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1. LINE-1 RNA triggers matrix formation in bone cells via a PKR-mediated inflammatory response

Author

Mangiavacchi, Arianna, Morelli, Gabriele, Reppe, Sjur, Saera-Vila, Alfonso, Liu, Peng, Eggerschwiler, Benjamin, Zhang, Huoming, Bensaddek, Dalila, Casanova, Elisa A, Medina Gomez, Carolina, Prijatelj, Vid, Della Valle, Francesco, Atinbayeva, Nazerke, Izpisua Belmonte, Juan Carlos, Rivadeneira, Fernando, Cinelli, Paolo, Gautvik, Kaare Morten, and Orlando, Valerio

Published
2024
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2. Author Correction: LINE-1 RNA triggers matrix formation in bone cells via a PKR-mediated inflammatory response

Author

Mangiavacchi, Arianna, Morelli, Gabriele, Reppe, Sjur, Saera-Vila, Alfonso, Liu, Peng, Eggerschwiler, Benjamin, Zhang, Huoming, Bensaddek, Dalila, Casanova, Elisa A, Medina Gomez, Carolina, Prijatelj, Vid, Della Valle, Francesco, Atinbayeva, Nazerke, Izpisua Belmonte, Juan Carlos, Rivadeneira, Fernando, Cinelli, Paolo, Gautvik, Kaare Morten, and Orlando, Valerio

Published
2024
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4. Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

Author

Young, Kristin L, Fisher, Virginia, Deng, Xuan, Brody, Jennifer A, Graff, Misa, Lim, Elise, Lin, Bridget M, Xu, Hanfei, Amin, Najaf, An, Ping, Aslibekyan, Stella, Fohner, Alison E, Hidalgo, Bertha, Lenzini, Petra, Kraaij, Robert, Medina-Gomez, Carolina, Prokić, Ivana, Rivadeneira, Fernando, Sitlani, Colleen, Tao, Ran, van Rooij, Jeroen, Zhang, Di, Broome, Jai G, Buth, Erin J, Heavner, Benjamin D, Jain, Deepti, Smith, Albert V, Barnes, Kathleen, Boorgula, Meher Preethi, Chavan, Sameer, Darbar, Dawood, De Andrade, Mariza, Guo, Xiuqing, Haessler, Jeffrey, Irvin, Marguerite R, Kalyani, Rita R, Kardia, Sharon LR, Kooperberg, Charles, Kim, Wonji, Mathias, Rasika A, McDonald, Merry-Lynn, Mitchell, Braxton D, Peyser, Patricia A, Regan, Elizabeth A, Redline, Susan, Reiner, Alexander P, Rich, Stephen S, Rotter, Jerome I, Smith, Jennifer A, Weiss, Scott, Wiggins, Kerri L, Yanek, Lisa R, Arnett, Donna, Heard-Costa, Nancy L, Leal, Suzanne, Lin, Danyu, McKnight, Barbara, Province, Michael, van Duijn, Cornelia M, North, Kari E, Cupples, L Adrienne, and Liu, Ching-Ti

Subjects
Biological Sciences, Genetics, Clinical Research, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, Good Health and Well Being, Humans, Genome-Wide Association Study, Exome, Body Mass Index, Quantitative Trait Loci, Anthropometry, Intercellular Signaling Peptides and Proteins, Cell Cycle Proteins, body mass index, central obesity, exome sequencing, height
Abstract

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

Published
2023

5. 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

6. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Author

Ramdas, Shweta, Judd, Jonathan, Graham, Sarah E, Kanoni, Stavroula, Wang, Yuxuan, Surakka, Ida, Wenz, Brandon, Clarke, Shoa L, Chesi, Alessandra, Wells, Andrew, Bhatti, Konain Fatima, Vedantam, Sailaja, Winkler, Thomas W, Locke, Adam E, Marouli, Eirini, Zajac, Greg JM, Wu, Kuan-Han H, Ntalla, Ioanna, Hui, Qin, Klarin, Derek, Hilliard, Austin T, Wang, Zeyuan, Xue, Chao, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F, Holm, Hilma, Olafsson, Isleifur, Hwang, Mi Yeong, Han, Sohee, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M, Rasheed, Humaira, Havulinna, Aki S, Veturi, Yogasudha, Pacheco, Jennifer Allen, Rosenthal, Elisabeth A, Lingren, Todd, Feng, QiPing, Kullo, Iftikhar J, Narita, Akira, Takayama, Jun, Martin, Hilary C, Hunt, Karen A, Trivedi, Bhavi, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E, Campbell, Archie, Lin, Kuang, Millwood, Iona Y, Rasheed, Asif, Hindy, George, Faul, Jessica D, Zhao, Wei, Weir, David R, Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Choudhury, Ananyo, Sengupta, Dhriti, Mahajan, Anubha, Brown, Michael R, Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M, Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian’an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Wood, Andrew R, Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E, Chai, Jin Fang, Aadahl, Mette, Bjerregaard, Anne A, Yao, Jie, Manichaikul, Ani, Lee, Wen-Jane, Hsiung, Chao Agnes, Warren, Helen R, Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L, Goel, Anuj, and Sabater-Lleal, Maria

Subjects
Human Genome, Biotechnology, Genetics, 2.1 Biological and endogenous factors, Aetiology, Chromatin, Genome-Wide Association Study, Genomics, Humans, Lipids, Polymorphism, Single Nucleotide, Million Veterans Program, Global Lipids Genetics Consortium, complex traits, fine-mapping, functional genomics, lipid biology, post-GWAS, regulatory mechanism, variant prioritization, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Published
2022

7. A perspective on muscle phenotyping in musculoskeletal research

Author

Foessl, Ines, Ackert-Bicknell, Cheryl L., Kague, Erika, Laskou, Faidra, Jakob, Franz, Karasik, David, Obermayer-Pietsch, Barbara, Alonso, Nerea, Bjørnerem, Åshild, Brandi, Maria Luisa, Busse, Björn, Calado, Ângelo, Cebi, Alper Han, Christou, Maria, Curran, Kathleen M., Hald, Jannie Dahl, Semeraro, Maria Donatella, Douni, Eleni, Duncan, Emma L., Duran, Ivan, Formosa, Melissa M., Gabet, Yankel, Ghatan, Samuel, Gkitakou, Artemis, Hassler, Eva Maria, Högler, Wolfgang, Heino, Terhi J., Hendrickx, Gretl, Khashayar, Patricia, Kiel, Douglas P., Koromani, Fjorda, Langdahl, Bente, Lopes, Philippe, Mäkitie, Outi, Maurizi, Antonio, Medina-Gomez, Carolina, Ntzani, Evangelia, Ohlsson, Claes, Prijatelj, Vid, Rabionet, Raquel, Reppe, Sjur, Rivadeneira, Fernando, Roshchupkin, Gennady, Sharma, Neha, Søe, Kent, Styrkarsdottir, Unnur, Szulc, Pavel, Teti, Anna, Tobias, Jon, Valjevac, Amina, van de Peppel, Jeroen, van der Eerden, Bram, van Rietbergen, Bert, Zekic, Tatjana, and Zillikens, M. Carola

Published
2024
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8. European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation

Author

Budu-Aggrey, Ashley, Kilanowski, Anna, Sobczyk, Maria K., Shringarpure, Suyash S., Mitchell, Ruth, Reis, Kadri, Reigo, Anu, Mägi, Reedik, Nelis, Mari, Tanaka, Nao, Brumpton, Ben M., Thomas, Laurent F., Sole-Navais, Pol, Flatley, Christopher, Espuela-Ortiz, Antonio, Herrera-Luis, Esther, Lominchar, Jesus V. T., Bork-Jensen, Jette, Marenholz, Ingo, Arnau-Soler, Aleix, Jeong, Ayoung, Fawcett, Katherine A., Baurecht, Hansjorg, Rodriguez, Elke, Alves, Alexessander Couto, Kumar, Ashish, Sleiman, Patrick M., Chang, Xiao, Medina-Gomez, Carolina, Hu, Chen, Xu, Cheng-jian, Qi, Cancan, El-Heis, Sarah, Titcombe, Philip, Antoun, Elie, Fadista, João, Wang, Carol A., Thiering, Elisabeth, Wu, Baojun, Kress, Sara, Kothalawala, Dilini M., Kadalayil, Latha, Duan, Jiasong, Zhang, Hongmei, Hadebe, Sabelo, Hoffmann, Thomas, Jorgenson, Eric, Choquet, Hélène, Risch, Neil, Njølstad, Pål, Andreassen, Ole A., Johansson, Stefan, Almqvist, Catarina, Gong, Tong, Ullemar, Vilhelmina, Karlsson, Robert, Magnusson, Patrik K. E., Szwajda, Agnieszka, Burchard, Esteban G., Thyssen, Jacob P., Hansen, Torben, Kårhus, Line L., Dantoft, Thomas M., Jeanrenaud, Alexander C.S.N., Ghauri, Ahla, Arnold, Andreas, Homuth, Georg, Lau, Susanne, Nöthen, Markus M., Hübner, Norbert, Imboden, Medea, Visconti, Alessia, Falchi, Mario, Bataille, Veronique, Hysi, Pirro, Ballardini, Natalia, Boomsma, Dorret I., Hottenga, Jouke J., Müller-Nurasyid, Martina, Ahluwalia, Tarunveer S., Stokholm, Jakob, Chawes, Bo, Schoos, Ann-Marie M., Esplugues, Ana, Bustamante, Mariona, Raby, Benjamin, Arshad, Syed, German, Chris, Esko, Tõnu, Milani, Lili A., Metspalu, Andres, Terao, Chikashi, Abuabara, Katrina, Løset, Mari, Hveem, Kristian, Jacobsson, Bo, Pino-Yanes, Maria, Strachan, David P., Grarup, Niels, Linneberg, Allan, Lee, Young-Ae, Probst-Hensch, Nicole, Weidinger, Stephan, Jarvelin, Marjo-Riitta, Melén, Erik, Hakonarson, Hakon, Irvine, Alan D., Jarvis, Deborah, Nijsten, Tamar, Duijts, Liesbeth, Vonk, Judith M., Koppelmann, Gerard H., Godfrey, Keith M., Barton, Sheila J., Feenstra, Bjarke, Pennell, Craig E., Sly, Peter D., Holt, Patrick G., Williams, L. Keoki, Bisgaard, Hans, Bønnelykke, Klaus, Curtin, John, Simpson, Angela, Murray, Clare, Schikowski, Tamara, Bunyavanich, Supinda, Weiss, Scott T., Holloway, John W., Min, Josine L., Brown, Sara J., Standl, Marie, and Paternoster, Lavinia

Published
2023
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12. The power of genetic diversity in genome-wide association studies of lipids

Author

Graham, Sarah E, Clarke, Shoa L, Wu, Kuan-Han H, Kanoni, Stavroula, Zajac, Greg JM, Ramdas, Shweta, Surakka, Ida, Ntalla, Ioanna, Vedantam, Sailaja, Winkler, Thomas W, Locke, Adam E, Marouli, Eirini, Hwang, Mi Yeong, Han, Sohee, Narita, Akira, Choudhury, Ananyo, Bentley, Amy R, Ekoru, Kenneth, Verma, Anurag, Trivedi, Bhavi, Martin, Hilary C, Hunt, Karen A, Hui, Qin, Klarin, Derek, Zhu, Xiang, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F, Holm, Hilma, Olafsson, Isleifur, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M, Rasheed, Humaira, Ruotsalainen, Sanni E, Havulinna, Aki S, Veturi, Yogasudha, Feng, QiPing, Rosenthal, Elisabeth A, Lingren, Todd, Pacheco, Jennifer Allen, Pendergrass, Sarah A, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E, Campbell, Archie, Lin, Kuang, Millwood, Iona Y, Hindy, George, Rasheed, Asif, Faul, Jessica D, Zhao, Wei, Weir, David R, Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Mahajan, Anubha, Brown, Michael R, Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M, Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian’an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Willemsen, Gonneke, Wood, Andrew R, Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E, Chai, Jin Fang, Aadahl, Mette, Yao, Jie, Manichaikul, Ani, Warren, Helen R, Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L, Goel, Anuj, Sabater-Lleal, Maria, Noordam, Raymond, Sidore, Carlo, Fiorillo, Edoardo, McDaid, Aaron F, Marques-Vidal, Pedro, Wielscher, Matthias, Trompet, Stella, and Sattar, Naveed

Subjects
Heart Disease, Prevention, Human Genome, Genetics, Cardiovascular, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Cardiovascular Diseases, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Population Groups, VA Million Veteran Program, Global Lipids Genetics Consortium*, General Science & Technology
Abstract

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.

Published
2021

13. Large-scale association analyses identify host factors influencing human gut microbiome composition

Author

Kurilshikov, Alexander, Medina-Gomez, Carolina, Bacigalupe, Rodrigo, Radjabzadeh, Djawad, Wang, Jun, Demirkan, Ayse, Le Roy, Caroline I, Raygoza Garay, Juan Antonio, Finnicum, Casey T, Liu, Xingrong, Zhernakova, Daria V, Bonder, Marc Jan, Hansen, Tue H, Frost, Fabian, Rühlemann, Malte C, Turpin, Williams, Moon, Jee-Young, Kim, Han-Na, Lüll, Kreete, Barkan, Elad, Shah, Shiraz A, Fornage, Myriam, Szopinska-Tokov, Joanna, Wallen, Zachary D, Borisevich, Dmitrii, Agreus, Lars, Andreasson, Anna, Bang, Corinna, Bedrani, Larbi, Bell, Jordana T, Bisgaard, Hans, Boehnke, Michael, Boomsma, Dorret I, Burk, Robert D, Claringbould, Annique, Croitoru, Kenneth, Davies, Gareth E, van Duijn, Cornelia M, Duijts, Liesbeth, Falony, Gwen, Fu, Jingyuan, van der Graaf, Adriaan, Hansen, Torben, Homuth, Georg, Hughes, David A, Ijzerman, Richard G, Jackson, Matthew A, Jaddoe, Vincent WV, Joossens, Marie, Jørgensen, Torben, Keszthelyi, Daniel, Knight, Rob, Laakso, Markku, Laudes, Matthias, Launer, Lenore J, Lieb, Wolfgang, Lusis, Aldons J, Masclee, Ad AM, Moll, Henriette A, Mujagic, Zlatan, Qibin, Qi, Rothschild, Daphna, Shin, Hocheol, Sørensen, Søren J, Steves, Claire J, Thorsen, Jonathan, Timpson, Nicholas J, Tito, Raul Y, Vieira-Silva, Sara, Völker, Uwe, Völzke, Henry, Võsa, Urmo, Wade, Kaitlin H, Walter, Susanna, Watanabe, Kyoko, Weiss, Stefan, Weiss, Frank U, Weissbrod, Omer, Westra, Harm-Jan, Willemsen, Gonneke, Payami, Haydeh, Jonkers, Daisy MAE, Arias Vasquez, Alejandro, de Geus, Eco JC, Meyer, Katie A, Stokholm, Jakob, Segal, Eran, Org, Elin, Wijmenga, Cisca, Kim, Hyung-Lae, Kaplan, Robert C, Spector, Tim D, Uitterlinden, Andre G, Rivadeneira, Fernando, Franke, Andre, Lerch, Markus M, Franke, Lude, Sanna, Serena, D’Amato, Mauro, and Pedersen, Oluf

Subjects
Microbiology, Biological Sciences, Genetics, Human Genome, Biotechnology, Clinical Research, Digestive Diseases, Nutrition, 2.1 Biological and endogenous factors, Aetiology, Oral and gastrointestinal, Adolescent, Adult, Bifidobacterium, Child, Child, Preschool, Cohort Studies, Female, Gastrointestinal Microbiome, Genetic Variation, Genome-Wide Association Study, Humans, Lactase, Linkage Disequilibrium, Male, Mendelian Randomization Analysis, Metabolism, Quantitative Trait Loci, RNA, Ribosomal, 16S, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P

Published
2021

14. Genome-wide association study identifies 48 common genetic variants associated with handedness

Author

Cuellar-Partida, Gabriel, Tung, Joyce Y, Eriksson, Nicholas, Albrecht, Eva, Aliev, Fazil, Andreassen, Ole A, Barroso, Inês, Beckmann, Jacques S, Boks, Marco P, Boomsma, Dorret I, Boyd, Heather A, Breteler, Monique MB, Campbell, Harry, Chasman, Daniel I, Cherkas, Lynn F, Davies, Gail, de Geus, Eco JC, Deary, Ian J, Deloukas, Panos, Dick, Danielle M, Duffy, David L, Eriksson, Johan G, Esko, Tõnu, Feenstra, Bjarke, Geller, Frank, Gieger, Christian, Giegling, Ina, Gordon, Scott D, Han, Jiali, Hansen, Thomas F, Hartmann, Annette M, Hayward, Caroline, Heikkilä, Kauko, Hicks, Andrew A, Hirschhorn, Joel N, Hottenga, Jouke-Jan, Huffman, Jennifer E, Hwang, Liang-Dar, Ikram, M Arfan, Kaprio, Jaakko, Kemp, John P, Khaw, Kay-Tee, Klopp, Norman, Konte, Bettina, Kutalik, Zoltan, Lahti, Jari, Li, Xin, Loos, Ruth JF, Luciano, Michelle, Magnusson, Sigurdur H, Mangino, Massimo, Marques-Vidal, Pedro, Martin, Nicholas G, McArdle, Wendy L, McCarthy, Mark I, Medina-Gomez, Carolina, Melbye, Mads, Melville, Scott A, Metspalu, Andres, Milani, Lili, Mooser, Vincent, Nelis, Mari, Nyholt, Dale R, O’Connell, Kevin S, Ophoff, Roel A, Palmer, Cameron, Palotie, Aarno, Palviainen, Teemu, Pare, Guillaume, Paternoster, Lavinia, Peltonen, Leena, Penninx, Brenda WJH, Polasek, Ozren, Pramstaller, Peter P, Prokopenko, Inga, Raikkonen, Katri, Ripatti, Samuli, Rivadeneira, Fernando, Rudan, Igor, Rujescu, Dan, Smit, Johannes H, Smith, George Davey, Smoller, Jordan W, Soranzo, Nicole, Spector, Tim D, Pourcain, Beate St, Starr, John M, Stefánsson, Hreinn, Steinberg, Stacy, Teder-Laving, Maris, Thorleifsson, Gudmar, Stefánsson, Kári, Timpson, Nicholas J, Uitterlinden, André G, van Duijn, Cornelia M, van Rooij, Frank JA, Vink, Jaqueline M, Vollenweider, Peter, Vuoksimaa, Eero, and Waeber, Gérard

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Human Genome, Mental Health, Brain Disorders, Neurosciences, Schizophrenia, Genetics, 2.1 Biological and endogenous factors, Aetiology, Mental health, Adult, Aged, Female, Functional Laterality, Gene Frequency, Genetic Loci, Genetic Variation, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Male, Middle Aged, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Sex Factors, Biomedical and clinical sciences, Health sciences
Abstract

Handedness has been extensively studied because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and the International Handedness Consortium, we conducted a genome-wide association meta-analysis of handedness (N = 1,766,671). We found 41 loci associated (P

Published
2021

15. Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity

Author

Yaghootkar, Hanieh, Zhang, Yiying, Spracklen, Cassandra N, Karaderi, Tugce, Huang, Lam Opal, Bradfield, Jonathan, Schurmann, Claudia, Fine, Rebecca S, Preuss, Michael H, Kutalik, Zoltan, Wittemans, Laura BL, Lu, Yingchang, Metz, Sophia, Willems, Sara M, Li-Gao, Ruifang, Grarup, Niels, Wang, Shuai, Molnos, Sophie, Sandoval-Zárate, América A, Nalls, Mike A, Lange, Leslie A, Haesser, Jeffrey, Guo, Xiuqing, Lyytikäinen, Leo-Pekka, Feitosa, Mary F, Sitlani, Colleen M, Venturini, Cristina, Mahajan, Anubha, Kacprowski, Tim, Wang, Carol A, Chasman, Daniel I, Amin, Najaf, Broer, Linda, Robertson, Neil, Young, Kristin L, Allison, Matthew, Auer, Paul L, Blüher, Matthias, Borja, Judith B, Bork-Jensen, Jette, Carrasquilla, Germán D, Christofidou, Paraskevi, Demirkan, Ayse, Doege, Claudia A, Garcia, Melissa E, Graff, Mariaelisa, Guo, Kaiying, Hakonarson, Hakon, Hong, Jaeyoung, Ida Chen, Yii-Der, Jackson, Rebecca, Jakupović, Hermina, Jousilahti, Pekka, Justice, Anne E, Kähönen, Mika, Kizer, Jorge R, Kriebel, Jennifer, LeDuc, Charles A, Li, Jin, Lind, Lars, Luan, Jian'an, Mackey, David A, Mangino, Massimo, Männistö, Satu, Martin Carli, Jayne F, Medina-Gomez, Carolina, Mook-Kanamori, Dennis O, Morris, Andrew P, de Mutsert, Renée, Nauck, Matthias, Prokic, Ivana, Pennell, Craig E, Pradhan, Arund D, Psaty, Bruce M, Raitakari, Olli T, Scott, Robert A, Skaaby, Tea, Strauch, Konstantin, Taylor, Kent D, Teumer, Alexander, Uitterlinden, Andre G, Wu, Ying, Yao, Jie, Walker, Mark, North, Kari E, Kovacs, Peter, Ikram, M Arfan, van Duijn, Cornelia M, Ridker, Paul M, Lye, Stephen, Homuth, Georg, Ingelsson, Erik, Spector, Tim D, McKnight, Barbara, Province, Michael A, Lehtimäki, Terho, Adair, Linda S, Rotter, Jerome I, Reiner, Alexander P, and Wilson, James G

Subjects
Genetics, Nutrition, Prevention, Obesity, Aetiology, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Metabolic and endocrine, Cardiovascular, Stroke, Cancer, Adiposity, Gene Expression Regulation, Developmental, Genetic Variation, Genotype, Humans, Leptin, Models, Molecular, Protein Conformation, Racial Groups, Medical and Health Sciences, Endocrinology & Metabolism
Abstract

Leptin influences food intake by informing the brain about the status of body fat stores. Rare LEP mutations associated with congenital leptin deficiency cause severe early-onset obesity that can be mitigated by administering leptin. However, the role of genetic regulation of leptin in polygenic obesity remains poorly understood. We performed an exome-based analysis in up to 57,232 individuals of diverse ancestries to identify genetic variants that influence adiposity-adjusted leptin concentrations. We identify five novel variants, including four missense variants, in LEP, ZNF800, KLHL31, and ACTL9, and one intergenic variant near KLF14. The missense variant Val94Met (rs17151919) in LEP was common in individuals of African ancestry only, and its association with lower leptin concentrations was specific to this ancestry (P = 2 × 10-16, n = 3,901). Using in vitro analyses, we show that the Met94 allele decreases leptin secretion. We also show that the Met94 allele is associated with higher BMI in young African-ancestry children but not in adults, suggesting that leptin regulates early adiposity.

Published
2020

17. Associations of autozygosity with a broad range of human phenotypes.

Author

Clark, David W, Okada, Yukinori, Moore, Kristjan HS, Mason, Dan, Pirastu, Nicola, Gandin, Ilaria, Mattsson, Hannele, Barnes, Catriona LK, Lin, Kuang, Zhao, Jing Hua, Deelen, Patrick, Rohde, Rebecca, Schurmann, Claudia, Guo, Xiuqing, Giulianini, Franco, Zhang, Weihua, Medina-Gomez, Carolina, Karlsson, Robert, Bao, Yanchun, Bartz, Traci M, Baumbach, Clemens, Biino, Ginevra, Bixley, Matthew J, Brumat, Marco, Chai, Jin-Fang, Corre, Tanguy, Cousminer, Diana L, Dekker, Annelot M, Eccles, David A, van Eijk, Kristel R, Fuchsberger, Christian, Gao, He, Germain, Marine, Gordon, Scott D, de Haan, Hugoline G, Harris, Sarah E, Hofer, Edith, Huerta-Chagoya, Alicia, Igartua, Catherine, Jansen, Iris E, Jia, Yucheng, Kacprowski, Tim, Karlsson, Torgny, Kleber, Marcus E, Li, Shengchao Alfred, Li-Gao, Ruifang, Mahajan, Anubha, Matsuda, Koichi, Meidtner, Karina, Meng, Weihua, Montasser, May E, van der Most, Peter J, Munz, Matthias, Nutile, Teresa, Palviainen, Teemu, Prasad, Gauri, Prasad, Rashmi B, Priyanka, Tallapragada Divya Sri, Rizzi, Federica, Salvi, Erika, Sapkota, Bishwa R, Shriner, Daniel, Skotte, Line, Smart, Melissa C, Smith, Albert Vernon, van der Spek, Ashley, Spracklen, Cassandra N, Strawbridge, Rona J, Tajuddin, Salman M, Trompet, Stella, Turman, Constance, Verweij, Niek, Viberti, Clara, Wang, Lihua, Warren, Helen R, Wootton, Robyn E, Yanek, Lisa R, Yao, Jie, Yousri, Noha A, Zhao, Wei, Adeyemo, Adebowale A, Afaq, Saima, Aguilar-Salinas, Carlos Alberto, Akiyama, Masato, Albert, Matthew L, Allison, Matthew A, Alver, Maris, Aung, Tin, Azizi, Fereidoun, Bentley, Amy R, Boeing, Heiner, Boerwinkle, Eric, Borja, Judith B, de Borst, Gert J, Bottinger, Erwin P, Broer, Linda, Campbell, Harry, Chanock, Stephen, Chee, Miao-Li, and Chen, Guanjie

Subjects
Humans, Body Size, Risk-Taking, Cognition, Health Status, Consanguinity, Fertility, Haplotypes, Homozygote, Alleles, Inbreeding Depression
Abstract

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p

Published
2019

18. Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology

Author

Spracklen, Cassandra N, Karaderi, Tugce, Yaghootkar, Hanieh, Schurmann, Claudia, Fine, Rebecca S, Kutalik, Zoltan, Preuss, Michael H, Lu, Yingchang, Wittemans, Laura BL, Adair, Linda S, Allison, Matthew, Amin, Najaf, Auer, Paul L, Bartz, Traci M, Blüher, Matthias, Boehnke, Michael, Borja, Judith B, Bork-Jensen, Jette, Broer, Linda, Chasman, Daniel I, Chen, Yii-Der Ida, Chirstofidou, Paraskevi, Demirkan, Ayse, van Duijn, Cornelia M, Feitosa, Mary F, Garcia, Melissa E, Graff, Mariaelisa, Grallert, Harald, Grarup, Niels, Guo, Xiuqing, Haesser, Jeffrey, Hansen, Torben, Harris, Tamara B, Highland, Heather M, Hong, Jaeyoung, Ikram, M Arfan, Ingelsson, Erik, Jackson, Rebecca, Jousilahti, Pekka, Kähönen, Mika, Kizer, Jorge R, Kovacs, Peter, Kriebel, Jennifer, Laakso, Markku, Lange, Leslie A, Lehtimäki, Terho, Li, Jin, Li-Gao, Ruifang, Lind, Lars, Luan, Jian'an, Lyytikäinen, Leo-Pekka, MacGregor, Stuart, Mackey, David A, Mahajan, Anubha, Mangino, Massimo, Männistö, Satu, McCarthy, Mark I, McKnight, Barbara, Medina-Gomez, Carolina, Meigs, James B, Molnos, Sophie, Mook-Kanamori, Dennis, Morris, Andrew P, de Mutsert, Renee, Nalls, Mike A, Nedeljkovic, Ivana, North, Kari E, Pennell, Craig E, Pradhan, Aruna D, Province, Michael A, Raitakari, Olli T, Raulerson, Chelsea K, Reiner, Alex P, Ridker, Paul M, Ripatti, Samuli, Roberston, Neil, Rotter, Jerome I, Salomaa, Veikko, Sandoval-Zárate, America A, Sitlani, Colleen M, Spector, Tim D, Strauch, Konstantin, Stumvoll, Michael, Taylor, Kent D, Thuesen, Betina, Tönjes, Anke, Uitterlinden, Andre G, Venturini, Cristina, Walker, Mark, Wang, Carol A, Wang, Shuai, Wareham, Nicholas J, Willems, Sara M, Willems van Dijk, Ko, Wilson, James G, Wu, Ying, Yao, Jie, Young, Kristin L, Langenberg, Claudia, and Frayling, Timothy M

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Nutrition, Obesity, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Cardiovascular, Adiponectin, Adipose Tissue, Adolescent, Adult, Black or African American, Aged, Aged, 80 and over, Exome, Female, Genetic Predisposition to Disease, Hispanic or Latino, Humans, Lipids, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, White People, Young Adult, adiponectin, cardio metabolic traits, exome, genetics, genome-wide association study, lipids, obesity, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p < 2 × 10-7). Comparison of exome array variants to regional linkage disequilibrium (LD) patterns and prior genome-wide association study (GWAS) results detected candidate variants (r2 > .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p < 1 × 10-4) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.

Published
2019

19. Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors

Author

Warrington, Nicole M, Beaumont, Robin N, Horikoshi, Momoko, Day, Felix R, Helgeland, Øyvind, Laurin, Charles, Bacelis, Jonas, Peng, Shouneng, Hao, Ke, Feenstra, Bjarke, Wood, Andrew R, Mahajan, Anubha, Tyrrell, Jessica, Robertson, Neil R, Rayner, N William, Qiao, Zhen, Moen, Gunn-Helen, Vaudel, Marc, Marsit, Carmen J, Chen, Jia, Nodzenski, Michael, Schnurr, Theresia M, Zafarmand, Mohammad H, Bradfield, Jonathan P, Grarup, Niels, Kooijman, Marjolein N, Li-Gao, Ruifang, Geller, Frank, Ahluwalia, Tarunveer S, Paternoster, Lavinia, Rueedi, Rico, Huikari, Ville, Hottenga, Jouke-Jan, Lyytikäinen, Leo-Pekka, Cavadino, Alana, Metrustry, Sarah, Cousminer, Diana L, Wu, Ying, Thiering, Elisabeth, Wang, Carol A, Have, Christian T, Vilor-Tejedor, Natalia, Joshi, Peter K, Painter, Jodie N, Ntalla, Ioanna, Myhre, Ronny, Pitkänen, Niina, van Leeuwen, Elisabeth M, Joro, Raimo, Lagou, Vasiliki, Richmond, Rebecca C, Espinosa, Ana, Barton, Sheila J, Inskip, Hazel M, Holloway, John W, Santa-Marina, Loreto, Estivill, Xavier, Ang, Wei, Marsh, Julie A, Reichetzeder, Christoph, Marullo, Letizia, Hocher, Berthold, Lunetta, Kathryn L, Murabito, Joanne M, Relton, Caroline L, Kogevinas, Manolis, Chatzi, Leda, Allard, Catherine, Bouchard, Luigi, Hivert, Marie-France, Zhang, Ge, Muglia, Louis J, Heikkinen, Jani, Morgen, Camilla S, van Kampen, Antoine HC, van Schaik, Barbera DC, Mentch, Frank D, Langenberg, Claudia, Luan, Jian’an, Scott, Robert A, Zhao, Jing Hua, Hemani, Gibran, Ring, Susan M, Bennett, Amanda J, Gaulton, Kyle J, Fernandez-Tajes, Juan, van Zuydam, Natalie R, Medina-Gomez, Carolina, de Haan, Hugoline G, Rosendaal, Frits R, Kutalik, Zoltán, Marques-Vidal, Pedro, Das, Shikta, Willemsen, Gonneke, Mbarek, Hamdi, Müller-Nurasyid, Martina, Standl, Marie, Appel, Emil VR, Fonvig, Cilius E, and Trier, Caecilie

Subjects
Biological Sciences, Genetics, Cardiovascular, Perinatal Period - Conditions Originating in Perinatal Period, Nutrition, Prevention, Preterm, Low Birth Weight and Health of the Newborn, Pediatric, Obesity, Conditions Affecting the Embryonic and Fetal Periods, Infant Mortality, 2.1 Biological and endogenous factors, Aetiology, Reproductive health and childbirth, Good Health and Well Being, Adult, Birth Weight, Blood Pressure, Body Height, Diabetes Mellitus, Type 2, Female, Fetal Development, Genetic Predisposition to Disease, Genome-Wide Association Study, Heart Diseases, Humans, Infant, Newborn, Male, Maternal Inheritance, Maternal-Fetal Exchange, Metabolic Diseases, Models, Genetic, Polymorphism, Single Nucleotide, Pregnancy, Risk Factors, EGG Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.

Published
2019

20. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution

Author

Justice, Anne E, Karaderi, Tugce, Highland, Heather M, Young, Kristin L, Graff, Mariaelisa, Lu, Yingchang, Turcot, Valérie, Auer, Paul L, Fine, Rebecca S, Guo, Xiuqing, Schurmann, Claudia, Lempradl, Adelheid, Marouli, Eirini, Mahajan, Anubha, Winkler, Thomas W, Locke, Adam E, Medina-Gomez, Carolina, Esko, Tõnu, Vedantam, Sailaja, Giri, Ayush, Lo, Ken Sin, Alfred, Tamuno, Mudgal, Poorva, Ng, Maggie CY, Heard-Costa, Nancy L, Feitosa, Mary F, Manning, Alisa K, Willems, Sara M, Sivapalaratnam, Suthesh, Abecasis, Goncalo, Alam, Dewan S, Allison, Matthew, Amouyel, Philippe, Arzumanyan, Zorayr, Balkau, Beverley, Bastarache, Lisa, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Broer, Linda, Burt, Amber A, Butterworth, Adam S, Caulfield, Mark J, Cesana, Giancarlo, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der Ida, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Collins, Francis S, Cook, James P, Cox, Amanda J, Crosslin, David S, Danesh, John, de Bakker, Paul IW, Denus, Simon de, Mutsert, Renée de, Dedoussis, George, Demerath, Ellen W, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Dörr, Marcus, Drenos, Fotios, Dubé, Marie-Pierre, Dunning, Alison M, Easton, Douglas F, Elliott, Paul, Evangelou, Evangelos, Farmaki, Aliki-Eleni, Feng, Shuang, Ferrannini, Ele, Ferrieres, Jean, Florez, Jose C, Fornage, Myriam, Fox, Caroline S, Franks, Paul W, Friedrich, Nele, Gan, Wei, Gandin, Ilaria, Gasparini, Paolo, Giedraitis, Vilmantas, Girotto, Giorgia, Gorski, Mathias, Grallert, Harald, Grarup, Niels, Grove, Megan L, Gustafsson, Stefan, Haessler, Jeff, Hansen, Torben, and Hattersley, Andrew T

Subjects
Clinical Research, Obesity, Genetics, Nutrition, Prevention, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Cardiovascular, Animals, Body Fat Distribution, Body Mass Index, Case-Control Studies, Drosophila, Exome, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Homeostasis, Humans, Lipids, Male, Proteins, Risk Factors, Waist-Hip Ratio, CHD Exome+ Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, InterAct, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF

Published
2019

21. Disentangling the genetics of lean mass.

Author

Karasik, David, Zillikens, M, Hsu, Yi-Hsiang, Aghdassi, Ali, Akesson, Kristina, Amin, Najaf, Barroso, Inês, Bennett, David, Bertram, Lars, Bochud, Murielle, Borecki, Ingrid, Broer, Linda, Buchman, Aron, Byberg, Liisa, Campbell, Harry, Campos-Obando, Natalia, Cauley, Jane, Cawthon, Peggy, Chambers, John, Chen, Zhao, Cho, Nam, Choi, Hyung, Chou, Wen-Chi, Cummings, Steven, de Groot, Lisette, De Jager, Phillip, Demuth, Ilja, Diatchenko, Luda, Econs, Michael, Eiriksdottir, Gudny, Enneman, Anke, Eriksson, Joel, Eriksson, Johan, Estrada, Karol, Evans, Daniel, Feitosa, Mary, Fu, Mao, Gieger, Christian, Grallert, Harald, Gudnason, Vilmundur, Lenore, Launer, Hayward, Caroline, Hofman, Albert, Homuth, Georg, Huffman, Kim, Husted, Lise, Illig, Thomas, Ingelsson, Erik, Ittermann, Till, Jansson, John-Olov, Johnson, Toby, Biffar, Reiner, Jordan, Joanne, Jula, Antti, Karlsson, Magnus, Khaw, Kay-Tee, Kilpeläinen, Tuomas, Klopp, Norman, Kloth, Jacqueline, Koller, Daniel, Kooner, Jaspal, Kraus, William, Kritchevsky, Stephen, Kutalik, Zoltán, Kuulasmaa, Teemu, Kuusisto, Johanna, Laakso, Markku, Lahti, Jari, Langdahl, Bente, Lerch, Markus, Lewis, Joshua, Lill, Christina, Lind, Lars, Lindgren, Cecilia, Liu, Yongmei, Livshits, Gregory, Ljunggren, Östen, Loos, Ruth, Lorentzon, Mattias, Luan, Jianan, Luben, Robert, Malkin, Ida, McGuigan, Fiona, Medina-Gomez, Carolina, Meitinger, Thomas, Melhus, Håkan, Mellström, Dan, Michaëlsson, Karl, Mitchell, Braxton, Morris, Andrew, Mosekilde, Leif, Nethander, Maria, Newman, Anne, OConnell, Jeffery, Oostra, Ben, Orwoll, Eric, Palotie, Aarno, Peacock, Munro, Perola, Markus, and Peters, Annette

Subjects
ADAMTS Proteins, Absorptiometry, Photon, Adipose Tissue, Adolescent, Adult, Aged, Aged, 80 and over, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Body Composition, Body Fluid Compartments, Electric Impedance, Extracellular Matrix Proteins, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Muscle, Skeletal, Phenotype, Polymorphism, Single Nucleotide, RNA-Binding Proteins, Receptor, Melanocortin, Type 4, Versicans, White People, Young Adult
Abstract

BACKGROUND: Lean body mass (LM) plays an important role in mobility and metabolic function. We previously identified five loci associated with LM adjusted for fat mass in kilograms. Such an adjustment may reduce the power to identify genetic signals having an association with both lean mass and fat mass. OBJECTIVES: To determine the impact of different fat mass adjustments on genetic architecture of LM and identify additional LM loci. METHODS: We performed genome-wide association analyses for whole-body LM (20 cohorts of European ancestry with n = 38,292) measured using dual-energy X-ray absorptiometry) or bioelectrical impedance analysis, adjusted for sex, age, age2, and height with or without fat mass adjustments (Model 1 no fat adjustment; Model 2 adjustment for fat mass as a percentage of body mass; Model 3 adjustment for fat mass in kilograms). RESULTS: Seven single-nucleotide polymorphisms (SNPs) in separate loci, including one novel LM locus (TNRC6B), were successfully replicated in an additional 47,227 individuals from 29 cohorts. Based on the strengths of the associations in Model 1 vs Model 3, we divided the LM loci into those with an effect on both lean mass and fat mass in the same direction and refer to those as sumo wrestler loci (FTO and MC4R). In contrast, loci with an impact specifically on LM were termed body builder loci (VCAN and ADAMTSL3). Using existing available genome-wide association study databases, LM increasing alleles of SNPs in sumo wrestler loci were associated with an adverse metabolic profile, whereas LM increasing alleles of SNPs in body builder loci were associated with metabolic protection. CONCLUSIONS: In conclusion, we identified one novel LM locus (TNRC6B). Our results suggest that a genetically determined increase in lean mass might exert either harmful or protective effects on metabolic traits, depending on its relation to fat mass.

Published
2019

22. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Author

Kanoni, Stavroula, Graham, Sarah E., Wang, Yuxuan, Surakka, Ida, Ramdas, Shweta, Zhu, Xiang, Clarke, Shoa L., Bhatti, Konain Fatima, Vedantam, Sailaja, Winkler, Thomas W., Locke, Adam E., Marouli, Eirini, Zajac, Greg J. M., Wu, Kuan-Han H., Ntalla, Ioanna, Hui, Qin, Klarin, Derek, Hilliard, Austin T., Wang, Zeyuan, Xue, Chao, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F., Holm, Hilma, Olafsson, Isleifur, Hwang, Mi Yeong, Han, Sohee, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M., Rasheed, Humaira, Havulinna, Aki S., Veturi, Yogasudha, Pacheco, Jennifer Allen, Rosenthal, Elisabeth A., Lingren, Todd, Feng, QiPing, Kullo, Iftikhar J., Narita, Akira, Takayama, Jun, Martin, Hilary C., Hunt, Karen A., Trivedi, Bhavi, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E., Campbell, Archie, Lin, Kuang, Millwood, Iona Y., Rasheed, Asif, Hindy, George, Faul, Jessica D., Zhao, Wei, Weir, David R., Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Choudhury, Ananyo, Sengupta, Dhriti, Mahajan, Anubha, Brown, Michael R., Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M., Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian’an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Wood, Andrew R., Ji, Yingji, Gao, Zishan, Haworth, Simon, Yousri, Noha A., Mitchell, Ruth E., Chai, Jin Fang, Aadahl, Mette, Bjerregaard, Anne A., Yao, Jie, Manichaikul, Ani, Hwu, Chii-Min, Hung, Yi-Jen, Warren, Helen R., Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L., Goel, Anuj, Sabater-Lleal, Maria, Noordam, Raymond, Mauro, Pala, Matteo, Floris, McDaid, Aaron F., Marques-Vidal, Pedro, Wielscher, Matthias, Trompet, Stella, Sattar, Naveed, Møllehave, Line T., Munz, Matthias, Zeng, Lingyao, Huang, Jianfeng, Yang, Bin, Poveda, Alaitz, Kurbasic, Azra, Lamina, Claudia, Forer, Lukas, Scholz, Markus, Galesloot, Tessel E., Bradfield, Jonathan P., Ruotsalainen, Sanni E., Daw, EWarwick, Zmuda, Joseph M., Mitchell, Jonathan S., Fuchsberger, Christian, Christensen, Henry, Brody, Jennifer A., Vazquez-Moreno, Miguel, Feitosa, Mary F., Wojczynski, Mary K., Wang, Zhe, Preuss, Michael H., Mangino, Massimo, Christofidou, Paraskevi, Verweij, Niek, Benjamins, Jan W., Engmann, Jorgen, Tsao, Noah L., Verma, Anurag, Slieker, Roderick C., Lo, Ken Sin, Zilhao, Nuno R., Le, Phuong, Kleber, Marcus E., Delgado, Graciela E., Huo, Shaofeng, Ikeda, Daisuke D., Iha, Hiroyuki, Yang, Jian, Liu, Jun, Demirkan, Ayşe, Leonard, Hampton L., Marten, Jonathan, Frank, Mirjam, Schmidt, Börge, Smyth, Laura J., Cañadas-Garre, Marisa, Wang, Chaolong, Nakatochi, Masahiro, Wong, Andrew, Hutri-Kähönen, Nina, Sim, Xueling, Xia, Rui, Huerta-Chagoya, Alicia, Fernandez-Lopez, Juan Carlos, Lyssenko, Valeriya, Nongmaithem, Suraj S., Bayyana, Swati, Stringham, Heather M., Irvin, Marguerite R., Oldmeadow, Christopher, Kim, Han-Na, Ryu, Seungho, Timmers, Paul R. H. J., Arbeeva, Liubov, Dorajoo, Rajkumar, Lange, Leslie A., Prasad, Gauri, Lorés-Motta, Laura, Pauper, Marc, Long, Jirong, Li, Xiaohui, Theusch, Elizabeth, Takeuchi, Fumihiko, Spracklen, Cassandra N., Loukola, Anu, Bollepalli, Sailalitha, Warner, Sophie C., Wang, Ya Xing, Wei, Wen B., Nutile, Teresa, Ruggiero, Daniela, Sung, Yun Ju, Chen, Shufeng, Liu, Fangchao, Yang, Jingyun, Kentistou, Katherine A., Banas, Bernhard, Nardone, Giuseppe Giovanni, Meidtner, Karina, Bielak, Lawrence F., Smith, Jennifer A., Hebbar, Prashantha, Farmaki, Aliki-Eleni, Hofer, Edith, Lin, Maoxuan, Concas, Maria Pina, Vaccargiu, Simona, van der Most, Peter J., Pitkänen, Niina, Cade, Brian E., van der Laan, Sander W., Chitrala, Kumaraswamy Naidu, Weiss, Stefan, Bentley, Amy R., Doumatey, Ayo P., Adeyemo, Adebowale A., Lee, Jong Young, Petersen, Eva R. B., Nielsen, Aneta A., Choi, Hyeok Sun, Nethander, Maria, Freitag-Wolf, Sandra, Southam, Lorraine, Rayner, Nigel W., Wang, Carol A., Lin, Shih-Yi, Wang, Jun-Sing, Couture, Christian, Lyytikäinen, Leo-Pekka, Nikus, Kjell, Cuellar-Partida, Gabriel, Vestergaard, Henrik, Hidalgo, Bertha, Giannakopoulou, Olga, Cai, Qiuyin, Obura, Morgan O., van Setten, Jessica, Li, Xiaoyin, Liang, Jingjing, Tang, Hua, Terzikhan, Natalie, Shin, Jae Hun, Jackson, Rebecca D., Reiner, Alexander P., Martin, Lisa Warsinger, Chen, Zhengming, Li, Liming, Kawaguchi, Takahisa, Thiery, Joachim, Bis, Joshua C., Launer, Lenore J., Li, Huaixing, Nalls, Mike A., Raitakari, Olli T., Ichihara, Sahoko, Wild, Sarah H., Nelson, Christopher P., Campbell, Harry, Jäger, Susanne, Nabika, Toru, Al-Mulla, Fahd, Niinikoski, Harri, Braund, Peter S., Kolcic, Ivana, Kovacs, Peter, Giardoglou, Tota, Katsuya, Tomohiro, de Kleijn, Dominique, de Borst, Gert J., Kim, Eung Kweon, Adams, Hieab H. H., Ikram, M. Arfan, Zhu, Xiaofeng, Asselbergs, Folkert W., Kraaijeveld, Adriaan O., Beulens, Joline W. J., Shu, Xiao-Ou, Rallidis, Loukianos S., Pedersen, Oluf, Hansen, Torben, Mitchell, Paul, Hewitt, Alex W., Kähönen, Mika, Pérusse, Louis, Bouchard, Claude, Tönjes, Anke, Chen, Yii-Der Ida, Pennell, Craig E., Mori, Trevor A., Lieb, Wolfgang, Franke, Andre, Ohlsson, Claes, Mellström, Dan, Cho, Yoon Shin, Lee, Hyejin, Yuan, Jian-Min, Koh, Woon-Puay, Rhee, Sang Youl, Woo, Jeong-Taek, Heid, Iris M., Stark, Klaus J., Zimmermann, Martina E., Völzke, Henry, Homuth, Georg, Evans, Michele K., Zonderman, Alan B., Polasek, Ozren, Pasterkamp, Gerard, Hoefer, Imo E., Redline, Susan, Pahkala, Katja, Oldehinkel, Albertine J., Snieder, Harold, Biino, Ginevra, Schmidt, Reinhold, Schmidt, Helena, Bandinelli, Stefania, Dedoussis, George, Thanaraj, Thangavel Alphonse, Kardia, Sharon L. R., Peyser, Patricia A., Kato, Norihiro, Schulze, Matthias B., Girotto, Giorgia, Böger, Carsten A., Jung, Bettina, Joshi, Peter K., Bennett, David A., De Jager, Philip L., Lu, Xiangfeng, Mamakou, Vasiliki, Brown, Morris, Caulfield, Mark J., Munroe, Patricia B., Guo, Xiuqing, Ciullo, Marina, Jonas, Jost B., Samani, Nilesh J., Kaprio, Jaakko, Pajukanta, Päivi, Tusié-Luna, Teresa, Aguilar-Salinas, Carlos A., Adair, Linda S., Bechayda, Sonny Augustin, de Silva, H. Janaka, Wickremasinghe, Ananda R., Krauss, Ronald M., Wu, Jer-Yuarn, Zheng, Wei, Hollander, Anneke Iden, Bharadwaj, Dwaipayan, Correa, Adolfo, Wilson, James G., Lind, Lars, Heng, Chew-Kiat, Nelson, Amanda E., Golightly, Yvonne M., Wilson, James F., Penninx, Brenda, Kim, Hyung-Lae, Attia, John, Scott, Rodney J., Rao, D. C., Arnett, Donna K., Hunt, Steven C., Walker, Mark, Koistinen, Heikki A., Chandak, Giriraj R., Mercader, Josep M., Costanzo, Maria C., Jang, Dongkeun, Burtt, Noël P., Villalpando, Clicerio Gonzalez, Orozco, Lorena, Fornage, Myriam, Tai, EShyong, van Dam, Rob M., Lehtimäki, Terho, Chaturvedi, Nish, Yokota, Mitsuhiro, Liu, Jianjun, Reilly, Dermot F., McKnight, Amy Jayne, Kee, Frank, Jöckel, Karl-Heinz, McCarthy, Mark I., Palmer, Colin N. A., Vitart, Veronique, Hayward, Caroline, Simonsick, Eleanor, van Duijn, Cornelia M., Jin, Zi-Bing, Qu, Jia, Hishigaki, Haretsugu, Lin, Xu, März, Winfried, Gudnason, Vilmundur, Tardif, Jean-Claude, Lettre, Guillaume, Hart, Leen M.‘t, Elders, Petra J. M., Damrauer, Scott M., Kumari, Meena, Kivimaki, Mika, van der Harst, Pim, Spector, Tim D., Loos, Ruth J. F., Province, Michael A., Parra, Esteban J., Cruz, Miguel, Psaty, Bruce M., Brandslund, Ivan, Pramstaller, Peter P., Rotimi, Charles N., Christensen, Kaare, Ripatti, Samuli, Widén, Elisabeth, Hakonarson, Hakon, Grant, Struan F. A., Kiemeney, Lambertus A. L. M., de Graaf, Jacqueline, Loeffler, Markus, Kronenberg, Florian, Gu, Dongfeng, Erdmann, Jeanette, Schunkert, Heribert, Franks, Paul W., Linneberg, Allan, Jukema, J. Wouter, Khera, Amit V., Männikkö, Minna, Jarvelin, Marjo-Riitta, Kutalik, Zoltan, Francesco, Cucca, Mook-Kanamori, Dennis O., van Dijk, Ko Willems, Watkins, Hugh, Strachan, David P., Grarup, Niels, Sever, Peter, Poulter, Neil, Chuang, Lee-Ming, Rotter, Jerome I., Dantoft, Thomas M., Karpe, Fredrik, Neville, Matt J., Timpson, Nicholas J., Cheng, Ching-Yu, Wong, Tien-Yin, Khor, Chiea Chuen, Li, Hengtong, Sabanayagam, Charumathi, Peters, Annette, Gieger, Christian, Hattersley, Andrew T., Pedersen, Nancy L., Magnusson, Patrik K. E., Boomsma, Dorret I., Willemsen, Allegonda H. M., Cupples, LAdrienne, van Meurs, Joyce B. J., Ghanbari, Mohsen, Gordon-Larsen, Penny, Huang, Wei, Kim, Young Jin, Tabara, Yasuharu, Wareham, Nicholas J., Langenberg, Claudia, Zeggini, Eleftheria, Kuusisto, Johanna, Laakso, Markku, Ingelsson, Erik, Abecasis, Goncalo, Chambers, John C., Kooner, Jaspal S., de Vries, Paul S., Morrison, Alanna C., Hazelhurst, Scott, Ramsay, Michèle, North, Kari E., Daviglus, Martha, Kraft, Peter, Martin, Nicholas G., Whitfield, John B., Abbas, Shahid, Saleheen, Danish, Walters, Robin G., Holmes, Michael V., Black, Corri, Smith, Blair H., Baras, Aris, Justice, Anne E., Buring, Julie E., Ridker, Paul M., Chasman, Daniel I., Kooperberg, Charles, Tamiya, Gen, Yamamoto, Masayuki, van Heel, David A., Trembath, Richard C., Wei, Wei-Qi, Jarvik, Gail P., Namjou, Bahram, Hayes, M. Geoffrey, Ritchie, Marylyn D., Jousilahti, Pekka, Salomaa, Veikko, Hveem, Kristian, Åsvold, Bjørn Olav, Kubo, Michiaki, Kamatani, Yoichiro, Okada, Yukinori, Murakami, Yoshinori, Kim, Bong-Jo, Thorsteinsdottir, Unnur, Stefansson, Kari, Zhang, Jifeng, Chen, YEugene, Ho, Yuk-Lam, Lynch, Julie A., Rader, Daniel J., Tsao, Philip S., Chang, Kyong-Mi, Cho, Kelly, O’Donnell, Christopher J., Gaziano, John M., Wilson, Peter W. F., Frayling, Timothy M., Hirschhorn, Joel N., Kathiresan, Sekar, Mohlke, Karen L., Sun, Yan V., Morris, Andrew P., Boehnke, Michael, Brown, Christopher D., Natarajan, Pradeep, Deloukas, Panos, Willer, Cristen J., Assimes, Themistocles L., and Peloso, Gina M.

Published
2022
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23. Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative.

Author

Wang, Jun, Kurilshikov, Alexander, Radjabzadeh, Djawad, Turpin, Williams, Croitoru, Kenneth, Bonder, Marc, Jackson, Matthew, Medina-Gomez, Carolina, Frost, Fabian, Homuth, Georg, Rühlemann, Malte, Hughes, David, Kim, Han-Na, Spector, Tim, Bell, Jordana, Steves, Claire, Timpson, Nicolas, Franke, Andre, Wijmenga, Cisca, Meyer, Katie, Kacprowski, Tim, Franke, Lude, Paterson, Andrew, Raes, Jeroen, Kraaij, Robert, and Zhernakova, Alexandra

Subjects
Genome-wide association studies (GWAS), Gut microbiome, Meta-analysis, Bacteria, Cohort Studies, Gastrointestinal Microbiome, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, RNA, Ribosomal, 16S
Abstract

BACKGROUND: In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture. RESULTS: Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories. CONCLUSION: We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.

Published
2018

24. 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

25. Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects

Author

Medina-Gomez, Carolina, Kemp, John P, Trajanoska, Katerina, Luan, Jian’an, Chesi, Alessandra, Ahluwalia, Tarunveer S, Mook-Kanamori, Dennis O, Ham, Annelies, Hartwig, Fernando P, Evans, Daniel S, Joro, Raimo, Nedeljkovic, Ivana, Zheng, Hou-Feng, Zhu, Kun, Atalay, Mustafa, Liu, Ching-Ti, Nethander, Maria, Broer, Linda, Porleifsson, Gudmar, Mullin, Benjamin H, Handelman, Samuel K, Nalls, Mike A, Jessen, Leon E, Heppe, Denise HM, Richards, J Brent, Wang, Carol, Chawes, Bo, Schraut, Katharina E, Amin, Najaf, Wareham, Nick, Karasik, David, Van der Velde, Nathalie, Ikram, M Arfan, Zemel, Babette S, Zhou, Yanhua, Carlsson, Christian J, Liu, Yongmei, McGuigan, Fiona E, Boer, Cindy G, Bønnelykke, Klaus, Ralston, Stuart H, Robbins, John A, Walsh, John P, Zillikens, M Carola, Langenberg, Claudia, Li-Gao, Ruifang, Williams, Frances MK, Harris, Tamara B, Akesson, Kristina, Jackson, Rebecca D, Sigurdsson, Gunnar, Heijer, Martin den, van der Eerden, Bram CJ, van de Peppel, Jeroen, Spector, Timothy D, Pennell, Craig, Horta, Bernardo L, Felix, Janine F, Zhao, Jing Hua, Wilson, Scott G, de Mutsert, Renée, Bisgaard, Hans, Styrkársdóttir, Unnur, Jaddoe, Vincent W, Orwoll, Eric, Lakka, Timo A, Scott, Robert, Grant, Struan FA, Lorentzon, Mattias, van Duijn, Cornelia M, Wilson, James F, Stefansson, Kari, Psaty, Bruce M, Kiel, Douglas P, Ohlsson, Claes, Ntzani, Evangelia, van Wijnen, Andre J, Forgetta, Vincenzo, Ghanbari, Mohsen, Logan, John G, Williams, Graham R, Bassett, JH Duncan, Croucher, Peter I, Evangelou, Evangelos, Uitterlinden, Andre G, Ackert-Bicknell, Cheryl L, Tobias, Jonathan H, Evans, David M, and Rivadeneira, Fernando

Subjects
Rare Diseases, Aging, Human Genome, Osteoporosis, Clinical Research, Genetics, Aetiology, 2.1 Biological and endogenous factors, Musculoskeletal, Good Health and Well Being, Adolescent, Age Factors, Animals, Bone Density, Child, Child, Preschool, Genetic Loci, Genome-Wide Association Study, Humans, Infant, Infant, Newborn, Mice, Knockout, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Regression Analysis, BMD, CREB3L1, ESR1, GWASs, RANKL, age-dependent effects, bone mineral density, fracture, genetic correlation, genome-wide association studies, meta-regression, total-body DXA, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Bone mineral density (BMD) assessed by DXA is used to evaluate bone health. In children, total body (TB) measurements are commonly used; in older individuals, BMD at the lumbar spine (LS) and femoral neck (FN) is used to diagnose osteoporosis. To date, genetic variants in more than 60 loci have been identified as associated with BMD. To investigate the genetic determinants of TB-BMD variation along the life course and test for age-specific effects, we performed a meta-analysis of 30 genome-wide association studies (GWASs) of TB-BMD including 66,628 individuals overall and divided across five age strata, each spanning 15 years. We identified variants associated with TB-BMD at 80 loci, of which 36 have not been previously identified; overall, they explain approximately 10% of the TB-BMD variance when combining all age groups and influence the risk of fracture. Pathway and enrichment analysis of the association signals showed clustering within gene sets implicated in the regulation of cell growth and SMAD proteins, overexpressed in the musculoskeletal system, and enriched in enhancer and promoter regions. These findings reveal TB-BMD as a relevant trait for genetic studies of osteoporosis, enabling the identification of variants and pathways influencing different bone compartments. Only variants in ESR1 and close proximity to RANKL showed a clear effect dependency on age. This most likely indicates that the majority of genetic variants identified influence BMD early in life and that their effect can be captured throughout the life course.

Published
2018

26. 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

27. A comparison of genotyping arrays

Author

Verlouw, Joost A. M., Clemens, Eva, de Vries, Jard H., Zolk, Oliver, Verkerk, Annemieke J. M. H., am Zehnhoff-Dinnesen, Antoinette, Medina-Gomez, Carolina, Lanvers-Kaminsky, Claudia, Rivadeneira, Fernando, Langer, Thorsten, van Meurs, Joyce B. J., van den Heuvel-Eibrink, Marry M., Uitterlinden, André G., and Broer, Linda

Published
2021
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29. Rare and low-frequency coding variants alter human adult height

Author

Marouli, Eirini, Graff, Mariaelisa, Medina-Gomez, Carolina, Lo, Ken Sin, Wood, Andrew R, Kjaer, Troels R, Fine, Rebecca S, Lu, Yingchang, Schurmann, Claudia, Highland, Heather M, Rüeger, Sina, Thorleifsson, Gudmar, Justice, Anne E, Lamparter, David, Stirrups, Kathleen E, Turcot, Valérie, Young, Kristin L, Winkler, Thomas W, Esko, Tõnu, Karaderi, Tugce, Locke, Adam E, Masca, Nicholas GD, Ng, Maggie CY, Mudgal, Poorva, Rivas, Manuel A, Vedantam, Sailaja, Mahajan, Anubha, Guo, Xiuqing, Abecasis, Goncalo, Aben, Katja K, Adair, Linda S, Alam, Dewan S, Albrecht, Eva, Allin, Kristine H, Allison, Matthew, Amouyel, Philippe, Appel, Emil V, Arveiler, Dominique, Asselbergs, Folkert W, Auer, Paul L, Balkau, Beverley, Banas, Bernhard, Bang, Lia E, Benn, Marianne, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bonnycastle, Lori L, Bork-Jensen, Jette, Bots, Michiel L, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Breen, Gerome, Brilliant, Murray H, Broer, Linda, Burt, Amber A, Butterworth, Adam S, Carey, David J, Caulfield, Mark J, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der Ida, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Cocca, Massimiliano, Collins, Francis S, Cook, James P, Corley, Janie, Galbany, Jordi Corominas, Cox, Amanda J, Cuellar-Partida, Gabriel, Danesh, John, Davies, Gail, de Bakker, Paul IW, de Borst, Gert J, de Denus, Simon, de Groot, Mark CH, de Mutsert, Renée, Deary, Ian J, Dedoussis, George, Demerath, Ellen W, den Hollander, Anneke I, Dennis, Joe G, Di Angelantonio, Emanuele, Drenos, Fotios, Du, Mengmeng, Dunning, Alison M, Easton, Douglas F, Ebeling, Tapani, Edwards, Todd L, Ellinor, Patrick T, Elliott, Paul, Evangelou, Evangelos, Farmaki, Aliki-Eleni, and Faul, Jessica D

Subjects
Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, ADAMTS Proteins, Adult, Alleles, Body Height, Cell Adhesion Molecules, Female, Gene Frequency, Genetic Variation, Genome, Human, Glycoproteins, Glycosaminoglycans, Hedgehog Proteins, Humans, Intercellular Signaling Peptides and Proteins, Interferon Regulatory Factors, Interleukin-11 Receptor alpha Subunit, Male, Multifactorial Inheritance, NADPH Oxidase 4, NADPH Oxidases, Phenotype, Pregnancy-Associated Plasma Protein-A, Procollagen N-Endopeptidase, Proteoglycans, Proteolysis, Receptors, Androgen, Somatomedins, EPIC-InterAct Consortium, CHD Exome+ Consortium, ExomeBP Consortium, T2D-Genes Consortium, GoT2D Genes Consortium, Global Lipids Genetics Consortium, ReproGen Consortium, MAGIC Investigators, General Science & Technology
Abstract

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

Published
2017

31. Association between BMD and coronary artery calcification: an observational and Mendelian randomization study

Author

CDL Onderzoek Pasterkamp, Circulatory Health, Lu, Haojie, Lary, Christine W., Hodonsky, Chani J., Peyser, Patricia A., Bos, Daniel, van der Laan, Sander W., Miller, Clint L., Rivadeneira, Fernando, Kiel, Douglas P., Kavousi, Maryam, Medina-Gomez, Carolina, CDL Onderzoek Pasterkamp, Circulatory Health, Lu, Haojie, Lary, Christine W., Hodonsky, Chani J., Peyser, Patricia A., Bos, Daniel, van der Laan, Sander W., Miller, Clint L., Rivadeneira, Fernando, Kiel, Douglas P., Kavousi, Maryam, and Medina-Gomez, Carolina

Published
2024

32. Identification of Transcripts with Shared Roles in the Pathogenesis of Postmenopausal Osteoporosis and Cardiovascular Disease

Author

Reppe, Sjur, Gundersen, Sveinung, Sandve, Geir K., Wang, Yunpeng, Andreassen, Ole A., Medina-Gomez, Carolina, Rivadeneira, Fernando, Utheim, Tor P., Hovig, Eivind, Gautvik, Kaare M., Reppe, Sjur, Gundersen, Sveinung, Sandve, Geir K., Wang, Yunpeng, Andreassen, Ole A., Medina-Gomez, Carolina, Rivadeneira, Fernando, Utheim, Tor P., Hovig, Eivind, and Gautvik, Kaare M.

Abstract

Epidemiological evidence suggests existing comorbidity between postmenopausal osteoporosis (OP) and cardiovascular disease (CVD), but identification of possible shared genes is lacking. The skeletal global transcriptomes were analyzed in trans-iliac bone biopsies (n = 84) from clinically well-characterized postmenopausal women (50 to 86 years) without clinical CVD using microchips and RNA sequencing. One thousand transcripts highly correlated with areal bone mineral density (aBMD) were further analyzed using bioinformatics, and common genes overlapping with CVD and associated biological mechanisms, pathways and functions were identified. Fifty genes (45 mRNAs, 5 miRNAs) were discovered with established roles in oxidative stress, inflammatory response, endothelial function, fibrosis, dyslipidemia and osteoblastogenesis/calcification. These pleiotropic genes with possible CVD comorbidity functions were also present in transcriptomes of microvascular endothelial cells and cardiomyocytes and were differentially expressed between healthy and osteoporotic women with fragility fractures. The results were supported by a genetic pleiotropy-informed conditional False Discovery Rate approach identifying any overlap in single nucleotide polymorphisms (SNPs) within several genes encoding aBMD- and CVD-associated transcripts. The study provides transcriptional and genomic evidence for genes of importance for both BMD regulation and CVD risk in a large collection of postmenopausal bone biopsies. Most of the transcripts identified in the CVD risk categories have no previously recognized roles in OP pathogenesis and provide novel avenues for exploring the mechanistic basis for the biological association between CVD and OP.

Published
2024

33. Glaucoma Patients Have a Lower Abundance of Butyrate-Producing Taxa in the Gut

Author

Vergroesen, Joëlle E, Jarrar, Zakariya A, Weiss, Stefan, Frost, Fabian, Ansari, Abdus S, Nguyen, Picard, Kraaij, Robert, Medina-Gomez, Carolina, Völzke, Henry, Tost, Frank, Amin, Najaf, van Duijn, Cornelia M, Klaver, Caroline C W, Jürgens, Clemens, Hammond, Chris J, Ramdas, Wishal D, Vergroesen, Joëlle E, Jarrar, Zakariya A, Weiss, Stefan, Frost, Fabian, Ansari, Abdus S, Nguyen, Picard, Kraaij, Robert, Medina-Gomez, Carolina, Völzke, Henry, Tost, Frank, Amin, Najaf, van Duijn, Cornelia M, Klaver, Caroline C W, Jürgens, Clemens, Hammond, Chris J, and Ramdas, Wishal D

Abstract

PURPOSE: Glaucoma is an eye disease that is the most common cause of irreversible blindness worldwide. It has been suggested that gut microbiota can produce reactive oxygen species and pro-inflammatory cytokines that may travel from the gastric mucosa to distal sites, for example, the optic nerve head or trabecular meshwork. There is evidence for a gut-eye axis, as microbial dysbiosis has been associated with retinal diseases. We investigated the microbial composition in patients with glaucoma and healthy controls. Moreover, we analyzed the association of the gut microbiome with intraocular pressure (IOP; risk factor of glaucoma) and vertical cup-to-disc ratio (VCDR; quantifying glaucoma severity).METHODS: The discovery analyses included participants of the Rotterdam Study and the Erasmus Glaucoma Cohort. A total of 225 patients with glaucoma and 1247 age- and sex-matched participants without glaucoma were included in our analyses. Stool samples were used to generate 16S rRNA gene profiles. We assessed associations with 233 genera and species. We used data from the TwinsUK and the Study of Health in Pomerania (SHIP) to replicate our findings.RESULTS: Several butyrate-producing taxa (e.g. Butyrivibrio, Caproiciproducens, Clostridium sensu stricto 1, Coprococcus 1, Ruminococcaceae UCG 007, and Shuttleworthia) were less abundant in people with glaucoma compared to healthy controls. The same taxa were also associated with lower IOP and smaller VCDR. The replication analyses confirmed the findings from the discovery analyses.CONCLUSIONS: Large human studies exploring the link between the gut microbiome and glaucoma are lacking. Our results suggest that microbial dysbiosis plays a role in the pathophysiology of glaucoma.

Published
2024

34. Oral microbiota of adolescents with dental caries:A systematic review

Author

Veenman, Francien, van Dijk, Anne, Arredondo, Alexandre, Medina-Gomez, Carolina, Wolvius, Eppo, Rivadeneira, Fernando, Àlvarez, Gerard, Blanc, Vanessa, Kragt, Lea, Veenman, Francien, van Dijk, Anne, Arredondo, Alexandre, Medina-Gomez, Carolina, Wolvius, Eppo, Rivadeneira, Fernando, Àlvarez, Gerard, Blanc, Vanessa, and Kragt, Lea

Abstract

Objective: This systematic review summarizes the current knowledge on the association between the oral microbiota and dental caries in adolescents. Design: An electronic search was carried out across five databases. Studies were included if they conducted research on generally healthy adolescents, applied molecular-based microbiological analyses and assessed caries status. Data extraction was performed by two reviewers and the Newcastle-Ottawa Scale was applied for quality assessment. Results: In total, 3935 records were reviewed which resulted in a selection of 20 cross-sectional studies (published 2005–2022) with a sample size ranging from 11 to 614 participants including adolescents between 11 and 19 years. The studies analyzed saliva, dental biofilm or tongue swabs with Checkerboard DNA-DNA hybridization, (q)PCR or Next-Generation Sequencing methods. Prevotella denticola, Scardoviae Wiggsiae, Streptococcus sobrinus and Streptococcus mutans were the most frequently reported species presenting higher abundance in adolescents with caries. The majority of the studies reported that the microbial diversity was similar between participants with and without dental caries. Conclusion: This systematic review is the first that shows how the oral microbiota composition in adolescents appears to differ between those with and without dental caries, suggesting certain taxa may be associated with increased caries risk. However, there is a need to replicate and expand these findings in larger, longitudinal studies that also focus on caries severity and take adolescent-specific factors into account.

Published
2024

36. Glaucoma Patients Have a Lower Abundance of Butyrate-Producing Taxa in the Gut

Author

Vergroesen, Joëlle E., primary, Jarrar, Zakariya A., additional, Weiss, Stefan, additional, Frost, Fabian, additional, Ansari, Abdus S., additional, Nguyen, Picard, additional, Kraaij, Robert, additional, Medina-Gomez, Carolina, additional, Völzke, Henry, additional, Tost, Frank, additional, Amin, Najaf, additional, van Duijn, Cornelia M., additional, Klaver, Caroline C. W., additional, Jürgens, Clemens, additional, Hammond, Chris J., additional, and Ramdas, Wishal D., additional

Published
2024
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37. Association between BMD and coronary artery calcification: an observational and Mendelian randomization study

Author

Lu, Haojie, primary, Lary, Christine W, additional, Hodonsky, Chani J, additional, Peyser, Patricia A, additional, Bos, Daniel, additional, van der Laan, Sander W, additional, Miller, Clint L, additional, Rivadeneira, Fernando, additional, Kiel, Douglas P, additional, Kavousi, Maryam, additional, and Medina-Gomez, Carolina, additional

Published
2024
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38. A higher abundance of butyrate‐producing taxa in the gut is associated with lower glaucoma prevalence

Author

Vergroesen, Joëlle, primary, Jarrar, Zakariya, additional, Weiss, Stefan, additional, Frost, Fabian, additional, Kraaij, Robert, additional, Medina‐Gomez, Carolina, additional, Amin, Najaf, additional, van Duijn, Cornelia, additional, Klaver, Caroline, additional, Jürgens, Clemens, additional, Hammond, Chris, additional, and Ramdas, Wishal, additional

Published
2024
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40. Genome-wide associations for birth weight and correlations with adult disease

Author

Horikoshi, Momoko, Beaumont, Robin N, Day, Felix R, Warrington, Nicole M, Kooijman, Marjolein N, Fernandez-Tajes, Juan, Feenstra, Bjarke, van Zuydam, Natalie R, Gaulton, Kyle J, Grarup, Niels, Bradfield, Jonathan P, Strachan, David P, Li-Gao, Ruifang, Ahluwalia, Tarunveer S, Kreiner, Eskil, Rueedi, Rico, Lyytikäinen, Leo-Pekka, Cousminer, Diana L, Wu, Ying, Thiering, Elisabeth, Wang, Carol A, Have, Christian T, Hottenga, Jouke-Jan, Vilor-Tejedor, Natalia, Joshi, Peter K, Boh, Eileen Tai Hui, Ntalla, Ioanna, Pitkänen, Niina, Mahajan, Anubha, van Leeuwen, Elisabeth M, Joro, Raimo, Lagou, Vasiliki, Nodzenski, Michael, Diver, Louise A, Zondervan, Krina T, Bustamante, Mariona, Marques-Vidal, Pedro, Mercader, Josep M, Bennett, Amanda J, Rahmioglu, Nilufer, Nyholt, Dale R, Ma, Ronald CW, Tam, Claudia HT, Tam, Wing Hung, Ganesh, Santhi K, van Rooij, Frank JA, Jones, Samuel E, Loh, Po-Ru, Ruth, Katherine S, Tuke, Marcus A, Tyrrell, Jessica, Wood, Andrew R, Yaghootkar, Hanieh, Scholtens, Denise M, Paternoster, Lavinia, Prokopenko, Inga, Kovacs, Peter, Atalay, Mustafa, Willems, Sara M, Panoutsopoulou, Kalliope, Wang, Xu, Carstensen, Lisbeth, Geller, Frank, Schraut, Katharina E, Murcia, Mario, van Beijsterveldt, Catharina EM, Willemsen, Gonneke, Appel, Emil VR, Fonvig, Cilius E, Trier, Caecilie, Tiesler, Carla MT, Standl, Marie, Kutalik, Zoltán, Bonàs-Guarch, Sílvia, Hougaard, David M, Sánchez, Friman, Torrents, David, Waage, Johannes, Hollegaard, Mads V, de Haan, Hugoline G, Rosendaal, Frits R, Medina-Gomez, Carolina, Ring, Susan M, Hemani, Gibran, McMahon, George, Robertson, Neil R, Groves, Christopher J, Langenberg, Claudia, Luan, Jian’an, Scott, Robert A, Zhao, Jing Hua, Mentch, Frank D, MacKenzie, Scott M, Reynolds, Rebecca M, Lowe, William L, Tönjes, Anke, Stumvoll, Michael, Lindi, Virpi, Lakka, Timo A, and van Duijn, Cornelia M

Subjects
Nutrition, Perinatal Period - Conditions Originating in Perinatal Period, Human Genome, Genetics, Clinical Research, Preterm, Low Birth Weight and Health of the Newborn, Obesity, Prevention, Pediatric, Infant Mortality, 2.1 Biological and endogenous factors, Aetiology, Adult, Aging, Anthropometry, Birth Weight, Blood Pressure, Chromatin Assembly and Disassembly, Cohort Studies, Coronary Artery Disease, Datasets as Topic, Diabetes Mellitus, Type 2, Female, Fetus, Genetic Loci, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genomic Imprinting, Genotype, Glucose, Glycogen, Humans, Insulin, Male, Phenotype, Signal Transduction, CHARGE Consortium Hematology Working Group, Early Growth Genetics (EGG) Consortium, General Science & Technology
Abstract

Birth weight (BW) has been shown to be influenced by both fetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease. These life-course associations have often been attributed to the impact of an adverse early life environment. Here, we performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where fetal genotype was associated with BW (P

Published
2016

42. CYP11B1 variants influence skeletal maturation via alternative splicing

Author

Grgic, Olja, Gazzara, Matthew R., Chesi, Alessandra, Medina-Gomez, Carolina, Cousminer, Diana L., Mitchell, Jonathan A., Prijatelj, Vid, de Vries, Jard, Shevroja, Enisa, McCormack, Shana E., Kalkwarf, Heidi J., Lappe, Joan M., Gilsanz, Vicente, Oberfield, Sharon E., Shepherd, John A., Kelly, Andrea, Mahboubi, Soroosh, Faucz, Fabio R., Feelders, Richard A., de Jong, Frank H., Uitterlinden, Andre G., Visser, Jenny A., Ghanem, Louis R., Wolvius, Eppo B., Hofland, Leo J., Stratakis, Constantine A., Zemel, Babette S., Barash, Yoseph, Grant, Struan F. A., and Rivadeneira, Fernando

Published
2021
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43. The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study.

Author

Winkler, Thomas W, Justice, Anne E, Graff, Mariaelisa, Barata, Llilda, Feitosa, Mary F, Chu, Su, Czajkowski, Jacek, Esko, Tõnu, Fall, Tove, Kilpeläinen, Tuomas O, Lu, Yingchang, Mägi, Reedik, Mihailov, Evelin, Pers, Tune H, Rüeger, Sina, Teumer, Alexander, Ehret, Georg B, Ferreira, Teresa, Heard-Costa, Nancy L, Karjalainen, Juha, Lagou, Vasiliki, Mahajan, Anubha, Neinast, Michael D, Prokopenko, Inga, Simino, Jeannette, Teslovich, Tanya M, Jansen, Rick, Westra, Harm-Jan, White, Charles C, Absher, Devin, Ahluwalia, Tarunveer S, Ahmad, Shafqat, Albrecht, Eva, Alves, Alexessander Couto, Bragg-Gresham, Jennifer L, de Craen, Anton JM, Bis, Joshua C, Bonnefond, Amélie, Boucher, Gabrielle, Cadby, Gemma, Cheng, Yu-Ching, Chiang, Charleston WK, Delgado, Graciela, Demirkan, Ayse, Dueker, Nicole, Eklund, Niina, Eiriksdottir, Gudny, Eriksson, Joel, Feenstra, Bjarke, Fischer, Krista, Frau, Francesca, Galesloot, Tessel E, Geller, Frank, Goel, Anuj, Gorski, Mathias, Grammer, Tanja B, Gustafsson, Stefan, Haitjema, Saskia, Hottenga, Jouke-Jan, Huffman, Jennifer E, Jackson, Anne U, Jacobs, Kevin B, Johansson, Åsa, Kaakinen, Marika, Kleber, Marcus E, Lahti, Jari, Mateo Leach, Irene, Lehne, Benjamin, Liu, Youfang, Lo, Ken Sin, Lorentzon, Mattias, Luan, Jian'an, Madden, Pamela AF, Mangino, Massimo, McKnight, Barbara, Medina-Gomez, Carolina, Monda, Keri L, Montasser, May E, Müller, Gabriele, Müller-Nurasyid, Martina, Nolte, Ilja M, Panoutsopoulou, Kalliope, Pascoe, Laura, Paternoster, Lavinia, Rayner, Nigel W, Renström, Frida, Rizzi, Federica, Rose, Lynda M, Ryan, Kathy A, Salo, Perttu, Sanna, Serena, Scharnagl, Hubert, Shi, Jianxin, Smith, Albert Vernon, Southam, Lorraine, Stančáková, Alena, Steinthorsdottir, Valgerdur, Strawbridge, Rona J, Sung, Yun Ju, and Tachmazidou, Ioanna

Subjects
CHARGE Consortium, DIAGRAM Consortium, GLGC Consortium, Global-BPGen Consortium, ICBP Consortium, MAGIC Consortium, Humans, Genetic Predisposition to Disease, Body Mass Index, Body Size, Waist-Hip Ratio, Chromosome Mapping, Age Factors, Sex Characteristics, Polymorphism, Single Nucleotide, Adult, Aged, Middle Aged, European Continental Ancestry Group, Female, Male, Genome-Wide Association Study, Developmental Biology, Genetics
Abstract

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR

Published
2015

44. Genome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels.

Author

van Leeuwen, Elisabeth M, Karssen, Lennart C, Deelen, Joris, Isaacs, Aaron, Medina-Gomez, Carolina, Mbarek, Hamdi, Kanterakis, Alexandros, Trompet, Stella, Postmus, Iris, Verweij, Niek, van Enckevort, David J, Huffman, Jennifer E, White, Charles C, Feitosa, Mary F, Bartz, Traci M, Manichaikul, Ani, Joshi, Peter K, Peloso, Gina M, Deelen, Patrick, van Dijk, Freerk, Willemsen, Gonneke, de Geus, Eco J, Milaneschi, Yuri, Penninx, Brenda WJH, Francioli, Laurent C, Menelaou, Androniki, Pulit, Sara L, Rivadeneira, Fernando, Hofman, Albert, Oostra, Ben A, Franco, Oscar H, Mateo Leach, Irene, Beekman, Marian, de Craen, Anton JM, Uh, Hae-Won, Trochet, Holly, Hocking, Lynne J, Porteous, David J, Sattar, Naveed, Packard, Chris J, Buckley, Brendan M, Brody, Jennifer A, Bis, Joshua C, Rotter, Jerome I, Mychaleckyj, Josyf C, Campbell, Harry, Duan, Qing, Lange, Leslie A, Wilson, James F, Hayward, Caroline, Polasek, Ozren, Vitart, Veronique, Rudan, Igor, Wright, Alan F, Rich, Stephen S, Psaty, Bruce M, Borecki, Ingrid B, Kearney, Patricia M, Stott, David J, Adrienne Cupples, L, Genome of The Netherlands Consortium, Jukema, J Wouter, van der Harst, Pim, Sijbrands, Eric J, Hottenga, Jouke-Jan, Uitterlinden, Andre G, Swertz, Morris A, van Ommen, Gert-Jan B, de Bakker, Paul IW, Eline Slagboom, P, Boomsma, Dorret I, Wijmenga, Cisca, and van Duijn, Cornelia M

Subjects
Genome of The Netherlands Consortium, Humans, Cholesterol, ATP-Binding Cassette Transporters, Gene Frequency, Mutation, Missense, Netherlands, Genetic Association Studies, Mutation, Missense
Abstract

Variants associated with blood lipid levels may be population-specific. To identify low-frequency variants associated with this phenotype, population-specific reference panels may be used. Here we impute nine large Dutch biobanks (~35,000 samples) with the population-specific reference panel created by the Genome of The Netherlands Project and perform association testing with blood lipid levels. We report the discovery of five novel associations at four loci (P value

Published
2015

45. New genetic loci link adipose and insulin biology to body fat distribution

Author

Shungin, Dmitry, Winkler, Thomas W, Croteau-Chonka, Damien C, Ferreira, Teresa, Locke, Adam E, Mägi, Reedik, Strawbridge, Rona J, Pers, Tune H, Fischer, Krista, Justice, Anne E, Workalemahu, Tsegaselassie, Wu, Joseph MW, Buchkovich, Martin L, Heard-Costa, Nancy L, Roman, Tamara S, Drong, Alexander W, Song, Ci, Gustafsson, Stefan, Day, Felix R, Esko, Tonu, Fall, Tove, Kutalik, Zoltán, Luan, Jian’an, Randall, Joshua C, Scherag, André, Vedantam, Sailaja, Wood, Andrew R, Chen, Jin, Fehrmann, Rudolf, Karjalainen, Juha, Kahali, Bratati, Liu, Ching-Ti, Schmidt, Ellen M, Absher, Devin, Amin, Najaf, Anderson, Denise, Beekman, Marian, Bragg-Gresham, Jennifer L, Buyske, Steven, Demirkan, Ayse, Ehret, Georg B, Feitosa, Mary F, Goel, Anuj, Jackson, Anne U, Johnson, Toby, Kleber, Marcus E, Kristiansson, Kati, Mangino, Massimo, Mateo Leach, Irene, Medina-Gomez, Carolina, Palmer, Cameron D, Pasko, Dorota, Pechlivanis, Sonali, Peters, Marjolein J, Prokopenko, Inga, Stančáková, Alena, Ju Sung, Yun, Tanaka, Toshiko, Teumer, Alexander, Van Vliet-Ostaptchouk, Jana V, Yengo, Loïc, Zhang, Weihua, Albrecht, Eva, Ärnlöv, Johan, Arscott, Gillian M, Bandinelli, Stefania, Barrett, Amy, Bellis, Claire, Bennett, Amanda J, Berne, Christian, Blüher, Matthias, Böhringer, Stefan, Bonnet, Fabrice, Böttcher, Yvonne, Bruinenberg, Marcel, Carba, Delia B, Caspersen, Ida H, Clarke, Robert, Warwick Daw, E, Deelen, Joris, Deelman, Ewa, Delgado, Graciela, Doney, Alex SF, Eklund, Niina, Erdos, Michael R, Estrada, Karol, Eury, Elodie, Friedrich, Nele, Garcia, Melissa E, Giedraitis, Vilmantas, Gigante, Bruna, Go, Alan S, Golay, Alain, Grallert, Harald, Grammer, Tanja B, Gräßler, Jürgen, Grewal, Jagvir, Groves, Christopher J, Haller, Toomas, and Hallmans, Goran

Subjects
Genetics, Human Genome, Diabetes, Obesity, Nutrition, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Cardiovascular, Stroke, Adipocytes, Adipogenesis, Adipose Tissue, Age Factors, Body Fat Distribution, Body Mass Index, Epigenesis, Genetic, Europe, Female, Genome, Human, Genome-Wide Association Study, Humans, Insulin, Insulin Resistance, Male, Models, Biological, Neovascularization, Physiologic, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Racial Groups, Sex Characteristics, Transcription, Genetic, Waist-Hip Ratio, ADIPOGen Consortium, CARDIOGRAMplusC4D Consortium, CKDGen Consortium, GEFOS Consortium, GENIE Consortium, GLGC, ICBP, International Endogene Consortium, LifeLines Cohort Study, MAGIC Investigators, MuTHER Consortium, PAGE Consortium, ReproGen Consortium, General Science & Technology
Abstract

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P

Published
2015

46. Genetic studies of body mass index yield new insights for obesity biology

Author

Locke, Adam E, Kahali, Bratati, Berndt, Sonja I, Justice, Anne E, Pers, Tune H, Day, Felix R, Powell, Corey, Vedantam, Sailaja, Buchkovich, Martin L, Yang, Jian, Croteau-Chonka, Damien C, Esko, Tonu, Fall, Tove, Ferreira, Teresa, Gustafsson, Stefan, Kutalik, Zoltán, Luan, Jian’an, Mägi, Reedik, Randall, Joshua C, Winkler, Thomas W, Wood, Andrew R, Workalemahu, Tsegaselassie, Faul, Jessica D, Smith, Jennifer A, Hua Zhao, Jing, Zhao, Wei, Chen, Jin, Fehrmann, Rudolf, Hedman, Åsa K, Karjalainen, Juha, Schmidt, Ellen M, Absher, Devin, Amin, Najaf, Anderson, Denise, Beekman, Marian, Bolton, Jennifer L, Bragg-Gresham, Jennifer L, Buyske, Steven, Demirkan, Ayse, Deng, Guohong, Ehret, Georg B, Feenstra, Bjarke, Feitosa, Mary F, Fischer, Krista, Goel, Anuj, Gong, Jian, Jackson, Anne U, Kanoni, Stavroula, Kleber, Marcus E, Kristiansson, Kati, Lim, Unhee, Lotay, Vaneet, Mangino, Massimo, Mateo Leach, Irene, Medina-Gomez, Carolina, Medland, Sarah E, Nalls, Michael A, Palmer, Cameron D, Pasko, Dorota, Pechlivanis, Sonali, Peters, Marjolein J, Prokopenko, Inga, Shungin, Dmitry, Stančáková, Alena, Strawbridge, Rona J, Ju Sung, Yun, Tanaka, Toshiko, Teumer, Alexander, Trompet, Stella, van der Laan, Sander W, van Setten, Jessica, Van Vliet-Ostaptchouk, Jana V, Wang, Zhaoming, Yengo, Loïc, Zhang, Weihua, Isaacs, Aaron, Albrecht, Eva, Ärnlöv, Johan, Arscott, Gillian M, Attwood, Antony P, Bandinelli, Stefania, Barrett, Amy, Bas, Isabelita N, Bellis, Claire, Bennett, Amanda J, Berne, Christian, Blagieva, Roza, Blüher, Matthias, Böhringer, Stefan, Bonnycastle, Lori L, Böttcher, Yvonne, Boyd, Heather A, Bruinenberg, Marcel, Caspersen, Ida H, Ida Chen, Yii-Der, Clarke, Robert, Warwick Daw, E, de Craen, Anton JM, Delgado, Graciela, and Dimitriou, Maria

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Nutrition, Obesity, Prevention, Aetiology, 2.1 Biological and endogenous factors, Stroke, Cardiovascular, Metabolic and endocrine, Oral and gastrointestinal, Cancer, Adipogenesis, Adiposity, Age Factors, Body Mass Index, Energy Metabolism, Europe, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Glutamic Acid, Humans, Insulin, Insulin Secretion, Male, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Racial Groups, Synapses, LifeLines Cohort Study, ADIPOGen Consortium, AGEN-BMI Working Group, CARDIOGRAMplusC4D Consortium, CKDGen Consortium, GLGC, ICBP, MAGIC Investigators, MuTHER Consortium, MIGen Consortium, PAGE Consortium, ReproGen Consortium, GENIE Consortium, International Endogene Consortium, General Science & Technology
Abstract

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P 20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

Published
2015

47. Fine mapping the CETP region reveals a common intronic insertion associated to HDL-C

Author

van Leeuwen, Elisabeth M, Huffman, Jennifer E, Bis, Joshua C, Isaacs, Aaron, Mulder, Monique, Sabo, Aniko, Smith, Albert V, Demissie, Serkalem, Manichaikul, Ani, Brody, Jennifer A, Feitosa, Mary F, Duan, Qing, Schraut, Katharina E, Navarro, Pau, van Vliet-Ostaptchouk, Jana V, Zhu, Gu, Mbarek, Hamdi, Trompet, Stella, Verweij, Niek, Lyytikäinen, Leo-Pekka, Deelen, Joris, Nolte, Ilja M, van der Laan, Sander W, Davies, Gail, Vermeij-Verdoold, Andrea JM, van Oosterhout, Andy ALJ, Vergeer-Drop, Jeannette M, Arking, Dan E, Trochet, Holly, Medina-Gomez, Carolina, Rivadeneira, Fernando, Uitterlinden, Andre G, Dehghan, Abbas, Franco, Oscar H, Sijbrands, Eric J, Hofman, Albert, White, Charles C, Mychaleckyj, Josyf C, Peloso, Gina M, Swertz, Morris A, Willemsen, Gonneke, de Geus, Eco J, Milaneschi, Yuri, Penninx, Brenda WJH, Ford, Ian, Buckley, Brendan M, de Craen, Anton JM, Starr, John M, Deary, Ian J, Pasterkamp, Gerard, Oldehinkel, Albertine J, Snieder, Harold, Slagboom, P Eline, Nikus, Kjell, Kähönen, Mika, Lehtimäki, Terho, Viikari, Jorma S, Raitakari, Olli T, van der Harst, Pim, Jukema, J Wouter, Hottenga, Jouke-Jan, Boomsma, Dorret I, Whitfield, John B, Montgomery, Grant, Martin, Nicholas G, Polasek, Ozren, Vitart, Veronique, Hayward, Caroline, Kolcic, Ivana, Wright, Alan F, Rudan, Igor, Joshi, Peter K, Wilson, James F, Lange, Leslie A, Wilson, James G, Gudnason, Vilmundur, Harris, Tamar B, Morrison, Alanna C, Borecki, Ingrid B, Rich, Stephen S, Padmanabhan, Sandosh, Psaty, Bruce M, Rotter, Jerome I, Smith, Blair H, Boerwinkle, Eric, Cupples, L Adrienne, and van Duijn, Cornelia

Subjects
Biological Sciences, Genetics, Aetiology, 2.1 Biological and endogenous factors, Generation Scotland, LifeLines Cohort Study, CHARGE Lipids Working Group, Biochemistry and Cell Biology, Medical Biotechnology, Clinical Sciences, Biochemistry and cell biology
Abstract

BackgroundIndividuals with exceptional longevity and their offspring have significantly larger high-density lipoprotein concentrations (HDL-C) particle sizes due to the increased homozygosity for the I405V variant in the cholesteryl ester transfer protein (CETP) gene. In this study, we investigate the association of CETP and HDL-C further to identify novel, independent CETP variants associated with HDL-C in humans.MethodsWe performed a meta-analysis of HDL-C within the CETP region using 59,432 individuals imputed with 1000 Genomes data. We performed replication in an independent sample of 47,866 individuals and validation was done by Sanger sequencing.ResultsThe meta-analysis of HDL-C within the CETP region identified five independent variants, including an exonic variant and a common intronic insertion. We replicated these 5 variants significantly in an independent sample of 47,866 individuals. Sanger sequencing of the insertion within a single family confirmed segregation of this variant. The strongest reported association between HDL-C and CETP variants, was rs3764261; however, after conditioning on the five novel variants we identified the support for rs3764261 was highly reduced (βunadjusted=3.179 mg/dl (P value=5.25×10-509), βadjusted=0.859 mg/dl (P value=9.51×10-25)), and this finding suggests that these five novel variants may partly explain the association of CETP with HDL-C. Indeed, three of the five novel variants (rs34065661, rs5817082, rs7499892) are independent of rs3764261.ConclusionsThe causal variants in CETP that account for the association with HDL-C remain unknown. We used studies imputed to the 1000 Genomes reference panel for fine mapping of the CETP region. We identified and validated five variants within this region that may partly account for the association of the known variant (rs3764261), as well as other sources of genetic contribution to HDL-C.

Published
2015

50. Defining the role of common variation in the genomic and biological architecture of adult human height

Author

Wood, Andrew R, Esko, Tonu, Yang, Jian, Vedantam, Sailaja, Pers, Tune H, Gustafsson, Stefan, Chu, Audrey Y, Estrada, Karol, Luan, Jian'an, Kutalik, Zoltán, Amin, Najaf, Buchkovich, Martin L, Croteau-Chonka, Damien C, Day, Felix R, Duan, Yanan, Fall, Tove, Fehrmann, Rudolf, Ferreira, Teresa, Jackson, Anne U, Karjalainen, Juha, Lo, Ken Sin, Locke, Adam E, Mägi, Reedik, Mihailov, Evelin, Porcu, Eleonora, Randall, Joshua C, Scherag, André, Vinkhuyzen, Anna AE, Westra, Harm-Jan, Winkler, Thomas W, Workalemahu, Tsegaselassie, Zhao, Jing Hua, Absher, Devin, Albrecht, Eva, Anderson, Denise, Baron, Jeffrey, Beekman, Marian, Demirkan, Ayse, Ehret, Georg B, Feenstra, Bjarke, Feitosa, Mary F, Fischer, Krista, Fraser, Ross M, Goel, Anuj, Gong, Jian, Justice, Anne E, Kanoni, Stavroula, Kleber, Marcus E, Kristiansson, Kati, Lim, Unhee, Lotay, Vaneet, Lui, Julian C, Mangino, Massimo, Leach, Irene Mateo, Medina-Gomez, Carolina, Nalls, Michael A, Nyholt, Dale R, Palmer, Cameron D, Pasko, Dorota, Pechlivanis, Sonali, Prokopenko, Inga, Ried, Janina S, Ripke, Stephan, Shungin, Dmitry, Stancáková, Alena, Strawbridge, Rona J, Sung, Yun Ju, Tanaka, Toshiko, Teumer, Alexander, Trompet, Stella, van der Laan, Sander W, van Setten, Jessica, Van Vliet-Ostaptchouk, Jana V, Wang, Zhaoming, Yengo, Loïc, Zhang, Weihua, Afzal, Uzma, Ärnlöv, Johan, Arscott, Gillian M, Bandinelli, Stefania, Barrett, Amy, Bellis, Claire, Bennett, Amanda J, Berne, Christian, Blüher, Matthias, Bolton, Jennifer L, Böttcher, Yvonne, Boyd, Heather A, Bruinenberg, Marcel, Buckley, Brendan M, Buyske, Steven, Caspersen, Ida H, Chines, Peter S, Clarke, Robert, Claudi-Boehm, Simone, Cooper, Matthew, Daw, E Warwick, De Jong, Pim A, Deelen, Joris, and Delgado, Graciela

Subjects
Biological Sciences, Genetics, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Adult, Analysis of Variance, Body Height, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, White People, Electronic Medical Records and Genomics (eMEMERGEGE) Consortium, MIGen Consortium, PAGEGE Consortium, LifeLines Cohort Study, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

Published
2014

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