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Browsing by Author "Vandenput, Liesbeth"
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Item Disentangling the genetics of lean mass(Oxford University Press, 2019-02-01) Karasik, David; Zillikens, M. Carola; Hsu, Yi-Hsiang; Aghdassi, Ali; Akesson, Kristina; Amin, Najaf; Barroso, Inês; Bennett, David A.; Bertram, Lars; Bochud, Murielle; Borecki, Ingrid B.; Broer, Linda; Buchman, Aron S.; Byberg, Liisa; Campbell, Harry; Campos-Obando, Natalia; Cauley, Jane A.; Cawthon, Peggy M.; Chambers, John C.; Chen, Zhao; Cho, Nam H.; Choi, Hyung Jin; Chou, Wen-Chi; Cummings, Steven R.; De Groot, Lisette C. P. G. M.; De Jager, Phillip L.; Demuth, Ilja; Diatchenko, Luda; Econs, Michael J.; Eiriksdottir, Gudny; Enneman, Anke W.; Eriksson, Joel; Eriksson, Johan G.; Estrada, Karol; Evans, Daniel S.; Feitosa, Mary F.; Fu, Mao; Gieger, Christian; Grallert, Harald; Gudnason, Vilmundur; Lenore, Launer J.; Hayward, Caroline; Hofman, Albert; Homuth, Georg; Huffman, Kim M.; Husted, Lise B.; Illig, Thomas; Ingelsson, Erik; Ittermann, Till; Jansson, John-Olov; Johnson, Toby; Biffar, Reiner; Jordan, Joanne M.; Jula, Antti; Karlsson, Magnus; Khaw, Kay-Tee; Kilpeläinen, Tuomas O.; Klopp, Norman; Kloth, Jacqueline S. L.; Koller, Daniel L.; Kooner, Jaspal S.; Kraus, William E.; Kritchevsky, Stephen; Kutalik, Zoltán; Kuulasmaa, Teemu; Kuusisto, Johanna; Laakso, Markku; Lahti, Jari; Lang, Thomas; Langdahl, Bente L.; Lerch, Markus M.; Lewis, Joshua R.; Lill, Christina; Lind, Lars; Lindgren, Cecilia; Liu, Yongmei; Livshits, Gregory; Ljunggren, Östen; Loos, Ruth J. F.; Lorentzon, Mattias; Luan, Jian'an; Luben, Robert N.; Malkin, Ida; McGuigan, Fiona E.; Medina-Gomez, Carolina; Meitinger, Thomas; Melhus, Håkan; Mellström, Dan; Michaëlsson, Karl; Mitchell, Braxton D.; Morris, Andrew P.; Mosekilde, Leif; Nethander, Maria; Newman, Anne B.; O'Connell, Jeffery R.; Oostra, Ben A.; Orwoll, Eric S.; Palotie, Aarno; Peacock, Munro; Perola, Markus; Peters, Annette; Prince, Richard L.; Psaty, Bruce M.; Räikkönen, Katri; Ralston, Stuart H.; Ripatti, Samuli; Rivadeneira, Fernando; Robbins, John A.; Rotter, Jerome I.; Rudan, Igor; Salomaa, Veikko; Satterfield, Suzanne; Schipf, Sabine; Shin, Chan Soo; Smith, Albert V.; Smith, Shad B.; Soranzo, Nicole; Spector, Timothy D.; Stančáková, Alena; Stefansson, Kari; Steinhagen-Thiessen, Elisabeth; Stolk, Lisette; Streeten, Elizabeth A.; Styrkarsdottir, Unnur; Swart, Karin M. A.; Thompson, Patricia; Thomson, Cynthia A.; Thorleifsson, Gudmar; Thorsteinsdottir, Unnur; Tikkanen, Emmi; Tranah, Gregory J.; Uitterlinden, André G.; Van Duijn, Cornelia M.; Van Schoor, Natasja M.; Vandenput, Liesbeth; Vollenweider, Peter; Völzke, Henry; Wactawski-Wende, Jean; Walker, Mark; Wareham, Nicholas J.; Waterworth, Dawn; Weedon, Michael N.; Wichmann, H-Erich.; Widen, Elisabeth; Williams, Frances M. K.; Wilson, James F.; Wright, Nicole C.; Yerges-Armstrong, Laura M.; Yu, Lei; Zhang, Weihua; Zhao, Jing Hua; Zhou, Yanhua; Nielson, Carrie M.; Harris, Tamara B.; Demissie, Serkalem; Kiel, Douglas P.; Ohlsson, Claes; Medicine, School of MedicineBackground: 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.Item Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels(Nature Publishing Group, 2018-01-17) Jiang, Xia; O’Reilly, Paul F.; Aschard, Hugues; Hsu, Yi-Hsiang; Richards, J. Brent; Dupuis, Josée; Ingelsson, Erik; Karasik, David; Pilz, Stefan; Berry, Diane; Kestenbaum, Bryan; Zheng, Jusheng; Luan, Jianan; Sofianopoulou, Eleni; Streeten, Elizabeth A.; Albanes, Demetrius; Lutsey, Pamela L.; Yao, Lu; Tang, Weihong; Econs, Michael J.; Wallaschofski, Henri; Völzke, Henry; Zhou, Ang; Power, Chris; McCarthy, Mark I.; Michos, Erin D.; Boerwinkle, Eric; Weinstein, Stephanie J.; Freedman, Neal D.; Huang, Wen-Yi; Van Schoor, Natasja M.; Velde, Nathalie van der; de Groot, Lisette C. P. G. M.; Enneman, Anke; Cupples, L. Adrienne; Booth, Sarah L.; Vasan, Ramachandran S.; Liu, Ching-Ti; Zhou, Yanhua; Ripatti, Samuli; Ohlsson, Claes; Vandenput, Liesbeth; Lorentzon, Mattias; Eriksson, Johan G.; Shea, M. Kyla; Houston, Denise K.; Kritchevsky, Stephen B.; Liu, Yongmei; Lohman, Kurt K.; Ferrucci, Luigi; Peacock, Munro; Gieger, Christian; Beekman, Marian; Slagboom, Eline; Deelen, Joris; Heemst, Diana van; Kleber, Marcus E.; März, Winfried; de Boer, Ian H.; Wood, Alexis C.; Rotter, Jerome I.; Rich, Stephen S.; Robinson-Cohen, Cassianne; Heijer, Martin den; Jarvelin, Marjo-Riitta; Cavadino, Alana; Joshi, Peter K.; Wilson, James F.; Hayward, Caroline; Lind, Lars; Michaëlsson, Karl; Trompet, Stella; Zillikens, M. Carola; Uitterlinden, Andre G.; Rivadeneira, Fernando; Broer, Linda; Zgaga, Lina; Campbell, Harry; Theodoratou, Evropi; Farrington, Susan M.; Timofeeva, Maria; Dunlop, Malcolm G.; Valdes, Ana M.; Tikkanen, Emmi; Lehtimäki, Terho; Lyytikäinen, Leo-Pekka; Kähönen, Mika; Raitakari, Olli T.; Mikkilä, Vera; Ikram, M. Arfan; Sattar, Naveed; Jukema, J. Wouter; Wareham, Nicholas J.; Langenberg, Claudia; Forouhi, Nita G.; Gundersen, Thomas E.; Khaw, Kay-Tee; Butterworth, Adam S.; Danesh, John; Spector, Timothy; Wang, Thomas J.; Hyppönen, Elina; Kraft, Peter; Kiel, Douglas P.; Medicine, School of MedicineVitamin D is a steroid hormone precursor that is associated with a range of human traits and diseases. Previous GWAS of serum 25-hydroxyvitamin D concentrations have identified four genome-wide significant loci (GC, NADSYN1/DHCR7, CYP2R1, CYP24A1). In this study, we expand the previous SUNLIGHT Consortium GWAS discovery sample size from 16,125 to 79,366 (all European descent). This larger GWAS yields two additional loci harboring genome-wide significant variants (P = 4.7×10-9 at rs8018720 in SEC23A, and P = 1.9×10-14 at rs10745742 in AMDHD1). The overall estimate of heritability of 25-hydroxyvitamin D serum concentrations attributable to GWAS common SNPs is 7.5%, with statistically significant loci explaining 38% of this total. Further investigation identifies signal enrichment in immune and hematopoietic tissues, and clustering with autoimmune diseases in cell-type-specific analysis. Larger studies are required to identify additional common SNPs, and to explore the role of rare or structural variants and gene-gene interactions in the heritability of circulating 25-hydroxyvitamin D levelsItem Meta-Analysis of Genomewide Association Studies Reveals Genetic Variants for Hip Bone Geometry(Wiley, 2019-07) Hsu, Yi-Hsiang; Estrada, Karol; Evangelou, Evangelos; Ackert-Bicknell, Cheryl; Akesson, Kristina; Beck, Thomas; Brown, Suzanne J.; Capellini, Terence; Carbone, Laura; Cauley, Jane; Cheung, Ching-Lung; Cummings, Steven R.; Czerwinski, Stefan; Demissie, Serkalem; Econs, Michael; Evans, Daniel; Farber, Charles; Gautvik, Kaare; Harris, Tamara; Kammerer, Candace; Kemp, John; Koller, Daniel L.; Kung, Annie; Lawlor, Debbie; Lee, Miryoung; Lorentzon, Mattias; McGuigan, Fiona; Medina-Gomez, Carolina; Mitchell, Braxton; Newman, Anne; Nielson, Carrie; Ohlsson, Claes; Peacock, Munro; Reppe, Sjur; Richards, J. Brent; Robbins, John; Sigurdsson, Gunnar; Spector, Timothy D.; Stefansson, Kari; Streeten, Elizabeth; Styrkarsdottir, Unnur; Tobias, Jonathan; Trajanoska, Katerina; Uitterlinden, André; Vandenput, Liesbeth; Wilson, Scott G.; Yerges-Armstrong, Laura; Young, Mariel; Zillikens, Carola; Rivadeneira, Fernando; Kiel, Douglas P.; Karasik, David; Medicine, School of MedicineHip geometry is an important predictor of fracture. We performed a meta-analysis of GWAS studies in adults to identify genetic variants that are associated with proximal femur geometry phenotypes. We analyzed four phenotypes: (i) femoral neck length; (ii) neck-shaft angle; (iii) femoral neck width, and (iv) femoral neck section modulus, estimated from DXA scans using algorithms of hip structure analysis. In the Discovery stage, 10 cohort studies were included in the fixed-effect meta-analysis, with up to 18,719 men and women ages 16 to 93 years. Association analyses were performed with ∼2.5 million polymorphisms under an additive model adjusted for age, body mass index, and height. Replication analyses of meta-GWAS significant loci (at adjusted genomewide significance [GWS], threshold p ≤ 2.6 × 10-8 ) were performed in seven additional cohorts in silico. We looked up SNPs associated in our analysis, for association with height, bone mineral density (BMD), and fracture. In meta-analysis (combined Discovery and Replication stages), GWS associations were found at 5p15 (IRX1 and ADAMTS16); 5q35 near FGFR4; at 12p11 (in CCDC91); 11q13 (near LRP5 and PPP6R3 (rs7102273)). Several hip geometry signals overlapped with BMD, including LRP5 (chr. 11). Chr. 11 SNP rs7102273 was associated with any-type fracture (p = 7.5 × 10-5 ). We used bone transcriptome data and discovered several significant eQTLs, including rs7102273 and PPP6R3 expression (p = 0.0007), and rs6556301 (intergenic, chr.5 near FGFR4) and PDLIM7 expression (p = 0.005). In conclusion, we found associations between several genes and hip geometry measures that explained 12% to 22% of heritability at different sites. The results provide a defined set of genes related to biological pathways relevant to BMD and etiology of bone fragility.