Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2.

BACKGROUND: As precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method. RESULTS: The S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics. CONCLUSIONS: Taken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

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.

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

[This corrects the article DOI: 10.1371/journal.pgen.1007813.].

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.

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.

Patient-reported outcomes in RA care improve patient communication, decision-making, satisfaction and confidence: qualitative results.

OBJECTIVE: To evaluate the impact of integrating patient-reported outcomes (PROs) into routine clinics, from the perspective of patients with RA, clinicians and other staff. METHODS: We conducted a prospective cohort study using a mixed methods sequential explanatory design at an academic arthritis clinic. RA patients completed selected Patient-Reported Outcomes Measurement Information System measures on tablets in the waiting room. Results were immediately available to discuss during the visit. Post-visit surveys with patients and physicians evaluated topics discussed and their impact on decision making; patients rated confidence in treatment. Focus groups or interviews with patients, treating rheumatologists and clinic staff were conducted to understand perspectives and experiences. RESULTS: Some 196 patients and 20 rheumatologists completed post-visit surveys at 816 and 806 visits, respectively. Focus groups were conducted with 24 patients, 10 rheumatologists and 4 research/clinic staff. PROs influenced medical decision-making and RA treatment changes (38 and 18% of visits, respectively). Patients reported very high satisfaction and treatment confidence. Impact on clinical workflow was minimal after a period of initial adjustment. PROs were valued by patients and physicians, and provided new insight into how patients felt and functioned over time. Reviewing results together improved communication, and facilitated patient-centred care, shared decision making, and the identification of new symptoms and contributing psychosocial/behavioural factors. CONCLUSION: PRO use at RA visits was feasible, increased understanding of how disease affects how patients feel and function, facilitated shared decision-making, and was associated with high patient satisfaction and treatment confidence.

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.

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.

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.

CRTC3 links catecholamine signalling to energy balance.

The adipose-derived hormone leptin maintains energy balance in part through central nervous system-mediated increases in sympathetic outflow that enhance fat burning. Triggering of ß-adrenergic receptors in adipocytes stimulates energy expenditure by cyclic AMP (cAMP)-dependent increases in lipolysis and fatty-acid oxidation. Although the mechanism is unclear, catecholamine signalling is thought to be disrupted in obesity, leading to the development of insulin resistance. Here we show that the cAMP response element binding (CREB) coactivator Crtc3 promotes obesity by attenuating ß-adrenergic receptor signalling in adipose tissue. Crtc3 was activated in response to catecholamine signals, when it reduced adenyl cyclase activity by upregulating the expression of Rgs2, a GTPase-activating protein that also inhibits adenyl cyclase activity. As a common human CRTC3 variant with increased transcriptional activity is associated with adiposity in two distinct Mexican-American cohorts, these results suggest that adipocyte CRTC3 may play a role in the development of obesity in humans.

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.

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.

Fucosyltransferase 2 (FUT2) non-secretor status is associated with Crohn's disease.

Genetic variation in both innate and adaptive immune systems is associated with Crohn's disease (CD) susceptibility, but much of the heritability to CD remains unknown. We performed a genome-wide association study (GWAS) in 896 CD cases and 3204 healthy controls all of Caucasian origin as defined by multidimensional scaling. We found supportive evidence for 21 out of 40 CD loci identified in a recent CD GWAS meta-analysis, including two loci which had only nominally achieved replication (rs4807569, 19p13; rs991804, CCL2/CCL7). In addition, we identified associations with genes involved in tight junctions/epithelial integrity (ASHL, ARPC1A), innate immunity (EXOC2), dendritic cell biology [CADM1 (IGSF4)], macrophage development (MMD2), TGF-beta signaling (MAP3K7IP1) and FUT2 (a physiological trait that regulates gastrointestinal mucosal expression of blood group A and B antigens) (rs602662, P=3.4x10(-5)). Twenty percent of Caucasians are 'non-secretors' who do not express ABO antigens in saliva as a result of the FUT2 W134X allele. We demonstrated replication in an independent cohort of 1174 CD cases and 357 controls between the four primary FUT2 single nucleotide polymorphisms (SNPs) and CD (rs602662, combined P-value 4.90x10(-8)) and also association with FUT2 W143X (P=2.6x10(-5)). Further evidence of the relevance of this locus to CD pathogenesis was demonstrated by the association of the original four SNPs and CD in the recently published CD GWAS meta-analysis (rs602662, P=0.001). These findings strongly implicate this locus in CD susceptibility and highlight the role of the mucus layer in the development of CD.

Variants in ZNF365 isoform D are associated with Crohn's disease.

OBJECTIVE: Genome-wide association studies have identified multiple Crohn's disease (CD) susceptibility loci, including association with non-coding intergenic single-nucleotide polymorphisms (SNPs) at 10q21. DESIGN: To fine-map the 10q21 locus, the authors genotyped 86 SNPs in 1632 CD cases and 961 controls and performed single-marker and conditional analyses using logistic regression. RESULTS: Association with CD risk spanning 11 SNPs (p<0.001) was observed. The most significant association observed was at the non-synonymous SNP, rs7076156 (Ala62Thr), in ZNF365. The alanine allele was over-represented in CD (p=5.23×10⁻7; OR=1.39 (95% CI 1.22 to 1.58)); allele frequency of 76% in CD and 69.7% in controls). Conditional analysis on rs7076156 nullified all other significant associations, suggesting that this is the causative variant at this locus. Four isoforms of ZNF365 have previously been identified, and rs7076156 is located in an exon unique to ZNF365 isoform D. The authors demonstrated, using reverse transcription-PCR, expression of ZNF365D in intestinal resections from both CD subjects and controls. Markedly reduced mean expression levels of ZNF365D were identified in Epstein-Barr virus-transformed lymphoblastoid cell lines from CD subjects homozygous for the risk allele (Ala). A whole-genome microarray expression study further suggested that the Ala62Thr change in ZNF365 isoform D is related to differential expression of the genes ARL4A, MKKS, RRAGD, SUMF2, TDR1 and ZNF148 in CD. CONCLUSIONS: Collectively, these data support the hypothesis that the non-synonymous Ala62Thr SNP, rs7076156, underlies the association between 10q21 and CD risk and suggest that this SNP acts by altering expression of genes under the control of ZNF365 isoform D.

Identifying Minimal and Meaningful Change in a Patient-Reported Outcomes Measurement Information System for Rheumatoid Arthritis: Use of Multiple Methods and Perspectives.

OBJECTIVE: Rheumatoid arthritis (RA) is chronic, painful, disabling condition resulting in significant impairments in physical, emotional, and social health. Our objective was to use different methods and perspectives to evaluate the responsiveness of Patient-Reported Outcomes Measurement Information System (PROMIS) short forms (SFs) and to identify minimal and meaningful score changes. METHODS: Adults with RA who were enrolled in a multisite prospective observational cohort completed PROMIS physical function, pain interference, fatigue, and participation in social roles/activities SFs, the PROMIS 29-item form (PROMIS-29), and pain and patient global assessment, and rated change in specific symptoms and RA (a little versus lot better or worse) at the second visit. Physicians recorded joint counts, physician global assessment, and change in RA at visit 2. We compared mean score differences for minimal and meaningful improvement/worsening using patient and physician change ratings and distribution-based methods, and we visually inspected empirical cumulative distribution function curves by change categories. RESULTS: The 348 adults were mostly female (81%) with longstanding RA. Using patient ratings, generally 1-3-point differences were observed for minimal change and 3-7 points for meaningful change. Larger differences were observed with patient versus physician ratings and for symptom-specific versus RA change. Mean differences were similar among SF versions. Prespecified hypotheses about change in PROMIS physical function, pain interference, fatigue, and participation and legacy scales were supported. CONCLUSION: PROMIS SFs and the PROMIS-29 profiles are responsive to change and generally distinguish between minimal and meaningful improvement and worsening in key RA domains. These data add to a growing body of evidence demonstrating the robust psychometric properties of PROMIS and supporting its use in RA care, research, and decision-making.

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.

Nonreplication of the type 5 17beta-hydroxysteroid dehydrogenase gene association with polycystic ovary syndrome.

CONTEXT: Increased androgen production is a primary feature of polycystic ovary syndrome (PCOS) and appears to be an inherited trait. The gene for the steroidogenic enzyme type 5 17beta hydroxysteroid dehydrogenase (HSD17B5) was implicated as a candidate for the hyperandrogenemia of PCOS by a previous study that demonstrated an association of a single nucleotide polymorphism (SNP) in the promoter of this gene with PCOS. OBJECTIVE: The objective of the study was to replicate the previous report of association between the HSD17B5 gene and PCOS risk by genotyping the promoter SNP (as well as other SNPs in the region to provide improved coverage of the gene) in a large, well-characterized cohort suitable for replication study. DESIGN: Women with and without PCOS were genotyped for five SNPs in HSD17B5. SNPs and haplotypes were determined and tested for association with PCOS risk and phenotypic markers of PCOS. SETTING: Subjects were recruited from the reproductive endocrinology clinic at the University of Alabama at Birmingham; controls were recruited from the surrounding community. Genotyping took place at Cedars-Sinai Medical Center in Los Angeles. PARTICIPANTS: Participants included 287 white women with PCOS and 187 white controls. MAIN MEASUREMENTS: HSD17B5 genotype, PCOS risk, and testosterone levels were measured. RESULTS: No SNP or haplotype was significantly associated with PCOS risk, testosterone, or any of the traits tested. CONCLUSIONS: These data suggest that polymorphisms in the HSD17B5 gene are not associated with PCOS risk or elevated testosterone as previously reported.