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Systems level understanding of the genetic architecture of complex human traits

English title Systems level understanding of the genetic architecture of complex human traits
Applicant Kutalik Zoltan
Number 169929
Funding scheme Project funding (Div. I-III)
Research institution Policlinique Médicale Universitaire PMU
Institution of higher education University of Lausanne - LA
Main discipline Methods of Epidemiology and Preventive Medicine
Start/End 01.01.2017 - 29.02.2020
Approved amount 474'000.00
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All Disciplines (2)

Discipline
Methods of Epidemiology and Preventive Medicine
Mathematics

Keywords (7)

Genome-wide association study; gene-environment interaction; Bayesian inference; causal inference; copy number variants; Mendelian randomisation; fine-mapping

Lay Summary (French)

Lead
Certains des polymorphismes nucléotidiques (SNP), dans notre ADN, peuvent être partiellement responsables de l'obésité. Malgré le nombre impressionnant d'études d'association pan-génomique, l’interprétation et la compréhension du mécanisme génétique des SNPs découverts est toujours peu connu. La recherche proposée axes non seulement élucider l'architecture génétique fine de maladie complexe, mais va aussi faciliter prévention, le diagnostic dans l’ère de la médecine de précision.
Lay summary

Contenu et objectifs du travail de recherche

 Les études d'association pan-génomique (GWAS) sont effectuées avec le but d'identifier les SNPs liés à certains phénotypes. Grace aux études incluent énorme nombre d'échantillons, l'association robuste de milliers de marqueurs génétiques était découverte pour un large éventail de maladies complexes. Cependant, l'interprétation, cartographie fine de ces variantes et de leur interaction complexe avec l'environnement sont encore sous-étudié. Dans ce programme de recherche, nous proposons de 1) révéler des modificateurs importants des effets génétiques associées à l'obésité ; 2) déterminer les sous-classes de l’obésité et des sous-groupes des individus avec un profil similaire pour d'accélérer la compréhension des mécanismes ; 3) améliorer la précision de localisation des variantes génétiques impliquées dans l’obésité.

 

Contexte scientifique et social

La recherche proposée axes non seulement élucider l'architecture génétique fine de maladie complexe, mais aussi utiliser ces résultats génétiques à démêler un réseau de causalité complexe de facteurs de risque conduisant à la maladie dans divers milieux environnementaux. Comprendre ces mécanismes pathologiques complexes est essentielle pour la prévention, le diagnostic et la médecine de précision.

Direct link to Lay Summary Last update: 23.09.2016

Responsible applicant and co-applicants

Employees

Publications

Publication
Quantification of the overall contribution of gene-environment interaction for obesity-related traits
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.
Heterogeneity in Obesity: Genetic Basis and Metabolic Consequences
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.
Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits
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.
A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications
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.
Evaluation and application of summary statistic imputation to discover new height-associated loci
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.
CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits
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.
Rare and low-frequency coding variants alter human adult height
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.
Gene–obesogenic environment interactions in the UK Biobank study
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, dyw337-dyw337.

Collaboration

Group / person Country
Types of collaboration
Genetic Epidemiology, University of Regensburg Germany (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Department of Computational Biology, University of Lausanne Switzerland (Europe)
- Publication
- Exchange of personnel
Medical School, University of Exeter Great Britain and Northern Ireland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
- Exchange of personnel
Center for Integrative Genomics, University of Lausanne Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
- Exchange of personnel

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
European Society of Human Genetics Conference Talk given at a conference Maximum likelihood method quantifies the overall contribution of gene-environment interaction to complex traits: an application to obesity traits 15.06.2019 Gothenburg, Sweden Kutalik Zoltan;
European Society of Human Genetics Conference Talk given at a conference Leveraging correlated risks to increase power in Genome-Wide Association Studies 15.06.2019 Gothenburg, Sweden Mounier Ninon;
European Mathematical Genetics Meeting Talk given at a conference Bayesian approach to increase power in genome-wide association studies of complex traits 08.04.2019 Dublin, Ireland Mounier Ninon;
European Mathematical Genetics Meeting Talk given at a conference Components of obesity: genetic architecture, causes, and consequences 08.04.2019 Dublin, Ireland Sulc Jonathan;
European Mathematical Genetics Meeting Talk given at a conference Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits 08.04.2019 Dublin, Ireland Porcu Eleonora;
American Society of Human Genetics Conference Talk given at a conference Mendelian randomization combining GWAS and eQTL data reveals new loci, extensive pleiotropy and genetic determinants of complex and clinical traits 16.10.2018 San Diego, United States of America Porcu Eleonora;
American Society of Human Genetics Conference Talk given at a conference Mendelian randomization-derived priors substantially improve power of Bayesian GWAS on human lifespan. 16.10.2018 San Diego, United States of America Mounier Ninon;
European Mathematical Genetics Meeting Talk given at a conference Mendelian randomization combining GWAS and eQTL data reveals new loci, extensive pleiotropy and genetic determinants of complex and clinical traits 18.04.2018 Cagliari, Italy Porcu Eleonora;
European Mathematical Genetics Meeting Talk given at a conference A joint analysis of adiposity genetics unravels subtypes with different metabolic implications 18.04.2018 Cagliari, Italy Kutalik Zoltan;


Communication with the public

Communication Title Media Place Year
New media (web, blogs, podcasts, news feeds etc.) Detecting the environment-genetics interplay for obesity-related traits International 2020
New media (web, blogs, podcasts, news feeds etc.) A new approach to unravel genetic determinants of complex and clinical traits International 2019

Awards

Title Year
Swiss Institute of Bioinformatics Early Career Bioinformatician Award 2019
Young Investigator Award for the best talk in statistical genetics (Lodewijk Sandkuijl Award) 2018

Associated projects

Number Title Start Funding scheme
152724 From modules to models III: Towards a better understanding of disease through advanced analysis of large-scale data 01.10.2014 Project funding (Div. I-III)
189147 Mendelian randomisation to reveal context-specific exposome-disease networks 01.03.2020 Project funding (Div. I-III)
143914 Deciphering the missing heritability 01.06.2013 Project funding (Div. I-III)
148401 Cardiovascular diseases and psychiatric disorders in the general population: a prospective follow-up study 01.04.2014 Cohort Studies Large
140331 Blood pressure and renal function genetics 01.04.2012 SPUM
189147 Mendelian randomisation to reveal context-specific exposome-disease networks 01.03.2020 Project funding (Div. I-III)

Abstract

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.
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