Inferring causal relationships between metabolites and polycystic ovary syndrome using summary statistics from genome-wide association studies

Abstract

Abstract 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.