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.

中文翻译：

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STEPS：一种有效的前瞻性似然方法，用于极端表型测序中次要性状的遗传关联分析。


众所周知，病例对照设计（ST$_{\text{CC}}$）下的次要性状（ST）关联分析方法应仔细考虑抽样过程，以避免风险估计出现偏差。类似的情况也存在于极端表型测序（EPS）设计中，即选择具有连续初级表型极值的受试者进行测序。 EPS 设计常用于现代流行病学和临床研究，例如著名的国家心脏、肺和血液研究所外显子组测序项目。尽管可以应用朴素广义回归或 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，我们提出了一个通用的前瞻性似然框架，用于在 EPS 设计（STEPS）下对二元和连续 ST 进行关联测试，该框架还可以包含协变量和交互项。我们提供了一种计算高效且稳健的算法来获得最大似然估计。我们还提出了两个用于功效/样本量计算的经验数学公式，以方便在 EPS 设计下规划二元/连续 ST 关联分析。 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广泛模拟和应用证明了 STEPS 相对于上述所有替代方案的优越性。