Human Genetics Unit, Indian Statistical Institute, Kolkata, India.
Genet Epidemiol. 2009;33 Suppl 1(Suppl 1):S13-8. doi: 10.1002/gepi.20466.
The group that formed on the theme of genome-wide association analyses of quantitative traits (Group 2) in the Genetic Analysis Workshop 16 comprised eight sets of investigators. Three data sets were available: one on autoantibodies related to rheumatoid arthritis provided by the North American Rheumatoid Arthritis Consortium; the second on anthropometric, lipid, and biochemical measures provided by the Framingham Heart Study (FHS); and the third a simulated data set modeled after FHS. The different investigators in the group addressed a large set of statistical challenges and applied a wide spectrum of association methods in analyzing quantitative traits at the genome-wide level. While some previously reported genes were validated, some novel chromosomal regions provided significant evidence of association in multiple contributions in the group. In this report, we discuss the different strategies explored by the different investigators with the common goal of improving the power to detect association.
该小组以全基因组关联分析为主题，形成了 16 个遗传分析研讨会（第 2 组），由八组研究人员组成。有三个数据集可用：一个是由北美类风湿关节炎联盟提供的与类风湿关节炎相关的自身抗体；第二个是弗雷明汉心脏研究（FHS）提供的人体测量、脂质和生化指标；第三个是模拟的 FHS 数据集。该小组的不同研究人员解决了一系列重大的统计挑战，并在全基因组水平上应用了广泛的关联方法来分析定量特征。虽然一些先前报道的基因得到了验证，但一些新的染色体区域在该组的多项研究中提供了关联的显著证据。在本报告中，我们讨论了不同研究人员探索的不同策略，共同目标是提高检测关联的能力。
