Bioinformatic selection of putative epigenetically regulated loci associated with obesity using gene expression data

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• 作者：Valé ; rie Turcot^a ; ¹ ; Alexandra Groom^a ; James C. McConnell^a ; Mark S. Pearce^b ; Catherine Potter^a ; Nicholas D. Embleton^c ; Daniel C. Swan^d ; Caroline L. Relton^a ; ^{caroline.relton@ncl.ac.uk}
• 关键词：aa ; amino acid ; BMI ; body mass index ; bp ; base pair ; CD38 ; cluster of differentiation 38 ; CDC42 ; cell division cycle 42 ; CGI ; CpG island ; CpG ; cytosine&ndash ; phosphate&ndash ; guanine ; FDR ; false discovery rate ; IOTF ; International Obesity Task Force ; LTF
• 刊名：Gene
• 出版年：2012
• 期刊代码：73_03781119
• 类别：bio
• 出版时间：10 May, 2012
• 卷：499
• 期：1
• 页码：99-107
• 文件大小：492 K

摘要

There is considerable interest in defining the relationship between epigenetic variation and the risk of common complex diseases. Strategies which assist in the prioritisation of target loci that have the potential to be epigenetically regulated might provide a useful approach in identifying concrete examples of epigenotype-phenotype associations. Focusing on the postulated role of epigenetic factors in the aetiopathogenesis of obesity this report outlines an approach utilising gene expression data and a suite of bioinformatic tools to prioritise a list of target candidate genes for more detailed experimental scrutiny. Gene expression microarrays were performed using peripheral blood RNA from children aged 11-13 years selected from the Newcastle Preterm Birth Growth Study which were grouped by body mass index (BMI). Genes showing 鈮?#xA0;2.0 fold differential expression between low and high BMI groups were selected for in silico analysis. Several bioinformatic tools were used for each following step; 1) a literature search was carried out to identify whether the differentially expressed genes were associated with adiposity phenotypes. Of those obesity-candidate genes, putative epigenetically regulated promoters were identified by 2) defining the promoter regions, 3) then by selecting promoters with a CpG island (CGI), 4) and then by identifying any transcription factor binding modules covering CpG sites within the CGI. This bioinformatic processing culminated in the identification of a short list of target obesity-candidate genes putatively regulated by DNA methylation which can be taken forward for experimental analysis. The proposed workflow provides a flexible, versatile and low cost methodology for target gene prioritisation that is applicable to multiple species and disease contexts.