Dataset Information

Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations.

ABSTRACT: Body mass index (BMI) is a highly heritable polygenic trait. It is also affected by various environmental and behavioral risk factors. We used a BMI polygenic risk score (PRS) to study the interplay between the genetic and environmental factors defining BMI. First, we generated a BMI PRS that explained more variance than a BMI genetic risk score (GRS), which was using only genome-wide significant BMI-associated variants (R2 = 13.1% compared to 6.1%). Second, we analyzed interactions between BMI PRS and seven environmental factors. We found a significant interaction between physical activity and BMI PRS, even when the well-known effect of the FTO region was excluded from the PRS, using a small dataset of 6,179 samples. Third, we stratified the study population into two risk groups using BMI PRS. The top 22% of the studied populations were included in a high PRS risk group. Engagement in self-reported physical activity was associated with a 1.66 kg/m2 decrease in BMI in this group, compared to a 0.84 kg/m2 decrease in BMI in the rest of the population. Our results (i) confirm that genetic background strongly affects adult BMI in the general population, (ii) show a non-linear interaction between BMI genetics and physical activity, and (iii) provide a standardized framework for future gene-environment interaction analyses.

SUBMITTER: Borisevich D

PROVIDER: S-EPMC8523041 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations.

Borisevich Dmitrii D Schnurr Theresia M TM Engelbrechtsen Line L Rakitko Alexander A Ängquist Lars L Ilinsky Valery V Aadahl Mette M Grarup Niels N Pedersen Oluf O Sørensen Thorkild I A TIA Hansen Torben T

PloS one 20211018 10

Body mass index (BMI) is a highly heritable polygenic trait. It is also affected by various environmental and behavioral risk factors. We used a BMI polygenic risk score (PRS) to study the interplay between the genetic and environmental factors defining BMI. First, we generated a BMI PRS that explained more variance than a BMI genetic risk score (GRS), which was using only genome-wide significant BMI-associated variants (R2 = 13.1% compared to 6.1%). Second, we analyzed interactions between BMI ...[more]

PMID: 34662357

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Project description:ImportanceExcess weight, the prevalence of which is high and increasing in many countries, is linked to multiple adverse health outcomes, including increased colorectal cancer (CRC) risk. Better communication of health risks associated with excess weight might support efforts of prevention.ObjectiveTo evaluate the individual and joint associations of body mass index (BMI) and polygenic risk with CRC, to assess potential interactions among them, and to quantify by how much increased polygenic risk for CRC can be offset by having a BMI within reference range.Design, setting, and participantsThis population-based case-control study was conducted in the Rhine-Neckar region of southwest Germany, with recruitment from 2003 to 2017. Participants with both risk factor and genetic information were included for analysis. Data analysis was conducted from December 8, 2021, to February 17, 2022.ExposuresBMI was calculated as self-reported weight in kilograms approximately 10 years before diagnosis or interview and current height in meters squared. A polygenic risk score (PRS) was built based on 140 CRC-related risk loci.Main outcomes and measuresIndividual and joint associations of BMI and PRS with CRC were estimated using multiple logistic regression. Associations of excess weight with CRC were quantified by adjusted odds ratios (aORs) and genetic risk equivalents (GREs), the equivalent outcomes conveyed by defined differences in PRS percentiles.ResultsAmong 9169 participants (median [IQR] age, 69 [62-76] years; 5589 [61.0%] male participants) included, 5053 had CRC and 4116 did not. BMI of 30 or greater was associated with higher odds of having CRC compared with BMI less than 25 (aOR, 1.71; 95% CI, 1.49-1.97), independent of PRS levels (P for interaction = .45). Participants with BMI of 30 or greater and a PRS in the highest tertile had higher odds of CRC compared with participants with BMI less than 25 and a PRS in the lowest tertile (aOR, 3.82; 95% CI, 3.03-4.82). The estimated association of BMI greater than 30 with CRC risk was equivalent to that of having a 41 (95% CI, 29-53)-percentile higher PRS. BMI of 30 or greater was particularly associated with stage IV CRC (aOR, 2.21; 95% CI, 1.71-2.84).Conclusions and relevanceThese findings suggest that excess weight was associated with CRC regardless of PRS levels. The association of having a BMI within reference range may be similar to that of having a substantially lower polygenic risk for CRC.

Project description:Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unveiled many aspects of the genetic architecture of obesity and BMI. Information on genome-wide genetic variants has been used to estimate the genome-wide polygenic score (GPS) for a personalized prediction of obesity. However, the prediction power of GPS is affected by various factors, including the unequal variance in the distribution of a phenotype, known as heteroscedasticity. Here, we calculated a GPS for BMI using LDpred2, which was based on the BMI GWAS summary statistics from a European meta-analysis. Then, we tested the GPS in 354,761 European samples from the UK Biobank and found an effective prediction power of the GPS on BMI. To study a change in the variance of BMI, we investigated the heteroscedasticity of BMI across the GPS via graphical and statistical methods. We also studied the homoscedastic samples for BMI compared to the heteroscedastic sample, randomly selecting samples with various standard deviations of BMI residuals. Further, we examined the effect of the genetic interaction of GPS with environment (GPS×E) on the heteroscedasticity of BMI. We observed the changing variance (i.e., heteroscedasticity) of BMI along the GPS. The heteroscedasticity of BMI was confirmed by both the Breusch-Pagan test and the Score test. Compared to the heteroscedastic sample, the homoscedastic samples from small standard deviation of BMI residuals showed a decreased heteroscedasticity and an improved prediction accuracy, suggesting a quantitatively negative correlation between the phenotypic heteroscedasticity and the prediction accuracy of GPS. To further test the effects of the GPS×E on heteroscedasticity, first we tested the genetic interactions of the GPS with 21 environments and found 8 significant GPS×E interactions on BMI. However, the heteroscedasticity of BMI was not ameliorated after adjusting for the GPS×E interactions. Taken together, our findings suggest that the heteroscedasticity of BMI exists along the GPS and is not affected by the GPS×E interaction.

Project description:Polygenic risk scores (PRS) have led to enthusiasm for precision medicine. However, it is well documented that PRS do not generalize across groups differing in ancestry or sample characteristics e.g., age. Quantifying performance of PRS across different groups of study participants, using genome-wide association study (GWAS) summary statistics from multiple ancestry groups and sample sizes, and using different linkage disequilibrium (LD) reference panels may clarify which factors are limiting PRS transferability. To evaluate these factors in the PRS generation process, we generated body mass index (BMI) PRS (PRSBMI) in the Electronic Medical Records and Genomics (eMERGE) network (N=75,661). Analyses were conducted in two ancestry groups (European and African) and three age ranges (adult, teenagers, and children). For PRSBMI calculations, we evaluated five LD reference panels and three sets of GWAS summary statistics of varying sample size and ancestry. PRSBMI performance increased for both African and European ancestry individuals using cross-ancestry GWAS summary statistics compared to European-only summary statistics (6.3% and 3.7% relative R2 increase, respectively, pAfrican=0.038, pEuropean=6.26x10-4). The effects of LD reference panels were more pronounced in African ancestry study datasets. PRSBMI performance degraded in children; R2 was less than half of teenagers or adults. The effect of GWAS summary statistics sample size was small when modeled with the other factors. Additionally, the potential of using a PRS generated for one trait to predict risk for comorbid diseases is not well understood especially in the context of cross-ancestry analyses - we explored clinical comorbidities from the electronic health record associated with PRSBMI and identified significant associations with type 2 diabetes and coronary atherosclerosis. In summary, this study quantifies the effects that ancestry, GWAS summary statistic sample size, and LD reference panel have on PRS performance, especially in cross-ancestry and age-specific analyses.

Project description:BackgroundMammographic breast density, adjusted for age and body mass index, and a polygenic risk score (PRS), comprised of common genetic variation, are both strong risk factors for breast cancer and increase discrimination of risk models. Understanding their joint contribution will be important to more accurately predict risk.MethodsUsing 3628 breast cancer cases and 5126 controls of European ancestry from eight case-control studies, we evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS and quantitative mammographic density measures with breast cancer. Mammographic percent density and absolute dense area were evaluated using thresholding software and examined as residuals after adjusting for age, 1/BMI, and study. PRS and adjusted density phenotypes were modeled both continuously (per 1 standard deviation, SD) and categorically. We fit logistic regression models and tested the null hypothesis of multiplicative joint associations for PRS and adjusted density measures using likelihood ratio and global and tail-based goodness of fit tests within the subset of six cohort or population-based studies.ResultsAdjusted percent density (odds ratio (OR) = 1.45 per SD, 95% CI 1.38-1.52), adjusted absolute dense area (OR = 1.34 per SD, 95% CI 1.28-1.41), and the 77-SNP PRS (OR = 1.52 per SD, 95% CI 1.45-1.59) were associated with breast cancer risk. There was no evidence of interaction of the PRS with adjusted percent density or dense area on risk of breast cancer by either the likelihood ratio (P > 0.21) or goodness of fit tests (P > 0.09), whether assessed continuously or categorically. The joint association (OR) was 2.60 in the highest categories of adjusted PD and PRS and 0.34 in the lowest categories, relative to women in the second density quartile and middle PRS quintile.ConclusionsThe combined associations of the 77-SNP PRS and adjusted density measures are generally well described by multiplicative models, and both risk factors provide independent information on breast cancer risk.

Dataset Information

Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations.

Publications

Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations.

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