Feng Rui, Zhang Heping
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA.
Hum Genet. 2006 May;119(4):429-35. doi: 10.1007/s00439-006-0147-8. Epub 2006 Mar 10.
Most genetic studies recruit high risk families and the discoveries are based on non-random selected groups. We must consider the consequences of this ascertainment process in order to apply the results of genetic research to the general population. In previous reports, we developed a latent variable model to assess the familial aggregation and inheritability of ordinal-scaled diseases, and found a major gene component of alcoholism after applying the model to the data from the Yale family study of comorbidity of alcoholism and anxiety (YFSCAA). In this report, we examine the ascertainment effects on parameter estimates and correct potential bias in the latent variable model. The simulation studies for various ascertainment schemes suggest that our ascertainment adjustment is necessary and effective. We also find that the estimated effects are relatively unbiased for the particular ascertainment scheme used in the YFSCAA, which assures the validity of our earlier conclusion.
大多数基因研究招募的是高危家庭,其研究发现基于非随机选择的群体。为了将基因研究结果应用于普通人群,我们必须考虑这种确定过程的后果。在之前的报告中,我们开发了一个潜在变量模型来评估有序量表疾病的家族聚集性和遗传性,并在将该模型应用于耶鲁大学酒精中毒与焦虑症共病家庭研究(YFSCAA)的数据后,发现了酒精中毒的一个主要基因成分。在本报告中,我们研究了确定过程对参数估计的影响,并纠正潜在变量模型中的潜在偏差。针对各种确定方案的模拟研究表明,我们的确定调整是必要且有效的。我们还发现,对于YFSCAA中使用的特定确定方案,估计效应相对无偏,这确保了我们早期结论的有效性。