Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies
Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies
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DOI：
10.1093/biostatistics/kxn001
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发表时间：
2008-10-01
期刊：
影响因子：
2.1
通讯作者：
Prentice, Ross L.
中科院分区：
文献类型：
--
作者：
Zhong, Hua;Prentice, Ross L.
全基因组关联研究（GWAS）提供了一种重要的方法来识别易感人类疾病的常见遗传变异。在一组病例和对照中，典型的GWA可能会基因型数十万个位于整个人类基因组的单核苷酸多态性（SNP）。逻辑回归通常用于测试SNP基因型与病例与控制状态之间的关联，相应的优势比（ORS）通常仅针对那些SNP满足选择标准报告。但是，当这些估计基于用于检测变体的原始数据时，结果会受到选择偏差的影响，有时会引用“获胜者的诅咒”（Capen等人，1971年）。实际的遗传关联通常被高估。我们表明，这种选择偏见可能是严重的，因为标准或估计器的条件期望可能离基础参数很遥远。同样，标准置信区间（CIS）可能远远远离所选ORS所需的覆盖率。我们提出并评估了3个偏差减少估计器，还提出了相应的加权估计器，这些估计量结合了校正和未经校正的估计器，以减少选择偏差。还提出了它们相应的顺式。我们使用模拟数据集研究了这些估计量的性能，并表明它们在各种情况下，即使仅具有较小的统计能力，它们也可以使CI覆盖范围接近所需水平。
Genome-wide association studies (GWAS) provide an important approach to identifying common genetic variants that predispose to human disease. A typical GWAS may genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) located throughout the human genome in a set of cases and controls. Logistic regression is often used to test for association between a SNP genotype and case versus control status, with corresponding odds ratios (ORs) typically reported only for those SNPs meeting selection criteria. However, when these estimates are based on the original data used to detect the variant, the results are affected by a selection bias sometimes referred to the "winner's curse" (Capen and others, 1971). The actual genetic association is typically overestimated. We show that such selection bias may be severe in the sense that the conditional expectation of the standard OR estimator may be quite far away from the underlying parameter. Also standard confidence intervals (CIs) may have far from the desired coverage rate for the selected ORs. We propose and evaluate 3 bias-reduced estimators, and also corresponding weighted estimators that combine corrected and uncorrected estimators, to reduce selection bias. Their corresponding CIs are also proposed. We study the performance of these estimators using simulated data sets and show that they reduce the bias and give CI coverage close to the desired level under various scenarios, even for associations having only small statistical power.