An optimized instrument variable selection approach to improve causality estimation in association studies.

Mendelian randomization (MR) is an emerging tool for inferring causality in genetic epidemiology. MR studies suffer bias from weak genetic instrument variables (IVs) and horizontal pleiotropy. We introduce a robust integrative framework strictly adhering with STROBE-MR guidelines to improve causality inference through MR studies. We implemented novel t-statistics-based criteria to improve the reliability of selected IVs followed by various MR methods. Further, we include sensitivity analyses to remove horizontal-pleiotropy bias. For functional validation, we perform enrichment analysis of identified causal SNPs. We demonstrate effectiveness of our proposed approach on 5 different MR datasets selected from diverse populations. Our pipeline outperforms its counterpart MR analyses using default parameters on these datasets. Notably, we found a significant association between total cholesterol and coronary artery disease (P = 1.16 × 10) in a single-sample dataset using our pipeline. Contrarily, this same association was deemed ambiguous while using default parameters. Moreover, in a two-sample dataset, we uncover 13 new causal SNPs with enhanced statistical significance (P = 1.06 × 10) for liver-iron-content and liver-cell-carcinoma. Likewise, these SNPs remained undetected using the default parameters (P = 7.58 × 10). Furthermore, our analysis confirmed previously known pathways, such as hyperlipidemia in heart diseases and gene ME1 in liver cancer. In conclusion, we propose a robust and powerful framework to infer causality across diverse populations and easily adaptable to different diseases.