Liver and Adipose Expression Associated SNPs Are Enriched for Association to Type 2 Diabetes

Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

全基因组关联研究（Genome-wide association studies, GWAS）已被证实可在复杂人类疾病中筛选出效应最强的致病性常见变异体。然而截至目前，GWAS产生的海量数据尚未得到充分挖掘，以识别那些未达到全基因组显著性严格关联阈值的真实关联信号。基因表达遗传学（Genetics of gene expression, GGE）研究在筛选与疾病相关的DNA变异、并为GWAS发现的结果提供功能表征路径方面展现出良好应用前景。本研究首次开展系统性实证研究，对肝脏、皮下脂肪及网膜脂肪组织中与表达相关的单核苷酸多态性（expression-associated single nucleotide polymorphisms, eSNPs）集合进行系统表征，结果显示：相较于随机选取的匹配对照组单核苷酸多态性（single nucleotide polymorphisms, SNPs），上述三类组织中的eSNPs在三项大型GWAS中与2型糖尿病（type 2 diabetes, T2D）显著关联的富集程度显著更高。当将筛选范围限定在由携带2型糖尿病表型分离特征的小鼠群体的脂肪组织基因表达数据构建的基因网络所对应的eSNPs时，这类与T2D关联的富集程度进一步提升。最终，通过将范围进一步限定在被强预测为T2D致病相关的脂肪子网所对应的eSNPs，我们大幅提升了T2D关联SNPs的富集度，并成功鉴定出一组功能相关的糖尿病易感基因。我们鉴定并验证了苹果酸酶1（malic enzyme 1, Me1）作为该小鼠T2D子网的关键调控因子，并为该基因与人类T2D的关联提供了佐证。本研究整合eSNPs与基因网络的研究策略，为鉴定疾病易感网络（而非传统GWAS所识别的单个SNPs或基因）提供了全新路径，从而从当前GWAS产生的海量数据中挖掘出更多潜在价值。