Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies

Abstract

Genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with diverse complex phenotypes and diseases, and provided tremendous opportunities for further analyses using summary association statistics. Recently, Pickrell et al. developed a robust method for causal inference using independent putative causal SNPs. However, this method may fail to infer the causal relationship between two phenotypes when only a limited number of independent putative causal SNPs identified. Here, we extended Pickrell's method to make it more applicable for the general situations. We extended the causal inference method by replacing the putative causal SNPs with the lead SNPs (the set of the most significant SNPs in each independent locus) and tested the performance of our extended method using both simulation and empirical data. Simulations suggested that when the same number of genetic variants is used, our extended method had similar distribution of test statistic under the null model as well as comparable power under the causal model compared with the original method by Pickrell et al. But in practice, our extended method would generally be more powerful because the number of independent lead SNPs was often larger than the number of independent putative causal SNPs. And including more SNPs, on the other hand, would not cause more false positives. By applying our extended method to summary statistics from GWAS for blood metabolites and femoral neck bone mineral density (FN-BMD), we successfully identified ten blood metabolites that may causally influence FN-BMD. We extended a causal inference method for inferring putative causal relationship between two phenotypes using summary statistics from GWAS, and identified a number of potential causal metabolites for FN-BMD, which may provide novel insights into the pathophysiological mechanisms underlying osteoporosis.

Gut microbiota, plasma metabolites, and osteoporosis: unraveling links via Mendelian randomization

“…Previous investigations have delineated the relationships between blood metabolites and bone mineral density (BMD). For instance, one study pinpointed 10 blood metabolites that may influence femoral neck BMD ( Meng et al, 2018 ). Subsequently, another analysis revealed eight blood metabolites with a significant impact on hip BMD ( Liu et al, 2018 ), while a more recent study identified 13 plasma metabolites that markedly affect heel BMD ( Chen et al, 2023 ).…”

“…Now that large-scale genome-wide association studies (GWAS) data on GM, blood metabolites, and OP are publicly available ( Visscher et al, 2017 ), these resources afford a unique opportunity to explore the causal relationships between these variables using MR. Few studies have employed MR methods to explore the causal relationship between GM and OP ( Tu et al, 2021 ). While numerous studies have utilized MR to investigate the connection between blood metabolites and BMD ( Liu et al, 2018 ; Meng et al, 2018 ; Yu et al, 2022 ; Chen et al, 2023 ; Chen and He, 2023 ), research focusing specifically on the OP phenotype is scant. Moreover, comprehensive investigations into the associations among GM, blood metabolites, and OP remain limited.…”

Identifying Pleiotropic SNPs Associated With Femoral Neck and Heel Bone Mineral Density

“…Osteoporosis is a complex disease that prevalently occurs in postmenopausal women and is diagnosed primarily by measuring bone mineral density (BMD) ( Tella and Gallagher, 2014 ). Low femoral neck (FNK) BMD is a major risk factor of osteoporosis and remains the best predictor of primary osteoporotic fractures ( Meng et al, 2018 ). It is established that there are differences of genetic determination among various skeletal sites ( Yang et al, 2006 ).…”