Overcoming attenuation bias in regressions using polygenic indices.
Overcoming attenuation bias in regressions using polygenic indices.
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DOI：
10.1038/s41467-023-40069-4
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发表时间：
2023-07-25
影响因子：
16.6
通讯作者：
Rietveld, Cornelius A.
中科院分区：
文献类型：
--
作者：
van Kippersluis, Hans;Biroli, Pietro;Dias Pereira, Rita;Galama, Titus J.;von Hinke, Stephanie;Meddens, S. Fleur W.;Muslimova, Dilnoza;Slob, Eric A. W.;de Vlaming, Ronald;Rietveld, Cornelius A.
多基因指数（PGIs）的测量误差减弱了回归模型中对其影响的估计。我们分析和比较两种方法解决这种衰减偏差：明显相关的工具变量（ORIV）和PGI库校正（PGI-RC）。通过模拟，我们表明，PGI-RC的表现略好于ORIV，除非预测样本非常小（N < 1000）或当有相当大的竞争性交配。在家庭中，ORIV是最好的选择，因为PGI-RC校正因子通常不可用。我们通过预测来自英国生物银行的兄弟姐妹样本中的教育程度和身高来验证模拟的经验有效性。我们发现，与基于荟萃分析的PGI相比，在家庭之间应用ORIV使PGI的标准化效应增加了12%（身高）和22%（教育程度），但估计值仍略低于PGI-RC估计值。此外，家庭内ORIV回归为直接遗传效应提供了最严格的下限，与基于荟萃分析的PGI相比，标准化直接遗传效应对教育程度的下限从0.14增加到0.18（+29%），身高从0.54增加到0.61（+13%）。多基因指数的测量误差削弱了它们预测复杂性状的能力。在这里，作者比较了两种解决这种衰减偏差的方法，并提供了在各种情况下应用哪种方法的指导。
Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI. Measurement error in polygenic indices attenuates their power to predict complex traits. Here, the authors compare two approaches addressing this attenuation bias and provide guidance on which approach to apply in various scenarios.
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影响因子：
11
作者：
Davies G;Marioni RE;Liewald DC;Hill WD;Hagenaars SP;Harris SE;Ritchie SJ;Luciano M;Fawns-Ritchie C;Lyall D;Cullen B;Cox SR;Hayward C;Porteous DJ;Evans J;McIntosh AM;Gallacher J;Craddock N;Pell JP;Smith DJ;Gale CR;Deary IJ
通讯作者：
Deary IJ
影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
影响因子：
2.1
作者：
Dudbridge F
通讯作者：
Dudbridge F
影响因子：
5.9
作者：
de Vlaming R;Slob EAW;Jansen PR;Dagher A;Koellinger PD;Groenen PJF;Rietveld CA
通讯作者：
Rietveld CA
影响因子：
7.2
作者：
Belsky, Daniel W.;Harden, K. Paige
通讯作者：
Harden, K. Paige