DNA methylation and transcriptomic features are preserved throughout disease recurrence and chemoresistance in high grade serous ovarian cancers.

BACKGROUND: Little is known about the role of global DNA methylation in recurrence and chemoresistance of high grade serous ovarian cancer (HGSOC). METHODS: We performed whole genome bisulfite sequencing and transcriptome sequencing in 62 primary and recurrent tumors from 28 patients with stage III/IV HGSOC, of which 11 patients carried germline, pathogenic BRCA1 and/or BRCA2 mutations. RESULTS: Landscapes of genome-wide methylation (on average 24.2 million CpGs per tumor) and transcriptomes in primary and recurrent tumors showed extensive heterogeneity between patients but were highly preserved in tumors from the same patient. We identified significant differences in the burden of differentially methylated regions (DMRs) in tumors from BRCA1/2 compared to non-BRCA1/2 carriers (mean 659 DMRs and 388 DMRs in paired comparisons respectively). We identified overexpression of immune pathways in BRCA1/2 carriers compared to non-carriers, implicating an increased immune response in improved survival (P = 0.006) in these BRCA1/2 carriers. CONCLUSION: These findings indicate methylome and gene expression programs established in the primary tumor are conserved throughout disease progression, even after extensive chemotherapy treatment, and that changes in methylation and gene expression are unlikely to serve as drivers for chemoresistance in HGSOC.

Polygenic risk modeling for prediction of epithelial ovarian cancer risk.

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals.

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets.

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for clinical DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a strategy to assess imputation methods on clinical label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight imputation methods with multiple parameters at different levels of protein quantification: a dilution series data set, a small pilot data set, and a clinical proteomic data set comparing paired tumor and stroma tissue. We found that imputation methods based on local structures within the data, like local least-squares (LLS) and random forest (RF), worked well in our dilution series data set, whereas imputation methods based on global structures within the data, like BPCA, performed well in the other two data sets. We also found that imputation at the most basic protein quantification level-fragment level-improved accuracy and the number of proteins quantified. With this analytical framework, we quickly and cost-effectively evaluated different imputation methods using two smaller complementary data sets to narrow down to the larger proteomic data set's most accurate methods. This acquisition strategy allowed us to provide reproducible evidence of the accuracy of the imputation method, even in the absence of a ground truth. Overall, this study indicates that the most suitable imputation method relies on the overall structure of the data set and provides an example of an analytic framework that may assist in identifying the most appropriate imputation strategies for the differential analysis of proteins.

Ovarian Cancer Risk Variants Are Enriched in Histotype-Specific Enhancers and Disrupt Transcription Factor Binding Sites.

Quantifying the functional effects of complex disease risk variants can provide insights into mechanisms underlying disease biology. Genome-wide association studies have identified 39 regions associated with risk of epithelial ovarian cancer (EOC). The vast majority of these variants lie in the non-coding genome, where they likely function through interaction with gene regulatory elements. In this study we first estimated the heritability explained by known common low penetrance risk alleles for EOC. The narrow sense heritability (hg2) of EOC overall and high-grade serous ovarian cancer (HGSOCs) were estimated to be 5%-6%. Partitioned SNP heritability across broad functional categories indicated a significant contribution of regulatory elements to EOC heritability. We collated epigenomic profiling data for 77 cell and tissue types from Roadmap Epigenomics and ENCODE, and from H3K27Ac ChIP-seq data generated in 26 ovarian cancer and precursor-related cell and tissue types. We identified significant enrichment of risk single-nucleotide polymorphisms (SNPs) in active regulatory elements marked by H3K27Ac in HGSOCs. To further investigate how risk SNPs in active regulatory elements influence predisposition to ovarian cancer, we used motifbreakR to predict the disruption of transcription factor binding sites. We identified 469 candidate causal risk variants in H3K27Ac peaks that are predicted to significantly break transcription factor (TF) motifs. The most frequently broken motif was REST (p value = 0.0028), which has been reported as both a tumor suppressor and an oncogene. Overall, these systematic functional annotations with epigenomic data improve interpretation of EOC risk variants and shed light on likely cells of origin.

GENAVi: a shiny web application for gene expression normalization, analysis and visualization.

BACKGROUND: The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. RESULTS: We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. CONCLUSION: The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria.

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed based on the two criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a fixed-effect, inverse-weighted-variance meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Only one locus differed significantly in its association by diagnostic criteria; otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or non-NIH Rotterdam criteria across common variants at 13 loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Linkage disequilibrium score regression analysis revealed genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease, indicating shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. The data thus demonstrate 3 novel loci associated with PCOS and similar genetic architecture for all diagnostic criteria. The data also provide the first genetic evidence for a male phenotype for PCOS and a causal link to depression, a previously hypothesized comorbid disease. Thus, the genetics provide a comprehensive view of PCOS that encompasses multiple diagnostic criteria, gender, reproductive potential and mental health.

Genetic epidemiology of ovarian cancer and prospects for polygenic risk prediction.

Epithelial ovarian cancer (EOC) is a heterogeneous disease with a major heritable component. The different histotypes of invasive disease - high grade serous, clear cell, endometrioid and mucinous - are associated with different underlying genetic susceptibility and epidemiological and lifestyle risk factors, all of which contribute to the different biology and clinical characteristics of each histotype. A combination of familial and population based sequencing studies, and genome wide association studies (GWAS) have identified a range of genetic susceptibility alleles for EOC comprising rare but highly penetrant genes (e.g. BRCA1, BRCA2) that are responsible for familial clustering of ovarian cancer cases; more moderate penetrance susceptibility genes (e.g. BRIP1, RAD51C/D, MSH6); and multiple common but low penetrance susceptibility alleles identified by GWAS. Identifying genetic risk alleles for ovarian cancer has had a significant impact on disease prevention strategies; for example it is now routine clinical practice for individuals with germline BRCA1 and BRCA2 mutations to undergo risk reducing salpingo-oophorectomy. Because ovarian cancers are commonly diagnosed at a late clinical stage when the prognosis is poor, the continued development of genetic risk prediction and prevention strategies will represent an important approach to reduce mortality due to ovarian cancer. Advances in genomics technologies that enable more high-throughput genetic testing, combined with research studies that identify additional EOC risk alleles will likely provide further opportunities to establish polygenic risk prediction approaches, based on combinations of rare high/moderate penetrance susceptibility genes and common, low penetrance susceptibility alleles. This article reviews the current literature describing the genetic and epidemiological components of ovarian cancer risk, and discusses both the opportunities and challenges in using this information for clinical risk prediction and prevention.

Genetic determinants of polycystic ovary syndrome: progress and future directions.

The field of the genetics of polycystic ovary syndrome (PCOS) has relatively recently moved into the era of genome-wide association studies. This has led to the discovery of 16 robust loci for PCOS. Some loci contain genes with clear roles in reproductive (LHCGR, FSHR, and FSHB) and metabolic (INSR and HMGA2) dysfunction in the syndrome. The next challenge facing the field is the identification of causal variants and genes and the role they play in PCOS pathophysiology. The potential for gene discovery to improve diagnosis and treatment of PCOS is promising, though there is much to be done in the field before the current findings can be translated to the clinic.

Association study of androgen signaling pathway genes in polycystic ovary syndrome.

OBJECTIVE: To evaluate genes involved in androgen receptor (AR) signaling as candidate genes for polycystic ovary syndrome (PCOS). DESIGN: Two groups of women with PCOS and control women (discovery and replication cohorts), were genotyped for single-nucleotide polymorphisms (SNPs) in eight genes for AR chaperones and co-chaperones: HSPA1A, HSPA8, ST13, STIP1, PTGES3, FKBP4, BAG1, and STUB1. Single-nucleotide polymorphisms were tested for association with PCOS status and with androgenic and metabolic parameters. SETTING: Tertiary referral center. PATIENT(S): Discovery cohort: 354 women with PCOS and 161 control women. Replication cohort: 397 women with PCOS and 306 control women. INTERVENTION(S): Phenotypic and genotypic assessment. MAIN OUTCOME MEASURE(S): Single-nucleotide polymorphism genotypes, association with PCOS status, and androgenic and metabolic parameters. RESULT(S): In the discovery cohort, FKBP4 SNPs rs2968909 and rs4409904 were associated with lower odds of PCOS. This finding was not confirmed in the replication cohort analysis; however, when combining the two cohorts, rs4409904 was associated with lower odds of PCOS. In subjects with PCOS in the replication cohort as well as in the combined cohort, rs2968909 was associated with lower body mass index. CONCLUSION(S): Single-nucleotide polymorphisms in FKBP4, which codes for the AR co-chaperone FKBP52, may be associated with PCOS and body mass index in patients with PCOS. The remaining genes studied do not seem to be major contributors to the development of PCOS. These findings warrant confirmation in future studies, and genes encoding other androgen pathway components remain to be studied.

Systems Genetics Reveals the Functional Context of PCOS Loci and Identifies Genetic and Molecular Mechanisms of Disease Heterogeneity.

Genome wide association studies (GWAS) have revealed 11 independent risk loci for polycystic ovary syndrome (PCOS), a common disorder in young women characterized by androgen excess and oligomenorrhea. To put these risk loci and the single nucleotide polymorphisms (SNPs) therein into functional context, we measured DNA methylation and gene expression in subcutaneous adipose tissue biopsies to identify PCOS-specific alterations. Two genes from the LHCGR region, STON1-GTF2A1L and LHCGR, were overexpressed in PCOS. In analysis stratified by obesity, LHCGR was overexpressed only in non-obese PCOS women. Although not differentially expressed in the entire PCOS group, INSR was underexpressed in obese PCOS subjects only. Alterations in gene expression in the LHCGR, RAB5B and INSR regions suggest that SNPs in these loci may be functional and could affect gene expression directly or indirectly via epigenetic alterations. We identified reduced methylation in the LHCGR locus and increased methylation in the INSR locus, changes that are concordant with the altered gene expression profiles. Complex patterns of meQTL and eQTL were identified in these loci, suggesting that local genetic variation plays an important role in gene regulation. We propose that non-obese PCOS women possess significant alterations in LH receptor expression, which drives excess androgen secretion from the ovary. Alternatively, obese women with PCOS possess alterations in insulin receptor expression, with underexpression in metabolic tissues and overexpression in the ovary, resulting in peripheral insulin resistance and excess ovarian androgen production. These studies provide a genetic and molecular basis for the reported clinical heterogeneity of PCOS.

Further investigation in europeans of susceptibility variants for polycystic ovary syndrome discovered in genome-wide association studies of Chinese individuals.

CONTEXT: Two genome-wide association studies (GWAS) of polycystic ovary syndrome (PCOS) have identified 11 susceptibility loci in Chinese individuals. Some of the risk loci identified in Chinese cohorts, mostly from the first GWAS, have been replicated in Europeans. Replication of the loci from the second GWAS in European cohorts is necessary to determine whether the same variants confer risk for PCOS in multiple ethnicities. OBJECTIVE: The objective of the study was to determine the effects of the Chinese GWAS loci in European-origin individuals. DESIGN: This was a genetic association study. SETTING: The study was conducted at a tertiary care academic center. PATIENTS: Eight hundred forty-five European subjects with PCOS and 845 controls participated in the study. INTERVENTIONS: INTERVENTIONS included blood sampling and genotyping. MAIN OUTCOME MEASURE: The association between PCOS and 12 independent single-nucleotide polymorphisms mapping to seven of the Chinese GWAS loci in a European cohort was measured. RESULTS: Variants in DENND1A (P = .0002), THADA (P = .035), FSHR (P = .007), and INSR (P = .046) were associated with PCOS in Europeans. The genetic risk score, generated for each subject based on the total number of risk alleles, was associated with the diagnosis of PCOS (P < .0001) and remained associated (P = .02), even after exclusion of the four variants individually associated with PCOS. CONCLUSIONS: At least four of the PCOS susceptibility loci identified in the Chinese GWAS are associated with PCOS in Europeans. The overall genetic burden for PCOS, as demonstrated by the risk score, is also associated with the diagnosis of PCOS in Europeans. The PCOS susceptibility loci identified in the Chinese GWAS are thus likely to play an important role in the etiology of PCOS across ethnicities.

Steroidogenic regulatory factor FOS is underexpressed in polycystic ovary syndrome (PCOS) adipose tissue and genetically associated with PCOS susceptibility.

CONTEXT: Polycystic ovary syndrome (PCOS) is a heterogeneous common genetic disorder characterized by hyperandrogenemia and insulin resistance. Alterations in gene expression profiles of the ovary and adipose tissue identified the candidate gene FBJ murine osteosarcoma viral oncogene homolog (FOS) for further investigation of expression changes in metabolic tissues and genetic studies. OBJECTIVE: The objective of the study was to confirm the underexpression of the FOS gene in sc adipose and determine whether variants in this gene are risk factors for PCOS. DESIGN: RT-PCR was performed in sc fat from women with and without PCOS. Genotyping of single-nucleotide polymorphisms in the FOS locus was performed to test for association with PCOS. SETTING: The study was conducted at a tertiary care academic institution. PARTICIPANTS: Twenty-two PCOS and 13 control subjects were recruited for gene expression studies. We assembled a discovery genotyping cohort of 354 cases and 161 controls and a replication cohort of 476 cases and 315 controls, all of whom were Caucasian. MAIN MEASUREMENTS: Gene expression by quantitative real-time RT-PCR, FOS genotype, and PCOS status were measured. RESULTS: FOS expression was confirmed to be reduced in PCOS adipose tissue. Three single-nucleotide polymorphisms were significantly associated with PCOS in the discovery cohort (rs8006998, P = 0.0031; rs8013918, P = 0.0006; rs8013942, P = 0.0087). rs8006998 was also associated with PCOS in the replication cohort (P = 0.013). CONCLUSIONS: Differential gene expression in sc fat and genetic association at the FOS locus in PCOS subjects implicates a role for this transcription factor in PCOS. FOS dysfunction may be a common factor between hyperandrogenism and insulin resistance.

A genome-wide scan of Ashkenazi Jewish Crohn's disease suggests novel susceptibility loci.

Crohn's disease (CD) is a complex disorder resulting from the interaction of intestinal microbiota with the host immune system in genetically susceptible individuals. The largest meta-analysis of genome-wide association to date identified 71 CD-susceptibility loci in individuals of European ancestry. An important epidemiological feature of CD is that it is 2-4 times more prevalent among individuals of Ashkenazi Jewish (AJ) descent compared to non-Jewish Europeans (NJ). To explore genetic variation associated with CD in AJs, we conducted a genome-wide association study (GWAS) by combining raw genotype data across 10 AJ cohorts consisting of 907 cases and 2,345 controls in the discovery stage, followed up by a replication study in 971 cases and 2,124 controls. We confirmed genome-wide significant associations of 9 known CD loci in AJs and replicated 3 additional loci with strong signal (p<5×10⁻6). Novel signals detected among AJs were mapped to chromosomes 5q21.1 (rs7705924, combined p = 2×10⁻8; combined odds ratio OR = 1.48), 2p15 (rs6545946, p = 7×10⁻9; OR = 1.16), 8q21.11 (rs12677663, p = 2×10⁻8; OR = 1.15), 10q26.3 (rs10734105, p = 3×10⁻8; OR = 1.27), and 11q12.1 (rs11229030, p = 8×10⁻9; OR = 1.15), implicating biologically plausible candidate genes, including RPL7, CPAMD8, PRG2, and PRG3. In all, the 16 replicated and newly discovered loci, in addition to the three coding NOD2 variants, accounted for 11.2% of the total genetic variance for CD risk in the AJ population. This study demonstrates the complementary value of genetic studies in the Ashkenazim.

Metabolic and cardiovascular genes in polycystic ovary syndrome: a candidate-wide association study (CWAS).

The role of metabolic disturbance in polycystic ovary syndrome (PCOS) has been well established, with insulin resistance and the resulting compensatory hyperinsulinemia thought to promote hyperandrogenemia. Genome-wide association studies (GWAS) have established a large number of loci for metabolic conditions such as type 2 diabetes and obesity. A subset of these loci has been investigated for a role in PCOS; these studies generally have not revealed a confirmed role for these loci in PCOS risk. However, a large scale investigation of genes related to these pathways has not previously been performed. We conducted a two stage case control association study of 121,715 single nucleotide polymorphisms (SNPs) selected to represent susceptibility loci associated with traits such as type 2 diabetes, obesity measures, lipid levels and cardiovascular function using the Cardio-Metabochip in 847 PCOS cases and 845 controls. Several hypothesis-generating associations with PCOS were observed (top SNP rs2129107, P=3.8×10(-6)). We did not find any loci definitively associated with PCOS after strict correction for multiple testing, suggesting that cardio-metabolic loci are not major risk factors underlying the susceptibility to PCOS.

Replication of association of DENND1A and THADA variants with polycystic ovary syndrome in European cohorts.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with a strong familial component. PCOS is characterised by hyperandrogenaemia and irregular menses. A recent genome-wide association study (GWAS) of PCOS in a Chinese cohort identified three reproducible PCOS susceptibility loci mapping to 2p16.3 (luteinising hormone/choriogonadotropin receptor; LHCGR), 2p21 (thyroid associated protein; THADA), and 9q33.3 (DENN/MADD domain containing 1A; DENNDIA). The impact of these loci in non-Chinese PCOS cohorts remains to be determined. METHODS AND RESULTS: The study tested association with PCOS of seven single nucleotide polymorphisms mapping to the three Chinese PCOS loci in two European derived PCOS cohorts (cohort A = 939 cases and 957 controls; cohort B = 535 cases and 845 controls). Cases fulfilled the National Institute of Child Health & Human Development criteria for PCOS. Variation in DENND1A was strongly associated with PCOS in the study cohort (p(combined cohorts)=10(-8)); multiple variants in THADA were also associated with PCOS, while there was no significant evidence for association of LHCGR variation with PCOS. The present study had >80% power to detect an effect of similar size as was observed by Chen et al for DENND1A and THADA, but reduced power (at <40%) for LHCGR at p=0.0001. The study had sufficient power (57-88%) for LHCGR at p=0.01. CONCLUSIONS: At least two of the PCOS susceptibility loci identified in the Chinese PCOS GWAS (DENND1A and THADA) are also associated with PCOS in European derived populations, and are therefore likely to be important in the aetiology of PCOS regardless of ethnicity. The analysis of the LHCGR gene was not sufficiently powered to detect modest effects.

Harnessing expression data to identify novel candidate genes in polycystic ovary syndrome.

Novel pathways in polycystic ovary syndrome (PCOS) are being identified in gene expression studies in PCOS tissues; such pathways may contain key genes in disease etiology. Previous expression studies identified both dickkopf homolog 1 (DKK1) and DnaJ (Hsp40) homolog, subfamily B, member 1 (DNAJB1) as differentially expressed in PCOS tissue, implicating them as candidates for PCOS susceptibility. To test this, we genotyped a discovery cohort of 335 PCOS cases and 198 healthy controls for three DKK1 single nucleotide polymorphisms (SNPs) and four DNAJB1 SNPs and a replication cohort of 396 PCOS cases and 306 healthy controls for 1 DKK1 SNP and 1 DNAJB1 SNP. SNPs and haplotypes were determined and tested for association with PCOS and component phenotypes. We found that no single nucleotide polymorphisms were associated with PCOS risk; however, the major allele of rs1569198 from DKK1 was associated with increased total testosterone (discovery cohort P = 0.0035) and dehydroepiandrosterone sulfate (replication cohort P = 0.05). Minor allele carriers at rs3962158 from DNAJB1 had increased fasting insulin (discovery cohort P = 0.003), increased HOMA-IR (discovery cohort P = 0.006; replication cohort P = 0.036), and increased HOMA-%B (discovery cohort P = 0.004). Carriers of haplotype 2 at DNAJB1 also had increased fasting insulin, HOMA-IR, and HOMA-%B. These findings suggest that genetic variation in DKK1 and DNAJB1 may have a role in the hyperandrogenic and metabolic dysfunction of PCOS, respectively. Our results also demonstrate the utility of gene expression data as a source of novel candidate genes in PCOS, a complex and still incompletely defined disease, for which alternative methods of gene identification are needed.

Type 2 diabetes susceptibility single-nucleotide polymorphisms are not associated with polycystic ovary syndrome.

Two cohorts of women with polycystic ovary syndrome (PCOS), comprising 400 probands and affected sisters in 365 families and a case-control group including 395 women with PCOS and 171 healthy women with regular menstrual cycles, were studied to determine whether single-nucleotide polymorphisms (SNPs) identified as susceptibility loci in genomewide association studies of type 2 diabetes are also associated with PCOS. None of the 18 allelic variants in 10 genes previously shown to be associated with type 2 diabetes were found to be associated with PCOS, but some were associated with indices of beta cell function.

FTO and MC4R gene variants are associated with obesity in polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is the leading cause of anovulatory infertility in women. It is also associated with metabolic disturbances that place women at increased risk for obesity and type 2 diabetes. There is strong evidence for familial clustering of PCOS and a genetic predisposition. However, the gene(s) responsible for the PCOS phenotypes have not been elucidated. This two-phase family-based and case-control genetic study was designed to address the question of whether SNPs identified as susceptibility loci for obesity in genome-wide association studies (GWAS) are also associated with PCOS and elevated BMI. Members of 439 families having at least one offspring with PCOS were genotyped for 15 SNPs previously shown to be associated with obesity. Linkage and association with PCOS was assessed using the transmission/disequilibrium test (TDT). These SNPs were also analyzed in an independent case-control study involving 395 women with PCOS and 176 healthy women with regular menstrual cycles. Only one of these 15 SNPs (rs2815752 in NEGR1) was found to have a nominally significant association with PCOS (χ(2) = 6.11, P = 0.013), but this association failed to replicate in the case-control study. While not associated with PCOS itself, five SNPs in FTO and two in MC4R were associated with BMI as assessed with a quantitative-TDT analysis, several of which replicated association with BMI in the case-control cohort. These findings demonstrate that certain SNPs associated with obesity contribute to elevated BMI in PCOS, but do not appear to play a major role in PCOS per se. These findings support the notion that PCOS phenotypes are a consequence of an oligogenic/polygenic mechanism.