Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Scalable analysis of large multi-ancestry biobanks by leveraging sparse ancestry-adjusted sample-relatedness

Linear mixed-effects models (LMMs) and ridge regression are commonly applied in genetic association studies to control for population structure and sample-relatedness. To control for sample-relatedness, the existing methods use empirical genetic relatedness matrices (GRM) either explicitly or conceptually. This works well with mostly homogeneous populations, however, in multi-ancestry heterogeneous populations, GRMs are confounded with population structure which leads to inflated type I error rates, massively increased computation, and reduced power. Here, we propose FastSparseGRM, a scalable pipeline for multi-ancestry Genome-Wide Association studies (GWAS) and Whole Genome Sequencing (WGS) studies. It utilizes a block-diagonal sparse ancestry-adjusted (BDSA) GRM to model sample-relatedness, and ancestry PCs as fixed effects to control for population structure. It is ~ 2540/4100/54 times faster than BOLT-LMM/fast-GWA/REGENIE for fitting the null LMM on 50,000 heterogeneous subjects. Through numerical simulations and both single-variant GWAS and rare variant WGS analyses of five biomarkers (Triglycerides, HDL, LDL, BMI, Total Bilirubin) on the entire UK Biobank data, we demonstrate that our approach scales to nearly half-a-million subjects and provides accurate p-value calibration and improved power compared to the existing methods.

TOMM40 regulates hepatocellular and plasma lipid metabolism via an LXR-dependent pathway

OBJECTIVE

The gene encoding TOMM40 (Transporter of Outer Mitochondrial Membrane 40) is adjacent to that encoding APOE, which has a central role in lipid and lipoprotein metabolism. While human genetic variants near APOE and TOMM40 have been shown to be strongly associated with plasma lipid levels, a specific role for TOMM40 in lipid metabolism has not been established, and the present study was aimed at assessing this possibility.

METHODS

TOMM40 was knocked down by siRNA in human hepatoma HepG2 cells, and effects on mitochondrial function, lipid phenotypes, and crosstalk between mitochondria, ER, and lipid droplets were examined. Additionally, hepatic and plasma lipid levels were measured in mice following shRNA-induced knockdown of Tomm40 shRNA.

RESULTS

In HepG2 cells, TOMM40 knockdown upregulated expression of APOE and LDLR in part via activation of LXRB (NR1H2) by oxysterols, with consequent increased uptake of VLDL and LDL. This is in part due to disruption of mitochondria-endoplasmic reticulum contact sites, with resulting accrual of reactive oxygen species and non-enzymatically derived oxysterols. With TOMM40 knockdown, cellular triglyceride and lipid droplet content were increased, effects attributable in part to receptor-mediated VLDL uptake, since lipid staining was significantly reduced by concomitant suppression of either LDLR or APOE. In contrast, cellular cholesterol content was reduced due to LXRB-mediated upregulation of the ABCA1 transporter as well as increased production and secretion of oxysterol-derived cholic acid. Consistent with the findings in hepatoma cells, in vivo knockdown of TOMM40 in mice resulted in significant reductions of plasma triglyceride and cholesterol concentrations, reduced hepatic cholesterol and increased triglyceride content, and accumulation of lipid droplets leading to development of steatosis.

CONCLUSIONS

These findings demonstrate a role for TOMM40 in regulating hepatic lipid and plasma lipoprotein levels and identify mechanisms linking mitochondrial function with lipid metabolism.

Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height

The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N = 200,003), TOPMed (N = 87,652) and All of Us (N = 45,445). We performed rare ( < 0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal-regulatory, intergenic-regulatory and deep-intronic annotation. We observed 29 independent variants associated with height at P <  6 × 10 - 10 after conditioning on previously reported variants, with effect sizes ranging from -7cm to +4.7 cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5 cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed an approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits.

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.

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Genetic variation across and within individuals

Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.

Genetically transitional disease: conceptual understanding and applicability to rheumatic disease

In genomic medicine, the concept of genetically transitional disease (GTD) refers to cases in which gene mutation is necessary but not sufficient to cause disease. In this Perspective, we apply this novel concept to rheumatic diseases, which have been linked to hundreds of genetic variants via association studies. These variants are in the 'grey zone' between monogenic variants with large effect sizes and common susceptibility alleles with small effect sizes. Among genes associated with rare autoinflammatory diseases, many low-frequency and/or low-penetrance variants are known to increase susceptibility to systemic inflammation. In autoimmune diseases, hundreds of HLA and non-HLA genetic variants have been revealed to be modest- to moderate-risk alleles. These diseases can be reclassified as GTDs. The same concept could apply to many other human diseases. GTD could improve the reporting of genetic testing results, diagnostic yields, genetic counselling and selection of therapy, as well as facilitating research using a novel approach to human genetic diseases.

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

Genomic data in the All of Us Research Program

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1-4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Genome-wide association study of blood lipid levels in Southern Han Chinese adults with prediabetes

Background

Dyslipidemia is highly prevalent among individuals with prediabetes, further exacerbating their cardiovascular risk. However, the genetic determinants underlying diabetic dyslipidemia in Southern Han Chinese remain largely unexplored.

Methods

We performed a genome-wide association study (GWAS) of blood lipid traits in 451 Southern Han Chinese adults with prediabetes. Fasting plasma lipids, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were assayed. Genotyping was conducted using the Precision Medicine Diversity Array and Gene Titan platform, followed by genotype imputation using IMPUTE2 with the 1000 Genomes Project (Phase 3, Southern Han Chinese) as reference. Single nucleotide polymorphisms (SNPs) associated with lipid levels were identified using mixed linear regression, with adjustment for covariates.

Results

We identified 58, 215, 74 and 81 novel SNPs associated with TG, TC, HDL-C and LDL-C levels, respectively (P < 5×10-5). Several implicated loci were located in or near genes involved in lipid metabolism, including SRD5A2, PCSK7, PITPNC1, IRX3, BPI, and LBP. Pathway enrichment analysis highlighted lipid metabolism and insulin secretion.

Conclusion

This first GWAS of dyslipidemia in Southern Han Chinese with prediabetes identified novel genetic variants associated with lipid traits. Our findings provide new insights into genetic mechanisms underlying heightened cardiovascular risk in the prediabetic stage. Functional characterization of implicated loci is warranted.

An Update on the Genetics of IgA Nephropathy

Immunoglobulin A (IgA) nephropathy (IgAN), the most common form of glomerulonephritis, is one of the leading causes of end-stage kidney disease (ESKD). It is widely believed that genetic factors play a significant role in the development of IgAN. Previous studies of IgAN have provided important insights to unravel the genetic architecture of IgAN and its potential pathogenic mechanisms. The genome-wide association studies (GWASs) together have identified over 30 risk loci for IgAN, which emphasizes the importance of IgA production and regulation in the pathogenesis of IgAN. Follow-up fine-mapping studies help to elucidate the candidate causal variant and the potential pathogenic molecular pathway and provide new potential therapeutic targets. With the rapid development of next-generation sequencing technologies, linkage studies based on whole-genome sequencing (WGS)/whole-exome sequencing (WES) also identify rare variants associated with IgAN, accounting for some of the missing heritability. The complexity of pathogenesis and phenotypic variability may be better understood by integrating genetics, epigenetics, and environment. We have compiled a review summarizing the latest advancements in genetic studies on IgAN. We similarly summarized relevant studies examining the involvement of epigenetics in the pathogenesis of IgAN. Future directions and challenges in this field are also proposed.

Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification

Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific disease.

Elucidation of genetic determinants of dyslipidaemia using a global screening array for the early detection of coronary artery disease

Dyslipidemia is a major risk factor for the development of coronary artery disease (CAD). Understanding the genetic determinants of dyslipidemia can provide valuable information on the pathogenesis of CAD and aid in the development of early detection strategies. In this study, we used a Global Screening Array (GSA) to elucidate the genetic factors associated with dyslipidemia and their potential role in the prediction of CAD. We conducted a GSA-based association study in 265 subjects to identify the genetic loci associated with dyslipidemia traits using Multiple Linear Regression (MLR) and Logistic Regression (LR), Classification and Regression Tree (CART), and Manhattan plots. We identified an association between dyslipidemia and variants identified in genes such as JCAD, GLIS3, CD38, FN1, CELSR2, MTNR1B, GIPR, DYM, APOB, APOE, ADCY5. The MLR models explained 62%, 71%, and 81% of the variability in HDL, LDL, and triglycerides, respectively. The Area Under the Curve (AUC) values in the LR models of HDL, LDL, and triglycerides were 1.00, 0.94, and 0.95, respectively. CART models identified novel gene-gene interactions influencing the risk for dyslipidemia. To conclude, we have identified the association of 12 SNVs with dyslipidemia and demonstrated their clinical utility in four different models such as MLR, LR, CART, and Manhattan plots. The identified genetic variants and associated pathways shed light on the underlying biology of dyslipidemia and offer potential avenues for precision medicine strategies in the management of CAD.

Genome-Wide Association Study of Cardiovascular Resilience Identifies Protective Variation in the CETP Gene

Background The risk of atherosclerotic cardiovascular disease (ASCVD) increases sharply with age. Some older individuals, however, remain unaffected despite high predicted risk. These individuals may carry cardioprotective genetic variants that contribute to resilience. Our aim was to assess whether asymptomatic older individuals without prevalent ASCVD carry cardioprotective genetic variants that contribute to ASCVD resilience. Methods and Results We performed a genome-wide association study using a 10-year predicted ASCVD risk score as a quantitative trait, calculated only in asymptomatic older individuals aged ≥70 years without prevalent ASCVD. Our discovery genome-wide association study of N=12 031 ASCVD event-free individuals from the ASPREE (Aspirin in Reducing Events in the Elderly) trial identified 2 independent variants, rs9939224 (P<5×10-8) and rs56156922 (P<10-6), in the CETP (cholesteryl ester transfer protein) gene. The CETP gene is a regulator of plasma high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and lipoprotein(a) levels, and it is a therapeutic drug target. The associations were replicated in the UK Biobank (subpopulation of N=13 888 individuals aged ≥69 years without prevalent ASCVD). Carriers of the identified CETP variants (versus noncarriers) had higher plasma high-density lipoprotein cholesterol levels, lower plasma low-density lipoprotein cholesterol levels, and reduced risk of incident ASCVD events during follow-up. Expression quantitative trait loci analysis predicted the identified CETP variants reduce CETP gene expression across various tissues. Previously reported associations between genetic CETP inhibition and increased risk of age-related macular degeneration were not observed among the 3917 ASPREE trial participants with retinal imaging and genetic data available. Conclusions Common genetic variants in the CETP gene region are associated with cardiovascular resilience during aging. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01038583.

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

Variations in the Relative Abundance of Gut Bacteria Correlate with Lipid Profiles in Healthy Adults

The gut microbiome is a versatile system regulating numerous aspects of host metabolism. Among other traits, variations in the composition of gut microbial communities are related to blood lipid patterns and hyperlipidaemia, yet inconsistent association patterns exist. This study aims to assess the relationships between the composition of the gut microbiome and variations in lipid profiles among healthy adults. This study used data and samples from 23 adult participants of a previously conducted dietary intervention study. Circulating lipid measurements and whole-metagenome sequences of the gut microbiome were derived from 180 blood and faecal samples collected from eight visits distributed across an 11-week study. Lipid-related variables explained approximately 4.5% of the variation in gut microbiome compositions, with higher effects observed for total cholesterol and high-density lipoproteins. Species from the genera Odoribacter, Anaerostipes, and Parabacteroides correlated with increased serum lipid levels, whereas probiotic species like Akkermansia muciniphila were more abundant among participants with healthier blood lipid profiles. An inverse correlation with serum cholesterol was also observed for Massilistercora timonensis, a player in regulating lipid turnover. The observed correlation patterns add to the growing evidence supporting the role of the gut microbiome as an essential regulator of host lipid metabolism.

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Demonstrating paths for unlocking the value of cloud genomics through cross cohort analysis

Recently, large scale genomic projects such as All of Us and the UK Biobank have introduced a new research paradigm where data are stored centrally in cloud-based Trusted Research Environments (TREs). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conduct a Genome-Wide Association Study of standard lipid measures using two approaches: meta-analysis and pooled analysis. Comparison of full summary data from both approaches with an external study shows strong correlation of known loci with lipid levels (R2 ~ 83-97%). Importantly, 90 variants meet the significance threshold only in the meta-analysis and 64 variants are significant only in pooled analysis, with approximately 20% of variants in each of those groups being most prevalent in non-European, non-Asian ancestry individuals. These findings have important implications, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations.

An allelic-series rare-variant association test for candidate-gene discovery

Allelic series are of candidate therapeutic interest because of the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a collection of variants in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and we have developed a gene-based rare-variant association test specifically targeted to identifying genes containing allelic series. Building on the well-known burden test and sequence kernel association test (SKAT), we specify a variety of association models covering different genetic architectures and integrate these into a Coding-Variant Allelic-Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves the power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic-series genes for four circulating-lipid traits and five cell-count traits among 145,735 subjects with available whole-exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni-significant associations with circulating-lipid traits, on average, and 82% more with cell-count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant associations in the full cohort (Genebass, n = 400,000) or from common-variant associations in the GWAS Catalog. In addition to detecting many gene-trait associations present in Genebass by using only a fraction (36.9%) of the sample, COAST detects associations, such as that between ANGPTL4 and triglycerides, that are absent from Genebass but that have clear common-variant support.

Gut bacterial metabolism contributes to host global purine homeostasis

The microbes and microbial pathways that influence host inflammatory disease progression remain largely undefined. Here, we show that variation in atherosclerosis burden is partially driven by gut microbiota and is associated with circulating levels of uric acid (UA) in mice and humans. We identify gut bacterial taxa spanning multiple phyla, including Bacillota, Fusobacteriota, and Pseudomonadota, that use multiple purines, including UA as carbon and energy sources anaerobically. We identify a gene cluster that encodes key steps of anaerobic purine degradation and that is widely distributed among gut-dwelling bacteria. Furthermore, we show that colonization of gnotobiotic mice with purine-degrading bacteria modulates levels of UA and other purines in the gut and systemically. Thus, gut microbes are important drivers of host global purine homeostasis and serum UA levels, and gut bacterial catabolism of purines may represent a mechanism by which gut bacteria influence health.

Calling and Phasing of Single-Nucleotide and Structural Variants of the LDLR Gene Using Oxford Nanopore MinION

The LDLR locus has clinical significance for lipid metabolism, Mendelian familial hypercholesterolemia (FH), and common lipid metabolism-related diseases (coronary artery disease and Alzheimer's disease), but its intronic and structural variants are underinvestigated. The aim of this study was to design and validate a method for nearly complete sequencing of the LDLR gene using long-read Oxford Nanopore sequencing technology (ONT). Five PCR amplicons from LDLR of three patients with compound heterozygous FH were analyzed. We used standard workflows of EPI2ME Labs for variant calling. All rare missense and small deletion variants detected previously by massively parallel sequencing and Sanger sequencing were identified using ONT. One patient had a 6976 bp deletion (exons 15 and 16) that was detected by ONT with precisely located breakpoints between AluY and AluSx1. Trans-heterozygous associations between mutation c.530C>T and c.1054T>C, c.2141-966_2390-330del, and c.1327T>C, and between mutations c.1246C>T and c.940+3_940+6del of LDLR, were confirmed. We demonstrated the ability of ONT to phase variants, thereby enabling haplotype assignment for LDLR with personalized resolution. The ONT-based method was able to detect exonic variants with the additional benefit of intronic analysis in one run. This method can serve as an efficient and cost-effective tool for diagnosing FH and conducting research on extended LDLR haplotype reconstruction.

Genetic Heterogeneity of Familial Hypercholesterolemia: Repercussions for Molecular Diagnosis

Genetics of Familial Hypercholesterolemia (FH) is ascribable to pathogenic variants in genes encoding proteins leading to an impaired LDL uptake by the LDL receptor (LDLR). Two forms of the disease are possible, heterozygous (HeFH) and homozygous (HoFH), caused by one or two pathogenic variants, respectively, in the three main genes that are responsible for the autosomal dominant disease: LDLR, APOB and PCSK9 genes. The HeFH is the most common genetic disease in humans, being the prevalence about 1:300. Variants in the LDLRAP1 gene causes FH with a recessive inheritance and a specific APOE variant was described as causative of FH, contributing to increase FH genetic heterogeneity. In addition, variants in genes causing other dyslipidemias showing phenotypes overlapping with FH may mimic FH in patients without causative variants (FH-phenocopies; ABCG5, ABCG8, CYP27A1 and LIPA genes) or act as phenotype modifiers in patients with a pathogenic variant in a causative gene. The presence of several common variants was also considered a genetic basis of FH and several polygenic risk scores (PRS) have been described. The presence of a variant in modifier genes or high PRS in HeFH further exacerbates the phenotype, partially justifying its variability among patients. This review aims to report the updates on the genetic and molecular bases of FH with their implication for molecular diagnosis.