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

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

Objective

Osteoporosis, characterized by reduced bone density and heightened fracture risk, is influenced by genetic and environmental factors. This study investigates the interplay between gut microbiota, plasma metabolomics, and osteoporosis, identifying potential causal relationships mediated by plasma metabolites.

Methods

Utilizing aggregated genome-wide association studies (GWAS) data, a comprehensive two-sample Mendelian Randomization (MR) analysis was performed involving 196 gut microbiota taxa, 1,400 plasma metabolites, and osteoporosis indicators. Causal relationships between gut microbiota, plasma metabolites, and osteoporosis were explored.

Results

The MR analyses revealed ten gut microbiota taxa associated with osteoporosis, with five taxa positively linked to increased risk and five negatively associated. Additionally, 96 plasma metabolites exhibited potential causal relationships with osteoporosis, with 49 showing positive associations and 47 displaying negative associations. Mediation analyses identified six causal pathways connecting gut microbiota to osteoporosis through ten mediating relationships involving seven distinct plasma metabolites, two of which demonstrated suppression effects.

Conclusion

This study provides suggestive evidence of genetic correlations and causal links between gut microbiota, plasma metabolites, and osteoporosis. The findings underscore the complex, multifactorial nature of osteoporosis and suggest the potential of gut microbiota and plasma metabolite profiles as biomarkers or therapeutic targets in the management of osteoporosis.

Inferring Causal Relationships Between Metabolites and Polycystic Ovary Syndrome Using Summary Statistics from Genome‑Wide Association Studies

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. Previous studies have suggested that metabolites may play a pivotal mediating role in the progression of phenotypic variations. Although several metabolites had been identified as potential markers for PCOS, the relationship between blood metabolites and PCOS was not comprehensively explored. Previously, Pickrell et al. designed a robust approach to infer evidence of a causal relationship between different phenotypes using independently putative causal SNPs. Our previous paper extended this approach to make it more suitable for cases where only a few independently putative causal SNPs were identified to be significantly associated with the phenotypes (i.e., metabolites). When the most significant SNPs in each independent locus (the independent lead SNPs) with p-values of < 1 × 10-5 were used, 3 metabolites (2-tetradecenoyl carnitine, threitol, 1-docosahexaenoylglycerophosphocholine) causally influencing PCOS and 2 metabolites (asparagine and phenyllactate) influenced by PCOS were identified, (relative likelihood r < 0.01). Under a less stringent threshold of r < 0.05, 7 metabolites (trans-4-hydroxyproline, glutaroyl carnitine, stachydrine, undecanoate, 7-Hoca, N-acetylalanine and 2-hydroxyisobutyrate) were identified. Taken together, this study can provide novel insights into the pathophysiological mechanisms underlying PCOS; whether these metabolites can serve as biomarkers to predict PCOS in clinical practice warrants further investigations.

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

Background

Genome-wide association studies (GWASs) routinely identify loci associated with risk factors for osteoporosis. However, GWASs with relatively small sample sizes still lack sufficient power to ascertain the majority of genetic variants with small to modest effect size, which may together truly influence the phenotype. The loci identified only account for a small percentage of the heritability of osteoporosis. This study aims to identify novel genetic loci associated with DXA-derived femoral neck (FNK) bone mineral density (BMD) and quantitative ultrasound of the heel calcaneus estimated BMD (eBMD), and to detect shared/causal variants for the two traits, to assess whether the SNPs or putative causal SNPs associated with eBMD were also associated with FNK-BMD.

Methods

Novel loci associated with eBMD and FNK-BMD were identified by the genetic pleiotropic conditional false discovery rate (cFDR) method. Shared putative causal variants between eBMD and FNK-BMD and putative causal SNPs for each trait were identified by the colocalization method. Mendelian randomization analysis addresses the causal relationship between eBMD/FNK-BMD and fracture.

Results

We identified 9,500 (cFDR < 9.8E-6), 137 (cFDR < 8.9E-4) and 124 SNPs associated with eBMD, FNK-BMD, and both eBMD and FNK-BMD, respectively, with 37 genomic regions where there was a SNP that influences both eBMD and FNK-BMD. Most genomic regions only contained putative causal SNPs associated with eBMD and 3 regions contained two distinct putative causal SNPs influenced both traits, respectively. We demonstrated a causal effect of FNK-BMD/eBMD on fracture.

Conclusion

Most of SNPs or putative causal SNPs associated with FNK-BMD were also associated with eBMD. However, most of SNPs or putative causal SNPs associated with eBMD were not associated with FNK-BMD. The novel variants we identified may help to account for the additional proportion of variance of each trait and advance our understanding of the genetic mechanisms underlying osteoporotic fracture.