Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
PLoS One. 2012;7(2):e31134. doi: 10.1371/journal.pone.0031134. Epub 2012 Feb 1.
Family based association study (FBAS) has the advantages of controlling for population stratification and testing for linkage and association simultaneously. We propose a retrospective multilevel model (rMLM) approach to analyze sibship data by using genotypic information as the dependent variable. Simulated data sets were generated using the simulation of linkage and association (SIMLA) program. We compared rMLM to sib transmission/disequilibrium test (S-TDT), sibling disequilibrium test (SDT), conditional logistic regression (CLR) and generalized estimation equations (GEE) on the measures of power, type I error, estimation bias and standard error. The results indicated that rMLM was a valid test of association in the presence of linkage using sibship data. The advantages of rMLM became more evident when the data contained concordant sibships. Compared to GEE, rMLM had less underestimated odds ratio (OR). Our results support the application of rMLM to detect gene-disease associations using sibship data. However, the risk of increasing type I error rate should be cautioned when there is association without linkage between the disease locus and the genotyped marker.
基于家系的关联研究（FBAS）具有控制群体分层和同时检测连锁与关联的优势。我们提出了一种回顾性多层次模型（rMLM）方法，通过使用基因型信息作为因变量来分析家系数据。使用连锁和关联模拟（SIMLA）程序生成模拟数据集。我们比较了 rMLM 与同胞传递/不平衡检验（S-TDT）、同胞不平衡检验（SDT）、条件逻辑回归（CLR）和广义估计方程（GEE）在功效、I 型错误、估计偏差和标准误差方面的表现。结果表明，在连锁存在的情况下，rMLM 是一种有效的关联检验方法。在家系数据中，当包含一致的同胞家系时，rMLM 的优势更加明显。与 GEE 相比，rMLM 低估的比值比（OR）更少。我们的结果支持使用 rMLM 来检测家系数据中的基因-疾病关联。然而，当疾病位点与基因型标记之间没有连锁而存在关联时，应该警惕增加 I 型错误率的风险。
