Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program

A genome-wide association study of neonatal metabolites

Genetic factors significantly influence the concentration of metabolites in adults. Nevertheless, the genetic influence on neonatal metabolites remains uncertain. To bridge this gap, we employed genotype imputation techniques on large-scale low-pass genome data obtained from non-invasive prenatal testing. Subsequently, we conducted association studies on a total of 75 metabolic components in neonates. The study identified 19 previously reported associations and 11 novel associations between single-nucleotide polymorphisms and metabolic components. These associations were initially found in the discovery cohort (8,744 participants) and subsequently confirmed in a replication cohort (19,041 participants). The average heritability of metabolic components was estimated to be 76.2%, with a range of 69%-78.8%. These findings offer valuable insights into the genetic architecture of neonatal metabolism.

Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk

Lung cancer remains the leading cause of cancer mortality, despite declining smoking rates. Previous lung cancer GWAS have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging. Here, we perform multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program cohort (approximately 95% male cases) and a previous study of European-ancestry individuals, jointly comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We then carry out conditional meta-analyses on cigarettes per day and identify two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in squamous cell lung carcinoma. Overall, we report twelve novel risk loci for overall lung cancer, lung adenocarcinoma, and squamous cell lung carcinoma, nine of which are externally replicated. Finally, we perform PheWAS on polygenic risk scores for lung cancer, with and without conditioning on smoking. The unconditioned lung cancer polygenic risk score is associated with smoking status in controls, illustrating a reduced predictive utility in non-smokers. Additionally, our polygenic risk score demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.

Donor and recipient genetics: Implications for the development of posttransplant diabetes mellitus

Posttransplant diabetes mellitus (PTDM) is a prevalent complication of liver transplantation and is associated with cardiometabolic complications. We studied the consequences of genetic effects of liver donors and recipients on PTDM outcomes, focusing on the diverse genetic pathways related to insulin that play a role in the development of PTDM. One thousand one hundred fifteen liver transplant recipients without a pretransplant diagnosis of type 2 diabetes mellitus (T2D) and their paired donors recruited from 2 transplant centers had polygenic risk scores (PRS) for T2D, insulin secretion, and insulin sensitivity calculated. Among recipients in the highest T2D-PRS quintile, donor T2D-PRS did not contribute significantly to PTDM. However, in recipients with the lowest T2D genetic risk, donor livers with the highest T2D-PRS contributed to the development of PTDM (OR [95% CI] = 3.79 [1.10-13.1], P = .035). Recipient risk was linked to factors associated with insulin secretion (OR [95% CI] = 0.85 [0.74-0.98], P = .02), while donor livers contributed to PTDM via gene pathways involved in insulin sensitivity (OR [95% CI] = 0.86 [0.75-0.99], P = .03). Recipient and donor PRS independently and collectively serve as predictors of PTDM onset. The genetically influenced biological pathways in recipients primarily pertain to insulin secretion, whereas the genetic makeup of donors exerts an influence on insulin sensitivity.

Proprotein convertase subtilisin/kexin type 9 as a drug target for abdominal aortic aneurysm

PURPOSE OF REVIEW

There are no current drug therapies to limit abdominal aortic aneurysm (AAA) growth. This review summarizes evidence suggesting that inhibiting proprotein convertase subtilisin/kexin type 9 (PCSK9) may be a drug target to limit AAA growth.

RECENT FINDINGS

Mendelian randomization studies suggest that raised LDL and non-HDL-cholesterol are causal in AAA formation. PCSK9 was reported to be upregulated in human AAA samples compared to aortic samples from organ donors. PCSK9 gain of function viral vectors promoted aortic expansion in C57BL/6 mice infused with angiotensin II. The effect of altering PCSK9 expression in the aortic perfusion elastase model was reported to be inconsistent. Mutations in the gene encoding PCSK9, which increase serum cholesterol, were associated with increased risk of human AAA. Patients with AAA also have a high risk of cardiovascular death, myocardial infarction and stroke. Recent research suggests that PCSK9 inhibition would substantially reduce the risk of these events.

SUMMARY

Past research suggests that drugs that inhibit PCSK9 have potential as a novel therapy for AAA to both limit aneurysm growth and reduce risk of cardiovascular events. A large multinational randomized controlled trial is needed to test if PCSK9 inhibition limits AAA growth and cardiovascular events.

Ancestrally diverse genome-wide association analysis highlights ancestry-specific differences in genetic regulation of plasma protein levels

Fully characterizing the genetic architecture of circulating proteins in multi-ancestry populations provides an unprecedented opportunity to gain insights into the etiology of complex diseases. We characterized and contrasted the genetic associations of plasma proteomes in 9,455 participants of European and African (19.8%) ancestry from the Atherosclerosis Risk in Communities Study. Of 4,651 proteins, 1,408 and 2,565 proteins had protein-quantitative trait loci (pQTLs) identified in African and European ancestry respectively, and twelve unreported potentially causal protein-disease relationships were identified. Shared pQTLs across the two ancestries were detected in 1,113 aptamer-region pairs pQTLs, where 53 of them were not previously reported (all trans pQTLs). Sixteen unique protein-cardiovascular trait pairs were colocalized in both European and African ancestry with the same candidate causal variants. Our systematic cross-ancestry comparison provided a reliable set of pQTLs, highlighted the shared and distinct genetic architecture of proteome in two ancestries, and demonstrated possible biological mechanisms underlying complex diseases.

Joint models reveal genetic architecture of pubertal stage transitions and their association with BMI in admixed Chilean population

Early or late pubertal onset can lead to disease in adulthood, including cancer, obesity, type 2 diabetes, metabolic disorders, bone fractures, and psychopathologies. Thus, knowing the age at which puberty is attained is crucial as it can serve as a risk factor for future diseases. Pubertal development is divided into five stages of sexual maturation in boys and girls according to the standardized Tanner scale. We performed genome-wide association studies (GWAS) on the "Growth and Obesity Chilean Cohort Study" cohort composed of admixed children with mainly European and Native American ancestry. Using joint models that integrate time-to-event data with longitudinal trajectories of body mass index (BMI), we identified genetic variants associated with phenotypic transitions between pairs of Tanner stages. We identified $42$ novel significant associations, most of them in boys. The GWAS on Tanner $3\rightarrow 4$ transition in boys captured an association peak around the growth-related genes LARS2 and LIMD1 genes, the former of which causes ovarian dysfunction when mutated. The associated variants are expression and splicing Quantitative Trait Loci regulating gene expression and alternative splicing in multiple tissues. Further, higher individual Native American genetic ancestry proportions predicted a significantly earlier puberty onset in boys but not in girls. Finally, the joint models identified a longitudinal BMI parameter significantly associated with several Tanner stages' transitions, confirming the association of BMI with pubertal timing.

Exome wide association study for blood lipids in 1,158,017 individuals from diverse populations

Rare coding alleles play crucial roles in the molecular diagnosis of genetic diseases. However, the systemic identification of these alleles has been challenging due to their scarcity in the general population. Here, we discovered and characterized rare coding alleles contributing to genetic dyslipidemia, a principal risk for coronary artery disease, among over a million individuals combining three large contemporary genetic datasets (the Million Veteran Program, n = 634,535, UK Biobank, n = 431,178, and the All of Us Research Program, n = 92,304) totaling 1,158,017 multi-ancestral individuals. Unlike previous rare variant studies in lipids, this study included 238,243 individuals (20.6%) from non-European-like populations. Testing 2,997,401 rare coding variants from diverse backgrounds, we identified 800 exome-wide significant associations across 209 genes including 176 predicted loss of function and 624 missense variants. Among these exome-wide associations, 130 associations were driven by non-European-like populations. Associated alleles are highly enriched in functional variant classes, showed significant additive and recessive associations, exhibited similar effects across populations, and resolved pathogenicity for variants enriched in African or South-Asian populations. Furthermore, we identified 5 lipid-related genes associated with coronary artery disease (RORC, CFAP65, GTF2E2, PLCB3, and ZNF117). Among them, RORC is a potentially novel therapeutic target through the down regulation of LDLC by its silencing. This study provides resources and insights for understanding causal mechanisms, quantifying the expressivity of rare coding alleles, and identifying novel drug targets across diverse populations.

Blood metabolites, neurocognition and psychiatric disorders: a Mendelian randomization analysis to investigate causal pathways

BACKGROUND

Neurocognitive dysfunction is observationally associated with the risk of psychiatric disorders. Blood metabolites, which are readily accessible, may become highly promising biomarkers for brain disorders. However, the causal role of blood metabolites in neurocognitive function, and the biological pathways underlying their association with psychiatric disorders remain unclear.

METHODS

To explore their putative causalities, we conducted bidirectional two-sample Mendelian randomization (MR) using genetic variants associated with 317 human blood metabolites (nmax = 215,551), g-Factor (an integrated index of multiple neurocognitive tests with nmax = 332,050), and 10 different psychiatric disorders (n = 9,725 to 807,553) from the large-scale genome-wide association studies of European ancestry. Mediation analysis was used to assess the potential causal pathway among the candidate metabolite, neurocognitive trait and corresponding psychiatric disorder.

RESULTS

MR evidence indicated that genetically predicted acetylornithine was positively associated with g-Factor (0.035 standard deviation units increase in g-Factor per one standard deviation increase in acetylornithine level; 95% confidence interval, 0.021 to 0.049; P = 1.15 × 10-6). Genetically predicted butyrylcarnitine was negatively associated with g-Factor (0.028 standard deviation units decrease in g-Factor per one standard deviation increase in genetically proxied butyrylcarnitine; 95% confidence interval, -0.041 to -0.015; P = 1.31 × 10-5). There was no evidence of associations between genetically proxied g-Factor and metabolites. Furthermore, the mediation analysis via two-step MR revealed that the causal pathway from acetylornithine to bipolar disorder was partly mediated by g-Factor, with a mediated proportion of 37.1%. Besides, g-Factor mediated the causal pathway from butyrylcarnitine to schizophrenia, with a mediated proportion of 37.5%. Other neurocognitive traits from different sources provided consistent findings.

CONCLUSION

Our results provide genetic evidence that acetylornithine protects against bipolar disorder through neurocognitive abilities, while butyrylcarnitine has an adverse effect on schizophrenia through neurocognition. These findings may provide insight into interventions at the metabolic level for risk of neurocognitive and related disorders.

Ethnic and region-specific genetic risk variants of stroke and its comorbid conditions can define the variations in the burden of stroke and its phenotypic traits

Burden of stroke differs by region, which could be attributed to differences in comorbid conditions and ethnicity. Genomewide variation acts as a proxy marker for ethnicity, and comorbid conditions. We present an integrated approach to understand this variation by considering prevalence and mortality rates of stroke and its comorbid risk for 204 countries from 2009 to 2019, and Genome-wide association studies (GWAS) risk variant for all these conditions. Global and regional trend analysis of rates using linear regression, correlation, and proportion analysis, signifies ethnogeographic differences. Interestingly, the comorbid conditions that act as risk drivers for stroke differed by regions, with more of metabolic risk in America and Europe, in contrast to high systolic blood pressure in Asian and African regions. GWAS risk loci of stroke and its comorbid conditions indicate distinct population stratification for each of these conditions, signifying for population-specific risk. Unique and shared genetic risk variants for stroke, and its comorbid and followed up with ethnic-specific variation can help in determining regional risk drivers for stroke. Unique ethnic-specific risk variants and their distinct patterns of linkage disequilibrium further uncover the drivers for phenotypic variation. Therefore, identifying population- and comorbidity-specific risk variants might help in defining the threshold for risk, and aid in developing population-specific prevention strategies for stroke.

A Large-Scale Genome-Wide Gene-Sleep Interaction Study in 732,564 Participants Identifies Lipid Loci Explaining Sleep-Associated Lipid Disturbances

We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify novel genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. We collected data from 55 cohorts with a combined sample size of 732,564 participants (87% European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20% of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for the one-degree of freedom tests of interaction and two-degree of freedom joint tests of the main and interaction effect. In the cross-population meta-analyses, the one-degree of freedom variant-sleep interaction test identified 10 loci (P int <5.0e-9) not previously observed for lipids. Of interest, the ASPH locus (TG, LTST) is a target for aspartic and succinic acid metabolism previously shown to improve sleep and cardiovascular risk. The two-degree of freedom analyses identified an additional 7 loci that showed evidence for variant-sleep interaction (P joint <5.0e-9 in combination with P int <6.6e-6). Of these, the SLC8A1 locus (TG, STST) has been considered a potential treatment target for reduction of ischemic damage after acute myocardial infarction. Collectively, the 17 (9 with STST; 8 with LTST) loci identified in this large-scale initiative provides evidence into the biomolecular mechanisms underpinning sleep-duration-associated changes in lipid levels. The identified druggable targets may contribute to the development of novel therapies for dyslipidemia in people with sleep disturbances.

The metabolic signature of blood lipids: a causal inference study using twins

Dyslipidemia is one of the cardiometabolic risk factors that influences mortality globally. Unraveling the causality between blood lipids and metabolites and the complex networks connecting lipids, metabolites, and other cardiometabolic traits can help to more accurately reflect the body's metabolic disorders and even cardiometabolic diseases. We conducted targeted metabolomics of 248 metabolites in 437 twins from the Chinese National Twin Registry. Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) analysis was used for causal inference between metabolites and lipid parameters. Bidirectional mediation analysis was performed to explore the linkages between blood lipids, metabolites, and other seven cardiometabolic traits. We identified 44, 1, and 31 metabolites associated with triglyceride (TG), total cholesterol (TC), and high-density lipoprotein-cholesterol (HDL-C), most of which were gut microbiota-derived metabolites. There were 9, 1, and 14 metabolites that showed novel associations with TG, TC, and HDL-C, respectively. ICE FALCON analysis found that TG and HDL-C may have a predicted causal effect on 23 and six metabolites, respectively, and one metabolite may have a predicted causal effect on TG. Mediation analysis discovered 14 linkages connecting blood lipids, metabolites, and other cardiometabolic traits. Our study highlights the significance of gut microbiota-derived metabolites in lipid metabolism. Most of the identified cross-sectional associations may be due to the lipids having a predicted causal effect on metabolites, but not vice versa, nor are they due to family confounding. These findings shed new light on lipid metabolism and personalized management of cardiometabolic diseases.

Genetic variation in CCDC93 is associated with elevated central systolic blood pressure, impaired arterial relaxation, and mitochondrial dysfunction

Genetic studies of blood pressure (BP) traits to date have been performed on conventional measures by brachial cuff sphygmomanometer for systolic BP (SBP) and diastolic BP, integrating several physiologic occurrences. Genetic associations with central SBP (cSBP) have not been well-studied. Genetic discovery studies of BP have been most often performed in European-ancestry samples. Here, we investigated genetic associations with cSBP in a Chinese population and functionally validated the impact of a novel associated coiled-coil domain containing 93 (CCDC93) gene on BP regulation. An exome-wide association study (EWAS) was performed using a mixed linear model of non-invasive cSBP and peripheral BP traits in a Han Chinese population (N = 5,954) from Beijing, China genotyped with a customized Illumina ExomeChip array. We identified four SNP-trait associations with three SNPs, including two novel associations (rs2165468-SBP and rs33975708-cSBP). rs33975708 is a coding variant in the CCDC93 gene, c.535C>T, p.Arg179Cys (MAF = 0.15%), and was associated with increased cSBP (β = 29.3 mmHg, P = 1.23x10-7). CRISPR/Cas9 genome editing was used to model the effect of Ccdc93 loss in mice. Homozygous Ccdc93 deletion was lethal prior to day 10.5 of embryonic development. Ccdc93+/- heterozygous mice were viable and morphologically normal, with 1.3-fold lower aortic Ccdc93 protein expression (P = 0.0041) and elevated SBP as compared to littermate Ccdc93+/+ controls (110±8 mmHg vs 125±10 mmHg, P = 0.016). Wire myography of Ccdc93+/- aortae showed impaired acetylcholine-induced relaxation and enhanced phenylephrine-induced contraction. RNA-Seq transcriptome analysis of Ccdc93+/- mouse thoracic aortae identified significantly enriched pathways altered in fatty acid metabolism and mitochondrial metabolism. Plasma free fatty acid levels were elevated in Ccdc93+/- mice (96±7mM vs 124±13mM, P = 0.0031) and aortic mitochondrial dysfunction was observed through aberrant Parkin and Nix protein expression. Together, our genetic and functional studies support a novel role of CCDC93 in the regulation of BP through its effects on vascular mitochondrial function and endothelial function.

Genetic causal association between lipidomic profiles, inflammatory proteomics, and aortic stenosis: a Mendelian randomization investigation

BACKGROUND

Aortic stenosis (AS) is a prevalent and serious valvular heart disease with a complex etiology involving genetic predispositions, lipid dysregulation, and inflammation. The specific roles of lipid and protein biomarkers in AS development are not fully elucidated. This study aimed to elucidate the causal relationships between lipidome, inflammatory proteins, and AS using Mendelian randomization (MR), identifying potential therapeutic targets.

METHODS

Utilizing data from large-scale genome-wide association studies (GWAS) and genome-wide protein quantitative trait loci (pQTL) studies, we conducted MR analyses on 179 plasma lipidome and 91 inflammatory proteins to assess their causal associations with AS. Our approach included Inverse Variance Weighting (IVW), Wald ratio, and robust adjusted profile score (RAPS) analyses to refine these associations. MR-Egger regression was used to address directional horizontal pleiotropy.

RESULTS

Our MR analysis showed that genetically predicted 50 lipids were associated with AS, including 38 as risk factors [(9 Sterol ester, 18 Phosphatidylcholine, 4 Phosphatidylethanolamine, 1 Phosphatidylinositol and 6 Triacylglycerol)] and 12 as protective. Sterol ester (27:1/17:1) emerged as the most significant risk factor with an odds ratio (OR) of 3.11. Additionally, two inflammatory proteins, fibroblast growth factor 19 (FGF19) (OR = 0.830, P = 0.015), and interleukin 6 (IL-6) (OR = 0.729, P = 1.79E-04) were significantly associated with reduced AS risk. However, a two-step MR analysis showed no significant mediated correlations between these proteins and the lipid-AS pathway.

CONCLUSION

This study reveals complex lipid and protein interactions in AS, identifying potential molecular targets for therapy. These results go beyond traditional lipid profiling and significantly advance our genetic and molecular understanding of AS, highlighting potential pathways for intervention and prevention.

Unresolved alterations in bile acid composition and dyslipidemia in maternal and cord blood after ursodeoxycholic acid treatment for intrahepatic cholestasis of pregnancy

Intrahepatic cholestasis of pregnancy (ICP) is characterized by elevated plasma bile acid levels. ICP is linked to adverse metabolic outcomes, including a reported increased risk of gestational diabetes. The standard therapeutic approach for managing ICP is treatment with ursodeoxycholic acid (UDCA) and induction of labor prior to 40 weeks of gestation. To investigate bile acid and metabolic parameters after UDCA treatment, we enrolled 12 ICP patients with singleton pregnancies-half with and half without gestational diabetes-and 7 controls. Our study reveals that after UDCA treatment, notwithstanding a reduction in total bile acid and ALT levels, imbalances persist in the cholic acid (CA) to chenodeoxycholic acid (CDCA) ratio in maternal and cord blood plasma. This indicates a continued dysregulation of bile acid metabolism despite therapeutic intervention. Maternal plasma lipid analysis showed a distinct maternal dyslipidemia pattern among ICP patients, marked by elevated cholesterol levels on VLDL particles and heightened triglyceride concentrations on LDL particles, persisting even after UDCA treatment. Cord plasma lipid profiles in ICP patients exhibited elevated triglyceride and free fatty acid levels alongside a tendency toward increased β-hydroxybutyrate. The changes in lipid metabolism in both maternal and cord blood correlated with the high CA/CDCA ratio, but not total bile acid levels or gestational diabetes status. Understanding the imbalances in maternal and cord bile acid and lipid profiles that persist after standard UDCA therapy provides insights for improving management strategies and mitigating the long-term consequences of ICP.

News and Noteworthy

This study uncovers that despite ursodeoxycholic acid treatment, intrahepatic cholestasis of pregnancy (ICP) is associated with increases in the ratio of cholic acid to chenodeoxycholic acid in both maternal and cord blood, suggesting ongoing dysregulation of bile acid metabolism. The high cholic to chenodeoxycholic acid ratio is correlated with maternal dyslipidemia and high cord blood lipids. These findings may inform more targeted approaches to managing ICP.

Hypertriglyceridemia as a Key Contributor to Abdominal Aortic Aneurysm Development and Rupture: Insights from Genetic and Experimental Models

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease without effective medications. This study integrated genetic, proteomic, and metabolomic data to identify causation between increased triglyceride (TG)-rich lipoproteins and AAA risk. Three hypertriglyceridemia mouse models were employed to test the hypothesis that increased plasma TG concentrations accelerate AAA development and rupture. In the angiotensin II-infusion AAA model, most Lpl -deficient mice with severely high plasma TG concentrations died of aortic rupture. Consistently, Apoa5 -deficient mice with moderately increased TG concentrations had accelerated AAA development, while human APOC3 transgenic mice with dramatically increased TG concentrations exhibited aortic dissection and rupture. Increased TG concentrations and palmitate inhibited lysyl oxidase maturation. Administration of antisense oligonucleotide targeting Angptl3 profoundly inhibited AAA progression in human APOC3 transgenic mice and Apoe -deficient mice. These results indicate that hypertriglyceridemia is a key contributor to AAA pathogenesis, highlighting the importance of triglyceride-rich lipoprotein management in treating AAA.

Inferring causal direction between two traits using R2 with application to transcriptome-wide association studies

In Mendelian randomization, two single SNP-trait correlation-based methods have been developed to infer the causal direction between an exposure (e.g., a gene) and an outcome (e.g., a trait), called MR Steiger's method and its recent extension called Causal Direction-Ratio (CD-Ratio). Here we propose an approach based on R2, the coefficient of determination, to combine information from multiple (possibly correlated) SNPs to simultaneously infer the presence and direction of a causal relationship between an exposure and an outcome. Our proposed method generalizes Steiger's method from using a single SNP to multiple SNPs as IVs. It is especially useful in transcriptome-wide association studies (TWASs) (and similar applications) with typically small sample sizes for gene expression (or another molecular trait) data, providing a more flexible and powerful approach to inferring causal directions. It can be applied to GWAS summary data with a reference panel. We also discuss the influence of invalid IVs and introduce a new approach called R2S to select and remove invalid IVs (if any) to enhance the robustness. We compared the performance of the proposed method with existing methods in simulations to demonstrate its advantages. We applied the methods to identify causal genes for high/low-density lipoprotein cholesterol (HDL/LDL) using the individual-level GTEx gene expression data and UK Biobank GWAS data. The proposed method was able to confirm some well-known causal genes while identifying some novel ones. Additionally, we illustrated an application of the proposed method to GWAS summary to infer causal relationships between HDL/LDL and stroke/coronary artery disease (CAD).

Fast and Accurate Estimation of Selection Coefficients and Allele Histories from Ancient and Modern DNA

We here present CLUES2, a full-likelihood method to infer natural selection from sequence data that is an extension of the method CLUES. We make several substantial improvements to the CLUES method that greatly increases both its applicability and its speed. We add the ability to use ancestral recombination graphs on ancient data as emissions to the underlying hidden Markov model, which enables CLUES2 to use both temporal and linkage information to make estimates of selection coefficients. We also fully implement the ability to estimate distinct selection coefficients in different epochs, which allows for the analysis of changes in selective pressures through time, as well as selection with dominance. In addition, we greatly increase the computational efficiency of CLUES2 over CLUES using several approximations to the forward-backward algorithms and develop a new way to reconstruct historic allele frequencies by integrating over the uncertainty in the estimation of the selection coefficients. We illustrate the accuracy of CLUES2 through extensive simulations and validate the importance sampling framework for integrating over the uncertainty in the inference of gene trees. We also show that CLUES2 is well-calibrated by showing that under the null hypothesis, the distribution of log-likelihood ratios follows a χ2 distribution with the appropriate degrees of freedom. We run CLUES2 on a set of recently published ancient human data from Western Eurasia and test for evidence of changing selection coefficients through time. We find significant evidence of changing selective pressures in several genes correlated with the introduction of agriculture to Europe and the ensuing dietary and demographic shifts of that time. In particular, our analysis supports previous hypotheses of strong selection on lactase persistence during periods of ancient famines and attenuated selection in more modern periods.

Explaining adverse cholesterol levels and distinct gender patterns in an Indonesian population compared with the U.S

Cardiovascular disease is among the most common causes of death around the world. As rising incomes in low and middle-income countries are accompanied by increased obesity, the burden of disease shifts towards non-communicable diseases, and lower-income settings make up a growing share of cardiovascular disease deaths. Comparative investigation of the roles of body composition, behavioral and socioeconomic factors across countries can shed light on both the biological and social drivers of cardiovascular disease more broadly. Comparing rigorously-validated measures of HDL and non-HDL cholesterol among adults in the United States and in Aceh, Indonesia, we show that Indonesians present with adverse cholesterol biomarkers relative to Americans, despite being younger and having lower body mass index. Adjusting for age, the gaps increase. Body composition, behaviors, demographic and socioeconomic characteristics that affect cholesterol do not explain between-country HDL differences, but do explain non-HDL differences, after accounting for medication use. On average, gender differences are inconsistent across the two countries and persist after controlling observed characteristics. Leveraging the richness of the Indonesian data to draw comparisons of males and females within the same household, the gender gaps among Indonesians are not explained for HDL cholesterol but attenuated substantially for non-HDL cholesterol. This finding suggests that unmeasured household resources play an important role in determining non-HDL cholesterol. More generally, they appear to be affected by social and biological forces in complex ways that differ across countries and potentially operate differently for HDL and non-HDL biomarkers. These results point to the value of rigorous comparative studies to advance understanding of cardiovascular risks across the globe.

Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder

Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed health problems, prior pharmacological treatments, and polygenic scores (PGS) has potential to inform risk stratification. We examined self-reported SB and ideation using the Columbia Suicide Severity Rating Scale (C-SSRS) among 3,942 SCZ and 5,414 BPI patients receiving care within the Veterans Health Administration (VHA). These cross-sectional data were integrated with electronic health records (EHRs), and compared across lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. PGS were constructed using available genomic data for related traits. Genome-wide association studies were performed to identify and prioritize specific loci. Only 20% of the veterans who reported SB had a corroborating ICD-9/10 EHR code. Among those without prior SB, more than 20% reported new-onset SB at follow-up. SB were associated with a range of additional clinical diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking initiation, suicide attempt, and major depressive disorder were associated with SB. The GWAS for SB yielded no significant loci. Among individuals with a diagnosed mental illness, self-reported SB were strongly associated with clinical variables across several EHR domains. Analyses point to sequelae of substance-related and psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in health records, underscoring the value of regular screening with direct, in-person assessments, especially among high-risk individuals.

Genome-wide discovery for biomarkers using quantile regression at biobank scale

Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. Conventional GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. We draw attention here to an alternative, lesser known approach, namely quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest. Quantile regression can be applied efficiently at biobank scale, while having some unique advantages such as (1) identifying variants with heterogeneous effects across quantiles of the phenotype distribution; (2) accommodating a wide range of phenotype distributions including non-normal distributions, with invariance of results to trait transformations; and (3) providing more detailed information about genotype-phenotype associations even for those associations identified by conventional GWAS. We show in simulations that quantile regression is powerful across both homogeneous and various heterogeneous models. Applications to 39 quantitative traits in the UK Biobank demonstrate that quantile regression can be a helpful complement to linear regression in GWAS and can identify variants with larger effects on high-risk subgroups of individuals but with lower or no contribution overall.

Transitions in chromatin conformation shaped by fatty acids and the circadian clock underlie hepatic transcriptional reorganization in obese mice

The circadian clock system coordinates metabolic, physiological, and behavioral functions across a 24-h cycle, crucial for adapting to environmental changes. Disruptions in circadian rhythms contribute to major metabolic pathologies like obesity and Type 2 diabetes. Understanding the regulatory mechanisms governing circadian control is vital for identifying therapeutic targets. It is well characterized that chromatin remodeling and 3D structure at genome regulatory elements contributes to circadian transcriptional cycles; yet the impact of rhythmic chromatin topology in metabolic disease is largely unexplored. In this study, we explore how the spatial configuration of the genome adapts to diet, rewiring circadian transcription and contributing to dysfunctional metabolism. We describe daily fluctuations in chromatin contacts between distal regulatory elements of metabolic control genes in livers from lean and obese mice and identify specific lipid-responsive regions recruiting the clock molecular machinery. Interestingly, under high-fat feeding, a distinct interactome for the clock-controlled gene Dbp strategically promotes the expression of distal metabolic genes including Fgf21. Alongside, new chromatin loops between regulatory elements from genes involved in lipid metabolism control contribute to their transcriptional activation. These enhancers are responsive to lipids through CEBPβ, counteracting the circadian repressor REVERBa. Our findings highlight the intricate coupling of circadian gene expression to a dynamic nuclear environment under high-fat feeding, supporting a temporally regulated program of gene expression and transcriptional adaptation to diet.

Novel loci linked to serum lipid traits are identified in a genome-wide association study of a highly admixed Brazilian population - the 2015 ISA Nutrition

BACKGROUND

Cardiovascular diseases (CVDs) comprise major causes of death worldwide, leading to extensive burden on populations and societies. Alterations in normal lipid profiles, i.e., dyslipidemia, comprise important risk factors for CVDs. However, there is lack of comprehensive evidence on the genetic contribution to dyslipidemia in highly admixed populations. The identification of single nucleotide polymorphisms (SNPs) linked to blood lipid traits in the Brazilian population was based on genome-wide associations using data from the São Paulo Health Survey with Focus on Nutrition (ISA-Nutrition).

METHODS

A total of 667 unrelated individuals had genetic information on 330,656 SNPs available, and were genotyped with Axiom™ 2.0 Precision Medicine Research Array. Genetic associations were tested at the 10- 5 significance level for the following phenotypes: low-density lipoprotein cholesterol (LDL-c), very low-density lipoprotein cholesterol (VLDL-c), high-density lipoprotein cholesterol (HDL-c), HDL-c/LDL-c ratio, triglycerides (TGL), total cholesterol, and non-HDL-c.

RESULTS

There were 19 significantly different SNPs associated with lipid traits, the majority of which corresponding to intron variants, especially in the genes FAM81A, ZFHX3, PTPRD, and POMC. Three variants (rs1562012, rs16972039, and rs73401081) and two variants (rs8025871 and rs2161683) were associated with two and three phenotypes, respectively. Among the subtypes, non-HDL-c had the highest proportion of associated variants.

CONCLUSIONS

The results of the present genome-wide association study offer new insights into the genetic structure underlying lipid traits in underrepresented populations with high ancestry admixture. The associations were robust across multiple lipid phenotypes, and some of the phenotypes were associated with two or three variants. In addition, some variants were present in genes that encode ncRNAs, raising important questions regarding their role in lipid metabolism.

Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10-6) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.

Genetic Architecture and Clinical Outcomes of Combined Lipid Disturbances

BACKGROUND

Dyslipoproteinemia often involves simultaneous derangements of multiple lipid traits. We aimed to evaluate the phenotypic and genetic characteristics of combined lipid disturbances in a general population-based cohort.

METHODS

Among UK Biobank participants without prevalent coronary artery disease, we used blood lipid and apolipoprotein B concentrations to ascribe individuals into 1 of 6 reproducible and mutually exclusive dyslipoproteinemia subtypes. Incident coronary artery disease risk was estimated for each subtype using Cox proportional hazards models. Phenome-wide analyses and genome-wide association studies were performed for each subtype, followed by in silico causal gene prioritization and heritability analyses. Additionally, the prevalence of disruptive variants in causal genes for Mendelian lipid disorders was assessed using whole-exome sequence data.

RESULTS

Among 450 636 UK Biobank participants: 63 (0.01%) had chylomicronemia; 40 005 (8.9%) had hypercholesterolemia; 94 785 (21.0%) had combined hyperlipidemia; 13 998 (3.1%) had remnant hypercholesterolemia; 110 389 (24.5%) had hypertriglyceridemia; and 49 (0.01%) had mixed hypertriglyceridemia and hypercholesterolemia. Over a median (interquartile range) follow-up of 11.1 (10.4-11.8) years, incident coronary artery disease risk varied across subtypes, with combined hyperlipidemia exhibiting the largest hazard (hazard ratio, 1.92 [95% CI, 1.84-2.01]; P=2×10-16), even when accounting for non-HDL-C (hazard ratio, 1.45 [95% CI, 1.30-1.60]; P=2.6×10-12). Genome-wide association studies revealed 250 loci significantly associated with dyslipoproteinemia subtypes, of which 72 (28.8%) were not detected in prior single lipid trait genome-wide association studies. Mendelian lipid variant carriers were rare (2.0%) among individuals with dyslipoproteinemia, but polygenic heritability was high, ranging from 23% for remnant hypercholesterolemia to 54% for combined hyperlipidemia.

CONCLUSIONS

Simultaneous assessment of multiple lipid derangements revealed nuanced differences in coronary artery disease risk and genetic architectures across dyslipoproteinemia subtypes. These findings highlight the importance of looking beyond single lipid traits to better understand combined lipid and lipoprotein phenotypes and implications for disease risk.

Mendelian randomization analyses clarify the effects of height on cardiovascular diseases

An inverse correlation between stature and risk of coronary artery disease (CAD) has been observed in several epidemiologic studies, and recent Mendelian randomization (MR) experiments have suggested causal association. However, the extent to which the effect estimated by MR can be explained by cardiovascular, anthropometric, lung function, and lifestyle-related risk factors is unclear, with a recent report suggesting that lung function traits could fully explain the height-CAD effect. To clarify this relationship, we utilized a well-powered set of genetic instruments for human stature, comprising >1,800 genetic variants for height and CAD. In univariable analysis, we confirmed that a one standard deviation decrease in height (~6.5 cm) was associated with a 12.0% increase in the risk of CAD, consistent with previous reports. In multivariable analysis accounting for effects from up to 12 established risk factors, we observed a >3-fold attenuation in the causal effect of height on CAD susceptibility (3.7%, p = 0.02). However, multivariable analyses demonstrated independent effects of height on other cardiovascular traits beyond CAD, consistent with epidemiologic associations and univariable MR experiments. In contrast with published reports, we observed minimal effects of lung function traits on CAD risk in our analyses, indicating that these traits are unlikely to explain the residual association between height and CAD risk. In sum, these results suggest the impact of height on CAD risk beyond previously established cardiovascular risk factors is minimal and not explained by lung function measures.

Integrative regulation of hLMR1 by dietary and genetic factors in nonalcoholic fatty liver disease and hyperlipidemia

Long non-coding RNA (lncRNA) genes represent a large class of transcripts that are widely expressed across species. As most human lncRNAs are non-conserved, we recently employed a unique humanized liver mouse model to study lncRNAs expressed in human livers. We identified a human hepatocyte-specific lncRNA, hLMR1 (human lncRNA metabolic regulator 1), which is induced by feeding and promotes hepatic cholesterol synthesis. Recent genome-wide association studies (GWAS) found that several single-nucleotide polymorphisms (SNPs) from the hLMR1 gene locus are associated with blood lipids and markers of liver damage. These results suggest that dietary and genetic factors may regulate hLMR1 to affect disease progression. In this study, we first screened for nutritional/hormonal factors and found that hLMR1 was robustly induced by insulin/glucose in cultured human hepatocytes, and this induction is dependent on the transcription factor SREBP1. We then tested if GWAS SNPs genetically linked to hLMR1 could regulate hLMR1 expression. We found that DNA sequences flanking rs9653945, a SNP from the last exon of the hLMR1 gene, functions as an enhancer that can be robustly activated by SREBP1c depending on the presence of rs9653945 major allele (G). We further performed CRISPR base editing in human HepG2 cells and found that rs9653945 major (G) to minor (A) allele modification resulted in blunted insulin/glucose-induced expression of hLMR1. Finally, we performed genotyping and gene expression analyses using a published human NAFLD RNA-seq dataset and found that individuals homozygous for rs9653945-G have a higher expression of hLMR1 and risk of NAFLD. Taken together, our data support a model that rs9653945-G predisposes individuals to insulin/glucose-induced hLMR1, contributing to the development of hyperlipidemia and NAFLD.

Genome-wide association study of the common retinal disorder epiretinal membrane: Significant risk loci in each of three American populations

Epiretinal membrane (ERM) is a common retinal condition characterized by the presence of fibrocellular tissue on the retinal surface, often with visual distortion and loss of visual acuity. We studied European American (EUR), African American (AFR), and Latino (admixed American, AMR) ERM participants in the Million Veteran Program (MVP) for genome-wide association analysis-a total of 38,232 case individuals and 557,988 control individuals. We completed a genome-wide association study (GWAS) in each population separately, and then results were meta-analyzed. Genome-wide significant (GWS) associations were observed in all three populations studied: 31 risk loci in EUR subjects, 3 in AFR, and 2 in AMR, with 48 in trans-ancestry meta-analysis. Many results replicated in the FinnGen sample. Several GWS variants associate to alterations in gene expression in the macula. ERM showed significant genetic correlation to multiple traits. Pathway enrichment analyses implicated collagen and collagen-adjacent mechanisms, among others. This well-powered ERM GWAS identified novel genetic associations that point to biological mechanisms for ERM.

G protein-coupled receptor 146: new insights from genetics and model systems

PURPOSE OF REVIEW

Atherosclerotic cardiovascular diseases continue to be a significant global cause of death. Despite the availability of efficient treatments, there is an ongoing need for innovative strategies to lower lipid levels, especially for individuals experiencing refractory dyslipidemias or intolerable adverse effects. Based on human genetic findings and on mouse studies, the G protein-coupled receptor 146 (GPR146) emerges as a promising target against hypercholesterolemia and atherosclerosis. The present review aims at providing a thorough summary of the latest information acquired regarding GPR146, encompassing genetic evidence, functional insights, and its broader implications for cardiometabolic health.

RECENT FINDINGS

Human genetic studies uncovered associations between GPR146 variants, plasma lipid levels and metabolic parameters. Additionally, GPR146's influence extends beyond lipid regulation, impacting adipocyte differentiation, lipolysis, and inflammation pathways. Despite GPR146's orphan status, ongoing efforts to deorphanize it, suggest a potential ligand with downstream effects involving Gαi coupling.

SUMMARY

Here, we outline and deliberate on recent progress focused on: enhancing comprehension of the effects of inhibiting GPR146 in humans through genetic instruments, evaluating the extra-hepatic functions of GPR146, and discovering its natural ligand(s). Grasping these biological parameters and mechanisms is crucial in the exploration of GPR146 as a prospective therapeutic target.

Characterizing genetic profiles for high triglyceride levels in U.S. patients of African ancestry

Hypertriglyceridemia (HTG) is a common cardiovascular risk factor characterized by elevated triglyceride (TG) levels. Researchers have assessed the genetic factors that influence HTG in studies focused predominantly on individuals of European ancestry. However, relatively little is known about the contribution of genetic variation of HTG in people of African ancestry (AA), potentially constraining research and treatment opportunities. Our objective was to characterize genetic profiles among individuals of AA with mild-to-moderate HTG and severe HTG versus those with normal TGs by leveraging whole-genome sequencing data and longitudinal electronic health records available in the All of Us program. We compared the enrichment of functional variants within five canonical TG metabolism genes, an AA-specific polygenic risk score for TGs, and frequencies of 145 known potentially causal TG variants between HTG patients and normal TG among a cohort of AA patients (N = 15,373). Those with mild-to-moderate HTG (N = 342) and severe HTG (N ≤ 20) were more likely to carry APOA5 p.S19W (odds ratio = 1.94, 95% confidence interval = [1.48-2.54], P = 1.63 × 10-6 and OR = 3.65, 95% confidence interval: [1.22-10.93], P = 0.02, respectively) than those with normal TG. They were also more likely to have an elevated (top 10%) polygenic risk score, elevated carriage of potentially causal variant alleles, and carry any genetic risk factor. Alternative definitions of HTG yielded comparable results. In conclusion, individuals of AA with HTG were enriched for genetic risk factors compared to individuals with normal TGs.

A polygenic risk score for alcohol-associated cirrhosis among heavy drinkers with European ancestry

BACKGROUND

Polygenic Risk Scores (PRS) based on results from genome-wide association studies offer the prospect of risk stratification for many common and complex diseases. We developed a PRS for alcohol-associated cirrhosis by comparing single-nucleotide polymorphisms among patients with alcohol-associated cirrhosis (ALC) versus drinkers who did not have evidence of liver fibrosis/cirrhosis.

METHODS

Using a data-driven approach, a PRS for ALC was generated using a meta-genome-wide association study of ALC (N=4305) and an independent cohort of heavy drinkers with ALC and without significant liver disease (N=3037). It was validated in 2 additional independent cohorts from the UK Biobank with diagnosed ALC (N=467) and high-risk drinking controls (N=8981) and participants in the Indiana Biobank Liver cohort with alcohol-associated liver disease (N=121) and controls without liver disease (N=3239).

RESULTS

A 20-single-nucleotide polymorphisms PRS for ALC (PRSALC) was generated that stratified risk for ALC comparing the top and bottom deciles of PRS in the 2 validation cohorts (ORs: 2.83 [95% CI: 1.82 -4.39] in UK Biobank; 4.40 [1.56 -12.44] in Indiana Biobank Liver cohort). Furthermore, PRSALC improved the prediction of ALC risk when added to the models of clinically known predictors of ALC risk. It also stratified the risk for metabolic dysfunction -associated steatotic liver disease -cirrhosis (3.94 [2.23 -6.95]) in the Indiana Biobank Liver cohort -based exploratory analysis.

CONCLUSIONS

PRSALC incorporates 20 single-nucleotide polymorphisms, predicts increased risk for ALC, and improves risk stratification for ALC compared with the models that only include clinical risk factors. This new score has the potential for early detection of heavy drinking patients who are at high risk for ALC.

Association of thalassemia, hemoglobinopathies, and vitamin D levels with lipid profile in adults: Community-based research in southern Thai population

This study explored the frequency of lipid-lowering drug use in the thalassemia population and investigated the association of thalassemia, hemoglobinopathies, and serum 25(OH)D levels with lipid profile and red blood cell parameters. A combination of cross-sectional and community-based studies was conducted with 615 participants from the southern Thai population. Thalassemia and hemoglobinopathies were diagnosed using hemoglobin analysis and polymerase chain reaction-based methods to genotype globin genes. Biochemical parameters such as lipid profile, fasting blood sugar (FBS), and serum 25(OH)D levels were assessed using standard enzymatic methods and electrochemiluminescence immunoassays. Differences in the means of hematological and biochemical parameters between the thalassemia and non-thalassemia groups were compared and analyzed. A significantly lower frequency of lipid-lowering drug use was observed in the thalassemia group. Thalassemia, with clearly defined abnormalities in red blood cells, is associated with a 4.72-fold decreased risk of taking lipid-lowering drugs. Among thalassemia participants, the total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels were significantly lower than those in non-thalassemia participants. The prevalence of hypovitaminosis D in carriers of thalassemia and/or hemoglobinopathies in the southern Thai population was 53 % in females and 21 % in males. The highest lipid profile was observed in samples without thalassemia and hypovitaminosis D. The genetics of thalassemia and hemoglobinopathies with obviously abnormal red blood cells could explain the variable lipid levels, in addition to lipid metabolism-related genes and environmental factors. However, the effect of thalassemia on lipid levels in each population may differ according to its prevalence. A larger sample size is required to confirm this association, especially in countries with a high prevalence of thalassemia.

Clinical use of polygenic risk scores for detection of peripheral artery disease and cardiovascular events

We have previously shown that polygenic risk scores (PRS) can improve risk stratification of peripheral artery disease (PAD) in a large, retrospective cohort. Here, we evaluate the potential of PRS in improving the detection of PAD and prediction of major adverse cardiovascular and cerebrovascular events (MACCE) and adverse events (AE) in an institutional patient cohort. We created a cohort of 278 patients (52 cases and 226 controls) and fit a PAD-specific PRS based on the weighted sum of risk alleles. We built traditional clinical risk models and machine learning (ML) models using clinical and genetic variables to detect PAD, MACCE, and AE. The models' performances were measured using the area under the curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), and Brier score. We also evaluated the clinical utility of our PAD model using decision curve analysis (DCA). We found a modest, but not statistically significant improvement in the PAD detection model's performance with the inclusion of PRS from 0.902 (95% CI: 0.846-0.957) (clinical variables only) to 0.909 (95% CI: 0.856-0.961) (clinical variables with PRS). The PRS inclusion significantly improved risk re-classification of PAD with an NRI of 0.07 (95% CI: 0.002-0.137), p = 0.04. For our ML model predicting MACCE, the addition of PRS did not significantly improve the AUC, however, NRI analysis demonstrated significant improvement in risk re-classification (p = 2e-05). Decision curve analysis showed higher net benefit of our combined PRS-clinical model across all thresholds of PAD detection. Including PRS to a clinical PAD-risk model was associated with improvement in risk stratification and clinical utility, although we did not see a significant change in AUC. This result underscores the potential clinical utility of incorporating PRS data into clinical risk models for prevalent PAD and the need for use of evaluation metrics that can discern the clinical impact of using new biomarkers in smaller populations.

A Novel Mutation in LXRα Uncovers a Role for Cholesterol Sensing in Limiting Metabolic Dysfunction-Associated Steatohepatitis (MASH)

Liver x receptor alpha (LXRα, Nr1h3) functions as an important intracellular cholesterol sensor that regulates fat and cholesterol metabolism at the transcriptional level in response to the direct binding of cholesterol derivatives. We have generated mice with a mutation in LXRα that reduces activity in response to endogenous cholesterol derived LXR ligands while still allowing transcriptional activation by synthetic agonists. The mutant LXRα functions as a dominant negative that shuts down cholesterol sensing. When fed a high fat, high cholesterol diet LXRα mutant mice rapidly develop pathologies associated with Metabolic Dysfunction-Associated Steatohepatitis (MASH) including ballooning hepatocytes, liver inflammation, and fibrosis. Strikingly LXRα mutant mice have decreased liver triglycerides but increased liver cholesterol. Therefore, MASH-like phenotypes can arise in the absence of large increases in triglycerides. Reengaging LXR signaling by treatment with synthetic agonist reverses MASH suggesting that LXRα normally functions to impede the development of liver disease.

Characterizing genetic profiles for high triglyceride levels in U.S. patients of African ancestry

Hypertriglyceridemia (HTG) is a common cardiovascular risk factor characterized by elevated circulating triglyceride (TG) levels. Researchers have assessed the genetic factors that influence HTG in studies focused predominantly on individuals of European ancestry (EA). However, relatively little is known about the contribution of genetic variation to HTG in people of AA, potentially constraining research and treatment opportunities; the lipid profile for African ancestry (AA) populations differs from that of EA populations-which may be partially attributable to genetics. Our objective was to characterize genetic profiles among individuals of AA with mild-to-moderate HTG and severe HTG versus those with normal TGs by leveraging whole genome sequencing (WGS) data and longitudinal electronic health records (EHRs) available in the All of Us (AoU) program. We compared the enrichment of functional variants within five canonical TG metabolism genes, an AA-specific polygenic risk score for TGs, and frequencies of 145 known potentially causal TG variants between patients with HTG and normal TG among a cohort of AA patients (N=15,373). Those with mild-to-moderate HTG (N=342) and severe HTG (N≤20) were more likely to carry APOA5 p.S19W (OR=1.94, 95% CI [1.48-2.54], p=1.63×10 -6 and OR=3.65, 95% CI [1.22-10.93], p=0.02, respectively) than those with normal TG. They were also more likely to have an elevated (top 10%) PRS, elevated carriage of potentially causal variant alleles, and carry any genetic risk factor. Alternative definitions of HTG yielded comparable results. In conclusion, individuals of AA with HTG were enriched for genetic risk factors compared to individuals with normal TGs.

Cross-ancestry genetic architecture and prediction for cholesterol traits

While cholesterol is essential, a high level of cholesterol is associated with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have proven successful in identifying genetic variants that are linked to cholesterol levels, predominantly in white European populations. However, the extent to which genetic effects on cholesterol vary across different ancestries remains largely unexplored. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries. We find significant genetic heterogeneity between ancestries for cholesterol traits. Furthermore, we demonstrate that single nucleotide polymorphisms (SNPs) with concordant effects across ancestries for cholesterol are more frequently found in regulatory regions compared to other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog. These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings.

Liver regulatory mechanisms of noncoding variants at lipid and metabolic trait loci

Genome-wide association studies (GWASs) have identified hundreds of risk loci for liver disease and lipid-related metabolic traits, although identifying their target genes and molecular mechanisms remains challenging. We predicted target genes at GWAS signals by integrating them with molecular quantitative trait loci for liver gene expression (eQTL) and liver chromatin accessibility QTL (caQTL). We predicted specific regulatory caQTL variants at four GWAS signals located near EFHD1, LITAF, ZNF329, and GPR180. Using transcriptional reporter assays, we determined that caQTL variants rs13395911, rs11644920, rs34003091, and rs9556404 exhibit allelic differences in regulatory activity. We also performed a protein binding assay for rs13395911 and found that FOXA2 differentially interacts with the alleles of rs13395911. For variants rs13395911 and rs11644920 in putative enhancer regulatory elements, we used CRISPRi to demonstrate that repression of the enhancers altered the expression of the predicted target and/or nearby genes. Repression of the element at rs13395911 reduced the expression of EFHD1, and repression of the element at rs11644920 reduced the expression of LITAF, SNN, and TXNDC11. Finally, we showed that EFHD1 is a metabolically active gene in HepG2 cells. Together, these results provide key steps to connect genetic variants with cellular mechanisms and help elucidate the causes of liver disease.

Sex-dependent associations of plasma high-density lipoprotein cholesterol and mortality risk in healthy older men and women: two prospective cohort studies

The relationship between high plasma high-density lipoprotein cholesterol (HDL-C) and cause and mortality are not well established in healthy older people. This study examined the associations between HDL-C levels and mortality in initially healthy older men and women. This analysis included participants from the Aspirin in Reducing Events in the Elderly (ASPREE; n=18,668) trial and a matched cohort from the UK Biobank (UKB; n=62,849 ≥65 years). Cox regression was used to examine hazard ratios between HDL-C categories <1.03 mmol/L, 1.03-1.55 mmol/L (referent category), 1.55-2.07 mmol/L, and >2.07 mmol/L and all-cause, cancer, cardiovascular disease (CVD), and "non-cancer non-CVD" mortality. Genetic contributions were assessed using a polygenic score for HDL-C. Among ASPREE participants (aged 75±5 years), 1836 deaths occurred over a mean follow-up of 6.3±1.8 years. In men, the highest category of HDL-C levels was associated with increased risk of all-cause (HR 1.60, 95% CI 1.26-2.03), cancer (HR 1.37, 95% CI 0.96-2.00), and "non-cancer non-CVD" mortality (HR 2.35, 95% CI 1.41-3.42) but not CVD mortality (HR 1.08, 95% CI 0.60-1.94). The associations were replicated among UKB participants (aged 66.9±1.5 years), including 8739 deaths over a mean follow-up of 12.7±0.8 years. There was a non-linear association between HDL-C levels and all-cause and cause-specific mortality. The association between HDL-C levels and mortality was unrelated to variations in the HDL-C polygenic score. No significant association was found between HDL-C levels and mortality in women. Higher HDL-C levels are associated with increased risk from cancer and "non-cancer non-CVD" mortality in healthy older men but no such relationship was observed in women.

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Novel functions of Tribbles-homolog 1 in liver, adipocytes and atherosclerosis

PURPOSE OF REVIEW

Human genetics studies have sparked great interest in the pseudokinase Tribbles homolog 1, as variant at the TRIB1 gene locus were robustly linked to several cardiometabolic traits, including plasma lipids and coronary artery disease. In this review, we summarize recent findings from mouse models that investigated the function of hepatic and adipocyte Trib1 in lipid metabolism and its role in atherosclerosis.

RECENT FINDINGS

Studies in atherosclerosis prone low-density lipoprotein (LDL)-receptor knockout mice suggested that systemic Trib1 -deficiency promotes atherosclerotic lesion formation through the modulation of plasma lipids and inflammation. Further, investigations in mice with hepatocyte specific deletion of Trib1 identified a novel role in the catabolism of apoB-containing lipoproteins via regulation of the LDL-receptor. Moreover, recent studies on Trib1 in adipocytes uncovered critical functions in adipose tissue biology, including the regulation of plasma lipid and adiponectin levels and the response to β3-adrenergic receptor activation.

SUMMARY

Functional studies in mice have expanded our understanding of how Trib1 contributes to various aspects of cardiometabolic diseases. They support the notion that Trib1 exerts tissue-specific effects, which can result in opposing effects on cardiometabolic traits. Additional studies are required to fully elucidate the molecular mechanisms underlying the cellular and systemic effects of Trib1 .

A gut-derived hormone regulates cholesterol metabolism

The reciprocal coordination between cholesterol absorption in the intestine and de novo cholesterol synthesis in the liver is essential for maintaining cholesterol homeostasis, yet the mechanisms governing the opposing regulation of these processes remain poorly understood. Here, we identify a hormone, Cholesin, which is capable of inhibiting cholesterol synthesis in the liver, leading to a reduction in circulating cholesterol levels. Cholesin is encoded by a gene with a previously unknown function (C7orf50 in humans; 3110082I17Rik in mice). It is secreted from the intestine in response to cholesterol absorption and binds to GPR146, an orphan G-protein-coupled receptor, exerting antagonistic downstream effects by inhibiting PKA signaling and thereby suppressing SREBP2-controlled cholesterol synthesis in the liver. Therefore, our results demonstrate that the Cholesin-GPR146 axis mediates the inhibitory effect of intestinal cholesterol absorption on hepatic cholesterol synthesis. This discovered hormone, Cholesin, holds promise as an effective agent in combating hypercholesterolemia and atherosclerosis.

The genetic architecture of multimodal human brain age

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Genotypes of the UCP1 gene polymorphisms and cardiometabolic diseases: A multifactorial study of association with disease probability

Cardiometabolic diseases (CMDs) are complex disorders with a heterogenous phenotype, which are caused by multiple factors including genetic factors. Single nucleotide polymorphisms (SNPs) rs45539933 (p.Ala64Thr), rs10011540 (c.-112A>C), rs3811791 (c.-1766A>G), and rs1800592 (c.-3826A>G) in the UCP1 gene have been analyzed for association with CMDs in many studies providing controversial results. However, previous studies only considered individual UCP1 SNPs and did not evaluate them in an integrated manner, which is a more powerful approach to uncover genetic component of complex diseases. This study aimed to investigate associations between UCP1 genotype combinations and CMDs or CMD risk factors in the context of non-genetic factors. We performed multiple logistic regression analysis and proposed new methodology of testing different combinations of SNP genotypes. We found that probability of CMDs increased in presence of the three-SNP combination of genotypes with minor alleles of c.-3826A>G and p.Ala64Thr and wild allele of c.-112A>C, with increasing age, body mass index (BMI), body fat percentage (BF%) and may differ between sexes and between countries. The combination of genotypes with c.-3826A>G minor allele and wild homozygotes of c.-112A>C and p.Ala64Thr was associated with increased probability of diabetes. While combination of genotypes with minor alleles of all three SNPs reduced the CMD probability. The present results suggest that age, BMI, sex, and UCP1 three-SNP combinations of genotypes significantly contribute to CMD probability. Varying of c.-112A>C alleles in the genotype combination with minor alleles of c.-3826A>G and p.Ala64Thr markedly changes CMD probability.

Age effect on the shared etiology of glycemic traits and serum lipids: evidence from a Chinese twin study

PURPOSE

Diabetes and dyslipidemia are among the most common chronic diseases with increasing global disease burdens, and they frequently occur together. The study aimed to investigate differences in the heritability of glycemic traits and serum lipid indicators and differences in overlapping genetic and environmental influences between them across age groups.

METHODS

This study included 1189 twin pairs from the Chinese National Twin Registry and divided them into three groups: aged ≤ 40, 41-50, and > 50 years old. Univariate and bivariate structural equation models (SEMs) were conducted on glycemic indicators and serum lipid indicators, including blood glucose (GLU), glycated hemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C), in the total sample and three age groups.

RESULTS

All phenotypes showed moderate to high heritability (0.37-0.64). The heritability of HbA1c demonstrated a downward trend with age (HbA1c: 0.50-0.79), while others remained relatively stable (GLU: 0.55-0.62, TC: 0.58-0.66, TG: 0.50-0.63, LDL-C: 0.24-0.58, HDL-C: 0.31-0.57). The bivariate SEMs demonstrated that GLU and HbA1c were correlated with each serum lipid indicator (0.10-0.17), except HDL-C. Except for HbA1c and LDL-C, as well as HbA1c and HDL-C, differences in genetic correlations underlying glycemic traits and serum lipids between age groups were observed, with the youngest group showing a significantly higher genetic correlation than the oldest group.

CONCLUSION

Across the whole adulthood, genetic influences were consistently important for GLU, TC, TG, LDL-C and HDL-C, and age may affect the shared genetic influences between glycemic traits and serum lipids. Further studies are needed to elucidate the role of age in the interactions of genes related to glycemic traits and serum lipids.

Investigating the causal relationship between human blood/urine metabolites and periodontal disease using two-sample Mendelian randomization

Background and Aims

The aim is to investigate the cause-and-effect connection between metabolites found in blood/urine and the likelihood of developing periodontal disease (PD) through the utilization of a two-sample Mendelian randomization (MR) method.

Methods

Using an inverse variance weighted (IVW) method and two additional two-sample MR models, we examined the relationship between blood/urine metabolites and PD by analyzing data from a comprehensive metabolome-based genome-wide association study and the Genome-Wide Association Studies (GWAS) of PD. To assess the consistency and dependability of the findings, diversity, cross-effects, and sensitivity analyses were conducted.

Results

Out of the 35 metabolites found in blood and urine, a total of eight metabolites (C-reactive protein, Potassium in urine, Urea, Cystatin C, Non-albumin protein, Creatinine, estimated Glomerular Filtration Rate, and Phosphate) displayed a possible causal connection with the risk of dental caries/PD using the inverse variance weighted (IVW) method (p < 0.05). This includes five metabolites in the blood and three in the urine. No metabolites were statistically significant in IVW MR models (p < 3.68 × 10- 4). Even after conducting sensitivity analysis with the leave-one-out method and removing the confounding instrumental variables, the impact of these factors on dental caries/PD remained significant.

Conclusion

Based on the available evidence, it is not possible to establish a significant causal link between the 35 blood metabolites and the likelihood of developing dental caries and PD.

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.

Translational bioinformatics approach to combat cardiovascular disease and cancers

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

A method to estimate the contribution of rare coding variants to complex trait heritability

It has been postulated that rare coding variants (RVs; MAF < 0.01) contribute to the "missing" heritability of complex traits. We developed a framework, the Rare variant heritability (RARity) estimator, to assess RV heritability (h2RV) without assuming a particular genetic architecture. We applied RARity to 31 complex traits in the UK Biobank (n = 167,348) and showed that gene-level RV aggregation suffers from 79% (95% CI: 68-93%) loss of h2RV. Using unaggregated variants, 27 traits had h2RV > 5%, with height having the highest h2RV at 21.9% (95% CI: 19.0-24.8%). The total heritability, including common and rare variants, recovered pedigree-based estimates for 11 traits. RARity can estimate gene-level h2RV, enabling the assessment of gene-level characteristics and revealing 11, previously unreported, gene-phenotype relationships. Finally, we demonstrated that in silico pathogenicity prediction (variant-level) and gene-level annotations do not generally enrich for RVs that over-contribute to complex trait variance, and thus, innovative methods are needed to predict RV functionality.

Phenome-wide association study of ovarian cancer identifies common comorbidities and reveals shared genetics with complex diseases and biomarkers

BACKGROUND

Ovarian cancer (OC) is commonly diagnosed among older women who have comorbidities. This hypothesis-free phenome-wide association study (PheWAS) aimed to identify comorbidities associated with OC, as well as traits that share a genetic architecture with OC.

METHODS

We used data from 181,203 white British female UK Biobank participants and analysed OC and OC subtype-specific genetic risk scores (OC-GRS) for an association with 889 diseases and 43 other traits. We conducted PheWAS and colocalization analyses for individual variants to identify evidence for shared genetic architecture.

RESULTS

The OC-GRS was associated with 10 diseases, and the clear cell OC-GRS was associated with five diseases at the FDR threshold (p = 5.6 × 10-4 ). Mendelian randomizaiton analysis (MR) provided robust evidence for the association of OC with higher risk of "secondary malignant neoplasm of digestive systems" (OR 1.64, 95% CI 1.33, 2.02), "ascites" (1.48, 95% CI 1.17, 1.86), "chronic airway obstruction" (1.17, 95% CI 1.07, 1.29), and "abnormal findings on examination of the lung" (1.51, 95% CI 1.22, 1.87). Analyses of lung spirometry measures provided further support for compromised respiratory function. PheWAS on individual OC variants identified five genetic variants associated with other diseases, and seven variants associated with biomarkers (all, p ≤ 4.5 × 10-8 ). Colocalization analysis identified rs4449583 (from TERT locus) as the shared causal variant for OC and seborrheic keratosis.

CONCLUSIONS

OC is associated with digestive and respiratory comorbidities. Several variants affecting OC risk were associated with other diseases and biomarkers, with this study identifying a novel genetic locus shared between OC and skin conditions.

GWAS of lipids in Greenlanders finds association signals shared with Europeans and reveals an independent PCSK9 association signal

Perturbation of lipid homoeostasis is a major risk factor for cardiovascular disease (CVD), the leading cause of death worldwide. We aimed to identify genetic variants affecting lipid levels, and thereby risk of CVD, in Greenlanders. Genome-wide association studies (GWAS) of six blood lipids, triglycerides, LDL-cholesterol, HDL-cholesterol, total cholesterol, as well as apolipoproteins A1 and B, were performed in up to 4473 Greenlanders. For genome-wide significant variants, we also tested for associations with additional traits, including CVD events. We identified 11 genome-wide significant loci associated with lipid traits. Most of these loci were already known in Europeans, however, we found a potential causal variant near PCSK9 (rs12117661), which was independent of the known PCSK9 loss-of-function variant (rs11491147). rs12117661 was associated with lower LDL-cholesterol (βSD(SE) = -0.22 (0.03), p = 6.5 × 10-12) and total cholesterol (-0.17 (0.03), p = 1.1 × 10-8) in the Greenlandic study population. Similar associations were observed in Europeans from the UK Biobank, where the variant was also associated with a lower risk of CVD outcomes. Moreover, rs12117661 was a top eQTL for PCSK9 across tissues in European data from the GTEx portal, and was located in a predicted regulatory element, supporting a possible causal impact on PCSK9 expression. Combined, the 11 GWAS signals explained up to 16.3% of the variance of the lipid traits. This suggests that the genetic architecture of lipid levels in Greenlanders is different from Europeans, with fewer variants explaining the variance.