Exome-wide association study of plasma lipids in >300,000 individuals

An APOO Pseudogene on Chromosome 5q Is Associated With Low-Density Lipoprotein Cholesterol Levels

BACKGROUND

Elevated levels of low-density lipoprotein cholesterol (LDL-C) are a major risk factor for cardiovascular disease via its contribution to the development and progression of atherosclerotic lesions. Although the genetic basis of LDL-C has been studied extensively, currently known genetic variants account for only ≈20% of the variation in LDL-C levels.

METHODS

Through an array-based association analysis in 1102 Amish subjects, we identified a variant strongly associated with LDL-C levels. Using a combination of genetic analyses, zebrafish models, and in vitro experiments, we sought to identify the causal gene driving this association.

RESULTS

We identified a founder haplotype associated with a 15 mg/dL increase in LDL-C on chromosome 5. After recombination mapping, the associated region contained 8 candidate genes. Using a zebrafish model to evaluate the relevance of these genes to cholesterol metabolism, we found that expression of the transcribed pseudogene, APOOP1, increased LDL-C and vascular plaque formation.

CONCLUSIONS

Based on these data, we propose that APOOP1 regulates levels of LDL-C in humans, thus identifying a novel mechanism of lipid homeostasis.

Genetically elevated high-density lipoprotein cholesterol through the cholesteryl ester transfer protein gene does not associate with risk of Alzheimer's disease

INTRODUCTION

There is conflicting evidence whether high-density lipoprotein cholesterol (HDL-C) is a risk factor for Alzheimer's disease (AD) and dementia. Genetic variation in the cholesteryl ester transfer protein (CETP) locus is associated with altered HDL-C. We aimed to assess AD risk by genetically predicted HDL-C.

METHODS

Ten single nucleotide polymorphisms within the CETP locus predicting HDL-C were applied to the International Genomics of Alzheimer's Project (IGAP) exome chip stage 1 results in up 16,097 late onset AD cases and 18,077 cognitively normal elderly controls. We performed instrumental variables analysis using inverse variance weighting, weighted median, and MR-Egger.

RESULTS

Based on 10 single nucleotide polymorphisms distinctly predicting HDL-C in the CETP locus, we found that HDL-C was not associated with risk of AD (P > .7).

DISCUSSION

Our study does not support the role of HDL-C on risk of AD through HDL-C altered by CETP. This study does not rule out other mechanisms by which HDL-C affects risk of AD.

Patient Similarity Networks for Precision Medicine

Clinical research and practice in the 21st century is poised to be transformed by analysis of computable electronic medical records and population-level genome-scale patient profiles. Genomic data capture genetic and environmental state, providing information on heterogeneity in disease and treatment outcome, but genomic-based clinical risk scores are limited. Achieving the goal of routine precision medicine that takes advantage of these rich genomics data will require computational methods that support heterogeneous data, have excellent predictive performance, and ideally, provide biologically interpretable results. Traditional machine-learning approaches excel at performance, but often have limited interpretability. Patient similarity networks are an emerging paradigm for precision medicine, in which patients are clustered or classified based on their similarities in various features, including genomic profiles. This strategy is analogous to standard medical diagnosis, has excellent performance, is interpretable, and can preserve patient privacy. We review new methods based on patient similarity networks, including Similarity Network Fusion for patient clustering and netDx for patient classification. While these methods are already useful, much work is required to improve their scalability for contemporary genetic cohorts, optimize parameters, and incorporate a wide range of genomics and clinical data. The coming 5 years will provide an opportunity to assess the utility of network-based algorithms for precision medicine.

A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease

Genome-wide association studies (GWAS), relying on hundreds of thousands of individuals, have revealed >200 genomic loci linked to metabolic disease (MD). Loss of insulin sensitivity (IS) is a key component of MD and we hypothesized that discovery of a robust IS transcriptome would help reveal the underlying genomic structure of MD. Using 1,012 human skeletal muscle samples, detailed physiology and a tissue-optimized approach for the quantification of coding (>18,000) and non-coding (>15,000) RNA (ncRNA), we identified 332 fasting IS-related genes (CORE-IS). Over 200 had a proven role in the biochemistry of insulin and/or metabolism or were located at GWAS MD loci. Over 50% of the CORE-IS genes responded to clinical treatment; 16 quantitatively tracking changes in IS across four independent studies (P = 0.0000053: negatively: AGL, G0S2, KPNA2, PGM2, RND3 and TSPAN9 and positively: ALDH6A1, DHTKD1, ECHDC3, MCCC1, OARD1, PCYT2, PRRX1, SGCG, SLC43A1 and SMIM8). A network of ncRNA positively related to IS and interacted with RNA coding for viral response proteins (P < 1 × 10-48), while reduced amino acid catabolic gene expression occurred without a change in expression of oxidative-phosphorylation genes. We illustrate that combining in-depth physiological phenotyping with robust RNA profiling methods, identifies molecular networks which are highly consistent with the genetics and biochemistry of human metabolic disease.

Association of GCKR rs780094 polymorphism with circulating lipid levels in type 2 diabetes and hyperuricemia in Uygur Chinese

To investigate the relationship between a GCKR rs780094 polymorphism and lipid profiles in the Xinjiang Uygur population in China. 980 type 2 diabetes mellitus (T2DM) patients, 1017 hyperuricemia (HUA) and 1185 healthy controls were included in this study. After genotyping of rs780094 by Sequenom Mass ARRAY system, chi-square test and logistic regression analysis were used for association analysis as well as a genotype-phenotype analysis. We found that the serum concentration of TC (P<0.001) was significantly higher and HDL-C (P<0.001) was lower in T2DM than in control participants. Subjects with HUA had a significantly higher TG (P=0.003) and lower HDL-C (P<0.001) than control participants. Additionally, under the recessive model, rs780094 was shown to be associated with the risk of HUA (P=0.015, OR=1.311), particularly in males (P=0.047, OR=1.330). Subsequent interaction analysis between rs780094 and lipid parameters showed that the TG level was positively correlated with HUA in the rs780094- AA+AG carriers (P=0.005). The TC concentrations showed to be associated with T2DM in the rs780094- AA+AG carriers (P<0.001). The association between lipid parameters and gender showed that significantly higher TG levels (P<0.001) and lower HDL-C levels (P<0.001) were observed in female HUA. Higher LDL-C levels were found in male HUA (P=0.015). Moreover, statistically higher TC levels and lower HDL-C levels were found both in male and female T2DM cases (TC: male: P<0.001, female: P=0.014. HDL-C: male: P<0.001, female: P<0.001.).To conclude, our results demonstrated that different genotypes of rs780094 had different effects on blood lipids in HUA and T2DM patients in a Uygur population. Gender was also one of the factors influencing blood lipid levels.

Serum triglycerides predict first cardiovascular events in diabetic patients with hypercholesterolemia and retinopathy

AIMS

Low-density lipoprotein cholesterol predicts cardiovascular events in patients with diabetes. However, it is uncertain whether serum triglycerides level is also associated with an occurrence of future cardiovascular events in diabetic patients. We aimed to test whether serum triglycerides is associated with first cardiovascular events in diabetic patients.

METHODS AND RESULTS

We recruited 5042 participants with diabetes mellitus from the standard versus intEnsive statin therapy for hypercholesteroleMic Patients with diAbetic retinopaTHY (EMPATHY), multicenter, prospective, randomized, open-label, blinded-endpoint study. Median follow-up was three years. We evaluated an association of serum triglycerides with first cardiovascular events in cox-regression hazard models adjusted by age, sex, hypertension, current smoking, low-density lipoprotein cholesterol, and body mass index. Cardiovascular events were defined as (a) major adverse cardiac events including myocardial infarction, stroke, or cardiac death; and (b) cardiovascular diseases including myocardial infarction, unstable angina, ischemic stroke, or large artery disease or peripheral arterial disease. Serum triglycerides were associated with major adverse cardiac events (adjusted hazard ratio: 1.021 per 10 mg/dl; 95% confidence interval: 1.007-1.035; p = 0.0025) and cardiovascular diseases (adjusted hazard ratio: 1.023 per 10 mg/dl; 95% confidence interval: 1.013-1.034; p = 0.0000077). Comparing the top quintile (>185 mg/dl) with the bottom quintile (<79 mg/dl), the adjusted hazard ratio increased 1.89 (95% confidence interval: 1.03-2.80, p = 0.04) for major adverse cardiac events, and 1.90 (95% confidence interval: 1.18-3.07, p = 0.007) for cardiovascular diseases. There were no overall interactions of triglycerides and treatment assignment (standard/intensive statins) on both outcomes ( p-trend = 0.33 for major adverse cardiac events, p-trend = 0.62 for cardiovascular diseases).

CONCLUSIONS

Serum triglycerides were associated with first cardiovascular events among high-risk diabetes patients with hypercholesterolemia and retinopathy.

Using Genetic Variants in the Targets of Lipid Lowering Therapies to Inform Drug Discovery and Development: Current and Future Treatment Options

Mendelian randomization studies and "human knock-out" studies of rare loss-of-function coding variants suggest that plasma levels of low-density lipoprotein-cholesterol LDL-C, triglycerides (TGs), and lipoprotein(a) (Lp(a)) are causally associated with the risk of cardiovascular disease, and, therefore, therapies directed against these targets should reduce the risk of cardiovascular events. However, several therapies directed against these targets have failed to reduce the risk of cardiovascular events in large-scale randomized trials, thus suggesting that causality is not sufficient evidence to establish genetic target validation. Instead, the critical question that needs to be answered to improve drug discovery and development is how much a causal biomarker needs to be changed to produce a clinically meaningful benefit in a short-term trial. This review describes how to use naturally randomized genetic evidence to accurately anticipate the results of randomized trials evaluating current and future lipid lowering therapies, inform the design of randomized trials, and transform the drug discovery and development process.

Large-Scale Analysis of Genetic and Clinical Patient Data

Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.

Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.

It is of great interest to estimate the joint effects of multiple variants from large scale meta-analyses, in order to fine-map causal variants and understand the genetic architecture for complex traits. The summary association statistics from participating studies in a meta-analysis often contain missing values at some variant sites, as the imputation methods may not work well and the variants with low imputation quality will be filtered out. Missingness is especially likely when the underlying genetic variant is rare or the participating studies use targeted genotyping array that is not suitable for imputation. Existing methods for conditional meta-analysis do not properly handle missing data, and can incorrectly estimate correlations between score statistics. As a result, they can produce highly inflated type-I errors for conditional analysis, which will result in overestimated phenotypic variance explained and incorrect identification of causal variants. We systematically evaluated this bias and proposed a novel partial correlation based score statistic. The new statistic has valid type-I errors for conditional analysis and much higher power than the existing methods, even when the contributed summary statistics contain a large fraction of missing values. We expect this method to be highly useful in the sequencing age for complex trait genetics.

Genomic insights into the causes of type 2 diabetes

Genome-wide association studies have implicated around 250 genomic regions in predisposition to type 2 diabetes, with evidence for causal variants and genes emerging for several of these regions. Understanding of the underlying mechanisms, including the interplay between β-cell failure, insulin sensitivity, appetite regulation, and adipose storage has been facilitated by the integration of multidimensional data for diabetes-related intermediate phenotypes, detailed genomic annotations, functional experiments, and now multiomic molecular features. Studies in diverse ethnic groups and examples from population isolates have shown the value and need for a broad genomic approach to this global disease. Transethnic discovery efforts and large-scale biobanks in diverse populations and ancestries could help to address some of the Eurocentric bias. Despite rapid progress in the discovery of the highly polygenic architecture of type 2 diabetes, dominated by common alleles with small, cumulative effects on disease risk, these insights have been of little clinical use in terms of disease prediction or prevention, and have made only small contributions to subtype classification or stratified approaches to treatment. Successful development of academia-industry partnerships for exome or genome sequencing in large biobanks could help to deliver economies of scale, with implications for the future of genomics-focused research.

Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes

Angiopoietin-like 4 (ANGPTL4) is an endogenous inhibitor of lipoprotein lipase that modulates lipid levels, coronary atherosclerosis risk, and nutrient partitioning. We hypothesize that loss of ANGPTL4 function might improve glucose homeostasis and decrease risk of type 2 diabetes (T2D). We investigate protein-altering variants in ANGPTL4 among 58,124 participants in the DiscovEHR human genetics study, with follow-up studies in 82,766 T2D cases and 498,761 controls. Carriers of p.E40K, a variant that abolishes ANGPTL4 ability to inhibit lipoprotein lipase, have lower odds of T2D (odds ratio 0.89, 95% confidence interval 0.85-0.92, p = 6.3 × 10-10), lower fasting glucose, and greater insulin sensitivity. Predicted loss-of-function variants are associated with lower odds of T2D among 32,015 cases and 84,006 controls (odds ratio 0.71, 95% confidence interval 0.49-0.99, p = 0.041). Functional studies in Angptl4-deficient mice confirm improved insulin sensitivity and glucose homeostasis. In conclusion, genetic inactivation of ANGPTL4 is associated with improved glucose homeostasis and reduced risk of T2D.

Patatin-like phospholipase domain containing 3 variants differentially impact metabolic traits in individuals at high risk for cardiovascular events

Single nucleotide polymorphism (SNP) rs738409 C>G in the patatin‐like phospholipase domain containing 3 (PNPLA3) gene results in an amino acid exchange from isoleucin to methionine at position I148M of PNPLA3. The expression of this loss‐of‐function mutation leads to impaired hepatocellular triglyceride hydrolysis and is associated with the development of liver steatosis, fibrosis, and hepatocellular carcinoma. In contrast to these well‐established associations, the relationship of the PNPLA3 rs738409 variant with other metabolic traits is incompletely understood. We therefore assessed the association of the PNPLA3 rs738409 genotype with relevant metabolic traits in a prospective study of patients at high risk for cardiovascular events, i.e., patients undergoing coronary angiography. In a total of 270 patients, known associations of the PNPLA3 rs738409 GG genotype with nonalcoholic steatohepatitis and liver fibrosis were confirmed. In addition, we found an association of the PNPLA3 rs738409 G allele with the presence of diabetes (22% versus 28% versus 58% for CC versus CG versus GG genotype, respectively; P = 0.02). In contrast to its association with nonalcoholic fatty liver disease, liver fibrosis, and diabetes, the minor G allele of PNPLA3 rs738409 was inversely associated with total serum cholesterol and low‐density lipoprotein serum levels (P = 0.003 and P = 0.02, respectively). Finally, there was a trend toward an inverse association between the presence of the PNPLA3 rs738409 G allele and significant coronary heart disease. Comparable trends were observed for the transmembrane 6 superfamily member 2 (TM6SF2) 167 K variant, but the sample size was too small to evaluate this rarer variant. Conclusion: The PNPLA3 rs738409 G allele is associated with liver disease but also with a relatively benign cardiovascular risk profile. (Hepatology Communications 2018;2:798‐806)

DGAT2 Inhibition Alters Aspects of Triglyceride Metabolism in Rodents but Not in Non-human Primates

Diacylglycerol acyltransferase 2 (DGAT2) catalyzes the final step in triglyceride (TG) synthesis and has been shown to play a role in regulating hepatic very-low-density lipoprotein (VLDL) production in rodents. To explore the potential of DGAT2 as a therapeutic target for the treatment of dyslipidemia, we tested the effects of small-molecule inhibitors and gene silencing both in vitro and in vivo. Consistent with prior reports, chronic inhibition of DGAT2 in a murine model of obesity led to correction of multiple lipid parameters. In contrast, experiments in primary human, rhesus, and cynomolgus hepatocytes demonstrated that selective inhibition of DGAT2 has only a modest effect. Acute and chronic inhibition of DGAT2 in rhesus primates recapitulated the in vitro data yielding no significant effects on production of plasma TG or VLDL apolipoprotein B. These results call into question whether selective inhibition of DGAT2 is sufficient for remediation of dyslipidemia.

Loss of angiopoietin-like 4 (ANGPTL4) in mice with diet-induced obesity uncouples visceral obesity from glucose intolerance partly via the gut microbiota

AIMS/HYPOTHESIS

Angiopoietin-like 4 (ANGPTL4) is an important regulator of triacylglycerol metabolism, carrying out this role by inhibiting the enzymes lipoprotein lipase and pancreatic lipase. ANGPTL4 is a potential target for ameliorating cardiometabolic diseases. Although ANGPTL4 has been implicated in obesity, the study of the direct role of ANGPTL4 in diet-induced obesity and related metabolic dysfunction is hampered by the massive acute-phase response and development of lethal chylous ascites and peritonitis in Angptl4^-/- mice fed a standard high-fat diet. The aim of this study was to better characterise the role of ANGPTL4 in glucose homeostasis and metabolic dysfunction during obesity.

METHODS

We chronically fed wild-type (WT) and Angptl4^-/- mice a diet rich in unsaturated fatty acids and cholesterol, combined with fructose in drinking water, and studied metabolic function. The role of the gut microbiota was investigated by orally administering a mixture of antibiotics (ampicillin, neomycin, metronidazole). Glucose homeostasis was assessed via i.p. glucose and insulin tolerance tests.

RESULTS

Mice lacking ANGPTL4 displayed an increase in body weight gain, visceral adipose tissue mass, visceral adipose tissue lipoprotein lipase activity and visceral adipose tissue inflammation compared with WT mice. However, they also unexpectedly had markedly improved glucose tolerance, which was accompanied by elevated insulin levels. Loss of ANGPTL4 did not affect glucose-stimulated insulin secretion in isolated pancreatic islets. Since the gut microbiota have been suggested to influence insulin secretion, and because ANGPTL4 has been proposed to link the gut microbiota to host metabolism, we hypothesised a potential role of the gut microbiota. Gut microbiota composition was significantly different between Angptl4^-/- mice and WT mice. Interestingly, suppression of the gut microbiota using antibiotics largely abolished the differences in glucose tolerance and insulin levels between WT and Angptl4^-/- mice.

CONCLUSIONS/INTERPRETATION

Despite increasing visceral fat mass, inactivation of ANGPTL4 improves glucose tolerance, at least partly via a gut microbiota-dependent mechanism.

Myeloid cells regulate plasma LDL-cholesterol levels

PURPOSE OF REVIEW

Leukocytosis, elevated blood leukocyte levels, is associated with enhanced cardiovascular risk in humans. Hematopoietic stem and progenitor cells (HSPCs) drive leukocyte production in a process called hematopoiesis, which mainly occurs in the bone marrow, and under certain conditions also in other organs such as the spleen. Cholesterol accumulation in HSPCs enhances hematopoiesis, increasing levels of blood monocytes that infiltrate into atherosclerotic plaques. Although HSPC proliferation and monocytosis enhance atherogenesis in several studies, concomitant decreases in LDL-cholesterol levels have also been reported, associated with anti-atherogenic effects. This review focuses on the link between HSPC proliferation, leukocytosis, plasma LDL-cholesterol levels, and atherogenesis.

RECENT FINDINGS

Recent studies have shown that an acute infection enhances cholesterol accumulation in HSPCs, driving HSPC proliferation, and leading to the expansion of myeloid cells (monocytes, neutrophils, and macrophages). Enhanced hematopoiesis is associated with low plasma LDL-cholesterol levels in animal models and humans, probably because of the increased number of myeloid cells that take up LDL-cholesterol. Despite low-plasma LDL-cholesterol levels, specific patient populations with enhanced hematopoiesis show increased cardiovascular risk.

SUMMARY

Enhanced hematopoiesis and monocytosis may accelerate atherogenesis. Studies on these processes may lead to the identification of new therapeutic targets for cardiovascular diseases.

Unexplained reciprocal regulation of diabetes and lipoproteins

PURPOSE OF REVIEW

Type 2 diabetes is associated with a characteristic dyslipidemia that may exacerbate cardiovascular risk. The causes of, and the effects of new antihyperglycemia medications on, this dyslipidemia, are under investigation. In an unexpected reciprocal manner, lowering LDL-cholesterol with statins slightly increases the risk of diabetes. Here we review the latest findings.

RECENT FINDINGS

The inverse relationship between LDL-cholesterol and diabetes has now been confirmed by multiple lines of evidence. This includes clinical trials, genetic instruments using aggregate single nucleotide polymorphisms, as well as at least eight individual genes - HMGCR, NPC1L1, HNF4A, GCKR, APOE, PCKS9, TM6SF2, and PNPLA3 - support this inverse association. Genetic and pharmacologic evidence suggest that HDL-cholesterol may also be inversely associated with diabetes risk. Regarding the effects of diabetes on lipoproteins, new evidence suggests that insulin resistance but not diabetes per se may explain impaired secretion and clearance of VLDL-triglycerides. Weight loss, bariatric surgery, and incretin-based therapies all lower triglycerides, whereas SGLT2 inhibitors may slightly increase HDL-cholesterol and LDL-cholesterol.

SUMMARY

Diabetes and lipoproteins are highly interregulated. Further research is expected to uncover new mechanisms governing the metabolism of glucose, fat, and cholesterol. This topic has important implications for treating type 2 diabetes and cardiovascular disease.

Prioritising Risk Factors for Type 2 Diabetes: Causal Inference through Genetic Approaches

PURPOSE OF THE REVIEW

Causality has been demonstrated for few of the many putative risk factors for type 2 diabetes (T2D) emerging from observational epidemiology. Genetic approaches are increasingly being used to infer causality, and in this review, we discuss how genetic discoveries have shaped our understanding of the causal role of factors associated with T2D.

RECENT FINDINGS

Genetic discoveries have led to the identification of novel potential aetiological factors of T2D, including the protective role of peripheral fat storage capacity and specific metabolic pathways, such as the branched-chain amino acid breakdown. Consideration of specific genetic mechanisms contributing to overall lipid levels has suggested that distinct physiological processes influencing lipid levels may influence diabetes risk differentially. Genetic approaches have also been used to investigate the role of T2D and related metabolic traits as causal risk factors for other disease outcomes, such as cancer, but comprehensive studies are lacking. Genome-wide association studies of T2D and metabolic traits coupled with high-throughput molecular phenotyping and in-depth characterisation and follow-up of individual loci have provided better understanding of aetiological factors contributing to T2D.

Electronic health records: the next wave of complex disease genetics

The combination of electronic health records (EHRs) with genetic data has ushered in the next wave of complex disease genetics. Population-based biobanks and other large cohorts provide sufficient sample sizes to identify novel genetic associations across the hundreds to thousands of phenotypes gleaned from EHRs. In this review, we summarize the current state of these EHR-linked biobanks, explore ongoing methods development in the field and highlight recent discoveries of genetic associations. We enumerate the many existing biobanks with EHRs linked to genetic data, many of which are available to researchers via application and contain sample sizes >50 000. We also discuss the computational and statistical considerations for analysis of such large datasets including mixed models, phenotype curation and cloud computing. Finally, we demonstrate how genome-wide association studies and phenome-wide association studies have identified novel genetic findings for complex diseases, specifically cardiometabolic traits. As more researchers employ innovative hypotheses and analysis approaches to study EHR-linked biobanks, we anticipate a richer understanding of the genetic etiology of complex diseases.

Implications of publicly available genomic data resources in searching for therapeutic targets of obesity and type 2 diabetes

Obesity and type 2 diabetes (T2D) are two major conditions that are related to metabolic disorders and affect a large population. Although there have been significant efforts to identify their therapeutic targets, few benefits have come from comprehensive molecular profiling. This limited availability of comprehensive molecular profiling of obesity and T2D may be due to multiple challenges, as these conditions involve multiple organs and collecting tissue samples from subjects is more difficult in obesity and T2D than in other diseases, where surgical treatments are popular choices. While there is no repository of comprehensive molecular profiling data for obesity and T2D, multiple existing data resources can be utilized to cover various aspects of these conditions. This review presents studies with available genomic data resources for obesity and T2D and discusses genome-wide association studies (GWAS), a knockout (KO)-based phenotyping study, and gene expression profiles. These studies, based on their assessed coverage and characteristics, can provide insights into how such data can be utilized to identify therapeutic targets for obesity and T2D.

Organic cation transporter 1 (OCT1) modulates multiple cardiometabolic traits through effects on hepatic thiamine content

A constellation of metabolic disorders, including obesity, dysregulated lipids, and elevations in blood glucose levels, has been associated with cardiovascular disease and diabetes. Analysis of data from recently published genome-wide association studies (GWAS) demonstrated that reduced-function polymorphisms in the organic cation transporter, OCT1 (SLC22A1), are significantly associated with higher total cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride (TG) levels and an increased risk for type 2 diabetes mellitus, yet the mechanism linking OCT1 to these metabolic traits remains puzzling. Here, we show that OCT1, widely characterized as a drug transporter, plays a key role in modulating hepatic glucose and lipid metabolism, potentially by mediating thiamine (vitamin B1) uptake and hence its levels in the liver. Deletion of Oct1 in mice resulted in reduced activity of thiamine-dependent enzymes, including pyruvate dehydrogenase (PDH), which disrupted the hepatic glucose–fatty acid cycle and shifted the source of energy production from glucose to fatty acids, leading to a reduction in glucose utilization, increased gluconeogenesis, and altered lipid metabolism. In turn, these effects resulted in increased total body adiposity and systemic levels of glucose and lipids. Importantly, wild-type mice on thiamine deficient diets (TDs) exhibited impaired glucose metabolism that phenocopied Oct1 deficient mice. Collectively, our study reveals a critical role of hepatic thiamine deficiency through OCT1 deficiency in promoting the metabolic inflexibility that leads to the pathogenesis of cardiometabolic disease.

The liver is the major organ for glucose and lipid metabolism; impairment in liver energy metabolism is often found in metabolic disorders. Traditionally, excesses in macronutrients (fat and glucose) are linked to the development of metabolic disorders. Our study provides evidence that imbalances in a micronutrient, vitamin B1 (thiamine), can serve as an etiological cause of lipid and glucose disorders and implicates the organic cation transporter, OCT1, in these disorders. OCT1 is a key determinant of thiamine levels in the liver. In humans, reduced-function polymorphisms of OCT1 significantly associate with high LDL cholesterol levels. Using Oct1 knockout mice, we show that reduced OCT1-mediated thiamine uptake in the liver leads to reduced levels of TPP—the active metabolite of thiamine—and decreased activity of key TPP-dependent enzymes. As a result, a shift from glucose to fatty acid oxidation occurs, leading to imbalances in key metabolic intermediates, alterations in metabolic flux pathways, and disruptions of various metabolic regulatory mechanisms. The extensive characterization of Oct1 knockout mice provides evidence for the molecular mechanisms responsible for various metabolic traits and indicates an important role for imbalances in micronutrients in cardiometabolic disorders.

Polygenic influences on dyslipidemias

PURPOSE OF REVIEW

Rare large-effect genetic variants underlie monogenic dyslipidemias, whereas common small-effect genetic variants - single nucleotide polymorphisms (SNPs) - have modest influences on lipid traits. Over the past decade, these small-effect SNPs have been shown to cumulatively exert consistent effects on lipid phenotypes under a polygenic framework, which is the focus of this review.

RECENT FINDINGS

Several groups have reported polygenic risk scores assembled from lipid-associated SNPs, and have applied them to their respective phenotypes. For lipid traits in the normal population distribution, polygenic effects quantified by a score that integrates several common polymorphisms account for about 20-30% of genetic variation. Among individuals at the extremes of the distribution, that is, those with clinical dyslipidemia, the polygenic component includes both rare variants with large effects and common polymorphisms: depending on the trait, 20-50% of susceptibility can be accounted for by this assortment of genetic variants.

SUMMARY

Accounting for polygenic effects increases the numbers of dyslipidemic individuals who can be explained genetically, but a substantial proportion of susceptibility remains unexplained. Whether documenting the polygenic basis of dyslipidemia will affect outcomes in clinical trials or prospective observational studies remains to be determined.

Association of Genetic Instrumental Variables for Lung Function on Coronary Artery Disease Risk

Background:

Lung function, assessed by forced expiratory volume in 1 second (FEV 1 ) and forced vital capacity (FVC), is inversely associated with coronary artery disease (CAD), but these associations could be because of confounding or reversed causality. We conducted a 2-sample Mendelian randomization study, using publicly available data from relevant genome-wide association studies, to examine the role of FEV 1 or FVC on CAD.

Methods:

We used the most recent genome-wide association studies on lung function to extract genetic instruments related to FEV 1 and FVC (n=92 749). Data on the association between genetic instruments and CAD were obtained from Coronary Artery Disease Genome wide Replication and Meta-analysis plus The Coronary Artery Disease Genetics 1000 Genomes-based genome-wide association studies (60 801 CAD cases and 123 504 controls). We used inverse-variance weighting with a multiplicative random effect to estimate the genetic instrumented association of FEV 1 and FVC on CAD. Sensitivity analyses included weighted median and MR-Egger methods.

Results:

Each SD greater FEV 1 was associated with a lower risk of CAD (odds ratio, 0.78 per SD; 95% confidence interval, 0.62–0.98) with a similar magnitude for FVC on CAD risk (odds ratio, 0.82 per SD; 95% confidence interval, 0.64–1.06). Estimates for FEV 1 were similar when using MR-Egger method (odds ratio, 0.80 per SD; 95% confidence interval, 0.33–1.94) although the magnitude was smaller for weighted median method (odds ratio, 0.93 per SD; 95% confidence interval, 0.75–1.17). Estimates for FVC in the sensitivity analyses were attenuated (median) or changed direction (MR-Egger).

Conclusions:

Our study suggested an inverse relation between FEV 1 and CAD, but for FVC, evidence is less clear.

Causal relationship of hepatic fat with liver damage and insulin resistance in nonalcoholic fatty liver

BACKGROUND AND AIMS

Nonalcoholic fatty liver disease is epidemiologically associated with hepatic and metabolic disorders. The aim of this study was to examine whether hepatic fat accumulation has a causal role in determining liver damage and insulin resistance.

METHODS

We performed a Mendelian randomization analysis using risk alleles in PNPLA3, TM6SF2, GCKR and MBOAT7, and a polygenic risk score for hepatic fat, as instruments. We evaluated complementary cohorts of at-risk individuals and individuals from the general population: 1515 from the liver biopsy cohort (LBC), 3329 from the Swedish Obese Subjects Study (SOS) and 4570 from the population-based Dallas Heart Study (DHS).

RESULTS

Hepatic fat was epidemiologically associated with liver damage, insulin resistance, dyslipidemia and hypertension. The impact of genetic variants on liver damage was proportional to their effect on hepatic fat accumulation. Genetically determined hepatic fat was associated with aminotransferases, and with inflammation, ballooning and fibrosis in the LBC. Furthermore, in the LBC, the causal association between hepatic fat and fibrosis was independent of disease activity, suggesting that a causal effect of long-term liver fat accumulation on liver disease is independent of inflammation. Genetically determined hepatic steatosis was associated with insulin resistance in the LBC and SOS. However, this association was dependent on liver damage severity. Genetically determined hepatic steatosis was associated with liver fibrosis/cirrhosis and with a small increase in risk of type 2 diabetes in publicly available databases.

CONCLUSION

These data suggest that long-term hepatic fat accumulation plays a causal role in the development of chronic liver disease.

A large electronic-health-record-based genome-wide study of serum lipids

A genome-wide association study of 94,674 multi-ethnic Kaiser Permanente members utilizing 478,866 longitudinal untreated serum lipid electronic-health-record-derived measurements (EHRs) empowered multiple novel findings: 121 new SNP associations (46 primary, 15 conditional, 60 in meta-analysis with Global Lipids Genetic Consortium); increase of 33-42% in variance explained with multiple measurements; sex differences in genetic impact (greater in females for LDL, HDL, TC, the opposite for TG); differences in variance explained amongst non-Hispanic whites, Latinos, African Americans, and East Asians; genetic dominance and epistasis, with strong evidence for both at ABOxFUT2 for LDL; and eQTL tissue-enrichment implicating the liver, adipose, and pancreas. Utilizing EHR pharmacy data, both LDL and TG genetic risk scores (477 SNPs) were strongly predictive of age-at-initiation of lipid-lowering treatment. These findings highlight the value of longitudinal EHRs for identifying novel genetic features of cholesterol and lipoprotein metabolism with implications for lipid treatment and risk of coronary heart disease.

Insights from population-based analyses of plasma lipids across the allele frequency spectrum

Plasma lipid levels are heritable quantitative risk factors and therapeutic targets for cardiovascular disease. Plasma lipids have been a model for translating genetic observations across the allele frequency spectrum to unique biological and therapeutic insights. Most large studies to date predominately comprised of individuals of European ancestry. This review focuses on contemporary evidence from 2016 to 2017 looking at the effect of genetic variants on plasma lipid levels across the allele frequency spectrum with incrementally larger sample sizes and the contribution of non-European ancestry studies to the genetic etiology of plasma lipid levels. To date, over 250 loci have been associated with plasma lipid levels and several of these loci have additional evidence of association with rare coding variants providing evidence for causal genes at the locus.

Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease

Most genome-wide association studies have been conducted in European individuals, even though most genetic variation in humans is seen only in non-European samples. To search for novel loci associated with blood lipid levels and clarify the mechanism of action at previously identified lipid loci, we examined protein-coding genetic variants in 47,532 East Asian individuals using an exome array. We identified 255 variants at 41 loci reaching chip-wide significance, including 3 novel loci and 14 East Asian-specific coding variant associations. After meta-analysis with > 300,000 European samples, we identified an additional 9 novel loci. The same 16 genes were identified by the protein-altering variants in both East Asians and Europeans, likely pointing to the functional genes. Our data demonstrate that most of the low-frequency or rare coding variants associated with lipids are population-specific, and that examining genomic data across diverse ancestries may facilitate the identification of functional genes at associated loci.