Mendelian randomization analyses for PCOS: evidence, opportunities, and challenges

The key to preventing and treating polycystic ovary syndrome (PCOS) is to identify adjustable risk factors. Mendelian randomization (MR) is an emerging field that uses genetic variants to assess causal associations between modifiable exposures and outcomes. It can effectively avoid reverse causation and save manpower, reducing material and financial costs in the meantime. MR analysis of PCOS has provided evidence for causal associations between PCOS and various factors, including anti-Müllerian hormone level, sex hormone-binding globulin level, menopause age, adiposity, insulin resistance, depression, breast cancer, ovarian cancer, obsessive-compulsive disorder, and forced vital capacity . Current MR analyses did not support the causal association with PCOS in several disorders, such as type 2 diabetes mellitus, coronary heart disease, stroke, anxiety disorder, schizophrenia, and bipolar disorder, as well as offspring birth weight. Identifying etiological risk factors is significant for preventing and treating patients with polycystic ovary syndrome (PCOS). Through genetic variation, Mendelian randomization (MR) assesses causal associations between PCOS risk and related exposure factors. This emerging technology has provided evidence of causal associations of anti-Müllerian hormone (AMH) levels, sex hormone-binding globulin (SHBG) levels, menopause age, adiposity, insulin resistance (IR), depression, breast cancer, ovarian cancer, obsessive-compulsive disorder (OCD), and forced vital capacity (FVC) with PCOS, while lacking associations of type 2 diabetes mellitus (T2DM), coronary heart disease (CHD), stroke, anxiety disorder (AD), schizophrenia (SCZ), bipolar disorder (BIP), and offspring birth weight with PCOS. In this review, we briefly introduce the concept and methodology of MR in terms of the opportunities and challenges in this field based on recent results obtained from MR analyses involving PCOS. Identifying etiological risk factors is significant for preventing and treating patients with polycystic ovary syndrome (PCOS). Through genetic variation, Mendelian randomization (MR) assesses causal associations between PCOS risk and related exposure factors. This emerging technology has provided evidence of causal associations of anti-Müllerian hormone (AMH) levels, sex hormone-binding globulin (SHBG) levels, menopause age, adiposity, insulin resistance (IR), depression, breast cancer, ovarian cancer, obsessive-compulsive disorder (OCD), and forced vital capacity (FVC) with PCOS, while lacking associations of type 2 diabetes mellitus (T2DM), coronary heart disease (CHD), stroke, anxiety disorder (AD), schizophrenia (SCZ), bipolar disorder (BIP), and offspring birth weight with PCOS. In this review, we briefly introduce the concept and methodology of MR in terms of the opportunities and challenges in this field based on recent results obtained from MR analyses involving PCOS. due to the potential complexity of biological pathways, an overly simplistic interpretation based on MR results can be misleading. occurs when the exposure and outcome of interest independently influence a third risk factor, which can lead to bias in the estimate of causal association. individual adapts in response to a genetic change so that the effect of such genetic change is reduced or absent. research method that repeatedly verifies gene and disease association at the genome-wide level based on data from large sample, multicenter studies. It identifies the genetic factors related to complex diseases by genotyping DNA samples from large-scale population studies with genome-wide high-density genetic markers (such as SNP or copy number variations). one genetic variant is associated with multiple phenotypes on different pathways. estimation method that only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. genetic variations with similar gene loci tend to be inherited together. causal estimates are imprecise (wide confidence intervals) and the MR analysis lacks power to detect a causal effect. method that obtains a single causal effect estimate from multiple genetic instruments. The mode-based estimate is consistent when the largest number of similar (identical in infinite samples) individual-instrument estimates of causal effect comes from valid instruments, even if most instruments are invalid. sensitivity analysis for detecting violations of the instrumental variable assumptions. genetic instruments are sometimes associated with multiple aspects of traits (exposures). method that estimates the degree of pleiotropy initially and, in turn, corrects for it. If (i) a subsample exists for circumstances in which the genetic variants do not affect the exposure; (ii) the selection into this subsample is not a joint consequence of the instrumental variables and the outcome; and (iii) pleiotropic effects are homogeneous, then pleiotropy-robust MR obtains unbiased estimates of causal effects. frequency of genetic variation differs among people with different genetic backgrounds. approach for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. gene regions related to diseases or traits usually contain many genetic variations that meet the criteria of genome-wide significance, while GWAS only reports the most significant genetic variations, which can overestimate the association between genetic variations and exposure factors.