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Conditional inference in cis‐Mendelian randomization using weak genetic factors

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Listed:
  • Ashish Patel
  • Dipender Gill
  • Paul Newcombe
  • Stephen Burgess

Abstract

Mendelian randomization (MR) is a widely used method to estimate the causal effect of an exposure on an outcome by using genetic variants as instrumental variables. MR analyses that use variants from only a single genetic region (cis‐MR) encoding the protein target of a drug are able to provide supporting evidence for drug target validation. This paper proposes methods for cis‐MR inference that use many correlated variants to make robust inferences even in situations, where those variants have only weak effects on the exposure. In particular, we exploit the highly structured nature of genetic correlations in single gene regions to reduce the dimension of genetic variants using factor analysis. These genetic factors are then used as instrumental variables to construct tests for the causal effect of interest. Since these factors may often be weakly associated with the exposure, size distortions of standard t‐tests can be severe. Therefore, we consider two approaches based on conditional testing. First, we extend results of commonly‐used identification‐robust tests for the setting where estimated factors are used as instruments. Second, we propose a test which appropriately adjusts for first‐stage screening of genetic factors based on their relevance. Our empirical results provide genetic evidence to validate cholesterol‐lowering drug targets aimed at preventing coronary heart disease.

Suggested Citation

  • Ashish Patel & Dipender Gill & Paul Newcombe & Stephen Burgess, 2023. "Conditional inference in cis‐Mendelian randomization using weak genetic factors," Biometrics, The International Biometric Society, vol. 79(4), pages 3458-3471, December.
  • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3458-3471
    DOI: 10.1111/biom.13888
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Bai, Jushan & Ng, Serena, 2010. "Instrumental Variable Estimation In A Data Rich Environment," Econometric Theory, Cambridge University Press, vol. 26(6), pages 1577-1606, December.
    3. Isaiah Andrews & James H. Stock & Liyang Sun, 2019. "Weak Instruments in Instrumental Variables Regression: Theory and Practice," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 727-753, August.
    4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    5. John C. Chao & Norman R. Swanson, 2005. "Consistent Estimation with a Large Number of Weak Instruments," Econometrica, Econometric Society, vol. 73(5), pages 1673-1692, September.
    6. Whitney K. Newey & Frank Windmeijer, 2009. "Generalized Method of Moments With Many Weak Moment Conditions," Econometrica, Econometric Society, vol. 77(3), pages 687-719, May.
    7. Andrews, Donald W.K. & Moreira, Marcelo J. & Stock, James H., 2007. "Performance of conditional Wald tests in IV regression with weak instruments," Journal of Econometrics, Elsevier, vol. 139(1), pages 116-132, July.
    8. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
    9. Marcelo J. Moreira, 2003. "A Conditional Likelihood Ratio Test for Structural Models," Econometrica, Econometric Society, vol. 71(4), pages 1027-1048, July.
    10. Frank Kleibergen, 2005. "Testing Parameters in GMM Without Assuming that They Are Identified," Econometrica, Econometric Society, vol. 73(4), pages 1103-1123, July.
    11. Amand F. Schmidt & Nicholas B. Hunt & Maria Gordillo-Marañón & Pimphen Charoen & Fotios Drenos & Mika Kivimaki & Deborah A. Lawlor & Claudia Giambartolomei & Olia Papacosta & Nishi Chaturvedi & Joshua, 2021. "Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    12. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    13. Patrik Guggenberger & Gitanjali Kumar, 2012. "On the size distortion of tests after an overidentifying restrictions pretest," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1138-1160, November.
    14. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
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