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

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

A catalog of natural products occurring in watermelon— Citrullus lanatus

Sweet dessert watermelon ( Citrullus lanatus ) is one of the most important vegetable crops consumed throughout the world. The chemical composition of watermelon provides both high nutritional value and various health benefits. The present manuscript introduces a catalog of 1,679 small molecules occurring in the watermelon and their cheminformatics analysis for diverse features. In this catalog, the phytochemicals are associated with the literature describing their presence in the watermelon plant, and when possible, concentration values in various plant parts (flesh, seeds, leaves, roots, rind). Also cataloged are the chemical classes, molecular weight and formula, chemical structure, and certain physical and chemical properties for each phytochemical. In our view, knowing precisely what is in what we eat, as this catalog does for watermelon, supports both the rationale for certain controlled feeding studies in the field of precision nutrition, and plant breeding efforts for the development of new varieties with enhanced concentrations of specific phytochemicals. Additionally, improved and comprehensive collections of natural products accessible to the public will be especially useful to researchers in nutrition, cheminformatics, bioinformatics, and drug development, among other disciplines

Gene expression changes in mononuclear cells from patients with metabolic syndrome after acute intake of phenol-rich virgin olive oil

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract Background Previous studies have shown that acute intake of high-phenol virgin olive oil reduces pro-inflammatory, pro-oxidant and pro-thrombotic markers compared with low phenols virgin olive oil, but it still remains unclear whether effects attributed to its phenolic fraction are exerted at transcriptional level in vivo. To achieve this goal, we aimed at identifying expression changes in genes which could be mediated by virgin olive oil phenol compounds in the human. Results Postprandial gene expression microarray analysis was performed on peripheral blood mononuclear cells during postprandial period. Two virgin olive oil-based breakfasts with high (398 ppm) and low (70 ppm) content of phenolic compounds were administered to 20 patients suffering from metabolic syndrome following a double-blinded, randomized, crossover design. To eliminate the potential effect that might exist in their usual dietary habits, all subjects followed a similar low-fat, carbohydrate rich diet during the study period. Microarray analysis identified 98 differentially expressed genes (79 underexpressed and 19 overexpressed) when comparing the intake of phenol-rich olive oil with low-phenol olive oil. Many of these genes seem linked to obesity, dyslipemia and type 2 diabetes mellitus. Among these, several genes seem involved in inflammatory processes mediated by transcription factor NF-&#954;B, activator protein-1 transcription factor complex AP-1, cytokines, mitogen-activated protein kinases MAPKs or arachidonic acid pathways. Conclusion This study shows that intake of virgin olive oil based breakfast, which is rich in phenol compounds is able to repress in vivo expression of several pro-inflammatory genes, thereby switching activity of peripheral blood mononuclear cells to a less deleterious inflammatory profile. These results provide at least a partial molecular basis for reduced risk of cardiovascular disease observed in Mediterranean countries, where virgin olive oil represents a main source of dietary fat. Admittedly, other lifestyle factors are also likely to contribute to lowered risk of cardiovascular disease in this region.Published versio

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

Genome-wide contribution of genotype by environment interaction to variation of diabetes-related traits

While genome-wide association studies (GWAS) and candidate gene approaches have identified many genetic variants that contribute to disease risk as main effects, the impact of genotype by environment (GxE) interactions remains rather under-surveyed. To explore the importance of GxE interactions for diabetes-related traits, a tool for Genome-wide Complex Trait Analysis (GCTA) was used to examine GxE variance contribution of 15 macronutrients and lifestyle to the total phenotypic variance of diabetes-related traits at the genome-wide level in a European American population. GCTA identified two key environmental factors making significant contributions to the GxE variance for diabetes-related traits: carbohydrate for fasting insulin (25.1% of total variance, P-nominal = 0.032) and homeostasis model assessment of insulin resistance (HOMA-IR) (24.2% of total variance, P-nominal = 0.035), n-6 polyunsaturated fatty acid (PUFA) for HOMA-β-cell-function (39.0% of total variance, P-nominal = 0.005). To demonstrate and support the results from GCTA, a GxE GWAS was conducted with each of the significant dietary factors and a control E factor (dietary protein), which contributed a non-significant GxE variance. We observed that GxE GWAS for the environmental factor contributing a significant GxE variance yielded more significant SNPs than the control factor. For each trait, we selected all significant SNPs produced from GxE GWAS, and conducted anew the GCTA to estimate the variance they contributed. We noted the variance contributed by these SNPs is higher than that of the control. In conclusion, we utilized a novel method that demonstrates the importance of genome-wide GxE interactions in explaining the variance of diabetes-related traits

Dietary epicatechin improves survival and delays skeletal muscle degeneration in aged mice

We recently reported that epicatechin, a bioactive compound that occurs naturally in various common foods, promoted general health and survival of obese diabetic mice. It remains to be determined whether epicatechin extends health span and delays the process of aging. In the present study, epicatechin or its analogue epigallocatechin gallate (EGCG) (0.25% w/v in drinking water) was administered to 20-mo-old male C57BL mice fed a standard chow. The goal was to determine the antiaging effect. The results showed that supplementation with epicatechin for 37 wk strikingly increased the survival rate from 39 to 69%, whereas EGCG had no significant effect. Consistently, epicatechin improved physical activity, delayed degeneration of skeletal muscle (quadriceps), and shifted the profiles of the serum metabolites and skeletal muscle general mRNA expressions in aging mice toward the profiles observed in young mice. In particular, we found that dietary epicatechin significantly reversed age-altered mRNA and protein expressions of extracellular matrix and peroxisome proliferator–activated receptor pathways in skeletal muscle, and reversed the age-induced declines of the nicotinate and nicotinamide pathway both in serum and skeletal muscle. The present study provides evidence that epicatechin supplementation can exert an antiaging effect, including an increase in survival, an attenuation of the aging-related deterioration of skeletal muscles, and a protection against the aging-related decline in nicotinate and nicotinamide metabolism.—Si, H., Wang, X., Zhang, L., Parnell, L. D., Ahmed, B., LeRoith, T., Ansah, T.-A., Zhang, L., Li, J., Ordovás, J. M., Si, H., Liu, D., Lai, C.-Q. Dietary epicatechin improves survival and delays skeletal muscle degeneration in aged mice. FASEB J. 33, 965–977 (2019)

Polyunsaturated fatty acids modulate the association between PIK3CA-KCNMB3 genetic variants and insulin resistance

BACKGROUND:Neighboring genes PIK3CA and KCNMB3 are both important for insulin signaling and β-cell function, but their associations with glucose-related traits are unclear. OBJECTIVE:The objective was to examine associations of PIK3CA-KCNMB3 variants with glucose-related traits and potential interaction with dietary fat. DESIGN:We first investigated genetic associations and their modulation by dietary fat in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study (n = 820). Nine single-nucleotide polymorphisms (SNPs) were selected for analysis, covering more than 80% of the SNPs in the region. We then sought to replicate the findings in the Boston Puerto Rican Health Study (BPRHS) (n = 844). RESULTS:For KCNMB3 missense mutation rs7645550, meta-analysis indicated that homeostasis model assessment of insulin resistance (HOMA-IR) was significantly lower in minor allele T homozygotes compared with major allele C carriers (pooled P-value = 0.004); for another SNP rs1183319, which is in moderate LD with rs7645550, minor allele G carriers had higher HOMA-IR compared with non-carriers in both populations (pooled P-value = 0.028). In GOLDN, rs7645550 T allele homozygotes had lower HOMA-IR only when dietary n-3: n-6 PUFA ratio was low (≤0.11, P = 0.001), but not when it was high (>0.11, P-interaction = 0.033). Similar interaction was observed between rs1183319 and n-3: n-6 PUFA ratio on HOMA-IR (P-interaction = 0.001) in GOLDN. Variance contribution analyses in GOLDN confirmed the genetic association and gene-diet interaction. In BPRHS, dietary n-3: n-6 PUFA ratio significantly modulated the association between rs1183319 and HbA1c (P-interaction = 0.034). CONCLUSION:PIK3CA-KCNMB3 variants are associated with insulin resistance in populations of different ancestries, and are modified by dietary PUFA