Center for Human and Clinical Genetics, Leiden University Medical Center, S4-P, PO Box 9600, 2300, RC Leiden, Netherlands.
BMC Genomics. 2013 Dec 9;14:865. doi: 10.1186/1471-2164-14-865.
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.
全基因组关联研究 (GWAS) 已经确定了许多与临床表型相关的常见单核苷酸多态性 (SNP)，但这些 SNP 通常只解释了遗传率的一小部分，并且效应大小相对较小。相比之下，与代谢物水平相关的 SNP 通常可以解释更高比例的遗传变异，并且表现出更大的效应大小。尽管如此，发现与代谢物水平相关的 SNP 仍然具有挑战性，因为用所有 SNP 对典型代谢组学研究中测量的所有代谢物进行测试会带来严重的多重测试惩罚。我们开发了一种自动化工作流程方法，该方法利用数据库（如 KEGG 和 BioCyc）中存在的生化途径的先验知识，生成与代谢物相关的更小的 SNP 集。本文探讨了代谢组学表型 GWAS 分析中的机会和挑战，并通过重新分析已发表的 GWAS 数据集，为代谢变异性的遗传基础提供了新的见解。
使用基于途径的工作流程（http://www.myexperiment.org/packs/319.html）对 Illig 等人发表的 GWAS 数据集进行重新分析（《自然遗传学》，2010 年），证实了先前鉴定的命中，并鉴定了人类代谢个体性的新位点，该位点与血液中的醛脱氢酶家族 1 L1（ALDH1L1）与丝氨酸/甘氨酸比值相关。在磷脂的独立 GWAS 数据集（Demirkan 等人，《PLoS 遗传学》，2012 年）中的复制中，确定了两个新的、有额外文献证据支持的位点：GPAM（甘油-3-磷酸酰基转移酶）和 CBS（胱硫醚β-合酶）。此外，工作流程方法还提供了对受影响途径的新见解，以及这些基因-代谢物对疾病发展和进展的相关性。
我们证明了自动利用途径数据库中存在的背景知识来分析代谢组学表型 GWAS 数据集的实用性。我们报告了新的位点和潜在的生化机制，有助于我们理解代谢变异的遗传基础及其与疾病发展和进展的关系。
