Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification

Abstract

Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.

Keywords: PCOS; androgen access; epigenetics; genetics; genomics; polycystic ovary syndrome.

Graphical Abstract

Figure 1.

Pathophysiology of PCOS. The “vicious cycle” of PCOS (13), a self-sustaining constellation of reproductive abnormalities. The association with insulin resistance and hyperinsulinemia was discovered in the 1980s (reviewed in (7, 21)). Abbreviations: AMH, anti-Müllerian hormone; E₂, estradiol; FSH, follicle-stimulating hormone; GnRH, gonadotropin-releasing hormone; LH, luteinizing hormone; PCOS, polycystic ovary syndrome; SHBG, sex hormone–binding globulin; T, testosterone.

Figure 2.

Linkage. Alleles that are inherited together are “linked”. The probability that any 2 markers on the same chromosome are linked is inversely proportional to the distance between the 2 markers. Figure reproduced from Bush & Moore, 2012 (187).

Figure 3.

Linkage disequilibrium. Over many generations, linked chromosomal segments undergo successive recombination until all nonadjacent allele pairs reach equilibrium. The extent to which any 2 alleles coexist nonrandomly in a population is referred to as “linkage disequilibrium”. Figure reproduced from Bush & Moore, 2012 (187).

Figure 4.

Population stratification. Allele frequencies often vary by ancestry. Failing to control for unequal distributions of ancestry between cases and controls can result in false positive associations.

Figure 5.

Mendelian randomization. In Mendelian randomization analyses, genetic variants that are predictive of an intermediate risk factor but are otherwise independent of the outcome of interest can be used to determine to what extent the intermediate factor is causal for the outcome in question. Example variables are shown in parentheses: to determine whether BMI is causal for PCOS, the genetic variants that predict BMI should not be independently associated with PCOS or any confounding factors like insulin resistance.

Figure 6.

Significant causal factors for PCOS. Odds ratios for PCOS risk per standard deviation increase are shown for significant causal factors according to Mendelian randomization analyses (117, 136, 137, 171, 272, 334). Abbreviations: BMI, body mass index; EPIA-S, epiandrosterone sulfate; PCOS, polycystic ovary syndrome; SHBG, sex hormone–binding globulin; T, testosterone. *Insulin resistance was the tested causal factor. ^¶Bioavailable testosterone was the tested causal factor.

Figure 7.

Genetic correlations with PCOS. Genetic correlations between PCOS risk and various hormonal and metabolic traits, according to Day and colleagues (117). Positive and negative correlations correspond to the direction of effect. Correlation estimates are shown with standard error bars. Menarche and Menopause correspond to age of onset. Other binary traits correspond to risk of occurrence. Abbreviations: BMI, body mass index; PCOS, polycystic ovary syndrome.

Figure 8.

Odds ratio of PCOS as a function of diagnostic criteria applied. The odds ratios (OR) and 95% CI are shown for each significant GWAS locus from the PCOS meta-analysis, stratified by case definitions according to different diagnostic criteria. NIH: groups recruiting only NIH diagnostic criteria; Non-NIH_Rotterdam: Rotterdam diagnostic criteria excluding the subset fulfilling NIH diagnostic criteria; Rotterdam+NIH: all groups except self-reported; self-reported: 23andMe. rs804279 at the GATA4/NEIL2 locus demonstrated significant heterogeneity (Het P = 2.6 × 10^-5). The * indicates statistically significant associations for PCOS. Abbreviations: NIH, National Institutes of Health; PCOS, polycystic ovary syndrome. Figure reproduced from Day et al, 2018 (117).

Figure 9.

PCA plot of novel PCOS clusters. Clustered PCOS cases are plotted on the first 2 PCs of adjusted quantitative trait data, colored according to their identified subtype with 95% concentration ellipses. The relative magnitude and direction of trait correlations with the PCs are shown with black arrows. Abbreviations: BMI, body mass index; DHEAS, dehydroepiandrosterone sulfate; FSH, follicle-stimulating hormone; Glu0, fasting glucose; Ins0, fasting insulin; LH, luteinizing hormone; PC, principal component; PCA, principal component analysis; PCOS, polycystic ovary syndrome; SHBG, sex hormone–binding globulin; T, testosterone. Figure reproduced from Dapas et al, 2020 (440).

Figure 10.

Novel PCOS cluster GWAS results. Manhattan plots for (a) reproductive, (b) metabolic, and (c) indeterminate PCOS subtypes. The red horizontal line indicates genome-wide significance (P ≤ 1.67 × 10⁻⁸). Variants proximal to genome-wide significant loci (± 200kb) are colored in green and labeled according to nearby gene(s). Quantile–quantile plots with genomic inflation factor, λ _GC, are shown adjacent to corresponding Manhattan plots. Abbreviations: GWAS, genome-wide association studies; PCOS, polycystic ovary syndrome. Figure reproduced from Dapas et al, 2020 (440).