Apolipoprotein A1/C3/A5 haplotypes and serum lipid levels

<p>Abstract</p> <p>Background</p> <p>The association of single nucleotide polymorphisms (SNPs) in the apolipoprotein (Apo) A1/C3/A4/A5 gene cluster and serum lipid profiles is inconsistent. The present study was undertaken to detect the association between the ApoA1/C3/A5 gene polymorphisms and their haplotypes with serum lipid levels in the general Chinese population.</p> <p>Methods</p> <p>A total of 1030 unrelated subjects (492 males and 538 females) aged 15-89 were randomly selected from our previous stratified randomized cluster samples. Genotyping of the ApoA1 -75 bp G>A, ApoC3 3238C>G, ApoA5 -1131T>C, ApoA5 c.553G>T and ApoA5 c.457G>A was performed by polymerse chain reaction and restriction fragment length polymorphism combined with gel electrophoresis, and then confirmed by direct sequencing. Pair-wise linkage disequilibria and haplotype analysis among the five SNPs were estimated.</p> <p>Results</p> <p>The levels of high-density lipoprotein cholesterol (HDL-C) and ApoA1 were lower in males than in femailes (<it>P </it>< 0.05 for each). The allelic and genotypic frequencies of the SNPs were no significant difference between males and females except ApoC3 3238C>G. There were 11 haplotypes with a frequency >1% identified in the cluster in our population. At the global level, the haplotypes comprised of all five SNPs were significantly associated with all seven lipid traits. In particular, haplotype G-G-C-C-A (6%; in the order of ApoA5 c.553G>T, ApoA5 c.457G>A, ApoA5 -1131T>C, ApoC3 3238C>G, and ApoA1 -75bp G>A) and G-A-T-C-G (4%) showed consistent association with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), ApoA1, ApoB, and the ApoA1/ApoB ratio. In addition, carriers of haplotype G-G-T-C-G (26%) had increased serum concentration of HDL-C and ApoA1, whereas carriers of G-G-C-G-G (15%) had high concentrations of TC, triglyceride (TG) and ApoB. We also found that haplotypes with five SNPs explain much more serum lipid variation than any single SNP alone, especially for TG (4.4% for haplotype vs. 2.4% for -1131T>C max based on R-square) and HDL-C (5.1% for haplotype vs. 0.9% for c.553G>T based on R-square). Serum lipid parameters were also correlated with genotypes and several environment factors.</p> <p>Conclusions</p> <p>Several common SNPs and their haplotypes in the ApoA1/C3/A5 gene cluster are closely associated with modifications of serum lipid parameters in the general Chinese population.</p

A genome-wide survey for SNPs altering microRNA seed sites identifies functional candidates in GWAS

<p>Abstract</p> <p>Background</p> <p>Gene variants within regulatory regions are thought to be major contributors of the variation of complex traits/diseases. Genome wide association studies (GWAS), have identified scores of genetic variants that appear to contribute to human disease risk. However, most of these variants do not appear to be functional. Thus, the significance of the association may be brought up by still unknown mechanisms or by linkage disequilibrium (LD) with functional polymorphisms. In the present study, focused on functional variants related with the binding of microRNAs (miR), we utilized SNP data, including newly released 1000 Genomes Project data to perform a genome-wide scan of SNPs that abrogate or create miR recognition element (MRE) seed sites (MRESS).</p> <p>Results</p> <p>We identified 2723 SNPs disrupting, and 22295 SNPs creating MRESSs. We estimated the percent of SNPs falling within both validated (5%) and predicted conserved MRESSs (3%). We determined 87 of these MRESS SNPs were listed in GWAS association studies, or in strong LD with a GWAS SNP, and may represent the functional variants of identified GWAS SNPs. Furthermore, 39 of these have evidence of co-expression of target mRNA and the predicted miR. We also gathered previously published eQTL data supporting a functional role for four of these SNPs shown to associate with disease phenotypes. Comparison of F_STstatistics (a measure of population subdivision) for predicted MRESS SNPs against non MRESS SNPs revealed a significantly higher (P = 0.0004) degree of subdivision among MRESS SNPs, suggesting a role for these SNPs in environmentally driven selection.</p> <p>Conclusions</p> <p>We have demonstrated the potential of publicly available resources to identify high priority candidate SNPs for functional studies and for disease risk prediction.</p

Disparities in allele frequencies and population differentiation for 101 disease-associated single nucleotide polymorphisms between Puerto Ricans and Non-Hispanic Whites

BACKGROUND. Variations in gene allele frequencies can contribute to differences in the prevalence of some common complex diseases among populations. Natural selection modulates the balance in allele frequencies across populations. Population differentiation (FST) can evidence environmental selection pressures. Such genetic information is limited in Puerto Ricans, the second largest Hispanic ethnic group in the US, and a group with high prevalence of chronic disease. We determined allele frequencies and population differentiation for 101 single nucleotide polymorphisms (SNPs) in 30 genes involved in major metabolic and disease-relevant pathways in Puerto Ricans (n = 969, ages 45–75 years) and compared them to similarly aged non-Hispanic whites (NHW) (n = 597). RESULTS. Minor allele frequency (MAF) distributions for 45.5% of the SNPs assessed in Puerto Ricans were significantly different from those of NHW. Puerto Ricans carried risk alleles in higher frequency and protective alleles in lower frequency than NHW. Patterns of population differentiation showed that Puerto Ricans had SNPs with exceptional FST values in intronic, non-synonymous and promoter regions. NHW had exceptional FST values in intronic and promoter region SNPs only. CONCLUSION. These observations may serve to explain and broaden studies on the impact of gene polymorphisms on chronic diseases affecting Puerto Ricans.National Institutes of Health, National Institutes on Aging (P01AG02394, P01AG023394-SI); National Insitutes of Health (53-K06-5-10); US Department of Agriculture Research Service (58-1950-9-001, 58-1950-7-707); National Institutes of Health & Heart, Lung, and Blood Institute (U 01 HL72524, Genetic and Environmental Determinants of Triglycerides, HL54776

Observation of Non-Hermitian Skin Effect in Thermal Diffusion

The paradigm shift of the Hermitian systems into the non-Hermitian regime profoundly modifies the inherent topological property, leading to various unprecedented effects such as the non-Hermitian skin effect (NHSE). In the past decade, the NHSE effect has been demonstrated in quantum, optical and acoustic systems. Besides in those non-Hermitian wave systems, the NHSE in diffusive systems has not yet been explicitly demonstrated, despite recent abundant advances in the study of topological thermal diffusion. Here we first design a thermal diffusion lattice based on a modified Su-Schrieffer-Heeger model which enables the observation of diffusive NHSE. In the proposed model, the periodic heat exchange rate among adjacent unit cells and the asymmetric temperature field coupling inside unit cells can be judiciously realized by appropriate configurations of structural parameters of unit cells. The transient concentration feature of temperature field on the boundary regardless of initial excitation conditions can be clearly observed, indicating the occurrence of transient thermal skin effect. Nonetheless, we experimentally demonstrated the NHSE and verified the remarkable robustness against various defects. Our work provides a platform for exploration of non-Hermitian physics in the diffusive systems, which has important applications in efficient heat collection, highly sensitive thermal sensing and others.Comment: 23 pages, 5 figure

Dietary Intake of n-6 Fatty Acids Modulates Effect of Apolipoprotein A5 Gene on Plasma Fasting Triglycerides, Remnant Lipoprotein Concentrations, and Lipoprotein Particle Size

Background— Apolipoprotein A5 gene (APOA5) variation is associated with plasma triglycerides (TGs). However, little is known about whether dietary fat modulates this association. Methods and Results— We investigated the interaction between APOA5 gene variation and dietary fat in determining plasma fasting TGs, remnant-like particle (RLP) concentrations, and lipoprotein particle size in 1001 men and 1147 women who were Framingham Heart Study participants. Polymorphisms –1131T>C and 56C>G, representing 2 independent haplotypes, were analyzed. Significant gene–diet interactions between the –1131T>C polymorphism and polyunsaturated fatty acid (PUFA) intake were found (PG polymorphism. The –1131C allele was associated with higher fasting TGs and RLP concentrations (P6% of total energy). No heterogeneity by sex was found. These interactions showed a dose-response effect when PUFA intake was considered as a continuous variable (P<0.01). Similar interactions were found for the sizes of VLDL and LDL particles. Only in carriers of the –1131C allele did the size of these particles increase (VLDL) or decrease (LDL) as PUFA intake increased (P<0.01). We further analyzed the effects of n-6 and n-3 fatty acids and found that the PUFA–APOA5 interactions were specific for dietary n-6 fatty acids. Conclusions— Higher n-6 (but not n-3) PUFA intake increased fasting TGs, RLP concentrations, and VLDL size and decreased LDL size in APOA5 –1131C carriers, suggesting that n-6 PUFA–rich diets are related to a more atherogenic lipid profile in these subjects.Corella Piquer, Maria Dolores, [email protected]

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions.

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual's risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].This research was funded by the United States Department of Agriculture (USDA), Agriculture Research Service (ARS) under agreement no. 8050-51000-107-000D. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDA.S

Replication of a Gene-Diet Interaction at CD36, NOS3 and PPARG in Response to Omega-3 Fatty Acid Supplements on Blood Lipids: A Double-Blind Randomized Controlled Trial.

BACKGROUND: Modulation of genetic variants on the effect of omega-3 fatty acid supplements on blood lipids is still unclear. METHODS: In a double-blind randomized controlled trial, 150 patients with type 2 diabetes (T2D) were randomized into omega-3 fatty acid group (n = 56 for fish oil and 44 for flaxseed oil) and control group (n = 50) for 180 days. All patients were genotyped for genetic variants at CD36 (rs1527483), NOS3 (rs1799983) and PPARG (rs1801282). Linear regression was used to examine the interaction between omega-3 fatty acid intervention and CD36, NOS3 or PPARG variants for blood lipids. FINDINGS: Significant interaction with omega-3 fatty acid supplements was observed for CD36 on triglycerides (p-interaction = 0.042) and PPAGR on low-density lipoprotein-cholesterol (p-interaction = 0.02). We also found a significant interaction between change in erythrocyte phospholipid omega-3 fatty acid composition and NOS3 genotype on triglycerides (p-interaction = 0.042), total cholesterol (p-interaction = 0.013) and ratio of total cholesterol to high-density lipoprotein cholesterol (p-interaction = 0.015). The T2D patients of CD36-G allele, PPARG-G allele and NOS3-A allele tended to respond better to omega-3 fatty acids in improving lipid profiles. The interaction results of the omega-3 fatty acid group were mainly attributed to the fish oil supplements. INTERPRETATION: This study suggests that T2D patients with different genotypes at CD36, NOS3 and PPARG respond differentially to intervention of omega-3 supplements in blood lipid profiles

Epigenome-wide association study of triglyceride postprandial responses to a high-fat dietary challenge

Postprandial lipemia (PPL), the increased plasma TG concentration after consuming a high-fat meal, is an independent risk factor for CVD. Individual responses to a meal high in fat vary greatly, depending on genetic and lifestyle factors. However, only a few loci have been associated with TG-PPL response. Heritable epigenomic changes may be significant contributors to the unexplained inter-individual PPL variability. We conducted an epigenome-wide association study on 979 subjects with DNA methylation measured from CD4(+) T cells, who were challenged with a high-fat meal as a part of the Genetics of Lipid Lowering Drugs and Diet Network study. Eight methylation sites encompassing five genes, LPP, CPT1A, APOA5, SREBF1, and ABCG1, were significantly associated with PPL response at an epigenome-wide level (P < 1.1 × 10(−7)), but no methylation site reached epigenome-wide significance after adjusting for baseline TG levels. Higher methylation at LPP, APOA5, SREBF1, and ABCG1, and lower methylation at CPT1A methylation were correlated with an increased TG-PPL response. These PPL-associated methylation sites, also correlated with fasting TG, account for a substantially greater amount of phenotypic variance (14.9%) in PPL and fasting TG (16.3%) when compared with the genetic contribution of loci identified by our previous genome-wide association study (4.5%). In summary, the epigenome is a large contributor to the variation in PPL, and this has the potential to be used to modulate PPL and reduce CVD