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首页> 外文会议>The 7th Asia-Pacific Bioinformatics Conference（第七届亚太生物信息学大会） >Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies
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Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

机译：通过全基因组关联研究比较使用涉及2型糖尿病的基因的自动候选基因预测系统

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Background: Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.Results: Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genorne-wide association studies,eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.Conclusions: The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes.Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.
机译：背景：自动化的候选基因预测系统使遗传学家能够通过鉴定与研究中的疾病表型相关的最可能的候选基因，更快地适应疾病基因。在这里，我们通过将其性能与最近的全基因组关联研究所牵涉的基因进行基准比较，评估了八个不同候选基因预测系统在先前与2型糖尿病相关的区间中预测疾病基因的能力。结果：使用9556个基因的搜索空间，所有但是其中一个系统修剪了基因组，转而支持与中度至高度重要的SNP相关的基因。在全基因组关联研究中鉴定出的与高度重要的SNP相关的11个基因中，至少有一个预测系统将8个标记为可能的候选基因。由先前的共识方法产生的候选物清单与全基因组关联研究所标记的706个中等至高度重要的SNP所牵涉的任何基因均不匹配。我们对与中等重要性SNP相关的基因进行了优先排序。结论：本研究评估了针对独立遗传数据的几种候选基因预测系统的相对成功。即使面临挑战性的大间隔，候选基因预测系统也可以成功地选择可能的疾病基因，此外，它们还可以用于过滤统计依据不太完善的遗传数据以选择更多可能的候选基因。我们建议共识方法失败，因为它们会惩罚由独立基础数据库做出的新颖预测。为了充分发挥其潜能，需要对基因的优先顺序和注释进行进一步的工作。

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  • 会议地点 Beijing(CN);Beijing(CN)
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    Erdahl T Teber; Jason Y Liu; Sara Ballouz; Diane Fatkin; Merridee A Wouters;

  • 作者单位

    Victor Chang Cardiac Research Institute,384 Victoria St, Darlinghurst, 2010, NSW, Australia;

    Victor Chang Cardiac Research Institute,384 Victoria St, Darlinghurst, 2010, SW, Australia;

    Victor Chang Cardiac Research Institute,384 Victoria St, Darlinghurst, 2010, NSW, Australia;

    Victor Chang Cardiac Research Institute,384 Victoria St, Darlinghurst, 2010, NSW, Australia School of Medical Sciences, University of New South Wales, Sydney, Australia;

    Victor Chang Cardiac Research Institute,384 Victoria St, Darlinghurst, 2010, NSW, Australia School of Medical Sciences, University of New South Wales, Sydney, Australia;

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