Pan Wen-Harn, Lynn Ke-Shiuan, Chen Chun-Houh, Wu Yi-Lin, Lin Chung-Yen, Chang Hsing-Yi
Institute of Biomedical Sciences, Academia Sinica, No. 128 Section 2 Academia Road, Taipei, Taiwan 11529.
Genet Epidemiol. 2006 Feb;30(2):143-54. doi: 10.1002/gepi.20136.
Nature determines the complexity of disease etiology and the likelihood of revealing disease genes. While culprit genes for many monogenic diseases have been successfully unraveled, efforts to map major complex disease genes have not been as productive as hoped. The conceptual framework currently adopted to deal with the heterogeneous nature of complex diseases focuses on using homogeneous internal features of the disease phenotype for mapping. However, phenotypic homogeneity does not equal genotypic homogeneity. In this report, we advocate working with well-measured phenotypes portrayed by amounts of transcripts and activities of gene products or their metabolites, which are pertinent to relatively small pathway clusters. Reliable and controlled measures for oligogenic traits resulting from proper dissection efforts may enhance statistical power. The large amounts of information obtained on gene and protein expression from technological advances can add to the power of gene finding, particularly for diseases with unclear etiology. Data-mining tools for dimension reduction can assist biologists to reveal novel molecular endophenotypes. However, there are still hurdles to overcome, including high cost, relatively poor reproducibility and comparability among platforms, the cross-sectional nature of the information, and the accessibility of human tissues. Concerted efforts are required to carry out large-scale prospective studies that are integrated at the levels of phenotype characterization, high throughput experimental techniques, data analyses, and beyond.
自然决定了疾病病因的复杂性以及揭示疾病基因的可能性。虽然许多单基因疾病的致病基因已被成功破解，但定位主要复杂疾病基因的努力并未达到预期的成效。目前用于应对复杂疾病异质性的概念框架侧重于利用疾病表型的同质内部特征进行定位。然而，表型同质性并不等同于基因型同质性。在本报告中，我们主张研究由转录本数量、基因产物或其代谢物活性所描绘的经过充分测量的表型，这些表型与相对较小的通路簇相关。通过适当的剖析努力获得的寡基因性状的可靠且可控的测量方法可能会提高统计效力。技术进步所获得的关于基因和蛋白质表达的大量信息可以增强基因发现的能力，特别是对于病因不明的疾病。用于降维的数据挖掘工具可以帮助生物学家揭示新的分子内表型。然而，仍有障碍需要克服，包括高成本、平台之间相对较差的可重复性和可比性、信息的横断面性质以及人体组织的可获取性。需要共同努力开展大规模前瞻性研究，这些研究要在表型特征描述、高通量实验技术、数据分析等层面进行整合。
