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Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles.

ABSTRACT: Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) that associate with clinical phenotypes, but these SNPs usually explain just a small part of the heritability and have relatively modest effect sizes. In contrast, SNPs that associate with metabolite levels generally explain a higher percentage of the genetic variation and demonstrate larger effect sizes. Still, the discovery of SNPs associated with metabolite levels is challenging since testing all metabolites measured in typical metabolomics studies with all SNPs comes with a severe multiple testing penalty. We have developed an automated workflow approach that utilizes prior knowledge of biochemical pathways present in databases like KEGG and BioCyc to generate a smaller SNP set relevant to the metabolite. This paper explores the opportunities and challenges in the analysis of GWAS of metabolomic phenotypes and provides novel insights into the genetic basis of metabolic variation through the re-analysis of published GWAS datasets.Re-analysis of the published GWAS dataset from Illig et al. (Nature Genetics, 2010) using a pathway-based workflow (http://www.myexperiment.org/packs/319.html), confirmed previously identified hits and identified a new locus of human metabolic individuality, associating Aldehyde dehydrogenase family1 L1 (ALDH1L1) with serine/glycine ratios in blood. Replication in an independent GWAS dataset of phospholipids (Demirkan et al., PLoS Genetics, 2012) identified two novel loci supported by additional literature evidence: GPAM (Glycerol-3 phosphate acyltransferase) and CBS (Cystathionine beta-synthase). In addition, the workflow approach provided novel insight into the affected pathways and relevance of some of these gene-metabolite pairs in disease development and progression.We demonstrate the utility of automated exploitation of background knowledge present in pathway databases for the analysis of GWAS datasets of metabolomic phenotypes. We report novel loci and potential biochemical mechanisms that contribute to our understanding of the genetic basis of metabolic variation and its relationship to disease development and progression.

SUBMITTER: Dharuri H

PROVIDER: S-EPMC3879060 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles.

Dharuri Harish H Henneman Peter P Demirkan Ayse A van Klinken Jan Bert JB Mook-Kanamori Dennis Owen DO Wang-Sattler Rui R Gieger Christian C Adamski Jerzy J Hettne Kristina K Roos Marco M Suhre Karsten K Van Duijn Cornelia M CM van Dijk Ko Willems KW 't Hoen Peter A C PA

BMC genomics 20131209

<h4>Background</h4>Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) that associate with clinical phenotypes, but these SNPs usually explain just a small part of the heritability and have relatively modest effect sizes. In contrast, SNPs that associate with metabolite levels generally explain a higher percentage of the genetic variation and demonstrate larger effect sizes. Still, the discovery of SNPs associated with metabolite levels is ch ...[more]

PMID: 24320595

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Project description:BACKGROUND:In the past decade, transcriptome data have become an important component of many phylogenetic studies. They are a cost-effective source of protein-coding gene sequences, and have helped projects grow from a few genes to hundreds or thousands of genes. Phylogenetic studies now regularly include genes from newly sequenced transcriptomes, as well as publicly available transcriptomes and genomes. Implementing such a phylogenomic study, however, is computationally intensive, requires the coordinated use of many complex software tools, and includes multiple steps for which no published tools exist. Phylogenomic studies have therefore been manual or semiautomated. In addition to taking considerable user time, this makes phylogenomic analyses difficult to reproduce, compare, and extend. In addition, methodological improvements made in the context of one study often cannot be easily applied and evaluated in the context of other studies. RESULTS:We present Agalma, an automated tool that constructs matrices for phylogenomic analyses. The user provides raw Illumina transcriptome data, and Agalma produces annotated assemblies, aligned gene sequence matrices, a preliminary phylogeny, and detailed diagnostics that allow the investigator to make extensive assessments of intermediate analysis steps and the final results. Sequences from other sources, such as externally assembled genomes and transcriptomes, can also be incorporated in the analyses. Agalma is built on the BioLite bioinformatics framework, which tracks provenance, profiles processor and memory use, records diagnostics, manages metadata, installs dependencies, logs version numbers and calls to external programs, and enables rich HTML reports for all stages of the analysis. Agalma includes a small test data set and a built-in test analysis of these data. In addition to describing Agalma, we here present a sample analysis of a larger seven-taxon data set. Agalma is available for download at https://bitbucket.org/caseywdunn/agalma. CONCLUSIONS:Agalma allows complex phylogenomic analyses to be implemented and described unambiguously as a series of high-level commands. This will enable phylogenomic studies to be readily reproduced, modified, and extended. Agalma also facilitates methods development by providing a complete modular workflow, bundled with test data, that will allow further optimization of each step in the context of a full phylogenomic analysis.

Project description:Consumer-resource interactions are a central issue in evolutionary and community ecology because they play important roles in selection and population regulation. Most consumers encounter resource variation at multiple scales, and respond through phenotypic plasticity in the short term or evolutionary divergence in the long term. The key traits for these responses may influence resource acquisition, assimilation, and/or allocation. To identify relevant candidate genes, we experimentally assayed genome-wide gene expression in pond and lake Daphnia ecotypes exposed to alternate resource environments. One was a simple, high-quality laboratory diet, Ankistrodesmus falcatus. The other was the complex natural seston from a large lake. In temporary ponds, Daphnia generally experience high-quality, abundant resources, whereas lakes provide low-quality, seasonally shifting resources that are chronically limiting. For both ecotypes, we used replicate clones drawn from a number of separate populations. Fourteen genes were differentially regulated with respect to resources, including genes involved in gut processes, resource allocation, and activities with no obvious connection to resource exploitation. Three genes were differentially regulated in both ecotypes; the others may play a role in ecological divergence. Genes clearly linked to gut processes include two peritrophic matrix proteins, a Niemann-Pick type C2 gene, and a chymotrypsin. A pancreatic lipase, an epoxide hydrolase, a neuroparsin, and an UDP-dependent glucuronyltransferase are potentially involved in resource allocation through effects on energy processing and storage or hormone pathways. We performed quantitative rt-PCR for eight genes in independent samples of three clones of each of the two ecotypes. Though these largely confirmed observed differential regulation, some genes' expression was highly variable among clones. Our results demonstrate the value of matching the level of biological replication in genome-wide assays to the question, as it gave us insight into ecotype-level responses at ecological and evolutionary scales despite substantial variation within ecotypes.

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Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles.

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Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles.

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