Nuclear receptor coregulator SNP discovery and impact on breast cancer risk

<p>Abstract</p> <p>Background</p> <p>Coregulator proteins are "master regulators", directing transcriptional and posttranscriptional regulation of many target genes, and are critical in many normal physiological processes, but also in hormone driven diseases, such as breast cancer. Little is known on how genetic changes in these genes impact disease development and progression. Thus, we set out to identify novel single nucleotide polymorphisms (SNPs) within SRC-1 (NCoA1), SRC-3 (NCoA3, AIB1), NCoR (NCoR1), and SMRT (NCoR2), and test the most promising SNPs for associations with breast cancer risk.</p> <p>Methods</p> <p>The identification of novel SNPs was accomplished by sequencing the coding regions of these genes in 96 apparently normal individuals (48 Caucasian Americans, 48 African Americans). To assess their association with breast cancer risk, five SNPs were genotyped in 1218 familial BRCA1/2-mutation negative breast cancer cases and 1509 controls (rs1804645, rs6094752, rs2230782, rs2076546, rs2229840).</p> <p>Results</p> <p>Through our resequencing effort, we identified 74 novel SNPs (30 in NCoR, 32 in SMRT, 10 in SRC-3, and 2 in SRC-1). Of these, 8 were found with minor allele frequency (MAF) >5% illustrating the large amount of genetic diversity yet to be discovered. The previously shown protective effect of rs2230782 in SRC-3 was strengthened (OR = 0.45 [0.21-0.98], p = 0.04). No significant associations were found with the other SNPs genotyped.</p> <p>Conclusions</p> <p>This data illustrates the importance of coregulators, especially SRC-3, in breast cancer development and suggests that more focused studies, including functional analyses, should be conducted.</p

FGF receptor genes and breast cancer susceptibility: results from the Breast Cancer Association Consortium

Background:Breast cancer is one of the most common malignancies in women. Genome-wide association studies have identified FGFR2 as a breast cancer susceptibility gene. Common variation in other fibroblast growth factor (FGF) receptors might also modify risk. We tested this hypothesis by studying genotyped single-nucleotide polymorphisms (SNPs) and imputed SNPs in FGFR1, FGFR3, FGFR4 and FGFRL1 in the Breast Cancer Association Consortium. Methods:Data were combined from 49 studies, including 53 835 cases and 50 156 controls, of which 89 050 (46 450 cases and 42 600 controls) were of European ancestry, 12 893 (6269 cases and 6624 controls) of Asian and 2048 (1116 cases and 932 controls) of African ancestry. Associations with risk of breast cancer, overall and by disease sub-type, were assessed using unconditional logistic regression. Results:Little evidence of association with breast cancer risk was observed for SNPs in the FGF receptor genes. The strongest evidence in European women was for rs743682 in FGFR3; the estimated per-allele odds ratio was 1.05 (95 confidence interval=1.02-1.09, P=0.0020), which is substantially lower than that observed for SNPs in FGFR2. Conclusion:Our results suggest that common variants in the other FGF receptors are not associated with risk of breast cancer to the degree observed for FGFR2. © 2014 Cancer Research UK

Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon

[EN] Background: MiRNAs have emerged as key regulators of stress response in plants, suggesting their potential as candidates for knock-in/out to improve stress tolerance in agricultural crops. Although diverse assays have been performed, systematic and detailed studies of miRNA expression and function during exposure to multiple environments in crops are limited. Results: Here, we present such pioneering analysis in melon plants in response to seven biotic and abiotic stress conditions. Deep-sequencing and computational approaches have identified twenty-four known miRNAs whose expression was significantly altered under at least one stress condition, observing that down-regulation was preponderant. Additionally, miRNA function was characterized by high scale degradome assays and quantitative RNA measurements over the intended target mRNAs, providing mechanistic insight. Clustering analysis provided evidence that eight miRNAs showed a broad response range under the stress conditions analyzed, whereas another eight miRNAs displayed a narrow response range. Transcription factors were predominantly targeted by stressresponsive miRNAs in melon. Furthermore, our results show that the miRNAs that are down-regulated upon stress predominantly have as targets genes that are known to participate in the stress response by the plant, whereas the miRNAs that are up-regulated control genes linked to development. Conclusion: Altogether, this high-resolution analysis of miRNA-target interactions, combining experimental and computational work, Illustrates the close interplay between miRNAs and the response to diverse environmental conditions, in melon.The authors thank Dr. A. Monforte for providing melon seeds and Dra. B. Pico (Cucurbits Group - COMAV) for providing melon seeds and Monosporascus isolate respectively. This work was supported by grants AGL2016-79825-R, BIO2014-61826-EXP (GG), and BFU2015-66894-P (GR) from the Spanish Ministry of Economy and Competitiveness (co-supported by FEDER). The funders had no role in the experiment design, data analysis, decision to publish, or preparation of the manuscript.Sanz-Carbonell, A.; Marques Romero, MC.; Bustamante-González, AJ.; Fares Riaño, MA.; Rodrigo Tarrega, G.; Gomez, GG. (2019). Inferring the Regulatory Network of the miRNA-mediated Response to Biotic and Abiotic Stress in Melon. BMC Plant Biology. 1-17. https://doi.org/10.1186/s12870-019-1679-0S117Zhang B. MicroRNAs: a new target for improving plant tolerance to abiotic stress. J Exp Bot. 2015;66:1749–61.Zhu JK. Abiotic stress signaling and responses in plants. Cell. 2016;167:313–24.Bielach A, Hrtyan M, Tognetti VB. Plants under stress: involvement of auxin and Cytokinin. Int J Mol Sci. 2017;4(18):7.Zarattini M, Forlani G. Toward unveiling the mechanisms for transcriptional regulation of proline biosynthesis in the plant cell response to biotic and abiotic stress conditions. Front Plant Sci. 2017;2(8):927.Nolan T, Chen J, Yin Y. Cross-talk of Brassinosteroid signaling in controlling growth and stress responses. Biochem J. 2017;474:2641–61.Mittler R. Abiotic stress, the field environment and stress combinations. Trends Plant Sci. 2006;11:15–9.Djami-Tchatchou AT, Sanan-Mishra N, Ntushelo K, Dubery IA. Functional roles of microRNAs in Agronomically important plants—potential as targets for crop improvement and protection. Front Plant Sci. 2017;8:378.Baxter A, Mittler R, Suzuki N. ROS as key players in plant stress signaling. J Exp Bot. 2014;65:1229–40.Golldack D, Li C, Mohan H, Probst N. Tolerance to drought and salt stress in plants: unraveling the signaling networks. Front Plant Sci. 2014;5:151.Lee SH, Li HW, Koh KW, Chuang HY, Chen YR, Lin CS, Chan MT. MSRB7 reverses oxidation of GSTF2/3 to confer tolerance of Arabidopsis thaliana to oxidative stress. J Exp Bot. 2014;65:5049–62.Carrera J, Rodrigo G, Jaramillo A, Elena SF. Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biol. 2009;10(9):R96.Shriram V, Kumar V, Devarumath RM, Khare TS, Wani SH. MicroRNAs as potential targets for abiotic stress tolerance in plants. Front Plant Sci. 2016;7:817.Sunkar R, Chinnusamy V, Zhu J, Zhu JH. Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci. 2007;12:301–9.Kumar R. Role of microRNAs in biotic and abiotic stress responses in crop plants. Appl Biochem Biotechnology. 2014;174:93–115.Reis RS, Eamens AL, Waterhouse PM. Missing pieces in the puzzle of plant MicroRNAs. Trends Plant Sci. 2015;20:721–8.Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97.Borges F, Martienssen RA. The expanding world of small RNAs in plants. Nat Rev Mol Cell Biol. 2015;16:727–41.Axtell MJ, Bartel DP. Antiquity of microRNAs and their targets in land-plants. Plant Cell. 2005;17:1658–73.Cuperus JT, Fahlgren N, Carrington JC. Evolution and functional diversification of MIRNA genes. Plant Cell. 2011;23:431–42.Cui J, You C, Chen X. The evolution of microRNAs in plants. Current Opinions in Plant Biology. 2016;35:61–7.Sunkar R, Li YF, Jagadeeswaran G. Functions of microRNAs in plant stress responses. Trends Plant Sci. 2012;17:196–203.Zhang T, Zhao YL, Zhao JH, Wang S, Jin Y, Chen ZQ, Fang YY, Hua CL, Ding SW, Guo HS. Cotton plants export microRNAs to inhibit virulence gene expression in a fungal pathogen. Nature Plants. 2016;2(10):16153.Chaloner T, vanKan JA, Grant-Downton R. RNA ‘Information Warfare’ in pathogenic and mutualistic interactions. Trends Plant Sci. 2016;9:738–48.Niu D, Wang Z, Wang S, Qiao L Zhao H. Profiling of small RNAs involved in plant-pathogen interactions. Methods Molecular Biology. 2015;1287:61–79.Wei S, Wang L, Zhang Y, Huang D. Identification of early response genes to salt stress in roots of melon (Cucumis melo L.) seedlings. Molecular Biology Report. 2013;40:2915–26.Clepet C, Joobeur T, Zheng Y, Jublot D, Huang M, Truniger V, et al. Analysis of expressed sequence tags generated from full-length enriched cDNA libraries of melon. BMC Genomics. 2011;12:252.González M, Xu M, Esteras C, Roig C, Monforte AJ, Troadec C, et al. Towards a TILLING platform for functional genomics in Piel de Sapo melons. BMC Research Notes. 2011;4:289.García MJ. The genome of melon (Cucumis melo L.). Proc Natl Acad Sci U S A. 2012;109:11872–7.Pollack FG, Uecker FA. Monosporascus cannonballus: an unusual ascomycete in cantaloupe roots. Mycologia. 1974;66:346–9.Kofalvi S, Marcos J, Cañizares MC, Pallas V, Candresse T. Hop stunt viroid (HSVd) sequence variants from Prunus species: evidence for recombination between HSVd isolates. J Gen Virol. 1997;78:3177–86.Sattar S, Song Y, Anstead J, Sunkar R, Thompson G. Cucumis melo expression profile during aphid herbivory in a resistant and susceptible interaction. Mol Plant-Microbe Interact. 2012;25:839–48.Herranz MC, Navarro JA, Sommen E, Pallas V. Comparative analysis among the small RNA populations of source, sink and conductive tissues in two different plant-virus pathosystems. BMC Genomics. 2015;16:117.Jagadeeswaran G, Nimmakayala P, Zheng Y, Gowdu K, Reddy UK, Sunkar R. Characterization of the small RNA component of leaves and fruits from four different cucurbit species. BMC Genomics. 2012;13:329.Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42:D68–73.Barciszewska-Pacak M, Milanowska K, Knop K, Bielewicz D, Nuc P, Plewka P, et al. Arabidopsis microRNA expression regulation in a wide range of abiotic stress responses. Front Plant Sci. 2015;6:410.Zhou L, Liu Y, Liu Z, Kong D, Duan M, Luo L. Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. J Exp Bot. 2010;61:4157–68.Samad A, Sajad M, Nazaruddin N, Fauzi I, Murad A, Zainal Z, Ismanizan Ismail I. MicroRNA and transcription factor: key players in plant regulatory network. Front Plant Sci. 2017;8:565.Danisman S. TCP transcription factors at the Interface between environmental challenges and the Plant’s growth responses. Front Plant Sci. 2016;7:1930.Llave C, Xie Z, Kasschau KD, Carrington JC. Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science. 2002;297:2053–6.Gupta OP, Meena NL, Sharma I, et al. Differential regulation of microRNAs in response to osmotic, salt and cold stresses in wheat. Mol Biol Rep. 2014;41:4623.Wang M, Wang Q, Zhang B. 2013. Response of miRNAs and their targets to salt and drought stresses in cotton (Gossypium hirsutum ). Gene 30: 26–32.Savageau MA. Demand theory of gene regulation. I. Quantitative development of the theory. Genetics. 1998;149:1665–76.Negrão S, Schmöckel SM, Tester M. Evaluating physiological responses of plants to salinity stress. Ann Bot. 2017;119:1–11.Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004;5(2):101–13.Megraw M, Cumbie J, Ivanchenko M, Filichkin S. Small genetic circuits and MicroRNAs: big players in polymerase II transcriptional control in plants. Plant Cell. 2016;28:286–303.Wang St, Sun Xl, Hoshino Y, Yu Y, Jia B, et al. 2014. MicroRNA319 Positively Regulates Cold Tolerance by Targeting OsPCF6 and OsTCP21 in Rice (Oryza sativa). PLoS ONE 9(3): e91357.Fang Y, Xie K, Xiong L. Conserved miR164-targeted NAC genes regulate drought resistence in rice. J Exp Bot. 2014;65:2119–35.Goossens A, de la Fuente N, Forment J, Serrano R, Portillo F. Regulation of yeast H+-ATPase by protein kinases belonging to a family dedicated to activation of plasma membrane transporters. Mol Cell Biol. 2000;20:7654–61.Roig C, Fita A, Ríos G, Hammond JP, Nuez F, Picó B. Root transcriptional responses of two melon genotypes with contrasting resistance to Monosporascus cannonballus (Pollack et Uecker) infection. BMC Genomics. 2012;13:601.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal. 2011;17:10–2.R Core Team 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3–900051–07-0, URL http://www.R-project.org /.Tarazona S, Furió-Tarí P, Turrà D, Di Pietro A, Nueda MJ, Ferrer A, Conesa A. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/bioc package. Nucleic Acids Res. 2015;43:e140.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.Czimmerer Z, Hulvely J, Simandi Z, Varallyay E, Havelda Z, Szabo E, Balint BL. A versatile method to design stem-loop primer-based quantitative PCR assays for detecting small regulatory RNA molecules. PLoS One. 2013;8(1):e55168.Zhai J, Arikit S, Simon S, Kingham B, Meyers B. Rapid construction of parallel analysis of RNA end (PARE) libraries for Illumina sequencing. Methods. 2014;67:84–90.Pink S, Vogel S. 2014. D3NETWORK: Stata module to create network visualizations using D3.js http://EconPapers.repec.org/RePEc:boc:bocode:s457844 .Csardi G, Nepusz T. The igraph software package for complex network research. Int J Complex Systems. 2006;1695:1–9

Mechanisms and role of microRNA deregulation in cancer onset and progression

MicroRNAs are key regulators of various fundamental biological processes and, although representing only a small portion of the genome, they regulate a much larger population of target genes. Mature microRNAs (miRNAs) are single-stranded RNA molecules of 20–23 nucleotide (nt) length that control gene expression in many cellular processes. These molecules typically reduce the stability of mRNAs, including those of genes that mediate processes in tumorigenesis, such as inflammation, cell cycle regulation, stress response, differentiation, apoptosis and invasion. MicroRNA targeting is mostly achieved through specific base-pairing interactions between the 5′ end (‘seed’ region) of the miRNA and sites within coding and untranslated regions (UTRs) of mRNAs; target sites in the 3′ UTR diminish mRNA stability. Since miRNAs frequently target hundreds of mRNAs, miRNA regulatory pathways are complex. Calin and Croce were the first to demonstrate a connection between microRNAs and increased risk of developing cancer, and meanwhile the role of microRNAs in carcinogenesis has definitively been evidenced. It needs to be considered that the complex mechanism of gene regulation by microRNAs is profoundly influenced by variation in gene sequence (polymorphisms) of the target sites. Thus, individual variability could cause patients to present differential risks regarding several diseases. Aiming to provide a critical overview of miRNA dysregulation in cancer, this article reviews the growing number of studies that have shown the importance of these small molecules and how these microRNAs can affect or be affected by genetic and epigenetic mechanisms

Genomic analysis of microRNA time-course expression in liver of mice treated with genotoxic carcinogen N-ethyl-N-nitrosourea

<p>Abstract</p> <p>Background</p> <p>Dysregulated expression of microRNAs (miRNAs) has been previously observed in human cancer tissues and shown promise in defining tumor status. However, there is little information as to if or when expression changes of miRNAs occur in normal tissues after carcinogen exposure.</p> <p>Results</p> <p>To explore the possible time-course changes of miRNA expression induced by a carcinogen, we treated mice with one dose of 120 mg/kg <it>N</it>-ethyl-<it>N</it>-nitrosourea (ENU), a model genotoxic carcinogen, and vehicle control. The miRNA expression profiles were assessed in the mouse livers in a time-course design. miRNAs were isolated from the livers at days 1, 3, 7, 15, 30 and 120 after the treatment and their expression was determined using a miRNA PCR Array. Principal component analysis of the miRNA expression profiles showed that miRNA expression at post-treatment days (PTDs) 7 and 15 were different from those at the other time points and the control. The number of differentially expressed miRNAs (DEMs) changed over time (3, 5, 14, 32, 5 and 5 at PTDs 1, 3, 7, 15, 30 and 120, respectively). The magnitude of the expression change varied with time with the highest changes at PTDs 7 or 15 for most of the DEMs. In silico functional analysis of the DEMs at PTDs 7 and 15 indicated that the major functions of these ENU-induced DEMs were associated with DNA damage, DNA repair, apoptosis and other processes related to carcinogenesis.</p> <p>Conclusion</p> <p>Our results showed that many miRNAs changed their expression to respond the exposure of the genotoxic carcinogen ENU and the number and magnitude of the changes were highest at PTDs 7 to 15. Thus, one to two weeks after the exposure is the best time for miRNA expression sampling.</p

Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus.

BACKGROUND: Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk. METHOD: We performed a fine-scale mapping study of a 700 kb region including 441 genotyped and more than 1300 imputed genetic variants in 48,155 cases and 43,612 controls of European descent, 6269 cases and 6624 controls of East Asian descent and 1116 cases and 932 controls of African descent in the Breast Cancer Association Consortium (BCAC; http://bcac.ccge.medschl.cam.ac.uk/ ), and in 15,252 BRCA1 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Stepwise regression analyses were performed to identify independent association signals. Data from the Encyclopedia of DNA Elements project (ENCODE) and the Cancer Genome Atlas (TCGA) were used for functional annotation. RESULTS: Analysis of data from European descendants found evidence for four independent association signals at 12p11, represented by rs7297051 (odds ratio (OR) = 1.09, 95 % confidence interval (CI) = 1.06-1.12; P = 3 × 10(-9)), rs805510 (OR = 1.08, 95 % CI = 1.04-1.12, P = 2 × 10(-5)), and rs1871152 (OR = 1.04, 95 % CI = 1.02-1.06; P = 2 × 10(-4)) identified in the general populations, and rs113824616 (P = 7 × 10(-5)) identified in the meta-analysis of BCAC ER-negative cases and BRCA1 mutation carriers. SNPs rs7297051, rs805510 and rs113824616 were also associated with breast cancer risk at P < 0.05 in East Asians, but none of the associations were statistically significant in African descendants. Multiple candidate functional variants are located in putative enhancer sequences. Chromatin interaction data suggested that PTHLH was the likely target gene of these enhancers. Of the six variants with the strongest evidence of potential functionality, rs11049453 was statistically significantly associated with the expression of PTHLH and its nearby gene CCDC91 at P < 0.05. CONCLUSION: This study identified four independent association signals at 12p11 and revealed potentially functional variants, providing additional insights into the underlying biological mechanism(s) for the association observed between variants at 12p11 and breast cancer risk.UK funding includes Cancer Research UK and NIH.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s13058-016-0718-

Fine-Scale Mapping of the 5q11.2 Breast Cancer Locus Reveals at Least Three Independent Risk Variants Regulating MAP3K1

Peer reviewe

Identification of six new susceptibility loci for invasive epithelial ovarian cancer.

Genome-wide association studies (GWAS) have identified 12 epithelial ovarian cancer (EOC) susceptibility alleles. The pattern of association at these loci is consistent in BRCA1 and BRCA2 mutation carriers who are at high risk of EOC. After imputation to 1000 Genomes Project data, we assessed associations of 11 million genetic variants with EOC risk from 15,437 cases unselected for family history and 30,845 controls and from 15,252 BRCA1 mutation carriers and 8,211 BRCA2 mutation carriers (3,096 with ovarian cancer), and we combined the results in a meta-analysis. This new study design yielded increased statistical power, leading to the discovery of six new EOC susceptibility loci. Variants at 1p36 (nearest gene, WNT4), 4q26 (SYNPO2), 9q34.2 (ABO) and 17q11.2 (ATAD5) were associated with EOC risk, and at 1p34.3 (RSPO1) and 6p22.1 (GPX6) variants were specifically associated with the serous EOC subtype, all with P < 5 × 10(-8). Incorporating these variants into risk assessment tools will improve clinical risk predictions for BRCA1 and BRCA2 mutation carriers.COGS project is funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 ] HEALTH ]F2 ]2009 ]223175). The CIMBA data management and data analysis were supported by Cancer Research.UK grants 12292/A11174 and C1287/A10118. The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME ]ON Post ]GWAS Initiative (U19 ]CA148112). This study made use of data generated by the Wellcome Trust Case Control consortium. Funding for the project was provided by the Wellcome Trust under award 076113. The results published here are in part based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancer Institute and National Human Genome Research Institute (dbGap accession number phs000178.v8.p7). The cBio portal is developed and maintained by the Computational Biology Center at Memorial Sloan ] Kettering Cancer Center. SH is supported by an NHMRC Program Grant to GCT. Details of the funding of individual investigators and studies are provided in the Supplementary Note. This study made use of data generated by the Wellcome Trust Case Control consortium, funding for which was provided by the Wellcome Trust under award 076113. The results published here are, in part, based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancerhttp://dx.doi.org/10.1038/ng.3185This is the Author Accepted Manuscript of 'Identification of six new susceptibility loci for invasive epithelial ovarian cancer' which was published in Nature Genetics 47, 164–171 (2015) © Nature Publishing Group - content may only be used for academic research

Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus

A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10-20), ER-negative BC (P=1.1 × 10-13), BRCA1-associated BC (P=7.7 × 10-16) and triple negative BC (P-diff=2 × 10-5). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10-3) and ABHD8 (P<2 × 10-3). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3′-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk