Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America.
Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.
PLoS Comput Biol. 2022 Aug 29;18(8):e1010268. doi: 10.1371/journal.pcbi.1010268. eCollection 2022 Aug.
Bias from weak instruments may undermine the ability to estimate causal effects in instrumental variable regression (IVR). We present here a new approach to handling weak instrument bias through the application of a new type of instrumental variable coined 'Cross-Fitted Instrument' (CFI). CFI splits the data at random and estimates the impact of the instrument on the exposure in each partition. These estimates are then used to perform an IVR on each partition. We adapt CFI to the Mendelian randomization (MR) setting and term this adaptation 'Cross-Fitting for Mendelian Randomization' (CFMR). We show that, even when using weak instruments, CFMR is, at worst, biased towards the null, which makes it a conservative one-sample MR approach. In particular, CFMR remains conservative even when the two samples used to perform the MR analysis completely overlap, whereas current state-of-the-art approaches (e.g., MR RAPS) display substantial bias in this setting. Another major advantage of CFMR lies in its use of all of the available data to select genetic instruments, which maximizes statistical power, as opposed to traditional two-sample MR where only part of the data is used to select the instrument. Consequently, CFMR is able to enhance statistical power in consortia-led meta-analyses by enabling a conservative one-sample MR to be performed in each cohort prior to a meta-analysis of the results across all the cohorts. In addition, CFMR enables a cross-ethnic MR analysis by accounting for ethnic heterogeneity, which is particularly important in meta-analyses where the participating cohorts may have different ethnicities. To our knowledge, none of the current MR approaches can account for such heterogeneity. Finally, CFMR enables the application of MR to exposures that are either rare or difficult to measure, which would normally preclude their analysis in the regular two-sample MR setting.
弱工具可能会破坏工具变量回归（IVR）中因果效应估计的能力。我们在这里提出了一种新的方法，通过应用一种新的工具变量——“交叉拟合工具”（CFI）来处理弱工具偏差。CFI 随机分割数据，并估计工具对每个分区中暴露的影响。然后，这些估计值被用于对每个分区进行 IVR。我们将 CFI 适应孟德尔随机化（MR）设置，并将此适应方法称为“孟德尔随机化的交叉拟合”（CFMR）。我们表明，即使使用弱工具，CFMR 最多也是向零值有偏差的，这使得它成为一种保守的单样本 MR 方法。特别是，即使用于进行 MR 分析的两个样本完全重叠，CFMR 仍然保持保守，而当前最先进的方法（例如，MR RAPS）在这种情况下会显示出很大的偏差。CFMR 的另一个主要优势在于它使用所有可用数据来选择遗传工具，这最大限度地提高了统计能力，而不是传统的两样本 MR 方法，其中只有部分数据用于选择工具。因此，CFMR 能够通过在每个队列中执行保守的单样本 MR 分析，然后对所有队列的结果进行荟萃分析，从而提高联盟主导的荟萃分析中的统计能力。此外，CFMR 通过考虑种族异质性来实现跨种族 MR 分析，这在荟萃分析中尤其重要，因为参与的队列可能具有不同的种族。据我们所知，目前没有任何一种 MR 方法可以考虑到这种异质性。最后，CFMR 使 MR 能够应用于罕见或难以测量的暴露，这通常会排除在常规的两样本 MR 设置中对它们进行分析。
