The impact of low-frequency and rare variants on lipid levels

Team players in the pathogenesis of metabolic dysfunctions-associated steatotic liver disease: The basis of development of pharmacotherapy

Nutrient metabolism is regulated by several factors. Social determinants of health with or without genetics are the primary regulator of metabolism, and an unhealthy lifestyle affects all modulators and mediators, leading to the adaptation and finally to the exhaustion of cellular functions. Hepatic steatosis is defined by presence of fat in more than 5% of hepatocytes. In hepatocytes, fat is stored as triglycerides in lipid droplet. Hepatic steatosis results from a combination of multiple intracellular processes. In a healthy individual nutrient metabolism is regulated at several steps. It ranges from the selection of nutrients in a grocery store to the last step of consumption of ATP as an energy or as a building block of a cell as structural component. Several hormones, peptides, and genes have been described that participate in nutrient metabolism. Several enzymes participate in each nutrient metabolism as described above from ingestion to generation of ATP. As of now several publications have revealed very intricate regulation of nutrient metabolism, where most of the regulatory factors are tied to each other bidirectionally, making it difficult to comprehend chronological sequence of events. Insulin hormone is the primary regulator of all nutrients’ metabolism both in prandial and fasting states. Insulin exerts its effects directly and indirectly on enzymes involved in the three main cellular function processes; metabolic, inflammation and repair, and cell growth and regeneration. Final regulators that control the enzymatic functions through stimulation or suppression of a cell are nuclear receptors in especially farnesoid X receptor and peroxisome proliferator-activated receptor/RXR ligands, adiponectin, leptin, and adiponutrin. Insulin hormone has direct effect on these final modulators. Whereas blood glucose level, serum lipids, incretin hormones, bile acids in conjunction with microbiota are intermediary modulators which are controlled by lifestyle. The purpose of this review is to overview the key players in the pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD) that help us understand the disease natural course, risk stratification, role of lifestyle and pharmacotherapy in each individual patient with MASLD to achieve personalized care and target the practice of precision medicine. PubMed and Google Scholar databases were used to identify publication related to metabolism of carbohydrate and fat in states of health and disease states; MASLD, cardiovascular disease and cancer. More than 1000 publications including original research and review papers were reviewed.

Serum proteome signatures associated with liver steatosis in adolescents with obesity

PURPOSE

Childhood obesity, a pressing global health issue, significantly increases the risk of metabolic complications, including metabolic dysfunction associated with steatotic liver disease (MASLD). Accurate non-invasive tests for early detection and screening of steatosis are crucial. In this study, we explored the serum proteome, identifying proteins as potential biomarkers for inclusion in non-invasive steatosis diagnosis tests.

METHODS

Fifty-nine obese adolescents underwent ultrasonography to assess steatosis. Serum samples were collected and analyzed by targeted proteomics with the Proximity Extension Assay technology. Clinical and biochemical parameters were evaluated, and correlations among them, the individuated markers, and steatosis were performed. Receiver operating characteristic (ROC) curves were used to determine the steatosis diagnostic performance of the identified candidates, the fatty liver index (FLI), and their combination in a logistic regression model.

RESULTS

Significant differences were observed between subjects with and without steatosis in various clinical and biochemical parameters. Gender-related differences in the serum proteome were also noted. Five circulating proteins, including Cathepsin O (CTSO), Cadherin 2 (CDH2), and Prolyl endopeptidase (FAP), were identified as biomarkers for steatosis. CDH2, CTSO, Leukocyte Immunoglobulin Like Receptor A5 (LILRA5), BMI, waist circumference, HOMA-IR, and FLI, among others, significantly correlated with the steatosis degree. CDH2, FAP, and LDL combined in a logit model achieved a diagnostic performance with an AUC of 0.91 (95% CI 0.75-0.97, 100% sensitivity, 84% specificity).

CONCLUSIONS

CDH2 and FAP combined with other clinical parameters, represent useful tools for accurate diagnosis of fatty liver, emphasizing the importance of integrating novel markers into diagnostic algorithms for MASLD.

Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.

Apolipoprotein O modulates cholesterol metabolism via NRF2/CYB5R3 independent of LDL receptor

Apolipoprotein O (APOO) plays a critical intracellular role in regulating lipid metabolism. Here, we investigated the roles of APOO in metabolism and atherogenesis in mice. Hepatic APOO expression was increased in response to hyperlipidemia but was inhibited after simvastatin treatment. Using a novel APOO global knockout (Apoo-/-) model, it was found that APOO depletion aggravated diet-induced obesity and elevated plasma cholesterol levels. Upon crossing with low-density lipoprotein receptor (LDLR) and apolipoprotein E (APOE) knockout hyperlipidemic mouse models, Apoo-/- Apoe-/- and Apoo-/- Ldlr-/- mice exhibited elevated plasma cholesterol levels, with more severe atherosclerotic lesions than littermate controls. This indicated the effects of APOO on cholesterol metabolism independent of LDLR and APOE. Moreover, APOO deficiency reduced cholesterol excretion through bile and feces while decreasing phospholipid unsaturation by inhibiting NRF2 and CYB5R3. Restoration of CYB5R3 expression in vivo by adeno-associated virus (AAV) injection reversed the reduced degree of phospholipid unsaturation while decreasing blood cholesterol levels. This represents the first in vivo experimental validation of the role of APOO in plasma cholesterol metabolism independent of LDLR and elucidates a previously unrecognized cholesterol metabolism pathway involving NRF2/CYB5R3. APOO may be a metabolic regulator of total-body cholesterol homeostasis and a target for atherosclerosis management. Apolipoprotein O (APOO) regulates plasma cholesterol levels and atherosclerosis through a pathway involving CYB5R3 that regulates biliary and fecal cholesterol excretion, independently of the LDL receptor. In addition, down-regulation of APOO may lead to impaired mitochondrial function, which in turn aggravates diet-induced obesity and fat accumulation.

Metabolic dysfunction-associated steatotic liver disease heterogeneity: Need of subtyping

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a widespread global disease with significant health burden. Unhealthy lifestyle, obesity, diabetes mellitus (DM), insulin resistance, and genetics have been implicated in the pathogenesis of MASLD. A significant degree of heterogeneity exists among each of above-mentioned risk factors. Heterogeneity of these risk factors translates into the heterogeneity of MASLD. On the other hand, MASLD can itself lead to insulin resistance and DM. Such heterogeneity makes it difficult to assess the natural course of an individual with MASLD in clinical practice. At present MASLD is considered as one disease despite the variability of etiopathogenic processes, and we lack the consensus definitions of unique subtypes of MASLD. In this review, pathogenic processes of MASLD are discussed and a need of subtyping is recommended.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Genetically predicted gut microbiota mediate the association between plasma lipidomics and primary sclerosing cholangitis

BACKGROUND

Primary sclerosing cholangitis (PSC) is a complex disease with pathogenic mechanisms that remain to be elucidated. Previous observational studies with small sample sizes have reported associations between PSC, dyslipidemia, and gut microbiota dysbiosis. However, the causality of these associations is uncertain, and there has been no systematic analysis to date.

METHODS

The datasets comprise data on PSC, 179 lipid species, and 412 gut microbiota species. PSC data (n = 14,890) were sourced from the International PSC Study Group, while the dataset pertaining to plasma lipidomics originated from a study involving 7174 Finnish individuals. Data on gut microbiota species were derived from the Dutch Microbiome Project study, which conducted a genome-wide association study involving 7738 participants. Furthermore, we employed a two-step Mendelian randomization (MR) analysis to quantify the proportion of the effect of gut microbiota-mediated lipidomics on PSC.

RESULTS

Following a rigorous screening process, our MR analysis revealed a causal relationship between higher levels of gene-predicted Phosphatidylcholine (O-16:1_18:1) (PC O-16:1_18:1) and an increased risk of developing PSC (inverse variance-weighted method, odds ratio (OR) 1.30, 95% confidence interval (CI) 1.03-1.63). There is insufficient evidence to suggest that gene-predicted PSC impacts the levels of PC O-16:1_18:1 (OR 1.01, 95% CI 0.98-1.05). When incorporating gut microbiota data into the analysis, we found that Eubacterium rectale-mediated genetic prediction explains 17.59% of the variance in PC O-16:1_18:1 levels.

CONCLUSION

Our study revealed a causal association between PC O-16:1_18:1 levels and PSC, with a minor portion of the effect mediated by Eubacterium rectale. This study aims to further explore the pathogenesis of PSC and identify promising therapeutic targets. For patients with PSC who lack effective treatment options, the results are encouraging.

Design of a Zn-based nanozyme injectable multifunctional hydrogel with ROS scavenging activity for myocardial infarction therapy

The existing strategies for myocardial infarction therapy mainly focus on reinstating myocardial blood supply, often disregarding the intrinsic and intricate microenvironment created by elevated levels of reactive oxygen species (ROS) that accompanies myocardial infarction. This microenvironment entails cardiomyocytes apoptosis, substantial vascular cell death, excessive inflammatory infiltration and fibrosis. In such situation, the present study introduces a zinc-based nanozyme injectable multifunctional hydrogel, crafted from ZIF-8, to counteract ROS effects after myocardial infarction. The hydrogel exhibits both superoxide dismutase (SOD)-like and catalase (CAT)-like enzymatic activities, proficiently eliminating surplus ROS in the infarcted region and interrupting ROS-driven inflammatory cascades. Furthermore, the hydrogel's exceptional immunomodulatory ability spurs a notable transformation of macrophages into the M2 phenotype, effectively neutralizing inflammatory factors and indirectly fostering vascularization in the infarcted region. For high ROS and demanding for zinc of the infarcted microenvironment, the gradual release of zinc ions as the hydrogel degrades further enhances the bioactive and catalytic performance of the nanozymes, synergistically promoting cardiac function post myocardial infarction. In conclusion, this system of deploying catalytic nanomaterials within bioactive matrices for ROS-related ailment therapy not only establishes a robust foundation for biomedical material development, but also promises a holistic approach towards addressing myocardial infarction complexities. STATEMENT OF SIGNIFICANCE: Myocardial infarction remains the leading cause of death worldwide. However, the existing strategies for myocardial infarction therapy mainly focus on reinstating myocardial blood supply. These therapies often ignore the intrinsic and intricate microenvironment created by elevated levels of reactive oxygen species (ROS). Hence, we designed an injectable Zn-Based nanozyme hydrogel with ROS scavenging activity for myocardial infarction therapy. ALG-(ZIF-8) can significantly reduce ROS in the infarcted area and alleviate the ensuing pathological process. ALG-(ZIF-8) gradually releases zinc ions to participate in the repair process and improves cardiac function. Overall, this multifunctional hydrogel equipped with ZIF-8 makes full use of the characteristics of clearing ROS and slowly releasing zinc ions, and we are the first to test the therapeutic efficacy of Zinc-MOFs crosslinked-alginate hydrogel for myocardial infarction.

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.

Increased Expression of LIPC Is Associated With the Clinicopathological Features and Development of Head and Neck Squamous Cell Carcinoma

Introduction Lipase C hepatic type (LIPC) is a member of the lipase family and plays a role in tumor development. However, its specific role in head and neck squamous cell carcinoma (HNSCC) is not well understood. Objective This study aims to investigate LIPC gene expression in HNSCC and elucidate its potential role in the context of the disease. Methods LIPC expression was analyzed using the Cancer Genome Atlas-HNSCC (TCGA-HNSCC) dataset. Real-time polymerase chain reaction (qPCR) was used to validate LIPC expression in oral squamous cell carcinoma (OSCC) tissue samples, which is the most common type of HNSCC. The LIPC was assessed to find out if there is a link with HNSCC clinicopathological features, prognosis, and tumor infiltration. Functional pathways associated with the LIPC network were also examined. Results LIPC expression was found to be elevated in both HNSCC and OSCC tissues. The heightened expression of LIPC correlated with various clinicopathological features and influenced the prognosis of HNSCC patients. The LIPC gene demonstrated connections with several oncogenic genes and proteins, participating in lipid catabolic processes and other pathways. These findings suggest that LIPC expression may play a role in the pathogenesis of HNSCC. Conclusion Our study affirms that LIPC expression is linked to the development of HNSCC, suggesting its potential utility as a biomarker or therapeutic target for the disease. However, further functional studies are imperative to validate and expand upon these findings.

Novel Finnish-enriched variants causing severe hypercholesterolemia and their clinical impact on coronary artery disease

BACKGROUND AND AIMS

Severe hypercholesterolemia (LDL-cholesterol ≥ 5 mmol/l) is a major risk factor for coronary artery disease (CAD). The etiology incudes both genetic and nongenetic factors, but persons carrying mutations in known hypercholesterolemia-associated genes are at significantly higher CAD risk than non-carriers. Yet, a significant proportion of mutation carriers remains undetected while the assessment of genetic candidate variants in clinical practice is challenging.

METHODS

To address these challenges, we set out to test the utility of a practical approach to leverage data from a large reference cohort, the FinnGen Study encompassing 356,082 persons with extensive longitudinal health record information, to aid the clinical evaluation of single genetic candidate genes variants detected by exome sequence analysis in a target population of 351 persons with severe hypercholesterolemia.

RESULTS

We identified 23 rare missense mutations in known hypercholesterolemia genes, 3 of which were previously described mutations (LDLR Pro309Lysfs, LDLR Arg595Gln and APOB Arg3527Gln). Subsequent in silico and clinical assessment of the remaining 20 variants pinpointed two likely hypercholesterolemia-associated variants in LDLR (Arg574Leu and Glu626Lys) and one in LDLRAP1 (Arg151Trp). Heterozygous carriers of the novel LDLR and LDLRAP1 variants received statin treatment more often than non-carriers (OR 2.1, p = 1.8e-6 and OR 1.4, p = 0.001) and untreated carriers had higher risk for ischemic heart disease (OR 2.0, p = 0.03 and OR 1.8, p = 0.008).

CONCLUSIONS

Our data elucidate the wide spectrum of genetic variants impacting hypercholesterolemia and demonstrate the utility of a large reference population to assess the heterogeneous impact of candidate gene variants on cardiovascular disease risk.

Identification of genomic-wide genetic links between cutaneous melanoma and obesity-related physical traits via cFDR

BACKGROUND

Both epidemiological and clinical studies have suggested the comorbidity between cutaneous melanoma (CM) and obesity-related physical traits. However, it remains unclear about their shared genetic architecture.

OBJECTIVE

To determine the shared genetic architecture between CM and obesity-related physical traits through conditional false discovery rate (cFDR) analysis.

METHOD

Quantile-quantile plots were firstly built to assess the pleiotropic enrichment of shared single nucleotide polymorphisms between CM and each trait. Then, cFDR and conjunctional cFDR (ccFDR) were used to identify the shared risk loci between CM and each trait. Moreover, the functional evaluation of shared risk genes was carried out through analyses of expression quantitative trait loci (eQTL), Kyoto Encyclopedia of Genes and Genomes and gene ontology, respectively. Finally, single-cell sequence analysis was performed to locate the expression of eQTL-mapped genes in tissues.

RESULTS

Successive pleiotropic enrichment was found between CM and 5 obesity-related traits or height. 24 shared risk loci were identified between CM and 13 traits except appendicular lean mass using ccFDR analysis, with 17 novel and 4 validated loci. The functions of ccFDR-identified and eQTL-mapped genes were revealed to be mainly involved in cellular senescence, proliferation, meiotic nuclear division, cell cycle, and the metabolism of lipid, cholesterol and glucose. Single-cell sequence analysis showed that keratinocytes contribute to the occurrence and aggressiveness of CM through secreting paracrine cytokines.

CONCLUSION

Our findings demonstrate the significant genetic correlation between CM and obesity-related physical traits, which may provide a novel genetical basis for the pathogenesis and treatment of CM.

Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review

Rapid advancements in high-throughput biological techniques have facilitated the generation of high-dimensional omics datasets, which have provided a solid foundation for precision medicine and prognosis prediction. Nonetheless, the problem of missing heritability persists. To solve this problem, it is essential to explain the genetic structure of disease incidence risk and prognosis by incorporating interactions. The development of the Bayesian theory has provided new approaches for developing models for interaction identification and estimation. Several Bayesian models have been developed to improve the accuracy of model and identify the main effect, gene-environment (G×E) and gene-gene (G×G) interactions. Studies based on single-nucleotide polymorphisms (SNPs) are significant for the exploration of rare and common variants. Models based on the effect heredity principle and group-based models are relatively flexible and do not require strict constraints when dealing with the hierarchical structure between the main effect and interactions (M-I). These models have a good interpretability of biological mechanisms. Machine learning-based Bayesian approaches are highly competitive in improving prediction accuracy. These models provide insights into the mechanisms underlying the occurrence and progression of complex diseases, identify more reliable biomarkers, and develop higher predictive accuracy. In this paper, we provide a comprehensive review of these Bayesian approaches.

Genome-Wide Assessment of Shared Genetic Architecture Between Rheumatoid Arthritis and Cardiovascular Diseases

Background Patients with rheumatoid arthritis (RA) have a 2- to 10-fold increased risk of cardiovascular disease (CVD), but the biological mechanisms and existence of causality underlying such associations remain to be investigated. We aimed to investigate the genetic associations and underlying mechanisms between RA and CVD by leveraging large-scale genomic data and genetic cross-trait analytic approaches. Methods and Results Within UK Biobank data, we examined the genetic correlation, shared genetics, and potential causality between RA (Ncases=6754, Ncontrols=452 384) and cardiovascular diseases (CVD, Ncases=44 238, Ncontrols=414 900) using linkage disequilibrium score regression, cross-trait meta-analysis, and Mendelian randomization. We observed significant genetic correlations of RA with myocardial infarction (rg:0.40 [95% CI, 0.24-0.56), angina (rg:0.42 [95% CI, 0.28-0.56]), coronary heart diseases (rg:0.41 [95% CI, 0.27-0.55]), and CVD (rg:0.43 [95% CI, 0.29-0.57]) after correcting for multiple testing (P<0.05/5). When stratified by frequent use of analgesics, we found increased genetic correlation between RA and CVD among participants without aspirin usage (rg:0.54 [95% CI, 0.30-0.78] for angina; Pvalue=6.69×10-6) and among participants with paracetamol usage (rg:0.75 [95% CI, 0.20-1.29] for myocardial infarction; Pvalue=8.90×10-3), whereas others remained similar. Cross-trait meta-analysis identified 9 independent shared loci between RA and CVD, including PTPN22 at chr1p13.2, BCL2L11 at chr2q13, and CCR3 at chr3p21.31 (Psingle trait<1×10-3 and Pmeta<5×10-8), highlighting potential shared pathogenesis including accelerating atherosclerosis, upregulating oxidative stress, and vascular permeability. Finally, Mendelian randomization estimates showed limited evidence of causality between RA and CVD. Conclusions Our results supported shared genetic pathogenesis rather than causality in explaining the observed association between RA and CVD. The identified shared genetic factors provided insights into potential novel therapeutic target for RA-CVD comorbidities.

Genome-wide association analysis of plasma lipidome identifies 495 genetic associations

The human plasma lipidome captures risk for cardiometabolic diseases. To discover new lipid-associated variants and understand the link between lipid species and cardiometabolic disorders, we perform univariate and multivariate genome-wide analyses of 179 lipid species in 7174 Finnish individuals. We fine-map the associated loci, prioritize genes, and examine their disease links in 377,277 FinnGen participants. We identify 495 genome-trait associations in 56 genetic loci including 8 novel loci, with a considerable boost provided by the multivariate analysis. For 26 loci, fine-mapping identifies variants with a high causal probability, including 14 coding variants indicating likely causal genes. A phenome-wide analysis across 953 disease endpoints reveals disease associations for 40 lipid loci. For 11 coronary artery disease risk variants, we detect strong associations with lipid species. Our study demonstrates the power of multivariate genetic analysis in correlated lipidomics data and reveals genetic links between diseases and lipid species beyond the standard lipids.

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.

Apolipoprotein-CIII O-Glycosylation, a Link between GALNT2 and Plasma Lipids

Apolipoprotein-CIII (apo-CIII) is involved in triglyceride-rich lipoprotein metabolism and linked to beta-cell damage, insulin resistance, and cardiovascular disease. Apo-CIII exists in four main proteoforms: non-glycosylated (apo-CIII0a), and glycosylated apo-CIII with zero, one, or two sialic acids (apo-CIII0c, apo-CIII1 and apo-CIII2). Our objective is to determine how apo-CIII glycosylation affects lipid traits and type 2 diabetes prevalence, and to investigate the genetic basis of these relations with a genome-wide association study (GWAS) on apo-CIII glycosylation. We conducted GWAS on the four apo-CIII proteoforms in the DiaGene study in people with and without type 2 diabetes (n = 2318). We investigated the relations of the identified genetic loci and apo-CIII glycosylation with lipids and type 2 diabetes. The associations of the genetic variants with lipids were replicated in the Diabetes Care System (n = 5409). Rs4846913-A, in the GALNT2-gene, was associated with decreased apo-CIII0a. This variant was associated with increased high-density lipoprotein cholesterol and decreased triglycerides, while high apo-CIII0a was associated with raised high-density lipoprotein-cholesterol and triglycerides. Rs67086575-G, located in the IFT172-gene, was associated with decreased apo-CIII2 and with hypertriglyceridemia. In line, apo-CIII2 was associated with low triglycerides. On a genome-wide scale, we confirmed that the GALNT2-gene plays a major role i O-glycosylation of apolipoprotein-CIII, with subsequent associations with lipid parameters. We newly identified the IFT172/NRBP1 region, in the literature previously associated with hypertriglyceridemia, as involved in apolipoprotein-CIII sialylation and hypertriglyceridemia. These results link genomics, glycosylation, and lipid metabolism, and represent a key step towards unravelling the importance of O-glycosylation in health and disease.

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.

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. Large-scale whole genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess the 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 blood lipid levels (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 variations and rare protein coding variations 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 lncRNA, implicating new therapeutic opportunities.

An adaptive test based on principal components for detecting multiple phenotype associations using GWAS summary data

Extensive evidence from genome-wide association studies (GWAS) has shown that jointly analyzing multiple phenotypes can improve the power of the association test compared to the traditional single variant versus single trait approach. Here we propose an adaptive test based on principal components (ATPC) that is powerful and efficient for discovering the association between a single variant and multiple traits. Our method only needs GWAS summary statistics that are often available. We first estimate the trait correlation matrix by LD score regression. Then, based on the correlation matrix, we construct a series of test statistics that contain different numbers of principal components. The ultimate test statistic combines the P values of these principal component-based statistics by using the aggregated Cauchy association test. The analytical P-value of the test statistic can be computed quickly without the permutation process, which is the notable feature of our proposed method. The extensive simulation studies demonstrate that ATPC can control the type I error rates and have powerful and robust performance compared to several existing tests in a wide range of simulation settings. The analysis of the lipids GWAS summary data from the Global Lipids Genetics Consortium shows that ATPC identifies 230 new SNPs that are missed by the original single trait association analysis. By searching the GWAS Catalog, some SNPs and mapped genes identified by ATPC are reported to be associated with lipid traits. Through further analysis for GWAS results, we also find some Gene Ontology terms and biological pathways related to lipids.

Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods

Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30-45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidome-genotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research.

Genome-wide association study of varicose veins identifies a protective missense variant in GJD3 enriched in the Finnish population

Varicose veins is the most common manifestation of chronic venous disease that displays female-biased incidence. To identify protein-inactivating variants that could guide identification of drug target genes for varicose veins and genetic evidence for the disease prevalence difference between the sexes, we conducted a genome-wide association study of varicose veins in Finns using the FinnGen dataset with 17,027 cases and 190,028 controls. We identified 50 associated genetic loci (P < 5.0 × 10-8) of which 29 were novel including one near ERG with female-specificity (rs2836405-G, OR[95% CI] = 1.09[1.05-1.13], P = 3.1 × 10-8). These also include two X-chromosomal (ARHGAP6 and SRPX) and two autosomal novel loci (TGFB2 and GJD3) with protein-coding lead variants enriched above 56-fold in Finns over non-Finnish non-Estonian Europeans. A low-frequency missense variant in GJD3 (p.Pro59Thr) is exclusively associated with a lower risk for varicose veins (OR = 0.62 [0.55-0.70], P = 1.0 × 10-14) in a phenome-wide scan of the FinnGen data. The absence of observed pleiotropy and its membership of the connexin gene family underlines GJD3 as a potential connexin-modulating therapeutic strategy for varicose veins. Our results provide insights into varicose veins etiopathology and highlight the power of isolated populations, including Finns, to discover genetic variants that inform therapeutic development.

The genetic architecture of changes in adiposity during adulthood

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.

FinnGen provides genetic insights from a well-phenotyped isolated population

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored^1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10^-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

BACKGROUND

Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.

RESULTS

To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism.

CONCLUSIONS

Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants

The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR-Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.

Non-alcoholic fatty liver disease establishment and progression: genetics and epigenetics as relevant modulators of the pathology

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) results from metabolic dysfunctions that affect more than one-third of the world population. Over the last decades, scientific investigations have clarified many details on the pathology establishment and development; however, effective therapeutics approaches are still evasive. In addition, studies demonstrated that NAFLD establishment and progression are related to several etiologies. Recently, genetics and epigenetics backgrounds have emerged as relevant elements to the pathology onset, and, hence, deserve deep investigation to clarify molecular details on NAFLD signaling, which may be correlated with population behavior. Thus, to minimize the global problem, public health and public policies should take advantage of studies on NAFLD over the next following decades.

METHODS

In this context, we have performed a selective literature review focusing on biochemistry of lipid metabolism, genetics, epigenetics, and the ethnicity as strong elements that drive NAFLD establishment.

RESULTS

Considering the etiological agents that acts on NAFLD development and progression, the genetics and the epigenetics emerged as relevant factors. Genetics acts as a powerful element in the establishment and progression of the NAFLD. Over the last decades, details concerning genes and their polymorphisms, as well as epigenetics, have been considered relevant elements in the systems biology of diseases, and their effects on NAFLD should be considered in-depth, as well as the ethnicity, clarifying whether people are susceptible to liver diseases. Moreover, the endemicity and social problems of hepatic disfunction are far to be solved, which require a combined effort of various sectors of society.

CONCLUSION

Hence, the elements presented and discussed in this short review demonstrated their relevance to the physiological control of NAFLD, opening perspectives for research to develop new strategy to treat fatty liver diseases.

Examining the source of increased bipolar disorder and major depressive disorder common risk variation burden in multiplex schizophrenia families

Psychotic and affective disorders often aggregate in the relatives of probands with schizophrenia, and genetic studies show substantial genetic correlation among schizophrenia, bipolar disorder, and major depressive disorder. In this study, we examined the polygenic risk burden of bipolar disorder and major depressive disorder in 257 multiplex schizophrenia families (N = 1005) from the Irish Study of High-Density Multiplex Schizophrenia Families versus 2205 ancestry-matched controls. Our results indicate that members of multiplex schizophrenia families have an increased polygenic risk for bipolar disorder and major depressive disorder compared to population controls. However, this observation is largely attributable to the part of the genetic risk that bipolar disorder or major depressive disorder share with schizophrenia due to genetic correlation, rather than the affective portion of the genetic risk unique to them. These findings suggest that a complete interpretation of cross-disorder polygenic risks in multiplex families requires an assessment of the relative contribution of shared versus unique genetic factors to account for genetic correlations across psychiatric disorders.

Insight into genetic, biological, and environmental determinants of sexual-dimorphism in type 2 diabetes and glucose-related traits

There is growing evidence that sex and gender differences play an important role in risk and pathophysiology of type 2 diabetes (T2D). Men develop T2D earlier than women, even though there is more obesity in young women than men. This difference in T2D prevalence is attenuated after the menopause. However, not all women are equally protected against T2D before the menopause, and gestational diabetes represents an important risk factor for future T2D. Biological mechanisms underlying sex and gender differences on T2D physiopathology are not yet fully understood. Sex hormones affect behavior and biological changes, and can have implications on lifestyle; thus, both sex-specific environmental and biological risk factors interact within a complex network to explain the differences in T2D risk and physiopathology in men and women. In addition, lifetime hormone fluctuations and body changes due to reproductive factors are generally more dramatic in women than men (ovarian cycle, pregnancy, and menopause). Progress in genetic studies and rodent models have significantly advanced our understanding of the biological pathways involved in the physiopathology of T2D. However, evidence of the sex-specific effects on genetic factors involved in T2D is still limited, and this gap of knowledge is even more important when investigating sex-specific differences during the life course. In this narrative review, we will focus on the current state of knowledge on the sex-specific effects of genetic factors associated with T2D over a lifetime, as well as the biological effects of these different hormonal stages on T2D risk. We will also discuss how biological insights from rodent models complement the genetic insights into the sex-dimorphism effects on T2D. Finally, we will suggest future directions to cover the knowledge gaps.

Whole-exome sequencing identifies novel protein-altering variants associated with serum apolipoprotein and lipid concentrations

BACKGROUND

Dyslipidemia is a major risk factor for cardiovascular disease, and diabetes impacts the lipid metabolism through multiple pathways. In addition to the standard lipid measurements, apolipoprotein concentrations provide added awareness of the burden of circulating lipoproteins. While common genetic variants modestly affect the serum lipid concentrations, rare genetic mutations can cause monogenic forms of hypercholesterolemia and other genetic disorders of lipid metabolism. We aimed to identify low-frequency protein-altering variants (PAVs) affecting lipoprotein and lipid traits.

METHODS

We analyzed whole-exome (WES) and whole-genome sequencing (WGS) data of 481 and 474 individuals with type 1 diabetes, respectively. The phenotypic data consisted of 79 serum lipid and apolipoprotein phenotypes obtained with clinical laboratory measurements and nuclear magnetic resonance spectroscopy.

RESULTS

The single-variant analysis identified an association between the LIPC p.Thr405Met (rs113298164) and serum apolipoprotein A1 concentrations (p=7.8×10^-8). The burden of PAVs was significantly associated with lipid phenotypes in LIPC, RBM47, TRMT5, GTF3C5, MARCHF10, and RYR3 (p<2.9×10^-6). The RBM47 gene is required for apolipoprotein B post-translational modifications, and in our data, the association between RBM47 and apolipoprotein C-III concentrations was due to a rare 21 base pair p.Ala496-Ala502 deletion; in replication, the burden of rare deleterious variants in RBM47 was associated with lower triglyceride concentrations in WES of >170,000 individuals from multiple ancestries (p=0.0013). Two PAVs in GTF3C5 were highly enriched in the Finnish population and associated with cardiovascular phenotypes in the general population. In the previously known APOB gene, we identified novel associations at two protein-truncating variants resulting in lower serum non-HDL cholesterol (p=4.8×10^-4), apolipoprotein B (p=5.6×10^-4), and LDL cholesterol (p=9.5×10^-4) concentrations.

CONCLUSIONS

We identified lipid and apolipoprotein-associated variants in the previously known LIPC and APOB genes, as well as PAVs in GTF3C5 associated with LDLC, and in RBM47 associated with apolipoprotein C-III concentrations, implicated as an independent CVD risk factor. Identification of rare loss-of-function variants has previously revealed genes that can be targeted to prevent CVD, such as the LDL cholesterol-lowering loss-of-function variants in the PCSK9 gene. Thus, this study suggests novel putative therapeutic targets for the prevention of CVD.

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

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

The human ATP-binding cassette (ABC) transporter superfamily

The ATP-binding cassette (ABC) transporter superfamily comprises membrane proteins that efflux various substrates across extra- and intracellular membranes. Mutations in ABC genes cause 21 human disorders or phenotypes with Mendelian inheritance, including cystic fibrosis, adrenoleukodystrophy, retinal degeneration, cholesterol, and bile transport defects. To provide tools to study the function of human ABC transporters we compiled data from multiple genomics databases. We analyzed ABC gene conservation within human populations and across vertebrates and surveyed phenotypes of ABC gene mutations in mice. Most mouse ABC gene disruption mutations have a phenotype that mimics human disease, indicating they are applicable models. Interestingly, several ABCA family genes, whose human function is unknown, have cholesterol level phenotypes in the mouse. Genome-wide association studies confirm and extend ABC traits and suggest several new functions to investigate. Whole-exome sequencing of tumors from diverse cancer types demonstrates that mutations in ABC genes are not common in cancer, but specific genes are overexpressed in select tumor types. Finally, an analysis of the frequency of loss-of-function mutations demonstrates that many human ABC genes are essential with a low level of variants, while others have a higher level of genetic diversity.

Exploring Lead loci shared between schizophrenia and Cardiometabolic traits

Individuals with schizophrenia (SCZ) have, on average, a 10- to 20-year shorter expected life span than the rest of the population, primarily due to cardiovascular disease comorbidity. Genome-wide association studies (GWAS) have previously been used to separately identify common variants in SCZ and cardiometabolic traits. However, genetic variants jointly influencing both traits remain to be fully characterised. To assess overlaps (if any) between the genetic architecture of SCZ and cardiometabolic traits, we used conditional false discovery rate (FDR) and local genetic correlation statistical framework analyses. A conjunctional FDR was used to identify shared genetic traits between SCZ and cardiometabolic risk factors. We identified 144 genetic variants which were shared between SCZ and body mass index (BMI), and 15 variants shared between SCZ and triglycerides (TG). Furthermore, we discovered four novel single nucleotide polymorphisms (SNPs) (rs3865350, rs9860913, rs13307 and rs9614186) and four proximate genes (DERL2, SNX4, LY75 and EFCAB6) which were shared by SCZ and BMI. We observed that the novel genetic variant rs13307 and the most proximate gene LY75 exerted potential effects on SCZ and BMI comorbidity. Also, we observed a mixture of concordant and opposite direction associations with shared genetic variants. We demonstrated a moderate to high genetic overlap between SCZ and cardiometabolic traits associated with a pattern of bidirectional associations. Our data suggested a complex interplay between metabolism-related gene pathways in SCZ pathophysiology.

Nutrigenetic Interaction of Spontaneously Hypertensive Rat Chromosome 20 Segment and High-Sucrose Diet Sensitizes to Metabolic Syndrome

Several corresponding regions of human and mammalian genomes have been shown to affect sensitivity to the manifestation of metabolic syndrome via nutrigenetic interactions. In this study, we assessed the effect of sucrose administration in a newly established congenic strain BN.SHR20, in which a limited segment of rat chromosome 20 from a metabolic syndrome model, spontaneously hypertensive rat (SHR), was introgressed into Brown Norway (BN) genomic background. We mapped the extent of the differential segment and compared the genomic sequences of BN vs. SHR within the segment in silico. The differential segment of SHR origin in BN.SHR20 spans about 9 Mb of the telomeric portion of the short arm of chromosome 20. We identified non-synonymous mutations e.g., in ApoM, Notch4, Slc39a7, Smim29 genes and other variations in or near genes associated with metabolic syndrome in human genome-wide association studies. Male rats of BN and BN.SHR20 strains were fed a standard diet for 18 weeks (control groups) or 16 weeks of standard diet followed by 14 days of high-sucrose diet (HSD). We assessed the morphometric and metabolic profiles of all groups. Adiposity significantly increased only in BN.SHR20 after HSD. Fasting glycemia and the glucose levels during the oral glucose tolerance test were higher in BN.SHR20 than in BN groups, while insulin levels were comparable. The fasting levels of triacylglycerols were the highest in sucrose-fed BN.SHR20, both compared to the sucrose-fed BN and the control BN.SHR20. The non-esterified fatty acids and total cholesterol concentrations were higher in BN.SHR20 compared to their respective BN groups, and the HSD elicited an increase in non-esterified fatty acids only in BN.SHR20. In a new genetically defined model, we have isolated a limited genomic region involved in nutrigenetic sensitization to sucrose-induced metabolic disturbances.

Genetic Variants at the APOE Locus Predict Cardiometabolic Traits and Metabolic Syndrome: A Taiwan Biobank Study

Several apolipoprotein genes are located at the APOE locus on chromosome 19q13.32. This study explored the genetic determinants of cardiometabolic traits and metabolic syndrome at the APOE locus in a Taiwanese population. A total of 81,387 Taiwan Biobank (TWB) participants were enrolled to undergo genotype−phenotype analysis using data from the Axiom Genome-Wide CHB arrays. Regional association analysis with conditional analysis revealed lead single-nucleotide variations (SNVs) at the APOE locus: APOE rs7412 and rs429358 for total, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) cholesterol levels; CLPTM1 rs3786505 and rs11672748 for LDL and HDL cholesterol levels; and APOC1 rs438811 and APOE-APOC1 rs439401 for serum triglyceride levels. Genotype−phenotype association analysis revealed a significant association of rs429358 and rs438811 with metabolic syndrome and of rs7412, rs438811, and rs439401 with serum albumin levels (p < 0.0015). Stepwise regression analysis indicated that CLPTM1 variants were independently associated with LDL and HDL cholesterol levels (p = 3.10 × 10−15 for rs3786505 and p = 1.48 × 10−15 for rs11672748, respectively). APOE rs429358 and APOC1 rs438811 were also independently associated with metabolic syndrome (p = 2.29 × 10−14) and serum albumin levels (p = 3.80 × 10−6), respectively. In conclusion, in addition to APOE variants, CLPTM1 is a novel candidate locus for LDL and HDL cholesterol levels at the APOE gene region in Taiwan. Our data also indicated that APOE and APOC1 variants were independently associated with metabolic syndrome and serum albumin levels, respectively. These results revealed the crucial role of genetic variants at the APOE locus in predicting cardiometabolic traits and metabolic syndrome.

Identification of RNA modification-associated single nucleotide polymorphisms in genomic loci for low-density lipoprotein cholesterol concentrations

Introduction: Genome-wide association studies have identified approximately 1000 lipid-associated loci, but functional variants are less known. Materials & methods: The authors identified RNA modification-related single nucleotide polymorphisms (RNAm-SNPs) in summary data from a genome-wide association study. By applying Mendelian randomization analysis, the authors identified gene expression levels involved in the regulation of RNAm-SNPs on low-density lipoprotein cholesterol (LDL-C) levels. Results: The authors identified 391 RNAm-SNPs that were significantly associated with LDL-C levels. RNAm-SNPs in NPC1L1, LDLR, APOB, MYLIP, LDLRAP1 and ABCA6 were identified. The RNAm-SNPs were associated with gene expression. The expression levels of 112 genes were associated with LDL-C levels, and some of them (e.g., APOB, SMARCA4 and SH2B3) were associated with coronary artery disease. Conclusion: This study identified many RNAm-SNPs in LDL-C loci and elucidated the relationship among the SNPs, gene expression and LDL-C.

Evaluating the role of common risk variation in the recurrence risk of schizophrenia in multiplex schizophrenia families

Multiplex families have higher recurrence risk of schizophrenia compared to the families of sporadic cases, but the source of this increased recurrence risk is unknown. We used schizophrenia genome-wide association study data (N = 156,509) to construct polygenic risk scores (PRS) in 1005 individuals from 257 multiplex schizophrenia families, 2114 ancestry-matched sporadic cases, and 2205 population controls, to evaluate whether increased PRS can explain the higher recurrence risk of schizophrenia in multiplex families compared to ancestry-matched sporadic cases. Using mixed-effects logistic regression with family structure modeled as a random effect, we show that SCZ PRS in familial cases does not differ significantly from sporadic cases either with, or without family history (FH) of psychotic disorders (All sporadic cases p = 0.90, FH+ cases p = 0.88, FH- cases p = 0.82). These results indicate that increased burden of common schizophrenia risk variation as indexed by current SCZ PRS, is unlikely to account for the higher recurrence risk of schizophrenia in multiplex families. In the absence of elevated PRS, segregation of rare risk variation or environmental influences unique to the families may explain the increased familial recurrence risk. These findings also further validate a genetically influenced psychosis spectrum, as shown by a continuous increase of common SCZ risk variation burden from unaffected relatives to schizophrenia cases in multiplex families. Finally, these results suggest that common risk variation loading are unlikely to be predictive of schizophrenia recurrence risk in the families of index probands, and additional components of genetic risk must be identified and included in order to improve recurrence risk prediction.

LDL cholesterol is associated with higher AD neuropathology burden independent of APOE

OBJECTIVE

APOE is a strong risk factor for Alzheimer's disease (AD) and associated with higher low-density lipoprotein cholesterol (LDL-C) levels. Moreover, LDL-C is associated with the development of clinically ascertained AD; however, whether this association is present with the underlying neuropathological manifestations of AD or whether it is independent of the effect of APOE is unknown and is the focus of this paper.

METHODS

Individuals in the Religious Orders Study/Memory and Ageing Project cohorts with longitudinal measures of blood lipids and detailed autopsies were studied. We modelled the relationship between blood lipids and 12 age-related brain pathologies using a linear mixed model adjusted for potential confounding factors and stratified by APOE genotype with overall significance determined by meta-analysis. Blood lipids considered were LDL-C, high-density lipoprotein cholesterol and triglycerides. Brain pathologies included AD pathology measured by silver staining (Braak stage, a modified Consortium to Establish a Registry for Alzheimer's Disease [CERAD] score and global AD pathology) and immunohistochemistry (beta-amyloid and neurofibrillary tangles) as well as cerebral microinfarct, cerebral macroinfarct, cerebral amyloid angiopathy, cerebral atherosclerosis, hippocampal sclerosis, TDP-43 cytoplasmic inclusions and Lewy bodies.

RESULTS

559 participants (69.1% female) had complete data for analysis. They were followed for a median of 7 years and a median of 3 years prior to dementia onset. LDL-C was associated with all measures of AD neuropathology (neurofibrillary tangles, beta-amyloid, Braak stage, modified CERAD score and global AD pathology) and cerebral amyloid angiopathy independent of APOE after adjusting for age, sex, cholesterol-lowering medication use, body mass index, smoking and education at false discovery rate (FDR) p-value <0.05.

CONCLUSIONS

These findings implicate LDL-C in the pathophysiology of AD independent of APOE and suggest LDL-C is a modifiable risk factor for AD.

An LDLR missense variant poses high risk of familial hypercholesterolemia in 30% of Greenlanders and offers potential of early cardiovascular disease intervention

The common Arctic-specific LDLR p.G137S variant was recently shown to be associated with elevated lipid levels. Motivated by this, we aimed to investigate the effect of p.G137S on metabolic health and cardiovascular disease risk among Greenlanders to quantify its impact on the population. In a population-based Greenlandic cohort (n = 5,063), we tested for associations between the p.G137S variant and metabolic health traits as well as cardiovascular disease risk based on registry data. In addition, we explored the variant’s impact on plasma NMR measured lipoprotein concentration and composition in another Greenlandic cohort (n = 1,629); 29.5% of the individuals in the cohort carried at least one copy of the p.G137S risk allele. Furthermore, 25.4% of the heterozygous and 54.7% of the homozygous carriers had high levels (>4.9 mmol/L) of serum LDL cholesterol, which is above the diagnostic level for familial hypercholesterolemia (FH). Moreover, p.G137S was associated with an overall atherosclerotic lipid profile, and increased risk of ischemic heart disease (HR [95% CI], 1.51 [1.18–1.92], p = 0.00096), peripheral artery disease (1.69 [1.01–2.82], p = 0.046), and coronary operations (1.78 [1.21–2.62], p = 0.0035). Due to its high frequency and large effect sizes, p.G137S has a marked population-level impact, increasing the risk of FH and cardiovascular disease for up to 30% of the Greenlandic population. Thus, p.G137S is a potential marker for early intervention in Arctic populations.

Gene-lifestyle interactions in the genomics of human complex traits

The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.

Exome Sequencing Data Analysis and a Case-Control Study in Mexican Population Reveals Lipid Trait Associations of New and Known Genetic Variants in Dyslipidemia-Associated Loci

Background: Plasma lipid levels are a major risk factor for cardiovascular diseases. Although international efforts have identified a group of loci associated with the risk of dyslipidemia, Latin American populations have been underrepresented in these studies.

Objective: To know the genetic variation occurring in lipid-related loci in the Mexican population and its association with dyslipidemia.

Methods: We searched for single-nucleotide variants in 177 lipid candidate genes using previously published exome sequencing data from 2838 Mexican individuals belonging to three different cohorts. With the extracted variants, we performed a case-control study. Logistic regression and quantitative trait analyses were implemented in PLINK software. We used an LD pruning using a 50-kb sliding window size, a 5-kb window step size and a r2 threshold of 0.1.

Results: Among the 34251 biallelic variants identified in our sample population, 33% showed low frequency. For case-control study, we selected 2521 variants based on a minor allele frequency ≥1% in all datasets. We found 19 variants in 9 genes significantly associated with at least one lipid trait, with the most significant associations found in the APOA1/C3/A4/A5-ZPR1-BUD13 gene cluster on chromosome 11. Notably, all 11 variants associated with hypertriglyceridemia were within this cluster; whereas variants associated with hypercholesterolemia were located at chromosome 2 and 19, and for low high density lipoprotein cholesterol were in chromosomes 9, 11, and 19. No significant associated variants were found for low density lipoprotein. We found several novel variants associated with different lipemic traits: rs3825041 in BUD13 with hypertriglyceridemia, rs7252453 in CILP2 with decreased risk to hypercholesterolemia and rs11076176 in CETP with increased risk to low high density lipoprotein cholesterol.

Conclusions: We identified novel variants in lipid-regulation candidate genes in the Mexican population, an underrepresented population in genomic studies, demonstrating the necessity of more genomic studies on multi-ethnic populations to gain a deeper understanding of the genetic structure of the lipemic traits.

Lipoprotein(a) beyond the kringle IV repeat polymorphism: The complexity of genetic variation in the LPA gene

High lipoprotein(a) [Lp(a)] concentrations are one of the most important genetically determined risk factors for cardiovascular disease. Lp(a) concentrations are an enigmatic trait largely controlled by one single gene (LPA) that contains a complex interplay of several genetic elements with many surprising effects discussed in this review. A hypervariable coding copy number variation (the kringle IV type-2 repeat, KIV-2) generates >40 apolipoprotein(a) protein isoforms and determines the median Lp(a) concentrations. Carriers of small isoforms with up to 22 kringle IV domains have median Lp(a) concentrations up to 5 times higher than those with large isoforms (>22 kringle IV domains). The effect of the apo(a) isoforms are, however, modified by many functional single nucleotide polymorphisms (SNPs) distributed over the complete range of allele frequencies (<0.1% to >20%) with very pronounced effects on Lp(a) concentrations. A complex interaction is present between the apo (a) isoforms and LPA SNPs, with isoforms partially masking the effect of functional SNPs and, vice versa, SNPs lowering the Lp(a) concentrations of affected isoforms. This picture is further complicated by SNP-SNP interactions, a poorly understood role of other polymorphisms such as short tandem repeats and linkage structures that are poorly captured by common R² values. A further layer of complexity derives from recent findings that several functional SNPs are located in the KIV-2 repeat and are thus not accessible to conventional sequencing and genotyping technologies. A critical impact of the ancestry on correlation structures and baseline Lp(a) values becomes increasingly evident.

This review provides a comprehensive overview on the complex genetic architecture of the Lp(a) concentrations in plasma, a field that has made tremendous progress with the introduction of new technologies. Understanding the genetics of Lp(a) might be a key to many mysteries of Lp(a) and booster new ideas on the metabolism of Lp(a) and possible interventional targets.

An Amish founder population reveals rare-population genetic determinants of the human lipidome

Identifying the genetic determinants of inter-individual variation in lipid species (lipidome) may provide deeper understanding and additional insight into the mechanistic effect of complex lipidomic pathways in CVD risk and progression beyond simple traditional lipids. Previous studies have been largely population based and thus only powered to discover associations with common genetic variants. Founder populations represent a powerful resource to accelerate discovery of previously unknown biology associated with rare population alleles that have risen to higher frequency due to genetic drift. We performed a genome-wide association scan of 355 lipid species in 650 individuals from the Amish founder population including 127 lipid species not previously tested. To the best of our knowledge, we report for the first time the lipid species associated with two rare-population but Amish-enriched lipid variants: APOB_rs5742904 and APOC3_rs76353203. We also identified novel associations for 3 rare-population Amish-enriched loci with several sphingolipids and with proposed potential functional/causal variant in each locus including GLTPD2_rs536055318, CERS5_rs771033566, and AKNA_rs531892793. We replicated 7 previously known common loci including novel associations with two sterols: androstenediol with UGT locus and estriol with SLC22A8/A24 locus. Our results show the double power of founder populations and detailed lipidome to discover novel trait-associated variants.

A GWAS of 355 lipid species in the Old Order Amish founder population reveals associations between Amish-enriched loci and several sphingolipids.

Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores

Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred+, which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred+ to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred+ attained similar improvements.

Genetic and Mechanistic Insights into the Modulation of Circulating Lipoprotein (a) Concentration by Apolipoprotein E Isoforms

PURPOSE OF REVIEW

Lipoprotein (a) [Lp(a)] is a highly atherogenic lipoprotein species. A unique feature of Lp(a) is the strong genetic determination of its concentration. The LPA gene is responsible for up to 90% of the variance in Lp(a), but other genes also have an impact.

RECENT FINDINGS

Genome-wide associations studies indicate that the APOE gene, encoding apolipoprotein E (apoE), is the second most important locus modulating Lp(a) concentrations. Population studies clearly show that carriers of the apoE2 variant (ε2) display reduced Lp(a) levels, the lowest concentrations being observed in ε2/ε2 homozygotes. This genotype can lead predisposed adults to develop dysbetalipoproteinemia, a lipid disorder characterized by sharp elevations in cholesterol and triglycerides. However, dysbetalipoproteinemia does not significantly modulate circulating Lp(a). Mechanistically, apoE appears to impair the production but not the catabolism of Lp(a). These observations underline the complexity of Lp(a) metabolism and provide key insights into the pathways governing Lp(a) synthesis and secretion.

The Arg82Cys polymorphism of the protein nepmucin implies a role in HDL metabolism

Context

Blood lipid levels are linked to the risk of cardiovascular disease and regulated by genetic factors. A low-frequency polymorphism Arg82Cys (rs72836561) in the membrane protein nepmucin, encoded by CD300LG, is associated with lower fasting concentration of high-density lipoprotein cholesterol (HDLc) and higher fasting triglycerides. However, whether the variant is linked to postprandial lipids and glycemic status remains elusive.

Objective

Here, we augment the genetic effect of Arg82Cys on fasting plasma concentrations of HDL subclasses, postprandial lipemia after a standardized high-fat meal, and glycemic status to further untangle its role in HDL metabolism.

Methods

We elucidated fasting associations with HDL subclasses in a population-based cohort study (Oxford BioBank, OBB), including 4522 healthy men and women. We investigated fasting and postprandial consequences on HDL metabolism in recall-by-genotype (RbG) studies (fasting: 20 carrier/20 noncarrier; postprandial: 7 carrier/17 noncarrier), and shed light on the synergistic interaction with glycemic status.

Results

A lower fasting plasma concentration of cholesterol in large HDL particles was found in healthy male carriers of the Cys82 polymorphism compared to noncarriers, both in the OBB (P = .004) and RbG studies (P = .005). In addition, the Cys82 polymorphism was associated with low fasting plasma concentrations of ApoA1 (P = .008) in the OBB cohort. On the contrary, we did not find differences in postprandial lipemia or 2-hour plasma glucose levels.

Conclusion

Taken together, our results indicate an association between the Arg82Cys variant and a lower concentration of HDL particles and HDLc, especially in larger HDL subclasses, suggesting a link between nepmucin and HDLc metabolism or maturation.

A multi-dimensional integrative scoring framework for predicting functional variants in the human genome

Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.

Multiparametric platform for profiling lipid trafficking in human leukocytes

Systematic insight into cellular dysfunction can improve understanding of disease etiology, risk assessment, and patient stratification. We present a multiparametric high-content imaging platform enabling quantification of low-density lipoprotein (LDL) uptake and lipid storage in cytoplasmic droplets of primary leukocyte subpopulations. We validate this platform with samples from 65 individuals with variable blood LDL-cholesterol (LDL-c) levels, including familial hypercholesterolemia (FH) and non-FH subjects. We integrate lipid storage data into another readout parameter, lipid mobilization, measuring the efficiency with which cells deplete lipid reservoirs. Lipid mobilization correlates positively with LDL uptake and negatively with hypercholesterolemia and age, improving differentiation of individuals with normal and elevated LDL-c. Moreover, combination of cell-based readouts with a polygenic risk score for LDL-c explains hypercholesterolemia better than the genetic risk score alone. This platform provides functional insights into cellular lipid trafficking and has broad possible applications in dissecting the cellular basis of metabolic disorders.

Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models

Integration of expression quantitative trait loci (eQTL) into genome-wide association studies (GWASs) is a promising manner to reveal functional roles of associated single-nucleotide polymorphisms (SNPs) in complex phenotypes and has become an active research field in post-GWAS era. However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.