Associations of autozygosity with a broad range of human phenotypes.

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.

Gene-lifestyle interaction on risk of type 2 diabetes: A systematic review.

The pathophysiological influence of gene-lifestyle interactions on the risk to develop type 2 diabetes (T2D) is currently under intensive research. This systematic review summarizes the evidence for gene-lifestyle interactions regarding T2D incidence. MEDLINE, EMBASE, and Web of Science were systematically searched until 31 January 2019 to identify publication with (a) prospective study design; (b) T2D incidence; (c) gene-diet, gene-physical activity, and gene-weight loss intervention interaction; and (d) population who are healthy or prediabetic. Of 66 eligible publications, 28 reported significant interactions. A variety of different genetic variants and dietary factors were studied. Variants at TCF7L2 were most frequently investigated and showed interactions with fiber and whole grain on T2D incidence. Further gene-diet interactions were reported for, eg, a western dietary pattern with a T2D-GRS, fat and carbohydrate with IRS1 rs2943641, and heme iron with variants of HFE. Physical activity showed interaction with HNF1B, IRS1, PPARγ, ADRA2B, SLC2A2, and ABCC8 variants and weight loss interventions with ENPP1, PPARγ, ADIPOR2, ADRA2B, TNFα, and LIPC variants. However, most findings represent single study findings obtained in European ethnicities. Although some interactions have been reported, their conclusiveness is still low, as most findings were not yet replicated across multiple study populations.

Insulin-Like Growth Factor Binding Protein 2 (IGFBP-2) and the Risk of Developing Type 2 Diabetes.

Recent studies suggest that insulin-like growth factor binding protein 2 (IGFBP-2) may protect against type 2 diabetes, but population-based human studies are scarce. We aimed to investigate the prospective association of circulating IGFBP-2 concentrations and of differential methylation in the IGFBP-2 gene with type 2 diabetes risk.

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 < 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.

Circulating Fetuin-A and Risk of Type 2 Diabetes: A Mendelian Randomization Analysis.

Fetuin-A, a hepatic-origin protein, is strongly positively associated with risk of type 2 diabetes in human observational studies, but it is unknown whether this association is causal. We aimed to study the potential causal relation of circulating fetuin-A to risk of type 2 diabetes in a Mendelian randomization study with single nucleotide polymorphisms located in the fetuin-A-encoding AHSG gene. We used data from eight European countries of the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study including 10,020 incident cases. Plasma fetuin-A concentration was measured in a subset of 965 subcohort participants and 654 case subjects. A genetic score of the AHSG single nucleotide polymorphisms was strongly associated with fetuin-A (28% explained variation). Using the genetic score as instrumental variable of fetuin-A, we observed no significant association of a 50 µg/mL higher fetuin-A concentration with diabetes risk (hazard ratio 1.02 [95% CI 0.97, 1.07]). Combining our results with those from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium (12,171 case subjects) also did not suggest a clear significant relation of fetuin-A with diabetes risk. In conclusion, although there is mechanistic evidence for an effect of fetuin-A on insulin sensitivity and secretion, this study does not support a strong, relevant relationship between circulating fetuin-A and diabetes risk in the general population.

Interaction of Dietary and Genetic Factors Influencing Body Iron Status and Risk of Type 2 Diabetes Within the EPIC-InterAct Study.

OBJECTIVE: Meat intake has been consistently shown to be positively associated with incident type 2 diabetes. Part of that association may be mediated by body iron status, which is influenced by genetic factors. We aimed to test for interactions of genetic and dietary factors influencing body iron status in relation to the risk of incident type 2 diabetes. RESEARCH DESIGN AND METHODS: The case-cohort comprised 9,347 case subjects and 12,301 subcohort participants from eight European countries. Single nucleotide polymorphisms (SNPs) were selected from genome-wide association studies on iron status biomarkers and candidate gene studies. A ferritin-related gene score was constructed. Multiplicative and additive interactions of heme iron and SNPs as well as the gene score were evaluated using Cox proportional hazards regression. RESULTS: Higher heme iron intake (per 1 SD) was associated with higher ferritin levels (ß = 0.113 [95% CI 0.082; 0.144]), but not with transferrin (-0.019 [-0.043; 0.006]) or transferrin saturation (0.016 [-0.006; 0.037]). Five SNPs located in four genes (rs1799945 [HFE H63D], rs1800562 [HFE C282Y], rs236918 [PCK7], rs744653 [SLC40A1], and rs855791 [TMPRSS6 V736A]) were associated with ferritin. We did not detect an interaction of heme iron and the gene score on the risk of diabetes in the overall study population (Padd = 0.16, Pmult = 0.21) but did detect a trend toward a negative interaction in men (Padd = 0.04, Pmult = 0.03). CONCLUSIONS: We found no convincing evidence that the interplay of dietary and genetic factors related to body iron status associates with type 2 diabetes risk above the level expected from the sum or product of the two individual exposures.

Genetic variants including markers from the exome chip and metabolite traits of type 2 diabetes.

Diabetes-associated metabolites may aid the identification of new risk variants for type 2 diabetes. Using targeted metabolomics within a subsample of the German EPIC-Potsdam study (n = 2500), we tested previously published SNPs for their association with diabetes-associated metabolites and conducted an additional exploratory analysis using data from the exome chip including replication within 2,692 individuals from the German KORA F4 study. We identified a total of 16 loci associated with diabetes-related metabolite traits, including one novel association between rs499974 (MOGAT2) and a diacyl-phosphatidylcholine ratio (PC aa C40:5/PC aa C38:5). Gene-based tests on all exome chip variants revealed associations between GFRAL and PC aa C42:1/PC aa C42:0, BIN1 and SM (OH) C22:2/SM C18:0 and TFRC and SM (OH) C22:2/SM C16:1). Selecting variants for gene-based tests based on functional annotation identified one additional association between OR51Q1 and hexoses. Among single genetic variants consistently associated with diabetes-related metabolites, two (rs174550 (FADS1), rs3204953 (REV3L)) were significantly associated with type 2 diabetes in large-scale meta-analysis for type 2 diabetes. In conclusion, we identified a novel metabolite locus in single variant analyses and four genes within gene-based tests and confirmed two previously known mGWAS loci which might be relevant for the risk of type 2 diabetes.

General Framework for Meta-Analysis of Haplotype Association Tests.

For complex traits, most associated single nucleotide variants (SNV) discovered to date have a small effect, and detection of association is only possible with large sample sizes. Because of patient confidentiality concerns, it is often not possible to pool genetic data from multiple cohorts, and meta-analysis has emerged as the method of choice to combine results from multiple studies. Many meta-analysis methods are available for single SNV analyses. As new approaches allow the capture of low frequency and rare genetic variation, it is of interest to jointly consider multiple variants to improve power. However, for the analysis of haplotypes formed by multiple SNVs, meta-analysis remains a challenge, because different haplotypes may be observed across studies. We propose a two-stage meta-analysis approach to combine haplotype analysis results. In the first stage, each cohort estimate haplotype effect sizes in a regression framework, accounting for relatedness among observations if appropriate. For the second stage, we use a multivariate generalized least square meta-analysis approach to combine haplotype effect estimates from multiple cohorts. Haplotype-specific association tests and a global test of independence between haplotypes and traits are obtained within our framework. We demonstrate through simulation studies that we control the type-I error rate, and our approach is more powerful than inverse variance weighted meta-analysis of single SNV analysis when haplotype effects are present. We replicate a published haplotype association between fasting glucose-associated locus (G6PC2) and fasting glucose in seven studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and we provide more precise haplotype effect estimates.

Association of Multiple Biomarkers of Iron Metabolism and Type 2 Diabetes: The EPIC-InterAct Study.

OBJECTIVE: Observational studies show an association between ferritin and type 2 diabetes (T2D), suggesting a role of high iron stores in T2D development. However, ferritin is influenced by factors other than iron stores, which is less the case for other biomarkers of iron metabolism. We investigated associations of ferritin, transferrin saturation (TSAT), serum iron, and transferrin with T2D incidence to clarify the role of iron in the pathogenesis of T2D. RESEARCH DESIGN AND METHODS: The European Prospective Investigation into Cancer and Nutrition-InterAct study includes 12,403 incident T2D cases and a representative subcohort of 16,154 individuals from a European cohort with 3.99 million person-years of follow-up. We studied the prospective association of ferritin, TSAT, serum iron, and transferrin with incident T2D in 11,052 cases and a random subcohort of 15,182 individuals and assessed whether these associations differed by subgroups of the population. RESULTS: Higher levels of ferritin and transferrin were associated with a higher risk of T2D (hazard ratio [HR] [95% CI] in men and women, respectively: 1.07 [1.01-1.12] and 1.12 [1.05-1.19] per 100 µg/L higher ferritin level; 1.11 [1.00-1.24] and 1.22 [1.12-1.33] per 0.5 g/L higher transferrin level) after adjustment for age, center, BMI, physical activity, smoking status, education, hs-CRP, alanine aminotransferase, and γ-glutamyl transferase. Elevated TSAT (≥45% vs. <45%) was associated with a lower risk of T2D in women (0.68 [0.54-0.86]) but was not statistically significantly associated in men (0.90 [0.75-1.08]). Serum iron was not associated with T2D. The association of ferritin with T2D was stronger among leaner individuals (Pinteraction < 0.01). CONCLUSIONS: The pattern of association of TSAT and transferrin with T2D suggests that the underlying relationship between iron stores and T2D is more complex than the simple link suggested by the association of ferritin with T2D.

Metabolically healthy obesity and cardiovascular events: A systematic review and meta-analysis.

AIMS: Previous studies have provided inconsistent results about the cardiovascular risks for participants with metabolically healthy obesity (MHO). These uncertainties might partly reflect the lack of a uniform definition of MHO. We conducted a systematic review and meta-analysis to examine whether there is a suitable approach that identifies obese participants who are not at an increased risk of cardiovascular events compared with healthy normal-weight participants. METHODS AND RESULTS: Twenty-two prospective studies were eligible for the meta-analysis. Using random-effect models, pooled relative risks (RRs) were calculated for the combined effects of obesity with the presence or absence of metabolic syndrome, insulin resistance, hypertension, diabetes, hyperlipidaemia and any of these metabolic factors. Participants with MHO defined by the absence of metabolic syndrome were at increased risk for cardiovascular events compared with healthy normal-weight participants (pooled RR 1.45, 95% confidence interval (CI) 1.20-1.70), but had lower risks than unhealthy normal-weight (RR 2.07, 95% CI 1.62-2.65) and obese (RR 2.31, 95% CI 1.99-2.69) participants. The risk associated with participants who had MHO was particularly high over the long term. Similar risk estimates were observed when MHO was defined by other approaches. CONCLUSIONS: None of the approaches clearly identified an obese subgroup not at increased risk of cardiovascular events compared with normal-weight healthy participants. A benign obese phenotype might be defined by strict definitions, but insufficient studies exist to support this. More research is needed to better define MHO.

Characterization of metabolically unhealthy normal-weight individuals: Risk factors and their associations with type 2 diabetes.

OBJECTIVE: A proportion of type 2 diabetes cases arise from normal-weight individuals who can therefore be considered to be "metabolically unhealthy normal-weight" (MUH-NW). It remains unclear which factors account for this access risk. Our aims were to identify risk factors for type 2 diabetes in normal-weight individuals and to compare the strengths of their associations with type 2 diabetes to that observed in overweight and obese participants. METHODS: A case-cohort, including 2027 sub-cohort participants and 706 incident type 2 cases, was designed within the population-based European Prospective Investigation into Cancer and Nutrition Potsdam study. Adjusted means and relative frequencies of anthropometric, lifestyle and biochemical risk factors were calculated in groups stratified by BMI and incident diabetes status. Cox regressions were applied to evaluate associations between these variables and diabetes risk stratified by BMI category. RESULTS: MUH-NW individuals were characterized by known diabetes risk factors, e.g. they were significantly more likely to be male, former smokers, hypertensive, and less physically active compared to normal-weight individuals without incident diabetes. Higher waist circumference (women: 75.5 vs. 73.1cm; men: 88.0 vs. 85.1cm), higher HbA1c (6.1 vs. 5.3%), higher triglycerides (1.47 vs. 1.11 mmol/l), and higher levels of high sensitive C-reactive protein (0.81 vs. 0.51 mg/l) as well as lower levels of HDL-cholesterol (1.28 vs. 1.49 mmol/l) and adiponectin (6.32 vs. 8.25 µg/ml) characterized this phenotype. Stronger associations with diabetes among normal-weight participants compared to overweight and obese (p for interaction<0.05) were observed for height, waist circumference, former smoking, and hypertension. CONCLUSIONS: Normal-weight individuals who develop diabetes have higher levels of diabetes risk factors, however, frequently still among the normal range. Still, hypertension, elevated HbA1c and lifestyle risk factors might be useful indicators of risk.

Joint effect of unlinked genotypes: application to type 2 diabetes in the EPIC-Potsdam case-cohort study.

Analyzing multiple single nucleotide polymorphisms (SNPs) is a promising approach to finding genetic effects beyond single-locus associations. We proposed the use of multilocus stepwise regression (MSR) to screen for allele combinations as a method to model joint effects, and compared the results with the often used genetic risk score (GRS), conventional stepwise selection, and the shrinkage method LASSO. In contrast to MSR, the GRS, conventional stepwise selection, and LASSO model each genotype by the risk allele doses. We reanalyzed 20 unlinked SNPs related to type 2 diabetes (T2D) in the EPIC-Potsdam case-cohort study (760 cases, 2193 noncases). No SNP-SNP interactions and no nonlinear effects were found. Two SNP combinations selected by MSR (Nagelkerke's R² = 0.050 and 0.048) included eight SNPs with mean allele combination frequency of 2%. GRS and stepwise selection selected nearly the same SNP combinations consisting of 12 and 13 SNPs (Nagelkerke's R² ranged from 0.020 to 0.029). LASSO showed similar results. The MSR method showed the best model fit measured by Nagelkerke's R² suggesting that further improvement may render this method a useful tool in genetic research. However, our comparison suggests that the GRS is a simple way to model genetic effects since it does not consider linkage, SNP-SNP interactions, and no non-linear effects.

Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility.

Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (ß=-0.09±0.01 mmol l(-1), P=3.4 × 10(-12)), T2D risk (OR[95%CI]=0.86[0.76-0.96], P=0.010), early insulin secretion (ß=-0.07±0.035 pmolinsulin mmolglucose(-1), P=0.048), but higher 2-h glucose (ß=0.16±0.05 mmol l(-1), P=4.3 × 10(-4)). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (ß=0.02±0.004 mmol l(-1), P=1.3 × 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.

Variation in genes related to hepatic lipid metabolism and changes in waist circumference and body weight.

We analysed single nucleotide polymorphisms (SNPs) tagging the genetic variability of six candidate genes (ATF6, FABP1, LPIN2, LPIN3, MLXIPL and MTTP) involved in the regulation of hepatic lipid metabolism, an important regulatory site of energy balance for associations with body mass index (BMI) and changes in weight and waist circumference. We also investigated effect modification by sex and dietary intake. Data of 6,287 individuals participating in the European prospective investigation into cancer and nutrition were included in the analyses. Data on weight and waist circumference were followed up for 6.9 ± 2.5 years. Association of 69 tagSNPs with baseline BMI and annual changes in weight as well as waist circumference were investigated using linear regression analysis. Interactions with sex, GI and intake of carbohydrates, fat as well as saturated, monounsaturated and polyunsaturated fatty acids were examined by including multiplicative SNP-covariate terms into the regression model. Neither baseline BMI nor annual weight or waist circumference changes were significantly associated with variation in the selected genes in the entire study population after correction for multiple testing. One SNP (rs1164) in LPIN2 appeared to be significantly interacting with sex (p = 0.0003) and was associated with greater annual weight gain in men (56.8 ± 23.7 g/year per allele, p = 0.02) than in women (-25.5 ± 19.8 g/year per allele, p = 0.2). With respect to gene-nutrient interaction, we could not detect any significant interactions when accounting for multiple testing. Therefore, out of our six candidate genes, LPIN2 may be considered as a candidate for further studies.

Evaluation of 41 candidate gene variants for obesity in the EPIC-Potsdam cohort by multi-locus stepwise regression.

OBJECTIVE: Obesity has become a leading preventable cause of morbidity and mortality in many parts of the world. It is thought to originate from multiple genetic and environmental determinants. The aim of the current study was to introduce haplotype-based multi-locus stepwise regression (MSR) as a method to investigate combinations of unlinked single nucleotide polymorphisms (SNPs) for obesity phenotypes. METHODS: In 2,122 healthy randomly selected men and women of the EPIC-Potsdam cohort, the association between 41 SNPs from 18 obesity-candidate genes and either body mass index (BMI, mean=25.9 kg/m(2), SD=4.1) or waist circumference (WC, mean=85.2 cm, SD=12.6) was assessed. Single SNP analyses were done by using linear regression adjusted for age, sex, and other covariates. Subsequently, MSR was applied to search for the 'best' SNP combinations. Combinations were selected according to specific AICc and p-value criteria. Model uncertainty was accounted for by a permutation test. RESULTS: The strongest single SNP effects on BMI were found for TBC1D1 rs637797 (ß = -0.33, SE=0.13), FTO rs9939609 (ß=0.28, SE=0.13), MC4R rs17700144 (ß=0.41, SE=0.15), and MC4R rs10871777 (ß=0.34, SE=0.14). All these SNPs showed similar effects on waist circumference. The two 'best' six-SNP combinations for BMI (global p-value= 3.45⋅10(-6) and 6.82⋅10(-6)) showed effects ranging from -1.70 (SE=0.34) to 0.74 kg/m(2) (SE=0.21) per allele combination. We selected two six-SNP combinations on waist circumference (global p-value = 7.80⋅10(-6) and 9.76⋅10(-6)) with an allele combination effect of -2.96 cm (SE=0.76) at maximum. Additional adjustment for BMI revealed 15 three-SNP combinations (global p-values ranged from 3.09⋅10(-4) to 1.02⋅10(-2)). However, after carrying out the permutation test all SNP combinations lost significance indicating that the statistical associations might have occurred by chance. CONCLUSION: MSR provides a tool to search for risk-related SNP combinations of common traits or diseases. However, the search process does not always find meaningful SNP combinations in a dataset.

Hemochromatosis (HFE) gene mutations and risk of gastric cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC) study.

Hereditary hemochromatosis (HH) is a strong risk factor for hepatocellular cancer, and mutations in the HFE gene associated with HH and iron overload may be related to other tumors, but no studies have been reported for gastric cancer (GC). A nested case-control study was conducted within the European Prospective Investigation into Cancer and Nutrition (EPIC), including 365 incident gastric adenocarcinoma and 1284 controls matched by center, sex, age and date of blood collection. Genotype analysis was performed for two functional polymorphisms (C282Y/rs1800562 and H63D/rs1799945) and seven tagSNPs of the HFE genomic region. Association with all gastric adenocarcinoma, and according to anatomical localization and histological subtype, was assessed by means of the odds ratio (OR) and 95% confidence interval (CI) estimated by unconditional logistic regression adjusted for the matching variables. We observed a significant association for H63D with OR (per rare allele) of 1.32 (CI = 1.03-1.69). In subgroup analyses, the association was stronger for non-cardia anatomical subsite (OR = 1.60, CI = 1.16-2.21) and intestinal histological subtype (OR = 1.82, CI = 1.27-2.62). Among intestinal cases, two tagSNPs (rs1572982 and rs6918586) also showed a significant association that disappeared after adjustment for H63D. No association with tumors located in the cardia or with diffuse subtype was found for any of the nine SNPs analyzed. Our results suggest that H63D variant in HFE gene seems to be associated with GC risk of the non-cardia region and intestinal type, possibly due to its association with iron overload although a role for other mechanisms cannot be entirely ruled out.

Dietary factors impact on the association between CTSS variants and obesity related traits.

BACKGROUND/AIMS: Cathepsin S, a protein coded by the CTSS gene, is implicated in adipose tissue biology--this protein enhances adipose tissue development. Our hypothesis is that common variants in CTSS play a role in body weight regulation and in the development of obesity and that these effects are influenced by dietary factors--increased by high protein, glycemic index and energy diets. METHODS: Four tag SNPs (rs7511673, rs11576175, rs10888390 and rs1136774) were selected to capture all common variation in the CTSS region. Association between these four SNPs and several adiposity measurements (BMI, waist circumference, waist for given BMI and being a weight gainer-experiencing the greatest degree of unexplained annual weight gain during follow-up or not) given, where applicable, both as baseline values and gain during the study period (6-8 years) were tested in 11,091 European individuals (linear or logistic regression models). We also examined the interaction between the CTSS variants and dietary factors--energy density, protein content (in grams or in % of total energy intake) and glycemic index--on these four adiposity phenotypes. RESULTS: We found several associations between CTSS polymorphisms and anthropometric traits including baseline BMI (rs11576175 (SNP N°2), p = 0.02, ß = -0.2446), and waist change over time (rs7511673 (SNP N°1), p = 0.01, ß = -0.0433 and rs10888390 (SNP N°3), p = 0.04, ß = -0.0342). In interaction with the percentage of proteins contained in the diet, rs11576175 (SNP N°2) was also associated with the risk of being a weight gainer (p(interaction) = 0.01, OR = 1.0526)--the risk of being a weight gainer increased with the percentage of proteins contained in the diet. CONCLUSION: CTSS variants seem to be nominally associated to obesity related traits and this association may be modified by dietary protein intake.

Influence of dietary protein intake and glycemic index on the association between TCF7L2 HapA and weight gain.

BACKGROUND: Genetic polymorphisms of transcription factor 7-like 2 (TCF7L2) have been associated with type 2 diabetes and BMI. OBJECTIVE: The objective was to investigate whether TCF7L2 HapA is associated with weight development and whether such an association is modulated by protein intake or by the glycemic index (GI). DESIGN: The investigation was based on prospective data from 5 cohort studies nested within the European Prospective Investigation into Cancer and Nutrition. Weight change was followed up for a mean (±SD) of 6.8 ± 2.5 y. TCF7L2 rs7903146 and rs10885406 were successfully genotyped in 11,069 individuals and used to derive HapA. Multiple logistic and linear regression analysis was applied to test for the main effect of HapA and its interaction with dietary protein or GI. Analyses from the cohorts were combined by random-effects meta-analysis. RESULTS: HapA was associated neither with baseline BMI (0.03 ± 0.07 BMI units per allele; P = 0.6) nor with annual weight change (8.8 ± 11.7 g/y per allele; P = 0.5). However, a previously shown positive association between intake of protein, particularly of animal origin, and subsequent weight change in this population proved to be attenuated by TCF7L2 HapA (P-interaction = 0.01). We showed that weight gain becomes independent of protein intake with an increasing number of HapA alleles. Substitution of protein with either fat or carbohydrates showed the same effects. No interaction with GI was observed. CONCLUSION: TCF7L2 HapA attenuates the positive association between animal protein intake and long-term body weight change in middle-aged Europeans but does not interact with the GI of the diet.

Association between FTO variant and change in body weight and its interaction with dietary factors: the DiOGenes study.

Although FTO is an established obesity-susceptibility locus, it remains unknown whether it influences weight change in adult life and whether diet attenuates this association. Therefore, we investigated the association of FTO-rs9939609 with changes in weight and waist circumference (WC) during 6.8 years follow-up in a large-scale prospective study and examined whether these associations were modified by dietary energy percentage from fat, protein, carbohydrate, or glycemic index (GI). This study comprised data from five countries of European Prospective Investigation into Cancer and Nutrition (EPIC) and was designed as a case-cohort study for weight gain. Analyses included 11,091 individuals, of whom 5,584 were cases (age (SD), 47.6 (7.5) years), defined as those with the greatest unexplained annual weight gain during follow-up and 5,507 were noncases (48.0 (7.3) years), who were compared in our case-noncase (CNC) analyses. Furthermore, 6,566 individuals (47.9 (7.3) years) selected from the total sample (all noncases and 1,059 cases) formed the random subcohort (RSC), used for continuous trait analyses. Interactions were tested by including interaction terms in the models. In the RSC-analyses, FTO-rs9939609 was associated with BMI (ß (SE), 0.17 (0.08) kg·m(-2)/allele; P = 0.034) and WC (0.47 (0.21) cm/allele; P = 0.026) at baseline, but not with weight change (5.55 (12.5) g·year(-1)/allele; P = 0.66) during follow up. In the CNC-analysis, FTO-rs9939609 was associated with increased risk of being a weight-gainer (OR: 1.1; P = 0.045). We observed no interaction between FTO-rs9939609 and dietary fat, protein and carbohydrate, and GI on BMI and WC at baseline or on change in weight and WC. FTO-rs9939609 is associated with BMI and WC at baseline, but association with weight gain is weak and only observed for extreme gain. Dietary factors did not influence the associations.

Prostate stem-cell antigen gene is associated with diffuse and intestinal gastric cancer in Caucasians: results from the EPIC-EURGAST study.

A genome-wide study performed in a Japanese population identified a strong association between SNP rs2294008 (Met1Thr) in the Prostate Stem Cell Antigen gene (PSCA) and diffuse-type gastric cancer (GC). This association was validated in different Asian populations, and, very recently, a study has been published in Caucasians. In this study, we analyzed the association between PSCA variation and GC risk in Caucasians from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Six tagSNPs covering the PSCA gene region were genotyped in 411 incident gastric adenocarcinoma cases and 1530 matched controls from a nested case-control study in the EPIC cohort. Associations were analyzed by unconditional logistic regression, adjusting for age, sex and country. The T allele of rs2294008 in PSCA was found to be a highly significant risk factor for GC (per allele OR = 1.42, 95% CI: 1.23-1.66, p-value = 6.5 × 10(-6) ), particularly of the noncardia-type (per allele OR = 1.47, 95% CI: 1.19-1.81, p-value = 3 × 10(-4) ). At contrast with previous studies, no significant differences were observed between the diffuse (per allele OR = 1.54, 95% CI: 1.20-1.96, p-value = 5 × 10(-4) ) and the intestinal (per allele OR = 1.52, 95% CI: 1.20-1.93, p-value = 5 × 10(-4) ) GC histological subtypes. Although rs12155758 and rs9297976 were also found associated with GC, this association appeared to be due to linkage disequilibrium with rs2294008. Haplotype analysis did not provide additional information. These results confirm the association between variation in the promoter region of PSCA and GC risk in Caucasians and also indicate that the rs2294008 variant is a similar risk factor for both the diffuse and intestinal-types of GC.