A Versatile and Efficient Novel Approach for Mendelian Randomization Analysis with Application to Assess the Causal Effect of Fetal Hemoglobin on Anemia in Sickle Cell Anemia

Janaka S. S. Liyanage, Jeremie H. Estepp, Kumar Srivastava, Sara R. Rashkin, Vivien A. Sheehan, Jane S. Hankins, Clifford M. Takemoto, Yun Li, Yuehua Cui, Motomi Mori, Stephen Burgess, Michael R. DeBaun and Guolian Kang ()
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Janaka S. S. Liyanage: Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
Jeremie H. Estepp: Departments of Global Pediatric Medicine and Hematology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
Kumar Srivastava: Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
Sara R. Rashkin: Department of Hematology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
Vivien A. Sheehan: Department of Pediatrics, Emory University School of Medicine, Children’s Healthcare of Atlanta, Atlanta, GA 30322, USA
Jane S. Hankins: Department of Hematology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
Clifford M. Takemoto: Department of Hematology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
Yun Li: Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Yuehua Cui: Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
Motomi Mori: Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
Stephen Burgess: MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
Michael R. DeBaun: Department of Pediatrics, Vanderbilt-Meharry Sickle Cell Disease Center of Excellence, Vanderbilt University Medical Center, Nashville, TN 37232, USA
Guolian Kang: Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA

Mathematics, 2022, vol. 10, issue 20, 1-22

Abstract: Mendelian randomization (MR) is increasingly employed as a technique to assess the causation of a risk factor on an outcome using observational data. The two-stage least-squares (2SLS) procedure is commonly used to examine the causation using genetic variants as the instrument variables. The validity of 2SLS relies on a representative sample randomly selected from a study cohort or a population for genome-wide association study (GWAS), which is not always true in practice. For example, the extreme phenotype sequencing (EPS) design is widely used to investigate genetic determinants of an outcome in GWAS as it bears many advantages such as efficiency, low sequencing or genotyping cost, and large power in detecting the involvement of rare genetic variants in disease etiology. In this paper, we develop a novel, versatile, and efficient approach, namely MR analysis under Extreme or random Phenotype Sampling (MREPS), for one-sample MR analysis based on samples drawn through either the random sampling design or the nonrandom EPS design. In simulations, MREPS provides unbiased estimates for causal effects, correct type I errors for causal effect testing. Furthermore, it is robust under different study designs and has high power. These results demonstrate the superiority of MREPS over the widely used standard 2SLS approach. We applied MREPS to assess and highlight the causal effect of total fetal hemoglobin on anemia risk in patients with sickle cell anemia using two independent cohort studies. A user-friendly Shiny app web interface was implemented for professionals to easily explore the MREPS.

Keywords: Mendelian randomization; extreme phenotype sequencing; causal inference; genome-wide association studies; next generation sequencing studies (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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