Search

Your search keyword '"Catamo, Eulalia"' showing total 6 results
Start Over Author "Catamo, Eulalia" Publisher escholarship, university of california
6 results on '"Catamo, Eulalia"'

1. Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits.

Author

Keaton, Jacob, Kamali, Zoha, Xie, Tian, Vaez, Ahmad, Williams, Ariel, Goleva, Slavina, Ani, Alireza, Evangelou, Evangelos, Hellwege, Jacklyn, Yengo, Loic, Young, William, Traylor, Matthew, Giri, Ayush, Zheng, Zhili, Zeng, Jian, Chasman, Daniel, Morris, Andrew, Caulfield, Mark, Hwang, Shih-Jen, Kooner, Jaspal, Conen, David, Attia, John, Morrison, Alanna, Loos, Ruth, Kristiansson, Kati, Schmidt, Reinhold, Hicks, Andrew, Pramstaller, Peter, Nelson, Christopher, Samani, Nilesh, Risch, Lorenz, Gyllensten, Ulf, Melander, Olle, Riese, Harriette, Wilson, James, Campbell, Harry, Rich, Stephen, Psaty, Bruce, Lu, Yingchang, Guo, Xiuqing, Rice, Kenneth, Vollenweider, Peter, Sundström, Johan, Langenberg, Claudia, Tobin, Martin, Giedraitis, Vilmantas, Luan, Jianan, Tuomilehto, Jaakko, Kutalik, Zoltan, Ripatti, Samuli, Salomaa, Veikko, Girotto, Giorgia, Trompet, Stella, Jukema, J, van der Harst, Pim, Ridker, Paul, Giulianini, Franco, Vitart, Veronique, Goel, Anuj, Watkins, Hugh, Harris, Sarah, Deary, Ian, van der Most, Peter, Oldehinkel, Albertine, Keavney, Bernard, Hayward, Caroline, Campbell, Archie, Boehnke, Michael, Scott, Laura, Boutin, Thibaud, Mamasoula, Chrysovalanto, Järvelin, Marjo-Riitta, Peters, Annette, Gieger, Christian, Lakatta, Edward, Cucca, Francesco, Hui, Jennie, Knekt, Paul, Enroth, Stefan, De Borst, Martin, Polašek, Ozren, Concas, Maria, Catamo, Eulalia, Cocca, Massimiliano, Li-Gao, Ruifang, Hofer, Edith, Schmidt, Helena, Spedicati, Beatrice, Waldenberger, Melanie, Strachan, David, Laan, Maris, Teumer, Alexander, Dörr, Marcus, Gudnason, Vilmundur, Cook, James, Ruggiero, Daniela, Kolcic, Ivana, Boerwinkle, Eric, Traglia, Michela, and Lehtimäki, Terho

Subjects
Female, Humans, Male, Blood Pressure, Genetic Predisposition to Disease, Genetic Risk Score, Genome-Wide Association Study, Hypertension, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Risk Factors
Abstract

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P

Published
2024

2. A saturated map of common genetic variants associated with human height

Author

Yengo, Loïc, Vedantam, Sailaja, Marouli, Eirini, Sidorenko, Julia, Bartell, Eric, Sakaue, Saori, Graff, Marielisa, Eliasen, Anders U, Jiang, Yunxuan, Raghavan, Sridharan, Miao, Jenkai, Arias, Joshua D, Graham, Sarah E, Mukamel, Ronen E, Spracklen, Cassandra N, Yin, Xianyong, Chen, Shyh-Huei, Ferreira, Teresa, Highland, Heather H, Ji, Yingjie, Karaderi, Tugce, Lin, Kuang, Lüll, Kreete, Malden, Deborah E, Medina-Gomez, Carolina, Machado, Moara, Moore, Amy, Rüeger, Sina, Sim, Xueling, Vrieze, Scott, Ahluwalia, Tarunveer S, Akiyama, Masato, Allison, Matthew A, Alvarez, Marcus, Andersen, Mette K, Ani, Alireza, Appadurai, Vivek, Arbeeva, Liubov, Bhaskar, Seema, Bielak, Lawrence F, Bollepalli, Sailalitha, Bonnycastle, Lori L, Bork-Jensen, Jette, Bradfield, Jonathan P, Bradford, Yuki, Braund, Peter S, Brody, Jennifer A, Burgdorf, Kristoffer S, Cade, Brian E, Cai, Hui, Cai, Qiuyin, Campbell, Archie, Cañadas-Garre, Marisa, Catamo, Eulalia, Chai, Jin-Fang, Chai, Xiaoran, Chang, Li-Ching, Chang, Yi-Cheng, Chen, Chien-Hsiun, Chesi, Alessandra, Choi, Seung Hoan, Chung, Ren-Hua, Cocca, Massimiliano, Concas, Maria Pina, Couture, Christian, Cuellar-Partida, Gabriel, Danning, Rebecca, Daw, E Warwick, Degenhard, Frauke, Delgado, Graciela E, Delitala, Alessandro, Demirkan, Ayse, Deng, Xuan, Devineni, Poornima, Dietl, Alexander, Dimitriou, Maria, Dimitrov, Latchezar, Dorajoo, Rajkumar, Ekici, Arif B, Engmann, Jorgen E, Fairhurst-Hunter, Zammy, Farmaki, Aliki-Eleni, Faul, Jessica D, Fernandez-Lopez, Juan-Carlos, Forer, Lukas, Francescatto, Margherita, Freitag-Wolf, Sandra, Fuchsberger, Christian, Galesloot, Tessel E, Gao, Yan, Gao, Zishan, Geller, Frank, Giannakopoulou, Olga, Giulianini, Franco, Gjesing, Anette P, Goel, Anuj, Gordon, Scott D, Gorski, Mathias, Grove, Jakob, and Guo, Xiuqing

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Humans, Body Height, Gene Frequency, Genome, Human, Genome-Wide Association Study, Haplotypes, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Europe, Sample Size, Phenotype, Chromosome Mapping, 23andMe Research Team, VA Million Veteran Program, DiscovEHR, eMERGE, Lifelines Cohort Study, PRACTICAL Consortium, Understanding Society Scientific Group, General Science & Technology
Abstract

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Published
2022

3. Genetic insights into biological mechanisms governing human ovarian ageing

Author

Ruth, Katherine S, Day, Felix R, Hussain, Jazib, Martínez-Marchal, Ana, Aiken, Catherine E, Azad, Ajuna, Thompson, Deborah J, Knoblochova, Lucie, Abe, Hironori, Tarry-Adkins, Jane L, Gonzalez, Javier Martin, Fontanillas, Pierre, Claringbould, Annique, Bakker, Olivier B, Sulem, Patrick, Walters, Robin G, Terao, Chikashi, Turon, Sandra, Horikoshi, Momoko, Lin, Kuang, Onland-Moret, N Charlotte, Sankar, Aditya, Hertz, Emil Peter Thrane, Timshel, Pascal N, Shukla, Vallari, Borup, Rehannah, Olsen, Kristina W, Aguilera, Paula, Ferrer-Roda, Mònica, Huang, Yan, Stankovic, Stasa, Timmers, Paul RHJ, Ahearn, Thomas U, Alizadeh, Behrooz Z, Naderi, Elnaz, Andrulis, Irene L, Arnold, Alice M, Aronson, Kristan J, Augustinsson, Annelie, Bandinelli, Stefania, Barbieri, Caterina M, Beaumont, Robin N, Becher, Heiko, Beckmann, Matthias W, Benonisdottir, Stefania, Bergmann, Sven, Bochud, Murielle, Boerwinkle, Eric, Bojesen, Stig E, Bolla, Manjeet K, Boomsma, Dorret I, Bowker, Nicholas, Brody, Jennifer A, Broer, Linda, Buring, Julie E, Campbell, Archie, Campbell, Harry, Castelao, Jose E, Catamo, Eulalia, Chanock, Stephen J, Chenevix-Trench, Georgia, Ciullo, Marina, Corre, Tanguy, Couch, Fergus J, Cox, Angela, Crisponi, Laura, Cross, Simon S, Cucca, Francesco, Czene, Kamila, Smith, George Davey, de Geus, Eco JCN, de Mutsert, Renée, De Vivo, Immaculata, Demerath, Ellen W, Dennis, Joe, Dunning, Alison M, Dwek, Miriam, Eriksson, Mikael, Esko, Tõnu, Fasching, Peter A, Faul, Jessica D, Ferrucci, Luigi, Franceschini, Nora, Frayling, Timothy M, Gago-Dominguez, Manuela, Mezzavilla, Massimo, García-Closas, Montserrat, Gieger, Christian, Giles, Graham G, Grallert, Harald, Gudbjartsson, Daniel F, Gudnason, Vilmundur, Guénel, Pascal, Haiman, Christopher A, Håkansson, Niclas, Hall, Per, Hayward, Caroline, He, Chunyan, He, Wei, and Heiss, Gerardo

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Estrogen, Prevention, Women's Health, Contraception/Reproduction, Aging, Infertility, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Good Health and Well Being, Adult, Alleles, Animals, Bone and Bones, Checkpoint Kinase 1, Checkpoint Kinase 2, Diabetes Mellitus, Type 2, Diet, Europe, Asia, Eastern, Female, Fertility, Fragile X Mental Retardation Protein, Genetic Predisposition to Disease, Genome-Wide Association Study, Healthy Aging, Humans, Longevity, Menopause, Menopause, Premature, Mice, Mice, Inbred C57BL, Middle Aged, Ovary, Primary Ovarian Insufficiency, Uterus, Biobank-based Integrative Omics Study (BIOS) Consortium, eQTLGen Consortium, Biobank Japan Project, China Kadoorie Biobank Collaborative Group, kConFab Investigators, LifeLines Cohort Study, InterAct consortium, 23andMe Research Team, General Science & Technology
Abstract

Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.

Published
2021

4. Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Author

Turcot, Valérie, Lu, Yingchang, Highland, Heather M, Schurmann, Claudia, Justice, Anne E, Fine, Rebecca S, Bradfield, Jonathan P, Esko, Tõnu, Giri, Ayush, Graff, Mariaelisa, Guo, Xiuqing, Hendricks, Audrey E, Karaderi, Tugce, Lempradl, Adelheid, Locke, Adam E, Mahajan, Anubha, Marouli, Eirini, Sivapalaratnam, Suthesh, Young, Kristin L, Alfred, Tamuno, Feitosa, Mary F, Masca, Nicholas GD, Manning, Alisa K, Medina-Gomez, Carolina, Mudgal, Poorva, Ng, Maggie CY, Reiner, Alex P, Vedantam, Sailaja, Willems, Sara M, Winkler, Thomas W, Abecasis, Gonçalo, Aben, Katja K, Alam, Dewan S, Alharthi, Sameer E, Allison, Matthew, Amouyel, Philippe, Asselbergs, Folkert W, Auer, Paul L, Balkau, Beverley, Bang, Lia E, Barroso, Inês, Bastarache, Lisa, Benn, Marianne, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bots, Michiel L, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Breen, Gerome, Brilliant, Murray H, Broer, Linda, Brumat, Marco, Burt, Amber A, Butterworth, Adam S, Campbell, Peter T, Cappellani, Stefania, Carey, David J, Catamo, Eulalia, Caulfield, Mark J, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der I, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Cocca, Massimiliano, Collins, Francis S, Cook, James P, Corley, Janie, Corominas Galbany, Jordi, Cox, Amanda J, Crosslin, David S, Cuellar-Partida, Gabriel, D'Eustacchio, Angela, Danesh, John, Davies, Gail, Bakker, Paul IW, Groot, Mark CH, Mutsert, Renée, Deary, Ian J, Dedoussis, George, Demerath, Ellen W, Heijer, Martin, Hollander, Anneke I, Ruijter, Hester M, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Drenos, Fotios, Du, Mengmeng, Dubé, Marie-Pierre, Dunning, Alison M, and Easton, Douglas F

Subjects
CHD Exome+ Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, EPIC InterAct Consortium, INTERVAL Study, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Understanding Society Scientific Group, Nutrition, Obesity, Developmental Biology, Medical and Health Sciences, Biological Sciences
Abstract

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.

Published
2018

5. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

Author

Turcot, Valérie, Lu, Yingchang, Highland, Heather M, Schurmann, Claudia, Justice, Anne E, Fine, Rebecca S, Bradfield, Jonathan P, Esko, Tõnu, Giri, Ayush, Graff, Mariaelisa, Guo, Xiuqing, Hendricks, Audrey E, Karaderi, Tugce, Lempradl, Adelheid, Locke, Adam E, Mahajan, Anubha, Marouli, Eirini, Sivapalaratnam, Suthesh, Young, Kristin L, Alfred, Tamuno, Feitosa, Mary F, Masca, Nicholas GD, Manning, Alisa K, Medina-Gomez, Carolina, Mudgal, Poorva, Ng, Maggie CY, Reiner, Alex P, Vedantam, Sailaja, Willems, Sara M, Winkler, Thomas W, Abecasis, Gonçalo, Aben, Katja K, Alam, Dewan S, Alharthi, Sameer E, Allison, Matthew, Amouyel, Philippe, Asselbergs, Folkert W, Auer, Paul L, Balkau, Beverley, Bang, Lia E, Barroso, Inês, Bastarache, Lisa, Benn, Marianne, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bots, Michiel L, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Breen, Gerome, Brilliant, Murray H, Broer, Linda, Brumat, Marco, Burt, Amber A, Butterworth, Adam S, Campbell, Peter T, Cappellani, Stefania, Carey, David J, Catamo, Eulalia, Caulfield, Mark J, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der I, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Cocca, Massimiliano, Collins, Francis S, Cook, James P, Corley, Janie, Corominas Galbany, Jordi, Cox, Amanda J, Crosslin, David S, Cuellar-Partida, Gabriel, D’Eustacchio, Angela, Danesh, John, Davies, Gail, Bakker, Paul IW, Groot, Mark CH, Mutsert, Renée, Deary, Ian J, Dedoussis, George, Demerath, Ellen W, Heijer, Martin, Hollander, Anneke I, Ruijter, Hester M, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Drenos, Fotios, Du, Mengmeng, Dubé, Marie-Pierre, Dunning, Alison M, and Easton, Douglas F

Subjects
Genetics, Biotechnology, Nutrition, Obesity, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Cancer, Stroke, Metabolic and endocrine, Cardiovascular, Oral and gastrointestinal, Adult, Animals, Body Mass Index, Drosophila, Energy Intake, Energy Metabolism, Female, Gene Frequency, Genetic Variation, Humans, Male, Proteins, Syndrome, CHD Exome+ Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, EPIC InterAct Consortium, INTERVAL Study, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Understanding Society Scientific Group, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

Published
2018

6. Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk.

Author

Day, Felix R, Thompson, Deborah J, Helgason, Hannes, Chasman, Daniel I, Finucane, Hilary, Sulem, Patrick, Ruth, Katherine S, Whalen, Sean, Sarkar, Abhishek K, Albrecht, Eva, Altmaier, Elisabeth, Amini, Marzyeh, Barbieri, Caterina M, Boutin, Thibaud, Campbell, Archie, Demerath, Ellen, Giri, Ayush, He, Chunyan, Hottenga, Jouke J, Karlsson, Robert, Kolcic, Ivana, Loh, Po-Ru, Lunetta, Kathryn L, Mangino, Massimo, Marco, Brumat, McMahon, George, Medland, Sarah E, Nolte, Ilja M, Noordam, Raymond, Nutile, Teresa, Paternoster, Lavinia, Perjakova, Natalia, Porcu, Eleonora, Rose, Lynda M, Schraut, Katharina E, Segrè, Ayellet V, Smith, Albert V, Stolk, Lisette, Teumer, Alexander, Andrulis, Irene L, Bandinelli, Stefania, Beckmann, Matthias W, Benitez, Javier, Bergmann, Sven, Bochud, Murielle, Boerwinkle, Eric, Bojesen, Stig E, Bolla, Manjeet K, Brand, Judith S, Brauch, Hiltrud, Brenner, Hermann, Broer, Linda, Brüning, Thomas, Buring, Julie E, Campbell, Harry, Catamo, Eulalia, Chanock, Stephen, Chenevix-Trench, Georgia, Corre, Tanguy, Couch, Fergus J, Cousminer, Diana L, Cox, Angela, Crisponi, Laura, Czene, Kamila, Davey Smith, George, de Geus, Eco JCN, de Mutsert, Renée, De Vivo, Immaculata, Dennis, Joe, Devilee, Peter, Dos-Santos-Silva, Isabel, Dunning, Alison M, Eriksson, Johan G, Fasching, Peter A, Fernández-Rhodes, Lindsay, Ferrucci, Luigi, Flesch-Janys, Dieter, Franke, Lude, Gabrielson, Marike, Gandin, Ilaria, Giles, Graham G, Grallert, Harald, Gudbjartsson, Daniel F, Guénel, Pascal, Hall, Per, Hallberg, Emily, Hamann, Ute, Harris, Tamara B, Hartman, Catharina A, Heiss, Gerardo, Hooning, Maartje J, Hopper, John L, Hu, Frank, Hunter, David J, Ikram, M Arfan, Im, Hae Kyung, Järvelin, Marjo-Riitta, Joshi, Peter K, Karasik, David, and Kellis, Manolis

Subjects
LifeLines Cohort Study, InterAct Consortium, kConFab/AOCS Investigators, Endometrial Cancer Association Consortium, Ovarian Cancer Association Consortium, PRACTICAL consortium, Humans, Neoplasms, Genetic Predisposition to Disease, Ubiquitin-Protein Ligases, Intercellular Signaling Peptides and Proteins, Calcium-Binding Proteins, Ribonucleoproteins, Membrane Proteins, Body Mass Index, Risk Factors, Age Factors, Genomic Imprinting, Puberty, Menarche, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Databases, Genetic, Adolescent, Female, Male, Genome-Wide Association Study, Aging, Breast Cancer, Biotechnology, Cancer, Human Genome, Genetics, Prostate Cancer, Urologic Diseases, 2.1 Biological and endogenous factors, Aetiology, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Using 1000 Genomes Project-imputed genotype data in up to ∼370,000 women, we identify 389 independent signals (P < 5 × 10-8) for age at menarche, a milestone in female pubertal development. In Icelandic data, these signals explain ∼7.4% of the population variance in age at menarche, corresponding to ∼25% of the estimated heritability. We implicate ∼250 genes via coding variation or associated expression, demonstrating significant enrichment in neural tissues. Rare variants near the imprinted genes MKRN3 and DLK1 were identified, exhibiting large effects when paternally inherited. Mendelian randomization analyses suggest causal inverse associations, independent of body mass index (BMI), between puberty timing and risks for breast and endometrial cancers in women and prostate cancer in men. In aggregate, our findings highlight the complexity of the genetic regulation of puberty timing and support causal links with cancer susceptibility.

Published
2017

Catalog

Books, media, physical & digital resources
See catalog results