Genetics and genomics of cholesterol and polyunsaturated fatty acid metabolism in relation to coronary heart disease risk

Background Coronary heart disease (CHD) continues to be a leading cause of morbidity and mortality among adults worldwide. Deregulated lipid metabolism (dyslipidemia) that manifests as hypercholesterolemia, hypertriglyceridemia, low high-density-lipoprotein (HDL) cholesterol levels or a combination of those, is an established risk factor for CHD among other established risk factors. Linoleic acid (LA, C18:2n-6) and alpha-linolenic acid (ALA, C18:3n-3) are polyunsaturated fatty acids (PUFAs) that cannot be synthesized de novo by human or animal cells, and therefore must be obtained from the diet. From these two PUFAs, two series of long-chain PUFAs are formed; the omega-6 series that are synthesized from LA, and the omega-3 series that are from ALA. Formation of these long-chain PUFAs involves a series of alternate desaturation and elongation processes. These PUFAs, especially, omega-3 PUFAs, have long been observed to reduce CHD risk. In contrast to the consistently observed cardiovascular protective effects of omega-3 PUFAs, accumulating evidence suggests a potential pro-atherogenic effects of omega-6 PUFAs, which is now still under debate. It has been estimated that genetic factors account for 26%-69% of inter-individual variation in CHD risk. These genetic factors are thought to influence CHD risk both directly and through effects on known CHD risk factors such as plasma lipid levels. The heritability of plasma lipid levels (total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides (TG)) is estimated to be about 50% (ranging from 28%-78%). With the success of recent genome-wide association studies (GWAS), many genetic variants underlying intermediate risk factors of CHD (including plasma lipid levels) and CHD itself have been identified. Whether this new genetic information could be used to improve CHD risk prediction is still marginally explored, and for some variants, the underlying mechanisms for their mediated effects on CHD risk are still unknown. The aim of this research is to investigate common genetic determinants of plasma lipid levels (cholesterol and polyunsaturated fatty acid levels) using a pathway-driven approach, and to explore whether such common genetic variants could be used to improve CHD prediction using a population based genetic approach. An additional aim was to explore the underlying mechanisms of cardiovascular protective effects of PUFAs using a genomic approach. Methods In order to explore whether common genetic variants are involved in determining plasma cholesterol levels, we used data from 3575 men and women from the Doetinchem cohort, which was examined thrice over 11 years. They were genotyped on 384 single nucleotide polymorphisms (SNPs) across 251 genes in regulatory pathways that control fatty acid, glucose, cholesterol and bile salt homeostasis. In order to explore whether common genetic variants could be used to predict future CHD risk,we used the data from CAREMA cohort that involved 15,236 middle-aged subjects and was followed up for a median of 12.1 years. 179 SNPs associated with CHD or its risk factors in GWAS published up to May 2, 2011 were genotyped in the 2221 subcohort members and 742 incident CHD cases. In addition, fatty acids from plasma cholesteryl esters were quantified in 1323 subcohort members and 537 CHD cases. They were used to explore whether δ-5 and δ-6 desaturase activities were associated with CHD risk. In order to perform a comparative analysis of the effects of fenofibrate and fish oil at transcriptome and metabolome level, 34 mice were randomized by weight-matching into three groups (n = 10 in control group, and n = 12 in fenofibrate or fish oil intervention group), and fed a research diet supplemented with sunflower oil (containing 81.3% oleic acid, 7% energy intake) in control group, sunflower oil (7% energy intake) and fenofibrate (0.03% w/w) in fenofibrate group, and fish oil (Marinol C-38 fish oil: 23.1% EPA and 21.1% DHA, 7% energy intake) in fish oil group for 2 weeks. At the end of treatment, mice were fasted with drinking water available, and were subsequently sacrificed by cervical dislocation under isoflurane anesthesia. Blood was collected via orbital puncture. Livers were dissected, directly frozen in liquid nitrogen and stored at −80°C until further analysis. Microarray analysis was performed on individual mouse livers. The LC-MS method was used for measuring plasma lipids and non-esterified free fatty acids, and the GC-MS method was used for measuring a broad range of metabolites. Results In chapter 2, 3, and 4, common genetic variants in the genes along known cholesterol metabolic pathways, such as bile acid and bile metabolic pathways, the HDL cholesterol metabolic pathway, and the plasma total cholesterol metabolic pathway, are involved in determining plasma cholesterol levels. The modest effect associated with each individual variant, however, caused the amount of heritability explained by them in aggregate to be relatively small: 13 single nucleotide polymorphisms (SNPs) explained 4% of inter-individual variation in HDL cholesterol levels (Chapter 3), whereas 12 SNPs explained 6.9% of inter-individual variation in total cholesterol levels (Chapter 4). In chapter 5, we found that genetic variants in the FADS1 gene potentially interact with dietary PUFA intake to affect plasma cholesterol levels. A high intake of omega-3 PUFAs was associated with increased plasma non-HDL cholesterol levels, consistent with increased plasma LDL cholesterol levels observed in fish oil intervention studies. Increased LDL cholesterol levels could be due to hepatic downregulation of the LDL receptor gene (LDLR) in subjects with high omega-3 PUFA intakes. This is further confirmed by the findings described in Chapter 6 that the hepatic LDLR gene was significantly downregulated in fish oil treated mice. This study also confirmed PUFAs to be weak PPAR ligands. The increased plasma HDL cholesterol levels in the subjects with high PUFA intakes in Chapter 5 could be due to PPARs-mediated genes that are directly involved in HDL lipoprotein metabolism. All these may explain the changes in blood cholesterol levels upon PUFA intake observed in human studies. In Chapter 6, we found that not only downregulation in the hepatic lipogenic pathway but also upregulation in hepatic fatty acid oxidation pathways are involved in lowering plasma TG levels upon fish oil treatment. The striking parallel between fenofibrate and fish oil in hepatic downregulation of blood coagulation and fibrinolysis pathways suggest that hepatic activation of PPARα is potentially one of the mechanisms responsible for anticoagulation effects of fish oil treatment observed in humans. In Chapter 7, with confirmed effects of rs174547 in FADS1 on PUFA levels and δ-5 desaturase activities and also protective effects of DHA on CHD, we observed a reduced CHD risk of increased δ-5 desaturase activity. Increased δ-5 desaturase activity could contribute to the intracellular increase of EPA and especially arachidonic acid (C20:4n-6) levels. Despite the potential pro-coagulant and pro-inflammatory effects of increased exposures of arachidonic acid and its derived eicosanoid metabolites, there is no evidence of increased CHD risk with increased habitual arachidonic acid intake so far. Some of the oxygenated metabolites of arachidonic acid were found to have anti-inflammatory and pro-resolving actions. High dietary n-6 PUFA intakes or high plasma n-6 PUFA levels are associated with increased blood HDL cholesterol levels and reduced TG (or VLDL particle) levels. All these point to a potential cardiovascular protective effect of n-6 PUFAs. The fact that increased EPA and/or DHA levels associated with increased δ-5 desaturase activity protect against CHD is consistent with the current established cardiovascular protective effect of increased n-3 PUFA exposure, especially EPA and DHA. In Chapter 8, the current known common genetic variants associated with CHD risk factors (blood pressure, obesity, blood lipid levels, and type 2 diabetes) and CHD itself from published GWAS are examined to see whether they provide additional value in CHD risk prediction beyond established traditional CHD risk factors. We constructed several gene risk scores (GRS) for CHD that consisted of SNPs directly associated with CHD or intermediate CHD risk factors in GWAS, and tested their relationship to incident CHD and their potential to improve risk prediction. The weighted GRS based on 29 CHD SNPs predicted future CHD independently from established traditional risk factors. However, the GRS based on 153 SNPs associated with intermediate risk factors and the GRS based on the total 179 SNPs did not. None of them improved risk discrimination. Risk classification of CHD, measured by the net reclassification index, improved only when the GRS based on the 29 CHD SNPs was used. These results are generally consistent with the results from other recent studies that took a similar approach as ours. However, the final conclusions on GRS application could not be drawn at this early stage. With a great understanding of the genetic architecture of CHD in the future, more research should be done on this topic. Conclusions Our studies in this thesis demonstrated that common genetic variants along the known candidate cholesterol metabolic pathways are involved in determining the plasma cholesterol levels. PUFAs are not only weak PPARα ligands, but also inhibit SREBPs’ activities. All these could explain part of the cardiovascular protective effects (increased HDL cholesterol levels and reduced TG levels) of PUFAs, increased LDL cholesterol levels upon fish oil treatment in humans, and potentially reduced CHD risk of high δ-5 desaturase activities. At present, many questions remain about the feasibility of genetic risk prediction of CHD. Clinicians should continue to inquire about family history of CHD for risk prediction, because this represents a simple, cheap, and useful risk factor for CHD that likely represents the net integrated effects from hundreds of genetic risk variants. </p

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P &lt; 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Meta-analysis of lipid-traits in Hispanics identifies novel loci, population-specific effects and tissue-specific enrichment of eQTLs

We performed genome-wide meta-analysis of lipid traits on three samples of Mexican and Mexican American ancestry comprising 4,383 individuals and followed up significant and highly suggestive associations in three additional Hispanic samples comprising 7,876 individuals. Genome-wide significant signals were observed in or near CELSR2, ZNF259/APOA5, KANK2/DOCK6 and NCAN/MAU2 for total cholesterol, LPL, ABCA1, ZNF259/APOA5, LIPC and CETP for HDL cholesterol, CELSR2, APOB and NCAN/MAU2 for LDL cholesterol and GCKR, TRIB1, ZNF259/APOA5 and NCAN/MAU2 for triglycerides. Linkage disequilibrium and conditional analyses indicate that signals observed at ABCA1 and LIPC for HDL cholesterol and NCAN/MAU2 for triglycerides are independent of previously reported lead SNP associations. Analyses of lead SNPs from the European Global Lipids Genetics Consortium (GLGC) dataset in our Hispanic samples show remarkable concordance of direction of effects as well as strong correlation in effect sizes. A meta-analysis of the European GLGC and our Hispanic datasets identified five novel regions reaching genome-wide significance: two for total cholesterol (FN1 and SAMM50), two for HDL cholesterol (LOC100996634 and COPB1) and one for LDL cholesterol (LINC00324/CTC1/PFAS). The top meta-analysis signals were found to be enriched for SNPs associated with gene expression in a tissue-specific fashion, suggesting an enrichment of tissue-specific function in lipid-associated loci

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution.

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF &lt;5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants

Discovery and Fine-Mapping of Adiposity Loci Using High Density Imputation of Genome-Wide Association Studies in Individuals of African Ancestry: African Ancestry Anthropometry Genetics Consortium

Genome-wide association studies (GWAS) have identified \u3e 300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHRadjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHRadjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHRadjBMI and eight previously established loci at P \u3c 5×10−8: seven for BMI, and one for WHRadjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHRadjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHRadjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (\u3c5%). In the trans-ethnic fine mapping of 47 BMI loci and 27 WHRadjBMI loci that were locus-wide significant (P \u3c 0.05 adjusted for effective number of variants per locus) from the African ancestry sex-combined and sex-stratified analyses, 26 BMI loci and 17 WHRadjBMI loci contained ≤ 20 variants in the credible sets that jointly account for 99% posterior probability of driving the associations. The lead variants in 13 of these loci had a high probability of being causal. As compared to our previous HapMap imputed GWAS for BMI and WHRadjBMI including up to 71,412 and 27,350 African ancestry individuals, respectively, our results suggest that 1000 Genomes imputation showed modest improvement in identifying GWAS loci including low frequency variants. Trans-ethnic meta-analyses further improved fine mapping of putative causal variants in loci shared between the African and European ancestry populations

Genetic Discovery and Risk Characterization in Type 2 Diabetes across Diverse Populations

Genomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in th

Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci

Genome-wide association studies have identified over 150 loci associated with lipid traits, however, no large-scale studies exist for Hispanics and other minority populations. Additionally, the genetic architecture of lipid-influencing loci remains largely unknown. We performed one of the most racially/ethnically diverse fine-mapping genetic studies of HDL-C, LDL-C, and triglycerides to-date using SNPs on the MetaboChip array on 54,119 individuals: 21,304 African Americans, 19,829 Hispanic Americans, 12,456 Asians, and 530 American Indians. The majority of signals found in these groups generalize to European Americans. While we uncovered signals unique to racial/ethnic populations, we also observed systematically consistent lipid associations across these groups. In African Americans, we identified three novel signals associated with HDL-C (LPL, APOA5, LCAT) and two associated with LDL-C (ABCG8, DHODH). In addition, using this population, we refined the location for 16 out of the 58 known MetaboChip lipid loci. These results can guide tailored screening efforts, reveal population-specific responses to lipid-lowering medications, and aid in the development of new targeted drug therapies

Genomewide meta-analysis identifies loci associated with IGF-I and IGFBP-3 levels with impact on age-related traits

The growth hormone/insulin-like growth factor (IGF) axis can be manipulated in animal models to promote longevity, and IGF-related proteins including IGF-I and IGF-binding protein-3 (IGFBP-3) have also been implicated in risk of human diseases including cardiovascular diseases, diabetes, and cancer. Throug

Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk

DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P <7.94 x 10(-7). Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. Significance: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.Peer reviewe