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To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10 -8 ) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10 -5 ). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Developing novel cancer prevention medication strategies is important for reducing mortality. Identification of common genetic variants associated with cancer risk suggests the potential to leverage these discoveries to define causal targets for cancer interception. Although each risk variant confers small increases in risk, researchers propose that blocking those that produce causal carcinogenic effects might have large impacts on cancer prevention. While a promising concept, we describe potential hurdles that may need to be scaled to reach this goal, including: (i) understanding the complexity of risk; (ii) achieving statistical power in studies with binary outcomes (cancer development: yes or no); (iii) characterization of cancer precursors; (iv) heterogeneity of cancer subtypes and the populations in which these diseases occur; (v) impact of static genetic markers across complex events of the life course; (vi) defining gene-gene and gene-environment interactions and (vii) demonstrating functional effects of markers in human populations. We assess short-term prospects for this research against the backdrop of these challenges and the potential to prevent cancer through other means. See related commentary by Peters and Tomlinson, p. 7., (©2024 American Association for Cancer Research.)

Background: Breast parenchymal texture features, including grayscale variation (V), capture the patterns of texture variation on a mammogram and are associated with breast cancer risk, independent of mammographic density (MD). However, our knowledge on the genetic basis of these texture features is limited., Methods: We conducted a genome-wide association study of V in 7040 European-ancestry women. V assessments were generated from digitized film mammograms. We used linear regression to test the single-nucleotide polymorphism (SNP)-phenotype associations adjusting for age, body mass index (BMI), MD phenotypes, and the top four genetic principal components. We further calculated genetic correlations and performed SNP-set tests of V with MD, breast cancer risk, and other breast cancer risk factors., Results: We identified three genome-wide significant loci associated with V: rs138141444 (6q24.1) in ECT2L, rs79670367 (8q24.22) in LINC01591, and rs113174754 (12q22) near PGAM1P5. 6q24.1 and 8q24.22 have not previously been associated with MD phenotypes or breast cancer risk, while 12q22 is a known locus for both MD and breast cancer risk. Among known MD and breast cancer risk SNPs, we identified four variants that were associated with V at the Bonferroni-corrected thresholds accounting for the number of SNPs tested: rs335189 (5q23.2) in PRDM6, rs13256025 (8p21.2) in EBF2, rs11836164 (12p12.1) near SSPN, and rs17817449 (16q12.2) in FTO. We observed significant genetic correlations between V and mammographic dense area (r g  = 0.79, P = 5.91 × 10 -5 ), percent density (r g  = 0.73, P = 1.00 × 10 -4 ), and adult BMI (r g  =  - 0.36, P = 3.88 × 10 -7 ). Additional significant relationships were observed for non-dense area (z =  - 4.14, P = 3.42 × 10 -5 ), estrogen receptor-positive breast cancer (z = 3.41, P = 6.41 × 10 -4 ), and childhood body fatness (z =  - 4.91, P = 9.05 × 10 -7 ) from the SNP-set tests., Conclusions: These findings provide new insights into the genetic basis of mammographic texture variation and their associations with MD, breast cancer risk, and other breast cancer risk factors., (© 2022. The Author(s).)

Background: The origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations, we tested for a sex-differentiated genetic architecture within and between traits., Methods: Using European ancestry genome-wide association summary statistics for 20 neuropsychiatric and behavioral traits, we tested for sex differences in single nucleotide polymorphism (SNP)-based heritability and genetic correlation (r g < 1). For each trait, we computed per-SNP z scores from sex-stratified regression coefficients and identified genes with sex-differentiated effects using a gene-based approach. We calculated correlation coefficients between z scores to test for shared sex-differentiated effects. Finally, we tested for sex differences in across-trait genetic correlations., Results: We observed no consistent sex differences in SNP-based heritability. Between-sex, within-trait genetic correlations were high, although <1 for educational attainment and risk-taking behavior. We identified 4 genes with significant sex-differentiated effects across 3 traits. Several trait pairs shared sex-differentiated effects. The top genes with sex-differentiated effects were enriched for multiple gene sets, including neuron- and synapse-related sets. Most between-trait genetic correlation estimates were not significantly different between sexes, with exceptions (educational attainment and risk-taking behavior)., Conclusions: Sex differences in the common autosomal genetic architecture of neuropsychiatric and behavioral phenotypes are small and polygenic and unlikely to fully account for observed sex-differentiated attributes. Larger sample sizes are needed to identify sex-differentiated effects for most traits. For well-powered studies, we identified genes with sex-differentiated effects that were enriched for neuron-related and other biological functions. This work motivates further investigation of genetic and environmental influences on sex differences., (Copyright © 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Bipolar disorder (BD) is associated with binge eating behavior (BE), and both conditions are heritable. Previously, using data from the Genetic Association Information Network (GAIN) study of BD, we performed genome-wide association (GWA) analyses of BD with BE comorbidity. Here, utilizing data from the Mayo Clinic BD Biobank (969 BD cases, 777 controls), we performed a GWA analysis of a BD subtype defined by BE, and case-only analysis comparing BD subjects with and without BE. We then performed a meta-analysis of the Mayo and GAIN results. The meta-analysis provided genome-wide significant evidence of association between single nucleotide polymorphisms (SNPs) in PRR5-ARHGAP8 and BE in BD cases (rs726170 OR = 1.91, P = 3.05E-08). In the meta-analysis comparing cases with BD with comorbid BE vs. non-BD controls, a genome-wide significant association was observed at SNP rs111940429 in an intergenic region near PPP1R2P5 (p = 1.21E-08). PRR5-ARHGAP8 is a read-through transcript resulting in a fusion protein of PRR5 and ARHGAP8. PRR5 encodes a subunit of mTORC2, a serine/threonine kinase that participates in food intake regulation, while ARHGAP8 encodes a member of the RhoGAP family of proteins that mediate cross-talk between Rho GTPases and other signaling pathways. Without BE information in controls, it is not possible to determine whether the observed association reflects a risk factor for BE in general, risk for BE in individuals with BD, or risk of a subtype of BD with BE. The effect of PRR5-ARHGAP8 on BE risk thus warrants further investigation.

We conducted a meta-analysis of three endometrial cancer genome-wide association studies (GWAS) and two follow-up phases totaling 7,737 endometrial cancer cases and 37,144 controls of European ancestry. Genome-wide imputation and meta-analysis identified five new risk loci of genome-wide significance at likely regulatory regions on chromosomes 13q22.1 (rs11841589, near KLF5), 6q22.31 (rs13328298, in LOC643623 and near HEY2 and NCOA7), 8q24.21 (rs4733613, telomeric to MYC), 15q15.1 (rs937213, in EIF2AK4, near BMF) and 14q32.33 (rs2498796, in AKT1, near SIVA1). We also found a second independent 8q24.21 signal (rs17232730). Functional studies of the 13q22.1 locus showed that rs9600103 (pairwise r(2) = 0.98 with rs11841589) is located in a region of active chromatin that interacts with the KLF5 promoter region. The rs9600103[T] allele that is protective in endometrial cancer suppressed gene expression in vitro, suggesting that regulation of the expression of KLF5, a gene linked to uterine development, is implicated in tumorigenesis. These findings provide enhanced insight into the genetic and biological basis of endometrial cancer., Competing Interests: The authors declare no competing financial interests.

Purpose: Chemotherapy resistance remains a major challenge in the treatment of ovarian cancer. We hypothesize that germline polymorphisms might be associated with clinical outcome., Experimental Design: We analyzed approximately 2.8 million genotyped and imputed SNPs from the iCOGS experiment for progression-free survival (PFS) and overall survival (OS) in 2,901 European epithelial ovarian cancer (EOC) patients who underwent first-line treatment of cytoreductive surgery and chemotherapy regardless of regimen, and in a subset of 1,098 patients treated with ≥ 4 cycles of paclitaxel and carboplatin at standard doses. We evaluated the top SNPs in 4,434 EOC patients, including patients from The Cancer Genome Atlas. In addition, we conducted pathway analysis of all intragenic SNPs and tested their association with PFS and OS using gene set enrichment analysis., Results: Five SNPs were significantly associated (P ≤ 1.0 × 10(-5)) with poorer outcomes in at least one of the four analyses, three of which, rs4910232 (11p15.3), rs2549714 (16q23), and rs6674079 (1q22), were located in long noncoding RNAs (lncRNAs) RP11-179A10.1, RP11-314O13.1, and RP11-284F21.8, respectively (P ≤ 7.1 × 10(-6)). ENCODE ChIP-seq data at 1q22 for normal ovary show evidence of histone modification around RP11-284F21.8, and rs6674079 is perfectly correlated with another SNP within the super-enhancer MEF2D, expression levels of which were reportedly associated with prognosis in another solid tumor. YAP1- and WWTR1 (TAZ)-stimulated gene expression and high-density lipoprotein (HDL)-mediated lipid transport pathways were associated with PFS and OS, respectively, in the cohort who had standard chemotherapy (pGSEA ≤ 6 × 10(-3))., Conclusions: We have identified SNPs in three lncRNAs that might be important targets for novel EOC therapies., (©2015 American Association for Cancer Research.)

High-risk mutations in several genes predispose to both colorectal cancer (CRC) and endometrial cancer (EC). We therefore hypothesised that some lower-risk genetic variants might also predispose to both CRC and EC. Using CRC and EC genome-wide association series, totalling 13,265 cancer cases and 40,245 controls, we found that the protective allele [G] at one previously-identified CRC polymorphism, rs2736100 near TERT, was associated with EC risk (odds ratio (OR) = 1.08, P = 0.000167); this polymorphism influences the risk of several other cancers. A further CRC polymorphism near TERC also showed evidence of association with EC (OR = 0.92; P = 0.03). Overall, however, there was no good evidence that the set of CRC polymorphisms was associated with EC risk, and neither of two previously-reported EC polymorphisms was associated with CRC risk. A combined analysis revealed one genome-wide significant polymorphism, rs3184504, on chromosome 12q24 (OR = 1.10, P = 7.23 × 10(-9)) with shared effects on CRC and EC risk. This polymorphism, a missense variant in the gene SH2B3, is also associated with haematological and autoimmune disorders, suggesting that it influences cancer risk through the immune response. Another polymorphism, rs12970291 near gene TSHZ1, was associated with both CRC and EC (OR = 1.26, P = 4.82 × 10(-8)), with the alleles showing opposite effects on the risks of the two cancers.

Background: Bipolar disorder (BD) is a highly heritable disease. While genome-wide association (GWA) studies have identified several genetic risk factors for BD, few of these studies have investigated the genetic etiology of specific disease subtypes. In particular, BD is positively associated with eating dysregulation traits such as binge eating behavior (BE), yet the genetic risk factors underlying BD with comorbid BE have not been investigated., Methods: Utilizing data from the Genetic Association Information Network study of BD, which included 729,454 single nucleotide polymorphisms (SNPs) genotyped in 1001 European American bipolar cases and 1034 controls, we performed GWA analyses of bipolar subtypes defined by the presence or absence of BE history, and performed a case-only analysis comparing BD subjects with and without BE history. Association signals were refined using imputation, and network analysis was performed with Ingenuity Pathway Analysis software. Based on these results, candidate SNPs were selected for replication in an independent sample of 855 cases and 857 controls., Results: Top ranking SNPs in the discovery set included rs6006893 in PRR5, rs17045162 in ANK2, rs13233490 near PER4, rs4665788 and rs10198175 downstream of APOB, rs2367911 in CACNA2D1, and rs7249968 near ZNF536. Rs10198175 in APOB also demonstrated evidence of association in the replication sample and a meta-analysis of the two samples., Limitations: Without information of BE history in controls, it is not possible to determine whether the observed association with APOB reflects a risk factor for BE behavior in general or a risk factor for a subtype of BD with BE. Further longitudinal and functional studies are needed to determine the causal pathways underlying the observed associations., Conclusions: This study identified new potential BD-susceptibility genes, highlighting the advantages of phenotypic sub-classification in genetic research and clinical practice., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Background: Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized gene-environment interactions are now fairly common in human genetic research, and with the shift toward genome-wide association studies, genome-wide gene-environment interaction studies are beginning to emerge., Methods: We summarize the basic ideas behind gene-environment interaction, and provide an overview of possible study designs and traditional analysis methods in the context of genome-wide analysis. We then discuss novel approaches beyond the traditional strategy of analyzing the interaction between the environmental factor and each polymorphism individually., Results: Two-step filtering approaches that reduce the number of polymorphisms tested for interactions can substantially increase the power of genome-wide gene-environment studies. New analytical methods including data-mining approaches, and gene-level and pathway-level analyses, also have the capacity to improve our understanding of how complex genetic and environmental factors interact to influence psychologic and psychiatric traits. Such methods, however, have not yet been utilized much in behavioral and mental health research., Conclusions: Although methods to investigate gene-environment interactions are available, there is a need for further development and extension of these methods to identify gene-environment interactions in the context of genome-wide association studies. These novel approaches need to be applied in studies of psychology and psychiatry., (© 2013 The Authors. Journal of Child Psychology and Psychiatry © 2013 Association for Child and Adolescent Mental Health.)

Background: Identifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional., Results: RF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions., Conclusions: While RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.