Towards the integration and development of a cross-European research network and infrastructure:the DEterminants of DIet and Physical ACtivity (DEDIPAC) Knowledge Hub

To address major societal challenges and enhance cooperation in research across Europe, the European Commission has initiated and facilitated ‘joint programming’. Joint programming is a process by which Member States engage in defining, developing and implementing a common strategic research agenda, based on a shared vision of how to address major societal challenges that no Member State is capable of resolving independently. Setting up a Joint Programming Initiative (JPI) should also contribute to avoiding unnecessary overlap and repetition of research, and enable and enhance the development and use of standardised research methods, procedures and data management. The Determinants of Diet and Physical Activity (DEDIPAC) Knowledge Hub (KH) is the first act of the European JPI ‘A Healthy Diet for a Healthy Life’. The objective of DEDIPAC is to contribute to improving understanding of the determinants of dietary, physical activity and sedentary behaviours. DEDIPAC KH is a multi-disciplinary consortium of 46 consortia and organisations supported by joint programming grants from 12 countries across Europe. The work is divided into three thematic areas: (I) assessment and harmonisation of methods for future research, surveillance and monitoring, and for evaluation of interventions and policies; (II) determinants of dietary, physical activity and sedentary behaviours across the life course and in vulnerable groups; and (III) evaluation and benchmarking of public health and policy interventions aimed at improving dietary, physical activity and sedentary behaviours. In the first three years, DEDIPAC KH will organise, develop, share and harmonise expertise, methods, measures, data and other infrastructure. This should further European research and improve the broad multi-disciplinary approach needed to study the interactions between multilevel determinants in influencing dietary, physical activity and sedentary behaviours. Insights will be translated into more effective interventions and policies for the promotion of healthier behaviours and more effective monitoring and evaluation of the impacts of such intervention

Population Substructure and Control Selection in Genome-Wide Association Studies

Determination of the relevance of both demanding classical epidemiologic criteria for control selection and robust handling of population stratification (PS) represents a major challenge in the design and analysis of genome-wide association studies (GWAS). Empirical data from two GWAS in European Americans of the Cancer Genetic Markers of Susceptibility (CGEMS) project were used to evaluate the impact of PS in studies with different control selection strategies. In each of the two original case-control studies nested in corresponding prospective cohorts, a minor confounding effect due to PS (inflation factor λ of 1.025 and 1.005) was observed. In contrast, when the control groups were exchanged to mimic a cost-effective but theoretically less desirable control selection strategy, the confounding effects were larger (λ of 1.090 and 1.062). A panel of 12,898 autosomal SNPs common to both the Illumina and Affymetrix commercial platforms and with low local background linkage disequilibrium (pair-wise r2<0.004) was selected to infer population substructure with principal component analysis. A novel permutation procedure was developed for the correction of PS that identified a smaller set of principal components and achieved a better control of type I error (to λ of 1.032 and 1.006, respectively) than currently used methods. The overlap between sets of SNPs in the bottom 5% of p-values based on the new test and the test without PS correction was about 80%, with the majority of discordant SNPs having both ranks close to the threshold. Thus, for the CGEMS GWAS of prostate and breast cancer conducted in European Americans, PS does not appear to be a major problem in well-designed studies. A study using suboptimal controls can have acceptable type I error when an effective strategy for the correction of PS is employed

Substituting prolonged sedentary time and cardiovascular risk in children and youth: a meta-analysis within the International Children’s Accelerometry database (ICAD)

Abstract: Background: Evidence on the association between sitting for extended periods (i.e. prolonged sedentary time (PST)) and cardio-metabolic health is inconsistent in children. We aimed to estimate the differences in cardio-metabolic health associated with substituting PST with non-prolonged sedentary time (non-PST), light (LIPA) or moderate-to-vigorous physical activity (MVPA) in children. Methods: Cross-sectional data from 14 studies (7 countries) in the International Children’s Accelerometry Database (ICAD, 1998–2009) was included. Accelerometry in 19,502 participants aged 3–18 years, together with covariate and outcome data, was pooled and harmonized. Iso-temporal substitution in linear regression models provided beta coefficients (95%CI) for substitution of 1 h/day PST (sedentary time accumulated in bouts > 15 min) with non-PST, LIPA or MVPA, for each study, which were meta-analysed. Results: Modelling substitution of 1 h/day of PST with non-PST suggested reductions in standardized BMI, but estimates were > 7-fold greater for substitution with MVPA (− 0.44 (− 0.62; − 0.26) SD units). Only reallocation by MVPA was beneficial for waist circumference (− 3.07 (− 4.47; − 1.68) cm), systolic blood pressure (− 1.53 (− 2.42; − 0.65) mmHg) and clustered cardio-metabolic risk (− 0.18 (− 0.3; − 0.1) SD units). For HDL-cholesterol and diastolic blood pressure, substitution with LIPA was beneficial; however, substitution with MVPA showed 5-fold stronger effect estimates (HDL-cholesterol: 0.05 (0.01; 0.10) mmol/l); diastolic blood pressure: − 0.81 (− 1.38; − 0.24) mmHg). Conclusions: Replacement of PST with MVPA may be the preferred scenario for behaviour change, given beneficial associations with a wide range of cardio-metabolic risk factors (including adiposity, HDL-cholesterol, blood pressure and clustered cardio-metabolic risk). Effect estimates are clinically relevant (e.g. an estimated reduction in waist circumference of ≈1.5 cm for 30 min/day replacement). Replacement with LIPA could be beneficial for some of these risk factors, however with substantially lower effect estimates

Accounting for Population Stratification in Practice: A Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies

Genome-Wide Association Studies are powerful tools to detect genetic variants associated with diseases. Their results have, however, been questioned, in part because of the bias induced by population stratification. This is a consequence of systematic differences in allele frequencies due to the difference in sample ancestries that can lead to both false positive or false negative findings. Many strategies are available to account for stratification but their performances differ, for instance according to the type of population structure, the disease susceptibility locus minor allele frequency, the degree of sampling imbalanced, or the sample size. We focus on the type of population structure and propose a comparison of the most commonly used methods to deal with stratification that are the Genomic Control, Principal Component based methods such as implemented in Eigenstrat, adjusted Regressions and Meta-Analyses strategies. Our assessment of the methods is based on a large simulation study, involving several scenarios corresponding to many types of population structures. We focused on both false positive rate and power to determine which methods perform the best. Our analysis showed that if there is no population structure, none of the tests led to a bias nor decreased the power except for the Meta-Analyses. When the population is stratified, adjusted Logistic Regressions and Eigenstrat are the best solutions to account for stratification even though only the Logistic Regressions are able to constantly maintain correct false positive rates. This study provides more details about these methods. Their advantages and limitations in different stratification scenarios are highlighted in order to propose practical guidelines to account for population stratification in Genome-Wide Association Studies

Correction of Population Stratification in Large Multi-Ethnic Association Studies

The vast majority of genetic risk factors for complex diseases have, taken individually, a small effect on the end phenotype. Population-based association studies therefore need very large sample sizes to detect significant differences between affected and non-affected individuals. Including thousands of affected individuals in a study requires recruitment in numerous centers, possibly from different geographic regions. Unfortunately such a recruitment strategy is likely to complicate the study design and to generate concerns regarding population stratification.We analyzed 9,751 individuals representing three main ethnic groups - Europeans, Arabs and South Asians - that had been enrolled from 154 centers involving 52 countries for a global case/control study of acute myocardial infarction. All individuals were genotyped at 103 candidate genes using 1,536 SNPs selected with a tagging strategy that captures most of the genetic diversity in different populations. We show that relying solely on self-reported ethnicity is not sufficient to exclude population stratification and we present additional methods to identify and correct for stratification.Our results highlight the importance of carefully addressing population stratification and of carefully “cleaning” the sample prior to analyses to obtain stronger signals of association and to avoid spurious results

A functional polymorphism under positive evolutionary selection in ADRB2 is associated with human intelligence with opposite effects in the young and the elderly

Comparative genomics offers a novel approach to unravel the genetic basis of complex traits. We performed a two stage analysis where genes ascertained for enhanced protein evolution in primates are subsequently searched for the presence of non-synonymous coding SNPs in the current human population at amino acid sites that differ between humans and chimpanzee. Positively selected genes among primates are generally presumed to determine phenotypic differences between humans and chimpanzee, such as the enhanced cognitive ability of our species. Amino acid substitutions segregating in humans at positively selected amino acid sites are expected to affect phenotypic differences among humans. Therefore we conducted an association study in two family based cohorts and one population based cohort between cognitive ability and the most likely candidate gene among the five that harbored more than one such polymorphism. The derived, human-specific allele of the beta-2 adrenergic receptor Arg16Gly polymorphism was found to be the increaser allele for performance IQ in the young, family based cohort but the decreaser allele for two different measures of cognition in the large Scottish cohort of unrelated individuals. The polymorphism is known to affect signaling activity and modulation of beta-2 adrenergic signaling has been shown to adjust memory consolidation, a trait related to cognition. The opposite effect of the polymorphism on cognition in the two age classes observed in the different cohorts resembles the effect of ADRB2 on hypertension, which also has been reported to be age dependent. This result illustrates the relevance of comparative genomics to detect genes that are involved in human behavior. © 2008 Springer Science+Business Media, LLC

Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples

The discordance in results of independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. We assessed the repeatibility of current Affymetrix technologies that support GWAS. Reasonable reproducibility was observed for both raw intensity and the genotypes/copy number variants. We also assessed consistencies between different SNP arrays and between genotype calling algorithms. We observed that the inconsistency in genotypes was generally small at the specimen level. To further examine whether the differences from genotyping and genotype calling are possible sources of variation in GWAS results, an association analysis was applied to compare the associated SNPs. We observed that the inconsistency in genotypes not only propagated to the association analysis, but was amplified in the associated SNPs. Our studies show that inconsistencies between SNP arrays and between genotype calling algorithms are potential sources for the lack of reproducibility in GWAS results