Interaction between SNPs in the RXRA and near ANGPTL3 gene region inhibits apoB reduction after statin-fenofibric acid therapy in individuals with mixed dyslipidemia

ANGPTL3 Variants Associate with Lower Levels of Irisin and C-Peptide in a Cohort of Arab Individuals

ANGPTL3 is an important regulator of lipid metabolism. Its inhibition in people with hypercholesteremia reduces plasma lipid levels dramatically. Genome-wide association studies have associated ANGPTL3 variants with lipid traits. Irisin, an exercise-modulated protein, has been associated with lipid metabolism. Intracellular accumulation of lipids impairs insulin action and contributes to metabolic disorders. In this study, we evaluate the impact of ANGPTL3 variants on levels of irisin and markers associated with lipid metabolism and insulin resistance. ANGPTL3 rs1748197 and rs12130333 variants were genotyped in a cohort of 278 Arab individuals from Kuwait. Levels of irisin and other metabolic markers were measured by ELISA. Significance of association signals was assessed using Bonferroni-corrected p-values and empirical p-values. The study variants were significantly associated with low levels of c-peptide and irisin. Levels of c-peptide and irisin were mediated by interaction between carrier genotypes (GA + AA) at rs1748197 and measures of IL13 and TG, respectively. While levels of c-peptide and IL13 were directly correlated in individuals with the reference genotype, they were inversely correlated in individuals with the carrier genotype. Irisin correlated positively with TG and was strong in individuals with carrier genotypes. These observations illustrate ANGPTL3 as a potential link connecting lipid metabolism, insulin resistance and cardioprotection.

ANGPLT3 in cardio-metabolic disorders

Dyslipidemia is associated with numerous health problems that include the combination of insulin resistance, hypertension and obesity, which is always grouped together asmetabolic syndrome. Given that metabolic syndrome leads to a high mortality and poses serious risks to human health worldwide, it is vital to explore the mechanisms whereby dyslipidemia modulates the risk and the severity of cardio-metabolic disorders. Recently, a specific secretory protein family, named angiopoietin-like protein (ANGPTL), is considered as one of the significant biomarkers which facilitate the development of angiogenesis. Among the eight proteins of ANGPTL family, ANGPTL3 has been demonstrated as an essential modulator of lipid catabolism within circulation by inhibiting the activity of lipoprotein lipase (LPL) and endothelial lipase (EL). Consistent with these notions, mice with ANGPTL3 gene-deficiency presented reduced circulating levels of low density lipoprotein cholesterol (LDL-C) and lower risk of atherosclerosis. On the other hand, participants carrying homozygous loss-of function (LOF) mutation in ANGPTL3 gene also displayed lower circulating LDL-C levels and atherosclerotic risk. In the current review, we summarized the recent understanding of ANGPTL3 in controlling the risk and the development of dyslipidemia and its related cardio-metabolic disorders. Moreover, we also provided the perspectives which potentially suggested that ANGPTL3 could be considered as a promising target in treating metabolic syndrome.

Tissue-specific epigenetics of atherosclerosis-related ANGPT and ANGPTL genes

Aim:

To understand tissue-specific regulation of angiopoietin/angiopoietin-like (ANGPT/ANGPTL) genes (especially the five genes embedded in introns of host genes) and their association with atherosclerosis.

Methods:

Transcription and epigenomic databases from various normal tissues were examined in the vicinity of ANGPT1, ANGPT2, ANGPTL1, ANGPTL2, ANGPTL3, ANGPTL4 and ANGPTL8.

Results:

We identified tissue-specific enhancer chromatin regions that are likely to regulate transcription of ANGPT/ANGPTL genes and were intragenic, intergenic or host gene-linked. In addition, we found atherosclerosis-linked differentially methylated regions associated with ANGPT2 and with sequences encoding miR-145, a microRNA that targets ANGPT2 mRNA in cancers.

Conclusion:

Our findings implicate enhancers as major contributors to tissue-specific expression of ANGPT/ANGPTL genes, which play critical roles in angiogenesis, atherosclerosis, cancer, and inflammatory and metabolic diseases.

A Novel Test for Detecting SNP-SNP Interactions in Case-Only Trio Studies

Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple-hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new SNP-SNP interaction test for use in case-only trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard 1 d.f. correlation test of interaction. We show via extensive simulations under a variety of disease models that our test substantially outperforms existing tests of interaction in case-only trio studies. We also demonstrate a bias in a previous case-only trio interaction test and identify its origin. Finally, we show that a previously proposed permutation scheme in trio studies mitigates the known biases of case-only tests in the presence of population stratification. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at www.github.com/BrunildaBalliu/TrioEpi.

Analysis of gene-gene interactions among common variants in candidate cardiovascular genes in coronary artery disease

Objective

Only a small fraction of coronary artery disease (CAD) heritability has been explained by common variants identified to date. Interactions between genes of importance to cardiovascular regulation may account for some of the missing heritability of CAD. This study aimed to investigate the role of gene-gene interactions in common variants in candidate cardiovascular genes in CAD.

Approach and Results

2,101 patients with CAD from the British Heart Foundation Family Heart Study and 2,426 CAD-free controls were included in the discovery cohort. All subjects were genotyped with the Illumina HumanCVD BeadChip enriched for genes and pathways relevant to the cardiovascular system and disease. The primary analysis in the discovery cohort examined pairwise interactions among 913 common (minor allele frequency >0.1) independent single nucleotide polymorphisms (SNPs) with at least nominal association with CAD in single locus analysis. A secondary exploratory interaction analysis was performed among all 11,332 independent common SNPs surviving quality control criteria. Replication analyses were conducted in 2,967 patients and 3,075 controls from the Myocardial Infarction Genetics Consortium. None of the interactions amongst 913 SNPs analysed in the primary analysis was statistically significant after correction for multiple testing (required P<1.2x10^-7). Similarly, none of the pairwise gene-gene interactions in the secondary analysis reached statistical significance after correction for multiple testing (required P = 7.8x10^-10). None of 36 suggestive interactions from the primary analysis or 31 interactions from the secondary analysis was significant in the replication cohort. Our study had 80% power to detect odds ratios > 1.7 for common variants in the primary analysis.

Conclusions

Moderately large additive interactions between common SNPs in genes relevant to cardiovascular disease do not appear to play a major role in genetic predisposition to CAD. The role of genetic interactions amongst less common SNPs and with medium and small magnitude effects remain to be investigated.

Biological mechanism analysis of acute renal allograft rejection: integrated of mRNA and microRNA expression profiles

OBJECTIVES

Renal transplantation is the preferred method for most patients with end-stage renal disease, however, acute renal allograft rejection is still a major risk factor for recipients leading to renal injury. To improve the early diagnosis and treatment of acute rejection, study on the molecular mechanism of it is urgent.

METHODS

MicroRNA (miRNA) expression profile and mRNA expression profile of acute renal allograft rejection and well-functioning allograft downloaded from ArrayExpress database were applied to identify differentially expressed (DE) miRNAs and DE mRNAs. DE miRNAs targets were predicted by combining five algorithm. By overlapping the DE mRNAs and DE miRNAs targets, common genes were obtained. Differentially co-expressed genes (DCGs) were identified by differential co-expression profile (DCp) and differential co-expression enrichment (DCe) methods in Differentially Co-expressed Genes and Links (DCGL) package. Then, co-expression network of DCGs and the cluster analysis were performed. Functional enrichment analysis for DCGs was undergone.

RESULTS

A total of 1270 miRNA targets were predicted and 698 DE mRNAs were obtained. While overlapping miRNA targets and DE mRNAs, 59 common genes were gained. We obtained 103 DCGs and 5 transcription factors (TFs) based on regulatory impact factors (RIF), then built the regulation network of miRNA targets and DE mRNAs. By clustering the co-expression network, 5 modules were obtained. Thereinto, module 1 had the highest degree and module 2 showed the most number of DCGs and common genes. TF CEBPB and several common genes, such as RXRA, BASP1 and AKAP10, were mapped on the co-expression network. C1R showed the highest degree in the network. These genes might be associated with human acute renal allograft rejection.

CONCLUSIONS

We conducted biological analysis on integration of DE mRNA and DE miRNA in acute renal allograft rejection, displayed gene expression patterns and screened out genes and TFs that may be related to acute renal allograft rejection.

Rare LPL gene variants attenuate triglyceride reduction and HDL cholesterol increase in response to fenofibric acid therapy in individuals with mixed dyslipidemia

OBJECTIVE

Individuals with mixed dyslipidemia have elevated triglycerides (TG), low high-density lipoprotein cholesterol (HDL-C), and increased risk for coronary disease. Fibrate therapy is commonly used to lower TG and increase HDL-C. Common genetic variants are known to affect the response to fibrate therapy. We sought to identify rare genetic variants (frequency ≤ 1%) in genes involved in TG and HDL-C metabolism that affect the response to fenofibric acid (FA) therapy.

METHODS

Four genes with a major role in HDL-C and TG metabolism APOA1, APOC2, APOC-III and LPL were sequenced in 2385 participants with mixed dyslipidemia in a randomized, double-blind, active-controlled study comparing therapy with FA alone, in combination with statins, or statin alone. Rare variants collapsing or SKAT methods were used for the analysis.

RESULTS

Synonymous rare variants in the LPL gene were significantly associated with absolute HDL-C change (P = 9 × 10(-4)) and TG percent change (P = 6.76 × 10(-4)) in those treated with FA only. Participants with these rare variants had a 2 mg/dL increase in HDL-C and 39 mg/dL decrease in TG as compared to 6.2 mg/dL increase in HDL-C and 100 mg/dL decrease in TG in those without these variants. Rare variants in the APOC-III gene were associated with a modest 3 mg/dL less reduction in APOB (P = 8.72 × 10(-4)) in those receiving FA and statin.

CONCLUSION

In individuals with mixed dyslipidemia rare synonymous variants within LPL gene were associated with attenuated response to FA therapy while APOCIII rare variants were associated with a modest effect on APOB response to FA-statin therapy. These results should be replicated in a similar clinical trial for further confirmation.

Analysis of multiple association studies provides evidence of an expression QTL hub in gene-gene interaction network affecting HDL cholesterol levels

Epistasis has been suggested to underlie part of the missing heritability in genome-wide association studies. In this study, we first report an analysis of gene-gene interactions affecting HDL cholesterol (HDL-C) levels in a candidate gene study of 2,091 individuals with mixed dyslipidemia from a clinical trial. Two additional studies, the Atherosclerosis Risk in Communities study (ARIC; n = 9,713) and the Multi-Ethnic Study of Atherosclerosis (MESA; n = 2,685), were considered for replication. We identified a gene-gene interaction between rs1532085 and rs12980554 (P = 7.1×10⁻⁷) in their effect on HDL-C levels, which is significant after Bonferroni correction (P_c = 0.017) for the number of SNP pairs tested. The interaction successfully replicated in the ARIC study (P = 7.0×10⁻⁴; P_c = 0.02). Rs1532085, an expression QTL (eQTL) of LIPC, is one of the two SNPs involved in another, well-replicated gene-gene interaction underlying HDL-C levels. To further investigate the role of this eQTL SNP in gene-gene interactions affecting HDL-C, we tested in the ARIC study for interaction between this SNP and any other SNP genome-wide. We found the eQTL to be involved in a few suggestive interactions, one of which significantly replicated in MESA. Importantly, these gene-gene interactions, involving only rs1532085, explain an additional 1.4% variation of HDL-C, on top of the 0.65% explained by rs1532085 alone. LIPC plays a key role in the lipid metabolism pathway and it, and rs1532085 in particular, has been associated with HDL-C and other lipid levels. Collectively, we discovered several novel gene-gene interactions, all involving an eQTL of LIPC, thus suggesting a hub role of LIPC in the gene-gene interaction network that regulates HDL-C levels, which in turn raises the hypothesis that LIPC's contribution is largely via interactions with other lipid metabolism related genes.

Rare APOA5 promoter variants associated with paradoxical HDL cholesterol decrease in response to fenofibric acid therapy

Individuals with mixed dyslipidemia, including high triglycerides (TGs) and low high density lipoprotein cholesterol (HDL-C), have increased risk for coronary events. We examined the effect of rare genetic variants in the APOA5 gene region on plasma HDL-C, apolipoprotein A-I (apoA-I), and TG response to fenofibric acid monotherapy and in combination with statins. The APOA5 gene region was sequenced in 1,612 individuals with mixed dyslipidemia in a randomized trial of fenofibric acid alone and in combination with statins. Student's t-test and rare variant burden tests were used to examine plasma HDL-C, apoA-I, and TG response. Rare APOA5 promoter region variants were associated with decreased HDL-C and apoA-I levels in response to fenofibric acid therapy; rare missense variants were associated with increased TG response to combination therapy. Further study is needed to examine the effect of these rare variants on coronary outcomes in this population in response to fenofibric acid monotherapy or combined with statins.

Gene-based testing of interactions in association studies of quantitative traits

Various methods have been developed for identifying gene–gene interactions in genome-wide association studies (GWAS). However, most methods focus on individual markers as the testing unit, and the large number of such tests drastically erodes statistical power. In this study, we propose novel interaction tests of quantitative traits that are gene-based and that confer advantage in both statistical power and biological interpretation. The framework of gene-based gene–gene interaction (GGG) tests combine marker-based interaction tests between all pairs of markers in two genes to produce a gene-level test for interaction between the two. The tests are based on an analytical formula we derive for the correlation between marker-based interaction tests due to linkage disequilibrium. We propose four GGG tests that extend the following P value combining methods: minimum P value, extended Simes procedure, truncated tail strength, and truncated P value product. Extensive simulations point to correct type I error rates of all tests and show that the two truncated tests are more powerful than the other tests in cases of markers involved in the underlying interaction not being directly genotyped and in cases of multiple underlying interactions. We applied our tests to pairs of genes that exhibit a protein–protein interaction to test for gene-level interactions underlying lipid levels using genotype data from the Atherosclerosis Risk in Communities study. We identified five novel interactions that are not evident from marker-based interaction testing and successfully replicated one of these interactions, between SMAD3 and NEDD9, in an independent sample from the Multi-Ethnic Study of Atherosclerosis. We conclude that our GGG tests show improved power to identify gene-level interactions in existing, as well as emerging, association studies.

Epistasis is likely to play a significant role in complex diseases or traits and is one of the many possible explanations for “missing heritability.” However, epistatic interactions have been difficult to detect in genome-wide association studies (GWAS) due to the limited power caused by the multiple-testing correction from the large number of tests conducted. Gene-based gene–gene interaction (GGG) tests might hold the key to relaxing the multiple-testing correction burden and increasing the power for identifying epistatic interactions in GWAS. Here, we developed GGG tests of quantitative traits by extending four P value combining methods and evaluated their type I error rates and power using extensive simulations. All four GGG tests are more powerful than a principal component-based test. We also applied our GGG tests to data from the Atherosclerosis Risk in Communities study and found five gene-level interactions associated with the levels of total cholesterol and high-density lipoprotein cholesterol (HDL-C). One interaction between SMAD3 and NEDD9 on HDL-C was further replicated in an independent sample from the Multi-Ethnic Study of Atherosclerosis.