Gene-environment interaction explains a part of missing heritability in human body mass index

Jung, Hae-Un; Kim, Dong Jun; Baek, Eun Ju; Chung, Ju Yeon; Ha, Tae Woong; Kim, Han-Kyul.; Kang, Ji‐One; Lim, Ji Eun; Oh, Bermseok

doi:10.1038/s42003-023-04679-4

“… Individual-level data advantages include better handling of LD and complex interactions. LDSC GxE 35 Estimates genome-wide GxE variance using GxE GWAS (GWIS) summary statistics. GWIS replaces GWAS in standard LDSC regression 37 to estimate .…”

Section: Discussionmentioning

confidence: 99%

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

Di Scipio,

Khan,

Mao

et al. 2023

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Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 – 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.

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“…Recent research from HICs has shown that environmental factors can influence an individual’s genetic susceptibility to obesity. For example, a recent study of 331,282 participants in the UK Biobank found that metabolic equivalent task (MET) score, pack-years of smoking, and alcohol intake frequency significantly interact with genetic factors related to obesity for BMI ( 58 ). Findings from GxE research in HICs cannot be assumed to be the same in LMICs.…”

Section: Discussionmentioning

confidence: 99%

Gene-environment interactions and the effect on obesity risk in low and middle-income countries: a scoping review

Pledger,

Ahmadizar

2023

Front. Endocrinol.

2

0

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BackgroundObesity represents a major and preventable global health challenge as a complex disease and a modifiable risk factor for developing other non-communicable diseases. In recent years, obesity prevalence has risen more rapidly in low- and middle-income countries (LMICs) compared to high-income countries (HICs). Obesity traits are shown to be modulated by an interplay of genetic and environmental factors such as unhealthy diet and physical inactivity in studies from HICs focused on populations of European descent; however, genetic heterogeneity and environmental differences prevent the generalisation of study results to LMICs. Primary research investigating gene-environment interactions (GxE) on obesity in LMICs is limited but expanding. Synthesis of current research would provide an overview of the interactions between genetic variants and environmental factors that underlie the obesity epidemic and identify knowledge gaps for future studies.MethodsThree databases were searched systematically using a combination of keywords such as “genes”, “obesity”, “LMIC”, “diet”, and “physical activity” to find all relevant observational studies published before November 2022.ResultsEighteen of the 1,373 articles met the inclusion criteria, of which one was a genome-wide association study (GWAS), thirteen used a candidate gene approach, and five were assigned as genetic risk score studies. Statistically significant findings were reported for 12 individual SNPs; however, most studies were small-scale and without replication.ConclusionAlthough the results suggest significant GxE interactions on obesity in LMICs, updated robust statistical techniques with more precise and standardised exposure and outcome measurements are necessary for translatable results. Future research should focus on improved quality replication efforts, emphasising large-scale and long-term longitudinal study designs using multi-ethnic GWAS.

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Generalizability of polygenic prediction models: how is the R2 defined on test data?

Staerk,

Klinkhammer,

Wistuba

et al. 2024

BMC Med Genomics

0

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Background Polygenic risk scores (PRS) quantify an individual’s genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the $$R^2$$ R 2 is a commonly used measure to evaluate prediction accuracy. While the $$R^2$$ R 2 is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. Methods Based on large-scale genotype data from the UK Biobank, we compare three definitions of the $$R^2$$ R 2 on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoprotein A, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. Results Our analysis shows that the choice of the $$R^2$$ R 2 definition can lead to considerably different results on test data, making the comparison of $$R^2$$ R 2 values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the $$R^2$$ R 2 based on the mean squared prediction error (MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis — whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of $$R^2$$ R 2 can provide valuable complementary information. Conclusions Awareness of the different definitions of the $$R^2$$ R 2 on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting $$R^2$$ R 2 values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.

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Gene-environment interaction explains a part of missing heritability in human body mass index

Cited by 3 publications

References 59 publications

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

Gene-environment interactions and the effect on obesity risk in low and middle-income countries: a scoping review

Generalizability of polygenic prediction models: how is the R2 defined on test data?

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