Huang Wenhui, Wang Pengyuan, Liu Zhen, Zhang Liqing
Department of Computer Science, Virginia Tech, 2050 Torgerson Hall, Blacksburg, VA 24061-0106, USA.
BMC Bioinformatics. 2009 Jan 30;10 Suppl 1(Suppl 1):S68. doi: 10.1186/1471-2105-10-S1-S68.
Genome-wide association studies prove to be a powerful approach to identify the genetic basis of different human diseases. We studied the relationship between seven diseases characterized in a previous genome-wide association study by the Wellcome Trust Case Control Consortium. Instead of doing a horizontal association of SNPs to diseases, we did a vertical analysis of disease associations by comparing the genetic similarities of diseases. Our analysis was carried out at four levels - the nucleotide level (SNPs), the gene level, the protein level (through protein-protein interaction network), and the phenotype level.
Our results show that Crohn's disease, rheumatoid arthritis, and type 1 diabetes share evidence of genetic associations at all levels of analysis, offering strong molecular support for the current grouping of the diseases. On the other hand, coronary artery disease, hypertension, and type 2 diabetes, despite being considered as a natural group with potential aetiological overlap, do not show any evidence of shared genetic basis at all levels.
Our study is a first attempt on mining of GWA data to examine genetic associations between different diseases. The positive result is apparently not a coincidence and hence demonstrates the promising use of our approach.
全基因组关联研究已被证明是确定不同人类疾病遗传基础的有力方法。我们研究了由威康信托病例对照协会在之前的全基因组关联研究中所确定的七种疾病之间的关系。我们并非对单核苷酸多态性(SNP)与疾病进行横向关联分析,而是通过比较疾病的遗传相似性对疾病关联进行纵向分析。我们的分析在四个层面展开——核苷酸层面(SNP)、基因层面、蛋白质层面(通过蛋白质 - 蛋白质相互作用网络)以及表型层面。
我们的结果表明,克罗恩病、类风湿性关节炎和1型糖尿病在所有分析层面均显示出遗传关联的证据,为当前对这些疾病的分类提供了强有力的分子支持。另一方面,冠状动脉疾病、高血压和2型糖尿病,尽管被视为具有潜在病因重叠的自然组,但在所有层面均未显示出任何共享遗传基础的证据。
我们的研究是首次尝试挖掘全基因组关联数据以检验不同疾病之间的遗传关联。这一积极结果显然并非巧合,因此证明了我们方法的良好应用前景。