Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
Biostatistics. 2020 Jan 1;21(1):33-49. doi: 10.1093/biostatistics/kxy030.
It has been well acknowledged that methods for secondary trait (ST) association analyses under a case-control design (ST${\text{CC}}$) should carefully consider the sampling process to avoid biased risk estimates. A similar situation also exists in the extreme phenotype sequencing (EPS) designs, which is to select subjects with extreme values of continuous primary phenotype for sequencing. EPS designs are commonly used in modern epidemiological and clinical studies such as the well-known National Heart, Lung, and Blood Institute Exome Sequencing Project. Although naïve generalized regression or ST${\text{CC}}$ method could be applied, their validity is questionable due to difference in statistical designs. Herein, we propose a general prospective likelihood framework to perform association testing for binary and continuous STs under EPS designs (STEPS), which can also incorporate covariates and interaction terms. We provide a computationally efficient and robust algorithm to obtain the maximum likelihood estimates. We also present two empirical mathematical formulas for power/sample size calculations to facilitate planning of binary/continuous STs association analyses under EPS designs. Extensive simulations and application to a genome-wide association study of benign ethnic neutropenia under an EPS design demonstrate the superiority of STEPS over all its alternatives above.
人们已经充分认识到，在病例对照设计（ST${\text{CC}}$）下进行二级性状（ST）关联分析的方法应该仔细考虑采样过程，以避免有偏差的风险估计。这种情况在极端表型测序（EPS）设计中也存在，即选择连续主要表型具有极值的个体进行测序。EPS 设计常用于现代流行病学和临床研究，例如著名的美国国立心肺血液研究所外显子测序计划。尽管可以应用简单的广义回归或 ST${\text{CC}}$方法，但由于统计设计的差异，其有效性值得怀疑。在此，我们提出了一种一般的前瞻性似然框架，用于在 EPS 设计（STEPS）下进行二元和连续 ST 的关联检验，该框架还可以包含协变量和交互项。我们提供了一种计算效率高且稳健的算法来获得最大似然估计。我们还提出了两种用于计算功效/样本量的经验数学公式，以方便规划 EPS 设计下的二元/连续 ST 关联分析。广泛的模拟和对 EPS 设计下良性种族中性粒细胞减少症的全基因组关联研究的应用表明，与所有替代方法相比，STEPS 具有优越性。
