Biology subjects

Hypponen, E.

Publications and source records attributed to Hypponen, E..

4 recordsLinked to original sources

bioRxiv2023

GxE PRS: Genotype-environment interaction in polygenic risk score models for quantitative and binary traits

The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individuals genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing GxE PRS models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose a novel GxE PRS model that correctly incorporates the GxE component to analyze complex traits and diseases. Through extensive simulations, we demonstrate that our proposed model outperforms existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed model to real data, and report significant GxE effects. Specifically, we highlight the impact of our model on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index (BMI) by alcohol intake frequency (ALC). In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension (HYP) by waist-to-hip ratio (WHR). These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxE PRS, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, while providing a practical tool to support further research in this area.

genetics↗

bioRxiv2022

Genomic contributions to infant and toddler vocabulary scores: Implications for association with health-, cognition- and behaviour-related outcomes

BackgroundThe number of words children produce (expressive vocabulary) and understand (receptive vocabulary) changes rapidly during early development, partially due to genetic factors, although mechanisms are not well understood. Here, we performed a meta-genome-wide association study within the EAGLE consortium and investigated polygenic overlap with later-life traits, including Attention-Deficit/Hyperactivity Disorder (ADHD) and cognition. MethodsWe studied 37,913 parent-reported vocabulary size measures (English, Dutch, Danish) for 17,298 children of European descent. Meta-analyses were performed for early-phase expressive (infancy, 15-18 months), late-phase expressive (toddlerhood, 24-38 months) and late-phase receptive (toddlerhood, 24-38 months) vocabulary. Subsequently, we estimated Single-Nucleotide Polymorphism heritability (SNP-h2), genetic correlations (rg) and modelled underlying genetic factor structures with multivariate models. ResultsContributions of common genetic variation to early-life vocabulary were modest (SNP-h2: 0.08(SE=0.01) to 0.24(SE=0.03)) and multi-factorial. Genetic overlap between infant expressive and toddler receptive vocabulary was near zero (rg=0.07(SE=0.10)), although both measures were genetically related to toddler expressive vocabulary (rg=0.69(SE=0.14) and rg=0.67(SE=0.16), respectively). Consistently, polygenic association patterns with later-life traits differed: Genetic links with cognition emerged only in toddlerhood (e.g. toddler receptive vocabulary and intelligence: rg=0.36(SE=0.12)), despite comparable study power for infant measures. Furthermore, increased polygenic ADHD risk was associated with larger infant expressive vocabulary (rg=0.23(SE=0.08)), as confirmed by ADHD-symptom-based follow-up analyses in the Avon Longitudinal Study of Parents and Children (ALSPAC-rg=0.54(SE=0.26)). Genetic relationships with toddler receptive vocabulary were, however, opposite (ALSPAC-rg=-0.74(SE=0.23)), highlighting developmental changes in genetic architectures. ConclusionsMultiple genetic components contribute to early-life vocabulary development, shaping polygenic association patterns with later-life ADHD symptoms and cognition.

genomics↗

bioRxiv2019

A Whole-Genome Approach Discovers Novel Genetic and Non-Genetic Variance Components Modulated by Lifestyle for Cardiovascular Health

Both genetic and non-genetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease (CVD) prevention. Here, we use a novel whole-genome framework to estimate genetic and non-genetic effects on--hence their predispositions to--cardiovascular risk and determine whether they vary with respect to lifestyle factors. We performed analyses on the Atherosclerosis Risk in Communities Study (ARIC, N=6,896-7,180) and validated findings using the UK Biobank (UKBB, N=14,076-34,538). Cardiovascular risk was measured using 23 traits in the ARIC and eight traits in the UKBB, such as body mass index (BMI), resting heart rate, white blood cell count and blood pressure; and lifestyle factors included information on physical activity, smoking, alcohol consumption and dietary intake. Physical activity altered both genetic and non-genetic effects on heart rate and BMI, genetic effects on HDL cholesterol level, and non-genetic effects on waist-to-hip ratio. Alcohol consumption altered both genetic and non-genetic effects on BMI, while smoking altered non-genetic effects on heart rate, pulse pressure, and white blood cell count. In addition, saturated fat intake modified genetic effects on BMI, and total daily energy intake modified non-genetic effects on waist-to-hip ratio. These results highlight the relevance of lifestyle changes for CVD prevention. We also stratified individuals according to their genetic predispositions and showed notable differences in the effects of lifestyle on cardiovascular risk across stratified groups, implying the need for individualizing lifestyle changes for CVD prevention. Finally, we showed that neglecting lifestyle modulation of genetic and non-genetic effects will on average reduce SNP heritability estimates of cardiovascular traits by a small yet significant amount, primarily owing to overestimation of residual variance. Thus, current SNP heritability estimates for cardiovascular traits, which commonly do not consider modulating effects of lifestyle covariates, are likely underestimated.

genetics↗

bioRxiv2018

Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model

The genomics era has brought useful tools to dissect the genetic architecture of complex traits. We propose a reaction norm model (RNM) to tackle genotype-environment correlation and interaction problems in the context of genome-wide association analyses of complex traits. In our approach, an environmental risk factor affecting the trait of interest can be modeled as dependent on a continuous covariate that is itself regulated by genetic as well as environmental factors. Our multivariate RNM approach allows the joint modelling of the relation between the genotype (G) and the covariate (C), so that both their correlation (association) and interaction (effect modification) can be estimated. Hence we jointly estimate genotype-covariate correlation and interaction (GCCI). We demonstrate using simulation that the proposed multivariate RNM performs better than the current state-of-the-art methods that ignore G-C correlation. We apply the method to data from the UK Biobank (N= 66,281) in analysis of body mass index using smoking quantity as a covariate. We find a highly significant G-C correlation, but a negligible G-C interaction. In contrast, when a conventional G-C interaction analysis is applied (i.e., G-C correlation is not included in the model), highly significant G-C interaction estimates are found. It is also notable that we find a significant heterogeneity in the estimated residual variances across different covariate levels probably due to residual-covariate interaction. Using simulation we also show that the residual variances estimated by genomic restricted maximum likelihood (GREML) or linkage disequilibrium score regression (LDSC) can be inflated in the presence of interactions, implying that the currently reported SNP-heritability estimates from these methods should be interpreted with caution. We conclude that it is essential to correctly account for both interaction and correlation in complex trait analyses and that the failure to do so may lead to substantial biases in inferences relating to genetic architecture of complex traits, including estimated SNP-heritability.

genetics↗