GWAS; integrative analysis; large-scale data; microarrays; transcription modules; interactions; Genomics; predictive medicine; system biology; large nested project (CoLaus)
Rueedi Rico (2014), Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links, in PLoS Genetics
, 10(2), e1004132-e1004132.
Kapur K, Schupbach T, Xenarios I, Kutalik Z, Bergmann S (2011), Comparison of Strategies to Detect Epistasis from eQTL Data, in PLOS ONE
, 6(12), 1-7.
Kutalik Zoltán, Johnson Toby, Bochud Murielle, Mooser Vincent, Vollenweider Peter, Waeber Gérard, Waterworth Dawn, Beckmann Jacques S, Bergmann Sven (2011), Methods for testing association between uncertain genotypes and quantitative traits., in Biostatistics (Oxford, England)
, 12(1), 1-17.
Kutalik Zoltán, Whittaker John, Waterworth Dawn, GIANT consortium, Beckmann Jacques S, Bergmann Sven (2011), Novel method to estimate the phenotypic variation explained by genome-wide association studies reveals large fraction of the missing heritability., in Genetic epidemiology
, 35(5), 341-9.
Brawand David, Soumillon Magali, Necsulea Anamaria, Julien Philippe, Csárdi Gábor, Harrigan Patrick, Weier Manuela, Liechti Angélica, Aximu-Petri Ayinuer, Kircher Martin, Albert Frank W, Zeller Ulrich, Khaitovich Philipp, Grützner Frank, Bergmann Sven, Nielsen Rasmus, Pääbo Svante, Kaessmann Henrik (2011), The evolution of gene expression levels in mammalian organs, in Nature
, 478(7369), 343-8.
Henrichsen Charlotte N, Csárdi Gábor, Zabot Marie-Thérèse, Fusco Carmela, Bergmann Sven, Merla Giuseppe, Reymond Alexandre (2011), Using transcription modules to identify expression clusters perturbed in Williams-Beuren syndrome., in PLoS computational biology
, 7(1), 1001054-1001054.
Vast amounts of financial and human resources have been invested in the last years into clinical and genomic profiling of large cohorts creating enormous amounts of data. While genome-wide association studies (GWAS) have already successfully revealed new candidate loci that potential affect human disease or related phenotypes, they still fail to predict a significant portion of the phenotypic variance for all common diseases, or related traits. We believe that part of this failure may be overcome by developing novel analysis concepts and methodologies. The main goal of this proposal is to integrate and adapt the modular technologies we developed within the framework of our first SNSF grant for the analysis of large-scale phenotypic data in order to address the challenges and limitation of current GWAS approaches. Specifically we propose to (1) use the Iterative Signature Algorithm (ISA) to perform a modular analysis of the HapMap expression data, (2) use our modular approaches for the unsupervised analysis of large sets of clinical phenotypes from the Cohorte Lausanne (CoLaus), (3) use the Ping-pong algorithm (PPA) to generate phenotypic co-modules for the integrative analysis of subsets of phenotypic data, (4) adapt the ISA for generating genotypic modules and the PPA for generating genotype-phenotype co-modules to identify meta-genotypes that may explain a larger fraction of the observed phenotypic variance and (5) explore to what extent the identification of genotypic modules or genotype-phenotype co-modules can facilitate the study of genetic interactions. Our first three aims concentrate on reducing the complexity (1-2) and integration of large phenotypic datasets (3), including both organismal (clinical) and molecular (transcriptomic or metabolomic) phenotypes. Based on our preliminary evidence, we expect that standard GWAS of the resulting meta-phenotypes may give rise to more significant or insightful associations. Aims 4-5 are directed towards exploring new concepts for GWAS that are not focused on single genetic markers but sets of markers whose cumulative (4) or more combinatory (5) scores may potentially explain a larger fraction of the observed phenotypic variance.While this project is focused on development and application of new methodologies using existing data, we propose also to generate an additional collection of 500 expression profiles from a random subpopulation of CoLaus that will facility our multi-level integrative analysis.In this project we focus and combine our expertise in modular analysis and GWAS through the development of new concepts and methods for the analysis of large-scale genomic and medical data. Thus our progress will have direct impact in terms on medical applications, like better prognostics and risk assessment based on genotypic profiles. Moreover our methodological advances will also contribute to the field of Genomics, where the rapidly growing and diversifying sets of data call for new integrative analysis tools.