in rare recessive diseases may also lead to milder and potentially preventable symptoms in carriers in the general population. Seizures are a common, extreme phenotype that can result from diverse and often elusive biological pathways in patients with ultrarare or undiagnosed disorders. In this pilot study, we present an approach to understand the biological pathways leading to seizures in patients from the Undiagnosed Diseases Network (UDN) by analyzing aggregated genotype and phenotype data from the UK Biobank (UKB). Specifically, we look for enriched phenotypes across UKB participants who harbor rare variants in the same gene known or suspected to be causally implicated in a UDN patient's recessively manifesting disorder. Analyzing these milder but related associated phenotypes in UKB participants can provide insight into the disease-causing molecular mechanisms at play in the rare disease UDN patient. We present six vignettes of undiagnosed patients experiencing seizures as part of their recessive genetic condition, and we discuss the potential mechanisms underlying the spectrum of symptoms associated with UKB participants to the severe presentations exhibited by UDN patients. We find that in our set of rare disease patients, seizures may result from diverse, multi-step pathways that involve multiple body systems. Analyses of large-scale population cohorts such as the UKB can be a critical tool to further our understanding of rare diseases in general.

Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing

We generated Japanese Encyclopedia of Whole-Genome/Exome Sequencing Library (JEWEL), a high-depth whole-genome sequencing dataset comprising 3256 individuals from across Japan. Analysis of JEWEL revealed genetic characteristics of the Japanese population that were not discernible using microarray data. First, rare variant-based analysis revealed an unprecedented fine-scale genetic structure. Together with population genetics analysis, the present-day Japanese can be decomposed into three ancestral components. Second, we identified unreported loss-of-function (LoF) variants and observed that for specific genes, LoF variants appeared to be restricted to a more limited set of transcripts than would be expected by chance, with PTPRD as a notable example. Third, we identified 44 archaic segments linked to complex traits, including a Denisovan-derived segment at NKX6-1 associated with type 2 diabetes. Most of these segments are specific to East Asians. Fourth, we identified candidate genetic loci under recent natural selection. Overall, our work provided insights into genetic characteristics of the Japanese population.

Multi-ancestry Whole-exome Sequencing Study of Alcohol Use Disorder in Two Cohorts

Alcohol use disorder (AUD) is a leading cause of death and disability worldwide. There has been substantial progress in identifying genetic variants underlying AUD. However, there are few whole-exome sequencing (WES) studies of AUD. We analyzed WES of 4,530 samples from the Yale-Penn cohort and 469,835 samples from the UK Biobank (UKB). After quality control, 1,420 AUD cases and 619 controls of European ancestry (EUR) and 1,142 cases and 608 controls of African ancestry (AFR) from Yale-Penn were retained for subsequent analyses. WES data from 415,617 EUR samples (12,861 cases), 6,142 AFR samples (130 cases) and 4,607 South Asian (SAS) samples (130 cases) from UKB were also analyzed. Single-variant association analysis identified the well-known functional variant rs1229984 in ADH1B ( P =4.88×10 -31 ) and several other common variants in ADH1C . Gene-based tests identified ADH1B ( P =1.00×10 -31 ), ADH1C ( P =5.23×10 -7 ), CNST ( P =1.19×10 -6 ), and IFIT5 (3.74×10 -6 ). This study extends our understanding of the genetic basis of AUD.

Angiopoietin-like protein 8: a multifaceted protein instrumental in regulating triglyceride metabolism

PURPOSE OF REVIEW

The angiopoietin-like (ANGPTL) proteins ANGPTL3 and ANGPTL4 are critical lipoprotein lipase (LPL) inhibitors. This review discusses the unique ability of the insulin-responsive protein ANGPTL8 to regulate triglyceride (TG) metabolism by forming ANGPTL3/8 and ANGPTL4/8 complexes that control tissue-specific LPL activities.

RECENT FINDINGS

After feeding, ANGPTL4/8 acts locally in adipose tissue, has decreased LPL-inhibitory activity compared to ANGPTL4, and binds tissue plasminogen activator (tPA) and plasminogen to generate plasmin, which cleaves ANGPTL4/8 and other LPL inhibitors. This enables LPL to be fully active postprandially to promote efficient fatty acid (FA) uptake and minimize ectopic fat deposition. In contrast, liver-derived ANGPTL3/8 acts in an endocrine manner, has markedly increased LPL-inhibitory activity compared to ANGPTL3, and potently inhibits LPL in oxidative tissues to direct TG toward adipose tissue for storage. Circulating ANGPTL3/8 levels are strongly correlated with serum TG, and the ANGPTL3/8 LPL-inhibitory epitope is blocked by the TG-lowering protein apolipoprotein A5 (ApoA5).

SUMMARY

ANGPTL8 plays a crucial role in TG metabolism by forming ANGPTL3/8 and ANGPTL4/8 complexes that differentially modulate LPL activities in oxidative and adipose tissues respectively. Selective ANGPTL8 inhibition in the context of the ANGPTL3/8 complex has the potential to be a promising strategy for treating dyslipidemia.

The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders

Despite its crucial role in the regulation of vital metabolic and neurological functions, the genetic architecture of the hypothalamus remains unknown. Here we conducted multivariate genome-wide association studies (GWAS) using hypothalamic imaging data from 32,956 individuals to uncover the genetic underpinnings of the hypothalamus and its involvement in neuropsychiatric traits. There were 23 significant loci associated with the whole hypothalamus and its subunits, with functional enrichment for genes involved in intracellular trafficking systems and metabolic processes of steroid-related compounds. The hypothalamus exhibited substantial genetic associations with limbic system structures and neuropsychiatric traits including chronotype, risky behaviour, cognition, satiety and sympathetic-parasympathetic activity. The strongest signal in the primary GWAS, the ADAMTS8 locus, was replicated in three independent datasets (N = 1,685-4,321) and was strengthened after meta-analysis. Exome-wide association analyses added evidence to the association for ADAMTS8, and Mendelian randomization showed lower ADAMTS8 expression with larger hypothalamic volumes. The current study advances our understanding of complex structure-function relationships of the hypothalamus and provides insights into the molecular mechanisms that underlie hypothalamic formation.

Genetic associations of protein-coding variants in venous thromboembolism

Previous genetic studies of venous thromboembolism (VTE) have been largely limited to common variants, leaving the genetic determinants relatively incomplete. We performed an exome-wide association study of VTE among 14,723 cases and 334,315 controls. Fourteen known and four novel genes (SRSF6, PHPT1, CGN, and MAP3K2) were identified through protein-coding variants, with broad replication in the FinnGen cohort. Most genes we discovered exhibited the potential to predict future VTE events in longitudinal analysis. Notably, we provide evidence for the additive contribution of rare coding variants to known genome-wide polygenic risk in shaping VTE risk. The identified genes were enriched in pathways affecting coagulation and platelet activation, along with liver-specific expression. The pleiotropic effects of these genes indicated the potential involvement of coagulation factors, blood cell traits, liver function, and immunometabolic processes in VTE pathogenesis. In conclusion, our study unveils the valuable contribution of protein-coding variants in VTE etiology and sheds new light on its risk stratification.

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

Clonal haematopoiesis of indeterminate potential predicts incident cardiac arrhythmias

BACKGROUND AND AIMS

Clonal haematopoiesis of indeterminate potential (CHIP), the age-related expansion of blood cells with preleukemic mutations, is associated with atherosclerotic cardiovascular disease and heart failure. This study aimed to test the association of CHIP with new-onset arrhythmias.

METHODS

UK Biobank participants without prevalent arrhythmias were included. Co-primary study outcomes were supraventricular arrhythmias, bradyarrhythmias, and ventricular arrhythmias. Secondary outcomes were cardiac arrest, atrial fibrillation, and any arrhythmia. Associations of any CHIP [variant allele fraction (VAF) ≥ 2%], large CHIP (VAF ≥10%), and gene-specific CHIP subtypes with incident arrhythmias were evaluated using multivariable-adjusted Cox regression. Associations of CHIP with myocardial interstitial fibrosis [T1 measured using cardiac magnetic resonance (CMR)] were also tested.

RESULTS

This study included 410 702 participants [CHIP: n = 13 892 (3.4%); large CHIP: n = 9191 (2.2%)]. Any and large CHIP were associated with multi-variable-adjusted hazard ratios of 1.11 [95% confidence interval (CI) 1.04-1.18; P = .001] and 1.13 (95% CI 1.05-1.22; P = .001) for supraventricular arrhythmias, 1.09 (95% CI 1.01-1.19; P = .031) and 1.13 (95% CI 1.03-1.25; P = .011) for bradyarrhythmias, and 1.16 (95% CI, 1.00-1.34; P = .049) and 1.22 (95% CI 1.03-1.45; P = .021) for ventricular arrhythmias, respectively. Associations were independent of coronary artery disease and heart failure. Associations were also heterogeneous across arrhythmia subtypes and strongest for cardiac arrest. Gene-specific analyses revealed an increased risk of arrhythmias across driver genes other than DNMT3A. Large CHIP was associated with 1.31-fold odds (95% CI 1.07-1.59; P = .009) of being in the top quintile of myocardial fibrosis by CMR.

CONCLUSIONS

CHIP may represent a novel risk factor for incident arrhythmias, indicating a potential target for modulation towards arrhythmia prevention and treatment.

Exome sequencing identifies genes associated with sleep-related traits

Sleep is vital for human health and has a moderate heritability. Previous genome-wide association studies have limitations in capturing the role of rare genetic variants in sleep-related traits. Here we conducted a large-scale exome-wide association study of eight sleep-related traits (sleep duration, insomnia symptoms, chronotype, daytime sleepiness, daytime napping, ease of getting up in the morning, snoring and sleep apnoea) among 450,000 participants from UK Biobank. We identified 22 new genes associated with chronotype (ADGRL4, COL6A3, CLK4 and KRTAP3-3), daytime sleepiness (ST3GAL1 and ANKRD12), daytime napping (PLEKHM1, ANKRD12 and ZBTB21), snoring (WDR59) and sleep apnoea (13 genes). Notably, 20 of these genes were confirmed to be significantly associated with sleep disorders in the FinnGen cohort. Enrichment analysis revealed that these discovered genes were enriched in circadian rhythm and central nervous system neurons. Phenotypic association analysis showed that ANKRD12 was associated with cognition and inflammatory traits. Our results demonstrate the value of large-scale whole-exome analysis in understanding the genetic architecture of sleep-related traits and potential biological mechanisms.

Discovery and SAR Study of Boronic Acid-Based Selective PDE3B Inhibitors from a Novel DNA-Encoded Library

Human genetic evidence shows that PDE3B is associated with metabolic and dyslipidemia phenotypes. A number of PDE3 family selective inhibitors have been approved by the FDA for various indications; however, given the undesirable proarrhythmic effects in the heart, selectivity for PDE3B inhibition over closely related family members (such as PDE3A; 48% identity) is a critical consideration for development of PDE3B therapeutics. Selectivity for PDE3B over PDE3A may be achieved in a variety of ways, including properties intrinsic to the compound or tissue-selective targeting. The high (>95%) active site homology between PDE3A and B represents a massive obstacle for obtaining selectivity at the active site; however, utilization of libraries with high molecular diversity in high throughput screens may uncover selective chemical matter. Herein, we employed a DNA-encoded library screen to identify PDE3B-selective inhibitors and identified potent and selective boronic acid compounds bound at the active site.

Inborn Errors of Immunity Contribute to the Burden of Skin Disease and Create Opportunities for Improving the Practice of Dermatology

Opportunities to improve the clinical management of skin disease are being created by advances in genomic medicine. Large-scale sequencing increasingly challenges notions about single-gene disorders. It is now apparent that monogenic etiologies make appreciable contributions to the population burden of disease and that they are underrecognized in clinical practice. A genetic diagnosis informs on molecular pathology and may direct targeted treatments and tailored prevention strategies for patients and family members. It also generates knowledge about disease pathogenesis and management that is relevant to patients without rare pathogenic variants. Inborn errors of immunity are a large class of monogenic etiologies that have been well-studied and contribute to the population burden of inflammatory diseases. To further delineate the contributions of inborn errors of immunity to the pathogenesis of skin disease, we performed a set of analyses that identified 316 inborn errors of immunity associated with skin pathologies, including common skin diseases. These data suggest that clinical sequencing is underutilized in dermatology. We next use these data to derive a network that illuminates the molecular relationships of these disorders and suggests an underlying etiological organization to immune-mediated skin disease. Our results motivate the further development of a molecularly derived and data-driven reorganization of clinical diagnoses of skin disease.