Browsing by Subject "biological networks"

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    Integrating Co-Expression Networks with GWAS to Detect Causal Genes For Agronomically Important Traits
    (2015-11) Schaefer, Robert
    The recent availability of high-throughput technologies in agricultural species provides an opportunity to advance our understanding of complex, agronomically important traits. Genome wide association studies (GWAS) have identified thousands of loci linked to these traits; however in most cases the causal genes remain unknown. Analysis of a single data type is typically unsatisfactory in explaining complex traits that exhibit variation across multiple levels of biological regulation. To address these issues, we developed a computational framework called Camoco (Co-analysis of molecular components) that systematically integrates loci identified by GWAS with gene co-expression networks to identify a focused set of candidate loci with functional coherence. This framework analyzes the overlap between candidate loci generated from GWAS and the co-expression interactions that occur between them and addresses several biological considerations important for integrating diverse data types. On average, using this integrated approach, candidate gene lists identified by GWAS were reduced by two orders of magnitude. By incorporating co-expression network information, we can rapidly evaluate hundreds of GWAS experiments, producing focused sets of candidates with both strong associations with the phenotype of interest as well as evidence for functional coherence in the co-expression network. Identifying these candidates in a systematic and integrated manner is an important step toward resolving genes responsible for agriculturally important traits.
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    Learning High-Order Relations for Network-Based Phenome-Genome Association Analysis
    (2019-08) Petegrosso, Raphael
    An organism's phenome is the expression of characteristics from genetic inheritance and interaction with the environment. This includes simple physical appearance and traits, and even complex diseases. In human, the understanding of the relationship of such features with genetic markers gives insights into the mechanisms involved in the expression, and can also help to design targeted therapies and new drugs. In other species, such as plants, correlation of phenotypes with genetic mutations and geoclimatic variables also assists in the understanding of evolutionary global diversity and important characteristics such as flowering time. In this thesis, we propose to use high-order machine learning methods to help in the analysis of phenome through the associations with biological networks and ontologies. We show that, by combining biological networks with functional annotation of genes, we can extract high-order relations to improve the discovery of new candidate associations between genes and phenotypes. We also propose to detect high-order relations among multiple genomics datasets, geoclimatic features, and interactions among genes, to find a feature representation that can be utilized to successfully predict phenotypes. Experiments using the Arabidopsis thaliana species shows that our approach does not only contribute with an accurate predictive tool, but also brings an intuitive alternative for the analysis of correlation among plant accessions, genetic markers, and geoclimatic variables. Finally, we propose a scalable approach to solve challenges inherited from the use of massive biological networks in phenome analysis. Our low-rank method can be used to process massive networks in parallel computing to enable large-scale prior knowledge to be incorporated and improve predictive power.