Response to Hartwig and Davies

1. Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 2003;32:1–22. 2. Burgess S, Timpson NJ, Ebrahim S, Davey Smith G. Mendelian randomization: where are we now and where are we going? Int J Epidemiol 2015;44:379–88. 3. Haycock PC, Burgess S, Wade KH, Bowden J, Relton C, Davey Smith G. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am J Clin Nutr 2016;103:965–78. 4. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 2016;40:304–14. 5. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015;44:512–25. 6. Lango Allen H, Estrada K, Lettre G et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 2010;467:832–38. 7. Davies NM, von Hinke Kessler Scholder S, Farbmacher H, Burgess S, Windmeijer F, Davey Smith G. The many weak instruments problem and Mendelian randomization. Stat Med 2015;34:454–68. 8. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013;3:658–65. 9. Wood AR, Esko T, Yang J et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet 2014;46:1173–86. 10. Kemp JP, Sayers A, Davey Smith G, Tobias JH, Evans DM. Using Mendelian randomization to investigate a possible causal relationship between adiposity and increased bone mineral density at different skeletal sites in children. Int J Epidemiol 2016;45:1560–72. 11. Locke AE, Kahali B, Berndt SI et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015;518:197–206. 12. Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol 2013;178:1177–84. 13. Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 2015;30:543–52.

[1]  F. Hartwig,et al.  Why internal weights should be avoided (not only) in MR-Egger regression. , 2016, International journal of epidemiology.

[2]  S. Thompson,et al.  Bias due to participant overlap in two‐sample Mendelian randomization , 2016, Genetic epidemiology.

[3]  N. Sheehan,et al.  Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic , 2016, International journal of epidemiology.

[4]  David M. Evans,et al.  Using Mendelian randomization to investigate a possible causal relationship between adiposity and increased bone mineral density at different skeletal sites in children , 2016, International journal of epidemiology.

[5]  G. Davey Smith,et al.  Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator , 2016, Genetic epidemiology.

[6]  G. Davey Smith,et al.  Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies1 , 2016, The American journal of clinical nutrition.

[7]  G. Davey Smith,et al.  Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression , 2015, International journal of epidemiology.

[8]  Stephen Burgess,et al.  Mendelian randomization: where are we now and where are we going? , 2015, International journal of epidemiology.

[9]  N. Timpson,et al.  Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors , 2015, European Journal of Epidemiology.

[10]  Ross M. Fraser,et al.  Genetic studies of body mass index yield new insights for obesity biology , 2015, Nature.

[11]  Neil M Davies,et al.  The many weak instruments problem and Mendelian randomization , 2014, Statistics in medicine.

[12]  Ross M. Fraser,et al.  Defining the role of common variation in the genomic and biological architecture of adult human height , 2014, Nature Genetics.

[13]  Tanya M. Teslovich,et al.  Discovery and refinement of loci associated with lipid levels , 2013, Nature Genetics.

[14]  A. Butterworth,et al.  Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data , 2013, Genetic epidemiology.

[15]  B. Pierce,et al.  Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators , 2013, American journal of epidemiology.

[16]  Ayellet V. Segrè,et al.  Hundreds of variants clustered in genomic loci and biological pathways affect human height , 2010, Nature.

[17]  G. Ronning,et al.  SIMEX estimation in case of correlated measurement errors , 2008 .

[18]  S. Ebrahim,et al.  'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? , 2003, International journal of epidemiology.

[19]  Joshua D. Angrist,et al.  Split-Sample Instrumental Variables Estimates of the Return to Schooling , 1995 .

[20]  J. R. Cook,et al.  Simulation-Extrapolation Estimation in Parametric Measurement Error Models , 1994 .