Genome-wide association study; gene-environment interaction; Bayesian inference; causal inference; copy number variants; Mendelian randomisation; fine-mapping
Sulc Jonathan, Mounier Ninon, Günther Felix, Winkler Thomas, Wood Andrew R., Frayling Timothy M., Heid Iris M., Robinson Matthew R., Kutalik Zoltán (2020), Quantification of the overall contribution of gene-environment interaction for obesity-related traits, in Nature Communications
, 11(1), 1385-1385.
Sulc Jonathan, Winkler Thomas W., Heid Iris M., Kutalik Zoltán (2020), Heterogeneity in Obesity: Genetic Basis and Metabolic Consequences, in Current Diabetes Reports
, 20(1), 1-1.
Porcu Eleonora, Rüeger Sina, Lepik Kaido, Santoni Federico A., Reymond Alexandre, Kutalik Zoltán (2019), Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits, in Nature Communications
, 10(1), 3300-3300.
Winkler Thomas W, Günther Felix, Höllerer Simon, Zimmermann Martina, Loos Ruth JF, Kutalik Zoltán, Heid Iris M (2018), A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications, in Nature Communications
, 9(1), 1946-1946.
Rüeger Sina, McDaid Aaron, Kutalik Zoltán (2018), Evaluation and application of summary statistic imputation to discover new height-associated loci, in PLOS Genetics
, 14(5), e1007371-e1007371.
Macé Aurélien, Tuke Marcus A., Deelen Patrick, Kristiansson Kati, Mattsson Hannele, Nõukas Margit, Sapkota Yadav, Schick Ursula, Porcu Eleonora, Rüeger Sina, McDaid Aaron F., Porteous David, Winkler Thomas W., Salvi Erika, Shrine Nick, Liu Xueping, Ang Wei Q., Zhang Weihua, Feitosa Mary F., Venturini Cristina, van der Most Peter J., Rosengren Anders, Wood Andrew R., Beaumont Robin N., et al. (2017), CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits, in Nature Communications
, 8(1), 744-744.
Marouli Eirini, Graff Mariaelisa, Medina-Gomez Carolina, Lo Ken Sin, Wood Andrew R., Kjaer Troels R., Fine Rebecca S., Lu Yingchang, Schurmann Claudia, Highland Heather M., Rüeger Sina, Thorleifsson Gudmar, Justice Anne E., Lamparter David, Stirrups Kathleen E., Turcot Valérie, Young Kristin L., Winkler Thomas W., Esko Tõnu, Karaderi Tugce, Locke Adam E., Masca Nicholas G. D., Ng Maggie C. Y., Mudgal Poorva, et al. (2017), Rare and low-frequency coding variants alter human adult height, in Nature
, 542(7640), 186-190.
Tyrrell Jessica, Wood Andrew R, Ames Ryan M, Yaghootkar Hanieh, Beaumont Robin N, Jones Samuel E, Tuke Marcus A, Ruth Katherine S, Freathy Rachel M, Davey Smith George, Joost Stéphane, Guessous Idris, Murray Anna, Strachan David P, Kutalik Zoltán, Weedon Michael N, Frayling Timothy M (2017), Gene–obesogenic environment interactions in the UK Biobank study, in International Journal of Epidemiology
With the advent of the deluge of Genome-wide association studies (GWASs) and advances in methodologies, the gap between the heritability estimated by twin-studies and explained by GWA studies, termed as the missing heritability, is closing rapidly. The enormous number of samples included in these analyses resulted in the robust association of thousands of genetic markers with a wide range of complex diseases. However, the interpretation, fine-mapping of such variants and their complex interplay with the environment are still under-investigated. In this research programme we propose to investigate the following three interconnected lines of research:1.We propose to reveal important modifiers of obesity-associated genetic effects through the following sub-projects:•We will test gene-lifestyle (nutrition, smoking, alcohol, caffeine) interactions•We will assess the pitfalls of gene-environment interaction analysis (with particular focus on index event bias)•We will explore parent-of-origin effect for obesity and gene expression regulation•We will identify genomic regions showing assortative mating pattern and examine their implication for disease via overlapping them with trait-associated loci2.To better fine-map association signals we will•implement substantial improvements for summary statistic imputation•introduce probabilistic copy number variant (CNV) calling and perform large-scale CNV association meta-analyses for obesity traits3.Estimating the joint causal effects of risk factors (such as lifestyle and molecular phenotypes) on obesity will facilitate the integration of previously published, exposure-associated GWAS findings into Bayesian priors to boost statistical power in order to detect novel obesity associations. Such approach will also enable the stratification of both diseases and samples into subgroups with similar profile and accelerate the understanding of disease mechanisms.The proposed research axes not only elucidate the fine genetic architecture of complex disease, but also utilize these genetic findings to unravel a complex causal network of genetic- and other risk factors leading to disease in various environmental settings. Comprehending these intricate disease mechanisms is key for prevention, diagnostics, and precision medicine.