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From modules to models II: Towards a better understanding of disease through advanced analysis of large-scale data

Titel Englisch From modules to models II: Towards a better understanding of disease through advanced analysis of large-scale data
Gesuchsteller/in Bergmann Sven
Nummer 130691
Förderungsinstrument Projektförderung (spezial)
Forschungseinrichtung Département de biologie computationnelle Faculté de biologie et de médecine Université de Lausanne
Hochschule Universität Lausanne - LA
Hauptdisziplin Genetik
Beginn/Ende 01.06.2010 - 31.05.2014
Bewilligter Betrag 288'000.00
Alle Daten anzeigen

Alle Disziplinen (2)

Disziplin
Genetik
Methoden der Epidemiologie und der Präventivmedizi

Keywords (10)

GWAS; integrative analysis; large-scale data; microarrays; transcription modules; interactions; Genomics; predictive medicine; system biology; large nested project (CoLaus)

Lay Summary (Englisch)

Lead
Lay summary
The promises of modern data acquisition for a better understanding of biological systems can only be matched by advanced data analysis and modeling. Large-scale genomic data require innovative tools for data normalization, visualization and organization. Combining related entities in modular units reduces the complexity of the data and is an important step towards understanding the intricate behavior of thousands of genes under a variety of internal and external changes. The comparison and integration of large data-sets from different sources present major conceptual and computational challenges. New approaches are needed to generate quantitative models from the massive information generated by array-based technologies. Ultimately such mathematical models should be not only descriptive, but also predictive and provide insight into the design features of the biological systems.The present research project funded by the Swiss National Science Foundation focuses on the modular analysis of large-scale mammalian data. In other words we would like to dissect large tables of data reporting the activity of thousands of genes for large collection of samples (e.g. from different individuals) into manageable blocks (sub-tables) featuring only those genes that exhibit a similar activity pattern over a subset of samples. The elementary building blocks are then used for further analysis, in order to learn which genes act together and how the regulatory program is structured in general. This project extends previous work by integrating also genotypic information. Specifically, we address the challenges and limitations of current Genome-Wide Association Studies (GWAS) by proposing to integrate modular features from high-throughput data with large-scale data of genetic markers. Particularly interesting data-sets that we will analyze are the Cohorte Lausannoise which has been genotyped using SNP arrays and the HapMap panel. We will explore various approaches for reducing the complexity of the respective large phenotypic data-sets, including both "organismal" (cellular or clinical) and molecular (transcriptomic or metabolomic) phenotypes. Our main working hypothesis is that association of the resulting meta-observables that describe the properties of the modules will give rise to more robust associations with genotypes than their individual constituents.Our project attacks pressing issues of contemporary life sciences and has the potential to generate testable hypothesis, provide practical analysis tools, as well as new concepts and ideas for the biological and biomedical communities.
Direktlink auf Lay Summary Letzte Aktualisierung: 21.02.2013

Verantw. Gesuchsteller/in und weitere Gesuchstellende

Mitarbeitende

Publikationen

Publikation
Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links
Rueedi Rico (2014), Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links, in PLoS Genetics, 10(2), e1004132-e1004132.
Comparison of Strategies to Detect Epistasis from eQTL Data
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.
Methods for testing association between uncertain genotypes and quantitative traits.
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.
Novel method to estimate the phenotypic variation explained by genome-wide association studies reveals large fraction of the missing heritability.
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.
The evolution of gene expression levels in mammalian organs
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.
Using transcription modules to identify expression clusters perturbed in Williams-Beuren syndrome.
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.

Zusammenarbeit

Gruppe / Person Land
Formen der Zusammenarbeit
UNIL Schweiz (Europa)
- vertiefter/weiterführender Austausch von Ansätzen, Methoden oder Resultaten
- Publikation
NESTEC Schweiz (Europa)
- vertiefter/weiterführender Austausch von Ansätzen, Methoden oder Resultaten
- Publikation
- Industrie/Wirtschaft/weitere anwendungs-orientierte Zusammenarbeit

Verbundene Projekte

Nummer Titel Start Förderungsinstrument
152724 From modules to models III: Towards a better understanding of disease through advanced analysis of large-scale data 01.10.2014 Projektförderung (Abt. I-III)
116323 From modules to models: towards a better understanding of disease through advanced analysis of large-scale data 01.06.2007 Projektförderung (Abt. I-III)

Abstract

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.
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