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

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