Transcriptomics in type 2 diabetes: Bridging the gap between genotype and phenotype.
Transcriptomics in type 2 diabetes: Bridging the gap between genotype and phenotype.
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DOI：
10.1016/j.gdata.2015.12.001
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发表时间：
2016-06
期刊：
影响因子：
--
通讯作者：
DeFronzo RA
中科院分区：
文献类型：
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作者：
Jenkinson CP;Göring HH;Arya R;Blangero J;Duggirala R;DeFronzo RA
2型糖尿病（T2D）是一种常见的多因素疾病，受遗传和环境因素及其相互作用的影响。然而，全基因组关联研究（GWAS）鉴定的常见变异仅解释了T2D总性状变异的约10%，肥胖变异的不到5%，这表明大部分遗传性仍然无法解释。这里描述的转录组学方法使用定量基因表达和疾病相关的生理数据（深度表型）来测量特定基因的表达和生理性状之间的直接相关性。转录组学分析架起了GWAS和生理学研究之间的桥梁。最近的GWAS研究利用了非常大的人群样本，数量达到数万（甚至数十万）人，但在强相关的遗传变异和疾病之间建立因果功能关系仍然是难以捉摸的。根据下面描述的发现，考虑小样本中的转录组学方法如何以及为什么能够识别在大样本中使用GWAS不明显的复杂疾病相关基因是合适的。
Type 2 diabetes (T2D) is a common, multifactorial disease that is influenced by genetic and environmental factors and their interactions. However, common variants identified by genome wide association studies (GWAS) explain only about 10% of the total trait variance for T2D and less than 5% of the variance for obesity, indicating that a large proportion of heritability is still unexplained. The transcriptomic approach described here uses quantitative gene expression and disease-related physiological data (deep phenotyping) to measure the direct correlation between the expression of specific genes and physiological traits. Transcriptomic analysis bridges the gulf between GWAS and physiological studies. Recent GWAS studies have utilized very large population samples, numbering in the tens of thousands (or even hundreds of thousands) of individuals, yet establishing causal functional relationships between strongly associated genetic variants and disease remains elusive. In light of the findings described below, it is appropriate to consider how and why transcriptomic approaches in small samples might be capable of identifying complex disease-related genes which are not apparent using GWAS in large samples.