A short-term, high-fat diet up-regulates lipid metabolism and gene expression in human skeletal muscle

Background: Dietary fatty acids may be important in regulating gene expression. However, little is known about the effect of changes in dietary fatty acids on gene regulation in human skeletal muscle.
Objective: The objective was to determine the effect of altered dietary fat intake on the expression of genes encoding proteins necessary for fatty acid transport and ß-oxidation in skeletal muscle.
Design: Fourteen well-trained male cyclists and triathletes with a mean (± SE) age of 26.9 ± 1.7 y, weight of 73.7 ± 1.7 kg, and peak oxygen uptake of 67.0 ± 1.3 mL ˙ kg-1 ˙ min-1 consumed either a high-fat diet (HFat: > 65% of energy as lipids) or an isoenergetic high-carbohydrate diet (HCho: 70–75% of energy as carbohydrate) for 5 d in a crossover design. On day 1 (baseline) and again after 5 d of dietary intervention, resting muscle and blood samples were taken. Muscle samples were analyzed for gene expression [fatty acid translocase (FAT/CD36), plasma membrane fatty acid binding protein (FABPpm), carnitine palmitoyltransferase I (CPT I), ß-hydroxyacyl-CoA dehydrogenase (ß-HAD), and uncoupling protein 3 (UCP3)] and concentrations of the proteins FAT/CD36 and FABPpm.
Results: The gene expression of FAT/CD36 and &szlig; -HAD and the gene abundance of FAT/CD36 were greater after the HFat than after the HCho diet (P < 0.05). Messenger RNA expression of FABPpm, CPT I, and UCP-3 did not change significantly with either diet.
Conclusions: A rapid and marked capacity for changes in dietary fatty acid availability to modulate the expression of mRNA-encoding proteins is necessary for fatty acid transport and oxidative metabolism. This finding is evidence of nutrient-gene interactions in human skeletal muscle.

Regulation of metabolic genes in human skeletal muscle by short-term exercise and diet manipulation

Changes in dietary macronutrient intake alter muscle and blood substrate availability and are important for regulating gene expression. However, few studies have examined the effects of diet manipulation on gene expression in human skeletal muscle. The aim of this study was to quantify the extent to which altering substrate availability impacts on subsequent mRNA abundance of a subset of carbohydrate (CHO)- and fat-related genes. Seven subjects consumed either a low- (LOW; 0.7 g/kg body mass CHO) or high- (HIGH; 10 g/kg body mass CHO) CHO diet for 48 h after performing an exhaustive exercise bout to deplete muscle glycogen stores. After intervention, resting muscle and blood samples were taken. Muscle was analyzed for the gene abundances of GLUT4, glycogenin, pyruvate dehydrogenase kinase-4 (PDK-4), fatty acid translocase (FAT/CD36), carnitine palmitoyltransferase I (CPT I), hormone-sensitive lipase (HSL), β-hydroxyacyl-CoA dehydrogenase (΄β-HAD), and uncoupling binding protein-3 (UCP3), and blood samples for glucose, insulin, and free fatty acid (FFA) concentrations. Glycogen-depleting exercise and HIGH-CHO resulted in a 300% increase in muscle glycogen content (P < 0.001) relative to the LOW-CHO condition. FFA concentrations were twofold higher after LOW- vs. HIGH-CHO (P < 0.05). The exercise-diet manipulation exerted a significant effect on transcription of all carbohydrate-related genes, with an increase in GLUT4 and glycogenin mRNA abundance and a reduction in PDK-4 transcription after HIGH-CHO (all P < 0.05). FAT/CD36 (P < 0.05) and UCP3 (P < 0.01) gene transcriptions were increased following LOW-CHO. We conclude that 1) there was a rapid capacity for a short-term exercise and diet intervention to exert coordinated changes in the mRNA transcription of metabolic related genes, and 2) genes involved in glucose regulation are increased following a high-carbohydrate diet.

Genome-wide gene-gene interaction analysis for cardiovascular disease

Numerous studies have been carried out to try to better understand the genetic predisposition for cardiovascular disease. Although it is widely believed that multifactorial diseases such as cardiovascular disease is the result from effects of many genes which working alone or interact with other genes, most genetic studies have been focused on identifying of cardiovascular disease susceptibility genes and usually ignore the effects of gene-gene interactions in the analysis. The current study applies a novel linkage disequilibrium based statistic for testing interactions between two linked loci using data from a genome-wide study of cardiovascular disease. A total of 53,394 single nucleotide polymorphisms (SNPs) are tested for pair-wise interactions, and 8,644 interactions are found to be significant with p-values less than 3.5×10-11. Results indicate that known cardiovascular disease susceptibility genes tend not to have many significantly interactions. One SNP in the CACNG1 (calcium channel, voltage-dependent, gamma subunit 1) gene and one SNP in the IL3RA (interleukin 3 receptor, alpha) gene are found to have the most significant pair-wise interactions. Findings from the current study should be replicated in other independent cohort to eliminate potential false positive results.^

Gene-gene interaction and gene-pathway analysis of genome-wide association for schizophrenia

Schizophrenia (SZ) is a complex disorder with high heritability and variable phenotypes that has limited success in finding causal genes associated with the disease development. Pathway-based analysis is an effective approach in investigating the molecular mechanism of susceptible genes associated with complex diseases. The etiology of complex diseases could be a network of genetic factors and within the genes, interaction may occur. In this work we argue that some genes might be of small effect that by itself are neither sufficient nor necessary to cause the disease however, their effect may induce slight changes to the gene expression or affect the protein function, therefore, analyzing the gene-gene interaction mechanism within the disease pathway would play crucial role in dissecting the genetic architecture of complex diseases, making the pathway-based analysis a complementary approach to GWAS technique. ^ In this study, we implemented three novel linkage disequilibrium based statistics, the linear combination, the quadratic, and the decorrelation test statistics, to investigate the interaction between linked and unlinked genes in two independent case-control GWAS datasets for SZ including participants of European (EA) and African (AA) ancestries. The EA population included 1,173 cases and 1,378 controls with 729,454 genotyped SNPs, while the AA population included 219 cases and 288 controls with 845,814 genotyped SNPs. We identified 17,186 interacting gene-sets at significant level in EA dataset, and 12,691 gene-sets in AA dataset using the gene-gene interaction method. We also identified 18,846 genes in EA dataset and 19,431 genes in AA dataset that were in the disease pathways. However, few genes were reported of significant association to SZ. ^ Our research determined the pathways characteristics for schizophrenia through the gene-gene interaction and gene-pathway based approaches. Our findings suggest insightful inferences of our methods in studying the molecular mechanisms of common complex diseases.^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Associations of gene polymorphisms and nutrition with calcium homeostasis and bone mineral density : Studies on skeletal nutrigenetics

Heredity explains a major part of the variation in calcium homeostasis and bone strength, and the susceptibility to osteoporosis is polygenetically regulated. Bone phenotype results from the interplay between lifestyle and genes, and several nutritional factors modulate bone health throughout life. Thus, nutrigenetics examining the genetic variation in nutrient intake and homeostatic control is an important research area in the etiology of osteoporosis. Despite continuing progress in the search for candidate genes for osteoporosis, the results thus far have been inconclusive. The main objective of this thesis was to investigate the associations of lactase, vitamin D receptor (VDR), calcium sensing receptor (CaSR) and parathyroid hormone (PTH) gene polymorphisms and lifestyle factors and their interactions with bone health in Finns at varying stages of the skeletal life span. Markers of calcium homeostasis and bone remodelling were measured from blood and urine samples. Bone strength was measured at peripheral and central bone sites. Lifestyle factors were assessed with questionnaires and interviews. Genetic lactase non-persistence (the C/C-13910 genotype) was associated with lower consumption of milk from childhood, predisposing females in particular to inadequate calcium intake. Consumption of low-lactose milk and milk products was shown to decrease the risk for inadequate calcium intake. In young adulthood, bone loss was more common in males than in females. Males with the lactase C/C-13910 genotype may be more susceptible to bone loss than males with the other lactase genotypes, although calcium intake predicts changes in bone mass more than the lactase genotype. The BsmI and FokI polymorphisms of the VDR gene were associated with bone mass in growing adolescents, but the associations weakened with age. In young adults, the A986S polymorphism of the calcium sensing receptor gene was associated with serum ionized calcium concentrations, and the BstBI polymorphism of the parathyroid gene was related to bone strength. The FokI polymorphism and sodium intake showed an interaction effect on urinary calcium excretion. A novel gene-gene interaction between the VDR FokI and PTH BstBI gene polymorphisms was found in the regulation of PTH secretion and urinary calcium excretion. Further research should be carried out with more number of Finns at varying stages of the skeletal life span and more detailed measurements of bone strength. Research should concern mechanisms by which genetic variants affect calcium homeostasis and bone strength, and the role of diet-gene and gene-gene interactions in the pathogenesis of osteoporosis.

PPARγ2 gene Pro12Ala and PPARα gene Leu162Val single nucleotide polymorphisms interact with dietary intake of fat in determination of plasma lipid concentrations

Background/Aims: The peroxisome proliferator-activated receptors (PPARs) are transcriptional regulators of lipid metabolism, activated by unsaturated fatty acids. We investigated independent and interactive effects of PPARγ2 gene PPARG Pro12Ala (rs1801282) andPPARαgene PPARA Leu162Val (rs1800206) genotypes with dietary intake of fatty acids on concentrations of plasma lipids in subjects of whom 47.5% had metabolic syndrome. Methods: The RISCK study is a parallel design, randomised controlled trial. Plasma lipids were quantified at baseline after a 4-week high saturated fatty acids diet and after three parallel 24-week interventions with reference (high saturated fatty acids), high monounsaturated fatty acids and low-fat diets. Single nucleotide polymorphisms were genotyped in 466 subjects. Results: At baseline, the PPARG Ala12allele was associated with increased plasma total cholesterol (n = 378; p = 0.04), LDL cholesterol (p = 0.05) and apoB (p =0.05) after adjustment for age, gender and ethnicity. At baseline, PPARA Leu162Val × PPARG Pro12Ala genotype interaction did not significantly influence plasma lipid concentrations. After dietary intervention, gene-gene interaction significantly influenced LDL cholesterol (p =0.0002) and small dense LDL as a proportion of LDL (p = 0.005) after adjustments. Conclusions: Interaction between PPARG Pro12Ala and PPARA Leu162Valgenotypes may influence plasma LDL cholesterol concentration and the proportion as small dense LDL after a high monounsaturated fatty acids diet.

A genome-wide asociation study identifies new psoriasis susceptibility loci and an interaction betwEn HLA-C and ERAP1

To identify new susceptibility loci for psoriasis, we undertOk a genome-wide asociation study of 594,224 SNPs in 2,622 individuals with psoriasis and 5,667 controls. We identified asociations at eight previously unreported genomic loci. Seven loci harbored genes with recognized iMune functions (IL28RA, REL, IFIH1, ERAP1, TRAF3IP2, NFKBIA and TYK2). These asociations were replicated in 9,079 European samples (six loci with a combined P < 5-10 -8 and two loci with a combined P < 5-10-7). We also report compeLing evidence for an interaction betwEn the HLA-C and ERAP1 loci (combined P = 6.95-10-6). ERAP1 plays an important role in MHC claS I peptide proceSing. ERAP1 variants only influenced psoriasis susceptibility in individuals carrying the HLA-C risk aLele. Our findings implicate pathways that integrate epidermal barrier dysfunction with iNate and adaptive iMune dysregulation in psoriasis pathogenesis.

Genetic predisposition influences plasma lipids of participants on habitual diet, but not the response to reductions in dietary intake of saturated fatty acids

Objective: SNPs identified from genome wide association studies associate with lipid risk markers of cardiovascular disease. This study investigated whether these SNPs altered the plasma lipid response to diet in the ‘RISCK’ study cohort. Methods: Participants (n = 490) from a dietary intervention to lower saturated fat by replacement with carbohydrate or monounsaturated fat, were genotyped for 39 lipid-associated SNPs. The association of each individual SNP, and of the SNPs combined (using genetic predisposition scores), with plasma lipid concentrations was assessed at baseline, and on change in response to 24 weeks on diets. Results: The associations between SNPs and lipid concentrations were directionally consistent with previous findings. The genetic predisposition scores were associated with higher baseline concentrations of plasma total(P = 0.02) and LDL (P = 0.002) cholesterol, triglycerides (P = 0.001) and apolipoprotein B (P = 0.004), and with lower baseline concentrations of HDL cholesterol (P < 0.001) and apolipoprotein A-I (P < 0.001). None of the SNPs showed significant association with the reduction of plasma lipids in response to the dietary interventions and there was no evidence of diet-gene interactions. Conclusion: Results from this exploratory study have shown that increased genetic predisposition was associated with an unfavourable plasma lipid profile at baseline, but did not influence the improvement in lipid profiles by the low-saturated-fat diets.

Influence of SNPs in nutrient-sensitive candidate genes and gene–diet interactions on blood lipids: the DiOGenes study

Blood lipid response to a given dietary intervention could be determined by the effect of diet, gene variants or gene–diet interactions. The objective of the present study was to investigate whether variants in presumed nutrient-sensitive genes involved in lipid metabolism modified lipid profile after weight loss and in response to a given diet, among overweight European adults participating in the Diet Obesity and Genes study. By multiple linear regressions, 240 SNPs in twenty-four candidate genes were investigated for SNP main and SNP–diet interaction effects on total cholesterol, LDL-cholesterol, HDL-cholesterol and TAG after an 8-week low-energy diet (only main effect), and a 6-month ad libitum weight maintenance diet, with different contents of dietary protein or glycaemic index. After adjusting for multiple testing, a SNP–dietary protein interaction effect on TAG was identified for lipin 1 (LPIN1) rs4315495, with a decrease in TAG of − 0·26 mmol/l per A-allele/protein unit (95 % CI − 0·38, − 0·14, P= 0·000043). In conclusion, we investigated SNP–diet interactions for blood lipid profiles for 240 SNPs in twenty-four candidate genes, selected for their involvement in lipid metabolism pathways, and identified one significant interaction between LPIN1 rs4315495 and dietary protein for TAG concentration.

Urinary metabolomic profiling to identify biomarkers of a flavonoid-rich and flavonoid-poor fruits and vegetables diet: the FLAVURS trial in adults: the FLAVURS trial

The present study aims to investigate the dose dependent effects of consuming diets enriched in flavonoid-rich and flavonoid-poor fruits and vegetables on the urine metabolome of adults who had a C1.5 fold increased risk of cardiovascular diseases. A single-blind, dose-dependent, parallel randomized controlled dietary intervention was conducted where volunteers (n = 126) were randomly assigned to one of three diets: high flavonoid diet, low flavonoid diet or habitual diet as a control for 18 weeks. High resolution LC– MS untargeted metabolomics with minimal sample cleanup was performed using an Orbitrap mass spectrometer. Putative biomarkers which characterize diets with high and low flavonoid content were selected by state-of-the-art data analysis strategies and identified by HR-MS and HR-MS/MS assays. Discrimination between diets was observed by application of two linear mixedmodels: one including a diet-time interaction effect and the second containing only a time effect. Valerolactones, phenolic acids and their derivatives were among sixteen biomarkers related to the high flavonoid dietary exposure. Four biomarkers related to the low flavonoid diet belonged to the family of phenolic acids. For the first time abscisic acid glucuronide was reported as a biomarker after a dietary intake, however its origins have to be examined by future hypothesis driven experiments using a more targeted approach. This metabolomic analysis has identified a number of dose dependent urinary biomarkers (i.e. proline betaine or iberin-N-acetyl cysteine), which can be used in future observation and intervention studies to assess flavonoids and nonflavonoid phenolic intakes and compliance to fruit and vegetable intervention.

Efeito da dieta hiperlipídica na programação fetal do metabolismo energético e atividade de enzimas digestivas em ratos

Pós-graduação em Zootecnia - FMVZ

Using gene-network landscape to dissect genotype effects of TCF7L2 genetic variant on diabetes and cardiovascular risk

Vaquero AR, Ferreira NE, Omae SV, Rodrigues MV, Teixeira SK, Krieger JE, Pereira AC. Using gene-network landscape to dissect genotype effects of TCF7L2 genetic variant on diabetes and cardiovascular risk. Physiol Genomics 44: 903-914, 2012. First published August 7, 2012; doi:10.1152/physiolgenomics.00030.2012.-The single nucleotide polymorphism (SNP) within the TCF7L2 gene, rs7903146, is, to date, the most significant genetic marker associated with Type 2 diabetes mellitus (T2DM) risk. Nonetheless, its functional role in disease pathology is poorly understood. The aim of the present study was to investigate, in vascular smooth muscle cells from 92 patients undergoing aortocoronary bypass surgery, the contribution of this SNP in T2DM using expression levels and expression correlation comparison approaches, which were visually represented as gene interaction networks. Initially, the expression levels of 41 genes (seven TCF7L2 splice forms and 40 other T2DM relevant genes) were compared between rs7903146 wild-type (CC) and T2DM-risk (CT + TT) genotype groups. Next, we compared the expression correlation patterns of these 41 genes between groups to observe if the relationships between genes were different. Five TCF7L2 splice forms and nine genes showed significant expression differences between groups. RXR alpha gene was pinpointed as showing the most different expression correlation pattern with other genes. Therefore, T2DM risk alleles appear to be influencing TCF7L2 splice form's expression in vascular smooth muscle cells, and RXR alpha gene is pointed out as a treatment target candidate for risk reduction in individuals with high risk of developing T2DM, especially individuals harboring TCF7L2 risk genotypes.

Multifactor effects and evidence of potential interaction between complement factor H Y402H and LOC387715 A69S in age-related macular degeneration.

BACKGROUND: Variants in the complement cascade genes and the LOC387715/HTRA1, have been widely reported to associate with age-related macular degeneration (AMD), the most common cause of visual impairment in industrialized countries. METHODS/PRINCIPAL FINDINGS: We investigated the association between the LOC387715 A69S and complement component C3 R102G risk alleles in the Finnish case-control material and found a significant association with both variants (OR 2.98, p = 3.75 x 10(-9); non-AMD controls and OR 2.79, p = 2.78 x 10(-19), blood donor controls and OR 1.83, p = 0.008; non-AMD controls and OR 1.39, p = 0.039; blood donor controls), respectively. Previously, we have shown a strong association between complement factor H (CFH) Y402H and AMD in the Finnish population. A carrier of at least one risk allele in each of the three susceptibility loci (LOC387715, C3, CFH) had an 18-fold risk of AMD when compared to a non-carrier homozygote in all three loci. A tentative gene-gene interaction between the two major AMD-associated loci, LOC387715 and CFH, was found in this study using a multiplicative (logistic regression) model, a synergy index (departure-from-additivity model) and the mutual information method (MI), suggesting that a common causative pathway may exist for these genes. Smoking (ever vs. never) exerted an extra risk for AMD, but somewhat surprisingly, only in connection with other factors such as sex and the C3 genotype. Population attributable risks (PAR) for the CFH, LOC387715 and C3 variants were 58.2%, 51.4% and 5.8%, respectively, the summary PAR for the three variants being 65.4%. CONCLUSIONS/SIGNIFICANCE: Evidence for gene-gene interaction between two major AMD associated loci CFH and LOC387715 was obtained using three methods, logistic regression, a synergy index and the mutual information (MI) index.

Gene by BMI Interactions Influencing C-Reactive Protein Levels in European-Americans

C-Reactive Protein (CRP) is a biomarker indicating tissue damage, inflammation, and infection. High-sensitivity CRP (hsCRP) is an emerging biomarker often used to estimate an individual’s risk for future coronary heart disease (CHD). hsCRP levels falling below 1.00 mg/l indicate a low risk for developing CHD, levels ranging between 1.00 mg/l and 3.00 mg/l indicate an elevated risk, and levels exceeding 3.00 mg/l indicate high risk. Multiple Genome-Wide Association Studies (GWAS) have identified a number of genetic polymorphisms which influence CRP levels. SNPs implicated in such studies have been found in or near genes of interest including: CRP, APOE, APOC, IL-6, HNF1A, LEPR, and GCKR. A strong positive correlation has also been found to exist between CRP levels and BMI, a known risk factor for CHD and a state of chronic inflammation. We conducted a series of analyses designed to identify loci which interact with BMI to influence CRP levels in a subsample of European-Americans in the ARIC cohort. In a stratified GWA analysis, 15 genetic regions were identified as having significantly (p-value < 2.00*10-3) distinct effects on hsCRP levels between the two obesity strata: lean (18.50 kg/m2 < BMI < 24.99 kg/m2) and obese (BMI ≥ 30.00 kg/m2). A GWA analysis performed on all individuals combined (i.e. not a priori stratified for obesity status) with the inclusion of an additional parameter for BMI by gene interaction, identified 11 regions which interact with BMI to influence hsCRP levels. Two regions containing the genes GJA5 and GJA8 (on chromosome 1) and FBXO11 (on chromosome 2) were identified in both methods of analysis suggesting that these genes possibly interact with BMI to influence hsCRP levels. We speculate that atrial fibrillation (AF), age-related cataracts and the TGF-β pathway may be the biological processes influenced by the interaction of GJA5, GJA8 and FBXO11, respectively, with BMI to cause changes in hsCRP levels. Future studies should focus on the influence of gene x bmi interaction on AF, age-related cataracts and TGF-β.