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

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258748
Author(s):  
Dmitrii Borisevich ◽  
Theresia M. Schnurr ◽  
Line Engelbrechtsen ◽  
Alexander Rakitko ◽  
Lars Ängquist ◽  
...  
Keyword(s):  
Physical Activity ◽  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Environment Interaction ◽  
Linear Interaction ◽  
Polygenic Risk ◽  
Non Linear

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.

scholarly journals Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Celine M. Vachon ◽  
Christopher G. Scott ◽  
Rulla M. Tamimi ◽  
Deborah J. Thompson ◽  
Peter A. Fasching ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Body Mass Index ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Body Mass ◽  
Mammographic Density ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Polygenic Risk

scholarly journals TU37. POLYGENIC RISK SCORE FOR BODY MASS INDEX AND MACRONUTRIENT INTAKE PROFILES DURING CHILDHOOD

2021 ◽  
Vol 51 ◽  
pp. e114-e115
Author(s):  
Mohamed Abdulkadir ◽  
Moritz Herle ◽  
Christopher Hübel ◽  
Ruth JF Loos ◽  
Gerome Breen ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Macronutrient Intake ◽  
Polygenic Risk

scholarly journals Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes

2021 ◽  
Vol 11 (6) ◽  
pp. 582
Author(s):  
Avigail Moldovan ◽  
Yedael Y. Waldman ◽  
Nadav Brandes ◽  
Michal Linial
Keyword(s):  
Type 2 Diabetes ◽  
Body Mass Index ◽  
Birth Weight ◽  
Body Mass ◽  
Risk Score ◽  
Early Life ◽  
Integrated Approach ◽  
Polygenic Risk Score ◽  
Polygenic Risk

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight (n = 172,239) and comparative body size at age ten (n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.

scholarly journals Polygenic risk score and risk of monoclonal B-cell lymphocytosis in caucasians and risk of chronic lymphocytic leukemia (CLL) in African Americans

Leukemia ◽  
2021 ◽  
Author(s):  
Geffen Kleinstern ◽  
J. Brice Weinberg ◽  
Sameer A. Parikh ◽  
Esteban Braggio ◽  
Sara J. Achenbach ◽  
...  
Keyword(s):  
Environmental Factors ◽  
B Cell ◽  
Risk Score ◽  
Genetic Factors ◽  
Strong Predictor ◽  
Lymphocytic Leukemia ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
C Statistic

AbstractMonoclonal B-cell lymphocytosis (MBL) is a precursor to CLL. Other than age, sex, and CLL family-history, little is known about factors associated with MBL risk. A polygenic-risk-score (PRS) of 41 CLL-susceptibility variants has been found to be associated with CLL risk among individuals of European-ancestry(EA). Here, we evaluate these variants, the PRS, and environmental factors for MBL risk. We also evaluate these variants and the CLL-PRS among African-American (AA) and EA-CLL cases and controls. Our study included 560 EA MBLs, 869 CLLs (696 EA/173 AA), and 2866 controls (2631 EA/235 AA). We used logistic regression, adjusting for age and sex, to estimate odds ratios (OR) and 95% confidence intervals within each race. We found significant associations with MBL risk among 21 of 41 variants and with the CLL-PRS (OR = 1.86, P = 1.9 × 10−29, c-statistic = 0.72). Little evidence of any association between MBL risk and environmental factors was observed. We observed significant associations of the CLL-PRS with EA-CLL risk (OR = 2.53, P = 4.0 × 10−63, c-statistic = 0.77) and AA-CLL risk (OR = 1.76, P = 5.1 × 10−5, c-statistic = 0.62). Inherited genetic factors and not environmental are associated with MBL risk. In particular, the CLL-PRS is a strong predictor for both risk of MBL and EA-CLL, but less so for AA-CLL supporting the need for further work in this population.

scholarly journals Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hae-Un Jung ◽  
Won Jun Lee ◽  
Tae-Woong Ha ◽  
Ji-One Kang ◽  
Jihye Kim ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Bayes Factor ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Interaction Analysis ◽  
Carbohydrate Intake ◽  
Calorie Intake ◽  
Environment Interaction

AbstractMultiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10−6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10−6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10−9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10−10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene–environment interaction affecting disease.

scholarly journals Interaction of a genetic risk score with physical activity, physical inactivity, and body mass index in relation to venous thromboembolism risk

2018 ◽  
Vol 42 (4) ◽  
pp. 354-365 ◽  
Author(s):  
Jihye Kim ◽  
Peter Kraft ◽  
Kaitlin A. Hagan ◽  
Laura B. Harrington ◽  
Sara Lindstroem ◽  
...  
Keyword(s):  
Physical Activity ◽  
Body Mass Index ◽  
Venous Thromboembolism ◽  
Body Mass ◽  
Risk Score ◽  
Genetic Risk ◽  
Physical Inactivity ◽  
Genetic Risk Score

scholarly journals Impact of Dietary, Socioeconomic, and Physical Factors on Obese and Overweight Schoolchildren Living in Sidi-Bel-Abbes (West of Algeria) and Ain Defla (Centre)

2018 ◽  
Vol 25 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Hayat Didaoui ◽  
Méghit Boumediene Khaled
Keyword(s):  
Physical Activity ◽  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
School Children ◽  
Task Force ◽  
The Body ◽  
Physical Factors ◽  
Obesity And Overweight ◽  
The Impact

Abstract Background and aims: The aim of the current study was to assess the impact of environmental factors; food, socio-economic, and physical activity, on a group of obese children living in Ain-Defla (Center Algeria) and Sidi-Bel-Abbes (West Algeria). Material and methods: The protocol was carried out on a cohort of 125 school children aged of 5 to 11 years, including 64 boys and 61 girls, and 139 school children, including 93 boys and 46 girls in Ain Defla and Sidi-Bel-Abbes respectively. Concerning the classification of obesity and overweight, we referred to the International Obesity Task Force and the French References' curves. Results: Regarding dietary intake our results showed that 34% of students from both regions took their breakfast, compared to 66% who did not take. Furthermore, 73% of students skipped at least one meal, however 23% respected meals frequency i.e. 4 meals a day. Regarding socio-economic factors and physical activity, our findings showed that obesity rates were high (36%) among children whose fathers are workers. However, for mothers who are housewives, obesity increases among their children (88%). The relationship was reversed between the parents' education level and the Body Mass Index. We found an opposite relationship between Body Mass Index and physical activity, and investigated children use screen devices for long time periods. Conclusions: Our study showed a positive relationship between obesity and overweight and environmental factors.

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