Detecting type 2 diabetes causal single nucleotide polymorphism combinations from a genome-wide association study dataset with optimal filtration

Chiyong Kang, Hyeji Yu, G. Yi
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引用次数: 2

Abstract

The identification of causal single nucleotide polymorphisms (SNPs) for complex diseases like type 2 diabetes (T2D) is a challenge because of the low statistical power of individual markers from a genome-wide association study (GWAS). SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity. Hence, we aim to detect T2D causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. The selected SNPs with SNP combinations are mapped with multi-dimensional levels of T2D-related information and gene set enrichment analysis (GSEA). A T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected, with an error rate of 10.25%. Matching with known disease genes and gene sets revealed the relationships between T2D and SNP combinations. We propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms.
从全基因组关联研究数据集中检测2型糖尿病致病单核苷酸多态性组合
由于来自全基因组关联研究(GWAS)的单个标记的统计能力较低,鉴定2型糖尿病(T2D)等复杂疾病的因果单核苷酸多态性(snp)是一项挑战。SNP组合可以弥补单个标记的低统计能力,但来自GWAS的SNP组合产生了很高的计算复杂度。因此,我们的目标是通过最佳过滤从GWAS数据集中检测T2D因果SNP组合,并发现检测到的SNP组合的生物学意义。通过比较不同Bonferroni阈值和基于p值范围的阈值结合链接不平衡(LD)修剪的SNP组合的错误率,最优过滤可以增强SNP组合的统计能力。使用随机森林从最佳SNP数据集中选择变量,选择T2D因果SNP组合。通过t2d相关信息和基因集富集分析(GSEA)的多维度水平对选定的SNP进行定位。选择了来自Wellcome Trust病例控制联盟(WTCCC) GWAS数据集的包含101个SNP的T2D因果SNP组合,错误率为10.25%。与已知疾病基因和基因集的匹配揭示了T2D与SNP组合之间的关系。我们提出了一种基于随机森林变量选择的最优SNP数据集的复杂致病SNP组合检测方法。绘制检测到的SNP组合的生物学意义可以帮助揭示复杂的疾病机制。
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