Gene-Lifestyle Interactions in Complex Diseases: Design and Description of the GLACIER and VIKING Studies

Kurbasic, Azra; Poveda, Alaitz; Chen, Yan; Ågren, Anneli; Engberg, Elisabeth; Hu, Frank B.; Johansson, Ingegerd; Barroso, Inês; Brändström, Anders; Hallmans, Göran; Renström, Frida; Franks, Paul W.

doi:10.1007/s13668-014-0100-8

Cited by 16 publications

(15 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The maximum theoretical range of the GRS was 0–40 risk alleles, and the GRS was normally distributed. To confirm this assumption of normality, we modeled 20 random, uncorrelated SNPs from the GLACIER cohort (n = 6,064)19 with MAFs of 0.20–0.50 (Supplementary Figure 1). …”

Section: Methodsmentioning

confidence: 99%

Statistical power considerations in genotype-based recall randomized controlled trials

Atabaki‐Pasdar

Ohlsson

Shungin

et al. 2016

Sci Rep

Self Cite

View full text Add to dashboard Cite

Randomized controlled trials (RCT) are often underpowered for validating gene-treatment interactions. Using published data from the Diabetes Prevention Program (DPP), we examined power in conventional and genotype-based recall (GBR) trials. We calculated sample size and statistical power for gene-metformin interactions (vs. placebo) using incidence rates, gene-drug interaction effect estimates and allele frequencies reported in the DPP for the rs8065082 SLC47A1 variant, a metformin transported encoding locus. We then calculated statistical power for interactions between genetic risk scores (GRS), metformin treatment and intensive lifestyle intervention (ILI) given a range of sampling frames, clinical trial sample sizes, interaction effect estimates, and allele frequencies; outcomes were type 2 diabetes incidence (time-to-event) and change in small LDL particles (continuous outcome). Thereafter, we compared two recruitment frameworks: GBR (participants recruited from the extremes of a GRS distribution) and conventional sampling (participants recruited without explicit emphasis on genetic characteristics). We further examined the influence of outcome measurement error on statistical power. Under most simulated scenarios, GBR trials have substantially higher power to observe gene-drug and gene-lifestyle interactions than same-sized conventional RCTs. GBR trials are becoming popular for validation of gene-treatment interactions; our analyses illustrate the strengths and weaknesses of this design.

show abstract

Section: Methodsmentioning

confidence: 99%

Statistical power considerations in genotype-based recall randomized controlled trials

Atabaki‐Pasdar

Ohlsson

Shungin

et al. 2016

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Gene-Lifestyle interactions And Complex traits Involved in Elevated disease Risk (GLACIER) Study [11,12], is a population-based prospective cohort study of ∼19,000 adults of European ancestry, nested within the Northern Sweden Health and Disease Study at the Department of Biobank Research, Umeå University [13]. Clinical characteristics and extensive information on lifestyle behaviours were obtained within the framework of the Västerbotten Health Survey (also known as the Västerbotten Intervention Program), a population-wide health screening initiative that began in 1985.…”

Section: Methodsmentioning

confidence: 99%

“…Clinical examinations were conducted throughout the year, as previously described [12,13]. The participants arrived at the healthcare centre in the morning following an overnight fast.…”

Section: Clinical Measurementsmentioning

confidence: 99%

Season-dependent associations of circadian rhythm-regulating loci (CRY1, CRY2 and MTNR1B) and glucose homeostasis: the GLACIER Study

et al. 2015

Self Cite

View full text Add to dashboard Cite

Aims/hypothesis The association of single nucleotide polymorphisms (SNPs) proximal to CRY2 and MTNR1B with fasting glucose is well established. CRY1/2 and MTNR1B encode proteins that regulate circadian rhythmicity and influence energy metabolism. Here we tested whether season modified the relationship of these loci with blood glucose concentration. Methods SNPs rs8192440 (CRY1), rs11605924 (CRY2) and rs10830963 (MTNR1B) were genotyped in a prospective cohort study from northern Sweden (n=16,499). The number of hours of daylight exposure during the year ranged from 4.5 to 22 h daily. Owing to the non-linear distribution of daylight throughout the year, season was dichotomised based on the vernal and autumnal equinoxes. Effect modification was assessed using linear regression models fitted with a SNP × season interaction term, marginal effect terms and putative confounding variables, with fasting or 2 h glucose concentrations as outcomes. Results The rs8192440 (CRY1) variant was only associated with fasting glucose among participants (n=2,318) examined during the light season ( β=−0.04 mmol/l per A allele, 95% CI −0.08, −0.01, p=0.02, p interaction =0.01). In addition to the established association with fasting glucose, the rs11605924 (CRY2) and rs10830963 (MTNR1B) loci were associated with 2 h glucose concentrations ( β=0.07 mmol/l per A allele, 95% CI 0.03, 0.12, p=0.0008, n=9,605, and β=−0.11 mmol/l per G allele, 95% CI −0.15, −0.06, p<0.0001, n=9,517, respectively), but only in participants examined during the dark season ( p interaction =0.006 and 0.04, respectively). Repeated measures analyses including data collected 10 years after baseline (n=3,500) confirmed the results for the CRY1 locus ( p interaction =0.01). Conclusions/interpretation In summary, these observations suggest a biologically plausible season-dependent association between SNPs at CRY1, CRY2 and MTNR1B and glucose homeostasis.

show abstract

“…Accordingly, we characterised the genealogical structure of a large northern Swedish population, within which detailed measures of environmental exposures, cardiometabolic traits and other personal characteristics exist [9].…”

Section: Introductionmentioning

confidence: 99%

The heritable basis of gene–environment interactions in cardiometabolic traits

et al. 2016

Self Cite

View full text Add to dashboard Cite

Aims/hypothesis Little is known about the heritable basis of gene-environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype-environment interactions. Methods Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software.Results All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities (h 2 ) ranging from 24% to 47%. Genotype-environment interactions were detected for age and sex (for the majority of traits), physical activity (for triacylglycerols, 2 h glucose and diastolic BP), smoking (for weight), alcohol intake (for weight, BMI and 2 h glucose) and diet pattern (for weight, BMI, glycaemic traits and systolic BP). Genotype-age interactions for weight and systolic BP, genotype-sex interactions for BMI and triacylglycerols and genotype-alcohol intake interactions for weight remained significant after multiple test correction. Conclusions/interpretation Age, sex and alcohol intake are likely to be major modifiers of genetic effects for a range of cardiometabolic traits. This information may prove valuable for studies that seek to identify specific loci that modify the effects of lifestyle in cardiometabolic disease.

show abstract

Gene-Lifestyle Interactions in Complex Diseases: Design and Description of the GLACIER and VIKING Studies

Cited by 16 publications

References 89 publications

Statistical power considerations in genotype-based recall randomized controlled trials

Statistical power considerations in genotype-based recall randomized controlled trials

Season-dependent associations of circadian rhythm-regulating loci (CRY1, CRY2 and MTNR1B) and glucose homeostasis: the GLACIER Study

The heritable basis of gene–environment interactions in cardiometabolic traits

Contact Info

Product

Resources

About