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1.  Sequence Variation in TMEM18 in Association with Body Mass Index: The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Targeted Sequencing Study 
Genome-wide association studies (GWAS) for body mass index (BMI) previously identified a locus near TMEM18. We conducted targeted sequencing of this region to investigate the role of common, low frequency, and rare variation influencing BMI.
Methods and Results
We sequenced TMEM18 and regions downstream of TMEM18 on chromosome 2 in 3976 individuals of European ancestry from three community-based cohorts (Atherosclerosis Risk in Communities, Cardiovascular Health Study and Framingham Heart Study), including 200 adults selected for high BMI. We examined the association between BMI and variants identified in the region from nucleotide position 586,432 to 677,539 (hg18). Rare variants (MAF <1%) were analyzed using a burden test and the Sequence Kernel of Association Test (SKAT). Results from the three cohort studies were meta-analyzed. We estimate that mean BMI is 0.43 kg/m2 higher for each copy of the G allele of SNP rs7596758 (MAF=29%, p=3.46 × 10−4) using a Bonferroni threshold of p <4.6 × 10−4). Analyses conditional on previous GWAS SNPs associated with BMI in the region led to attenuation of this signal and uncovered another independent (r2<0.2), statistically significant association, rs186019316 (p=2.11 × 10−4). Both rs186019316 and rs7596758 or proxies are located in transcription factor binding regions. No significant association with rare variants was found in either the exons of TMEM18 or the 3’ GWAS region.
Targeted sequencing around TMEM18 identified two novel BMI variants with possible regulatory function.
PMCID: PMC4135723  PMID: 24951660
body mass index; genetic association; targeted resequencing; TMEM18
2.  Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits 
Pankratz, Nathan | Schick, Ursula M | Zhou, Yi | Zhou, Wei | Ahluwalia, Tarunveer Singh | Allende, Maria Laura | Auer, Paul L | Bork-Jensen, Jette | Brody, Jennifer A | Chen, Ming-Huei | Clavo, Vinna | Eicher, John D | Grarup, Niels | Hagedorn, Elliott J | Hu, Bella | Hunker, Kristina | Johnson, Andrew D | Leusink, Maarten | Lu, Yingchang | Lyytikäinen, Leo-Pekka | Manichaikul, Ani | Marioni, Riccardo E | Nalls, Mike A | Pazoki, Raha | Smith, Albert Vernon | van Rooij, Frank J A | Yang, Min-Lee | Zhang, Xiaoling | Zhang, Yan | Asselbergs, Folkert W | Boerwinkle, Eric | Borecki, Ingrid B | Bottinger, Erwin P | Cushman, Mary | de Bakker, Paul I W | Deary, Ian J | Dong, Liguang | Feitosa, Mary F | Floyd, James S | Franceschini, Nora | Franco, Oscar H | Garcia, Melissa E | Grove, Megan L | Gudnason, Vilmundur | Hansen, Torben | Harris, Tamara B | Hofman, Albert | Jackson, Rebecca D | Jia, Jia | Kähönen, Mika | Launer, Lenore J | Lehtimäki, Terho | Liewald, David C | Linneberg, Allan | Liu, Yongmei | Loos, Ruth J F | Nguyen, Vy M | Numans, Mattijs E | Pedersen, Oluf | Psaty, Bruce M | Raitakari, Olli T | Rich, Stephen S | Rivadeneira, Fernando | Di Sant, Amanda M Rosa | Rotter, Jerome I | Starr, John M | Taylor, Kent D | Thuesen, Betina Heinsbæk | Tracy, Russell P | Uitterlinden, Andre G | Wang, Jiansong | Wang, Judy | Dehghan, Abbas | Huo, Yong | Cupples, L Adrienne | Wilson, James G | Proia, Richard L | Zon, Leonard I | O’Donnell, Christopher J | Reiner, Alex P | Ganesh, Santhi K
Nature genetics  2016;48(8):867-876.
Hematologic measures such as hematocrit and white blood cell (WBC) count are heritable and clinically relevant. Erythrocyte and WBC phenotypes were analyzed with Illumina HumanExome BeadChip genotypes in 52,531 individuals (37,775 of European ancestry; 11,589 African Americans; 3,167 Hispanic Americans) from 16 population-based cohorts. We then performed replication analyses of novel discoveries in 18,018 European American women and 5,261 Han Chinese. We identified and replicated four novel erythrocyte trait-locus associations (CEP89, SHROOM3, FADS2, and APOE) and six novel WBC loci for neutrophil count (S1PR4), monocyte count (BTBD8, NLRP12, and IL17RA), eosinophil count (IRF1), and total WBC (MYB). The novel association of a rare missense variant in S1PR4 supports the role of sphingosine-1-phosphate signaling in leukocyte trafficking and circulating neutrophil counts. Loss-of-function experiments of S1pr4 in mouse and zebrafish demonstrated phenotypes consistent with the association observed in humans and altered kinetics of neutrophil recruitment and resolution in response to tissue injury.
PMCID: PMC5145000  PMID: 27399967
3.  Genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease 
Scott, Robert A. | Freitag, Daniel F. | Li, Li | Chu, Audrey Y. | Surendran, Praveen | Young, Robin | Grarup, Niels | Stancáková, Alena | Chen, Yuning | V.Varga, Tibor | Yaghootkar, Hanieh | Luan, Jian'an | Zhao, Jing Hua | Willems, Sara M. | Wessel, Jennifer | Wang, Shuai | Maruthur, Nisa | Michailidou, Kyriaki | Pirie, Ailith | van der Lee, Sven J. | Gillson, Christopher | Olama, Ali Amin Al | Amouyel, Philippe | Arriola, Larraitz | Arveiler, Dominique | Aviles-Olmos, Iciar | Balkau, Beverley | Barricarte, Aurelio | Barroso, Inês | Garcia, Sara Benlloch | Bis, Joshua C. | Blankenberg, Stefan | Boehnke, Michael | Boeing, Heiner | Boerwinkle, Eric | Borecki, Ingrid B. | Bork-Jensen, Jette | Bowden, Sarah | Caldas, Carlos | Caslake, Muriel | Cupples, L. Adrienne | Cruchaga, Carlos | Czajkowski, Jacek | den Hoed, Marcel | Dunn, Janet A. | Earl, Helena M. | Ehret, Georg B. | Ferrannini, Ele | Ferrieres, Jean | Foltynie, Thomas | Ford, Ian | Forouhi, Nita G. | Gianfagna, Francesco | Gonzalez, Carlos | Grioni, Sara | Hiller, Louise | Jansson, Jan-Håkan | Jørgensen, Marit E. | Jukema, J. Wouter | Kaaks, Rudolf | Kee, Frank | Kerrison, Nicola D. | Key, Timothy J. | Kontto, Jukka | Kote-Jarai, Zsofia | Kraja, Aldi T. | Kuulasmaa, Kari | Kuusisto, Johanna | Linneberg, Allan | Liu, Chunyu | Marenne, Gaëlle | Mohlke, Karen L. | Morris, Andrew P. | Muir, Kenneth | Müller-Nurasyid, Martina | Munroe, Patricia B. | Navarro, Carmen | Nielsen, Sune F. | Nilsson, Peter M. | Nordestgaard, Børge G. | Packard, Chris J. | Palli, Domenico | Panico, Salvatore | Peloso, Gina M. | Perola, Markus | Peters, Annette | Poole, Christopher J. | Quirós, J. Ramón | Rolandsson, Olov | Sacerdote, Carlotta | Salomaa, Veikko | Sánchez, María-José | Sattar, Naveed | Sharp, Stephen J. | Sims, Rebecca | Slimani, Nadia | Smith, Jennifer A. | Thompson, Deborah J. | Trompet, Stella | Tumino, Rosario | van der A, Daphne L. | van der Schouw, Yvonne T. | Virtamo, Jarmo | Walker, Mark | Walter, Klaudia | Abraham, Jean E. | Amundadottir, Laufey T. | Aponte, Jennifer L. | Butterworth, Adam S. | Dupuis, Josée | Easton, Douglas F. | Eeles, Rosalind A. | Erdmann, Jeanette | Franks, Paul W. | Frayling, Timothy M. | Hansen, Torben | Howson, Joanna M. M. | Jørgensen, Torben | Kooner, Jaspal | Laakso, Markku | Langenberg, Claudia | McCarthy, Mark I. | Pankow, James S. | Pedersen, Oluf | Riboli, Elio | Rotter, Jerome I. | Saleheen, Danish | Samani, Nilesh J. | Schunkert, Heribert | Vollenweider, Peter | O'Rahilly, Stephen | Deloukas, Panos | Danesh, John | Goodarzi, Mark O. | Kathiresan, Sekar | Meigs, James B. | Ehm, Margaret G. | Wareham, Nicholas J. | Waterworth, Dawn M.
Science translational medicine  2016;8(341):341ra76.
Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to inform development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in 6 genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing, and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr;rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and lower T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomised controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.
PMCID: PMC5219001  PMID: 27252175
4.  A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape 
Ried, Janina S. | Jeff M., Janina | Chu, Audrey Y. | Bragg-Gresham, Jennifer L. | van Dongen, Jenny | Huffman, Jennifer E. | Ahluwalia, Tarunveer S. | Cadby, Gemma | Eklund, Niina | Eriksson, Joel | Esko, Tõnu | Feitosa, Mary F. | Goel, Anuj | Gorski, Mathias | Hayward, Caroline | Heard-Costa, Nancy L. | Jackson, Anne U. | Jokinen, Eero | Kanoni, Stavroula | Kristiansson, Kati | Kutalik, Zoltán | Lahti, Jari | Luan, Jian'an | Mägi, Reedik | Mahajan, Anubha | Mangino, Massimo | Medina-Gomez, Carolina | Monda, Keri L. | Nolte, Ilja M. | Pérusse, Louis | Prokopenko, Inga | Qi, Lu | Rose, Lynda M. | Salvi, Erika | Smith, Megan T. | Snieder, Harold | Stančáková, Alena | Ju Sung, Yun | Tachmazidou, Ioanna | Teumer, Alexander | Thorleifsson, Gudmar | van der Harst, Pim | Walker, Ryan W. | Wang, Sophie R. | Wild, Sarah H. | Willems, Sara M. | Wong, Andrew | Zhang, Weihua | Albrecht, Eva | Couto Alves, Alexessander | Bakker, Stephan J. L. | Barlassina, Cristina | Bartz, Traci M. | Beilby, John | Bellis, Claire | Bergman, Richard N. | Bergmann, Sven | Blangero, John | Blüher, Matthias | Boerwinkle, Eric | Bonnycastle, Lori L. | Bornstein, Stefan R. | Bruinenberg, Marcel | Campbell, Harry | Chen, Yii-Der Ida | Chiang, Charleston W. K. | Chines, Peter S. | Collins, Francis S | Cucca, Fracensco | Cupples, L Adrienne | D'Avila, Francesca | de Geus, Eco J .C. | Dedoussis, George | Dimitriou, Maria | Döring, Angela | Eriksson, Johan G. | Farmaki, Aliki-Eleni | Farrall, Martin | Ferreira, Teresa | Fischer, Krista | Forouhi, Nita G. | Friedrich, Nele | Gjesing, Anette Prior | Glorioso, Nicola | Graff, Mariaelisa | Grallert, Harald | Grarup, Niels | Gräßler, Jürgen | Grewal, Jagvir | Hamsten, Anders | Harder, Marie Neergaard | Hartman, Catharina A. | Hassinen, Maija | Hastie, Nicholas | Hattersley, Andrew Tym | Havulinna, Aki S. | Heliövaara, Markku | Hillege, Hans | Hofman, Albert | Holmen, Oddgeir | Homuth, Georg | Hottenga, Jouke-Jan | Hui, Jennie | Husemoen, Lise Lotte | Hysi, Pirro G. | Isaacs, Aaron | Ittermann, Till | Jalilzadeh, Shapour | James, Alan L. | Jørgensen, Torben | Jousilahti, Pekka | Jula, Antti | Marie Justesen, Johanne | Justice, Anne E. | Kähönen, Mika | Karaleftheri, Maria | Tee Khaw, Kay | Keinanen-Kiukaanniemi, Sirkka M. | Kinnunen, Leena | Knekt, Paul B. | Koistinen, Heikki A. | Kolcic, Ivana | Kooner, Ishminder K. | Koskinen, Seppo | Kovacs, Peter | Kyriakou, Theodosios | Laitinen, Tomi | Langenberg, Claudia | Lewin, Alexandra M. | Lichtner, Peter | Lindgren, Cecilia M. | Lindström, Jaana | Linneberg, Allan | Lorbeer, Roberto | Lorentzon, Mattias | Luben, Robert | Lyssenko, Valeriya | Männistö, Satu | Manunta, Paolo | Leach, Irene Mateo | McArdle, Wendy L. | Mcknight, Barbara | Mohlke, Karen L. | Mihailov, Evelin | Milani, Lili | Mills, Rebecca | Montasser, May E. | Morris, Andrew P. | Müller, Gabriele | Musk, Arthur W. | Narisu, Narisu | Ong, Ken K. | Oostra, Ben A. | Osmond, Clive | Palotie, Aarno | Pankow, James S. | Paternoster, Lavinia | Penninx, Brenda W. | Pichler, Irene | Pilia, Maria G. | Polašek, Ozren | Pramstaller, Peter P. | Raitakari, Olli T | Rankinen, Tuomo | Rao, D. C. | Rayner, Nigel W. | Ribel-Madsen, Rasmus | Rice, Treva K. | Richards, Marcus | Ridker, Paul M. | Rivadeneira, Fernando | Ryan, Kathy A. | Sanna, Serena | Sarzynski, Mark A. | Scholtens, Salome | Scott, Robert A. | Sebert, Sylvain | Southam, Lorraine | Sparsø, Thomas Hempel | Steinthorsdottir, Valgerdur | Stirrups, Kathleen | Stolk, Ronald P. | Strauch, Konstantin | Stringham, Heather M. | Swertz, Morris A. | Swift, Amy J. | Tönjes, Anke | Tsafantakis, Emmanouil | van der Most, Peter J. | Van Vliet-Ostaptchouk, Jana V. | Vandenput, Liesbeth | Vartiainen, Erkki | Venturini, Cristina | Verweij, Niek | Viikari, Jorma S. | Vitart, Veronique | Vohl, Marie-Claude | Vonk, Judith M. | Waeber, Gérard | Widén, Elisabeth | Willemsen, Gonneke | Wilsgaard, Tom | Winkler, Thomas W. | Wright, Alan F. | Yerges-Armstrong, Laura M. | Hua Zhao, Jing | Carola Zillikens, M. | Boomsma, Dorret I. | Bouchard, Claude | Chambers, John C. | Chasman, Daniel I. | Cusi, Daniele | Gansevoort, Ron T. | Gieger, Christian | Hansen, Torben | Hicks, Andrew A. | Hu, Frank | Hveem, Kristian | Jarvelin, Marjo-Riitta | Kajantie, Eero | Kooner, Jaspal S. | Kuh, Diana | Kuusisto, Johanna | Laakso, Markku | Lakka, Timo A. | Lehtimäki, Terho | Metspalu, Andres | Njølstad, Inger | Ohlsson, Claes | Oldehinkel, Albertine J. | Palmer, Lyle J. | Pedersen, Oluf | Perola, Markus | Peters, Annette | Psaty, Bruce M. | Puolijoki, Hannu | Rauramaa, Rainer | Rudan, Igor | Salomaa, Veikko | Schwarz, Peter E. H. | Shudiner, Alan R. | Smit, Jan H. | Sørensen, Thorkild I. A. | Spector, Timothy D. | Stefansson, Kari | Stumvoll, Michael | Tremblay, Angelo | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | Völker, Uwe | Vollenweider, Peter | Wareham, Nicholas J. | Watkins, Hugh | Wilson, James F. | Zeggini, Eleftheria | Abecasis, Goncalo R. | Boehnke, Michael | Borecki, Ingrid B. | Deloukas, Panos | van Duijn, Cornelia M. | Fox, Caroline | Groop, Leif C. | Heid, Iris M. | Hunter, David J. | Kaplan, Robert C. | McCarthy, Mark I. | North, Kari E. | O'Connell, Jeffrey R. | Schlessinger, David | Thorsteinsdottir, Unnur | Strachan, David P. | Frayling, Timothy | Hirschhorn, Joel N. | Müller-Nurasyid, Martina | Loos, Ruth J. F.
Nature Communications  2016;7:13357.
Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.
Past genome-wide associate studies have identified hundreds of genetic loci that influence body size and shape when examined one trait at a time. Here, Jeff and colleagues develop an aggregate score of various body traits, and use meta-analysis to find new loci linked to body shape.
PMCID: PMC5114527  PMID: 27876822
5.  Multiple Loci Are Associated with White Blood Cell Phenotypes 
Nalls, Michael A. | Couper, David J. | Tanaka, Toshiko | van Rooij, Frank J. A. | Chen, Ming-Huei | Smith, Albert V. | Toniolo, Daniela | Zakai, Neil A. | Yang, Qiong | Greinacher, Andreas | Wood, Andrew R. | Garcia, Melissa | Gasparini, Paolo | Liu, Yongmei | Lumley, Thomas | Folsom, Aaron R. | Reiner, Alex P. | Gieger, Christian | Lagou, Vasiliki | Felix, Janine F. | Völzke, Henry | Gouskova, Natalia A. | Biffi, Alessandro | Döring, Angela | Völker, Uwe | Chong, Sean | Wiggins, Kerri L. | Rendon, Augusto | Dehghan, Abbas | Moore, Matt | Taylor, Kent | Wilson, James G. | Lettre, Guillaume | Hofman, Albert | Bis, Joshua C. | Pirastu, Nicola | Fox, Caroline S. | Meisinger, Christa | Sambrook, Jennifer | Arepalli, Sampath | Nauck, Matthias | Prokisch, Holger | Stephens, Jonathan | Glazer, Nicole L. | Cupples, L. Adrienne | Okada, Yukinori | Takahashi, Atsushi | Kamatani, Yoichiro | Matsuda, Koichi | Tsunoda, Tatsuhiko | Tanaka, Toshihiro | Kubo, Michiaki | Nakamura, Yusuke | Yamamoto, Kazuhiko | Kamatani, Naoyuki | Stumvoll, Michael | Tönjes, Anke | Prokopenko, Inga | Illig, Thomas | Patel, Kushang V. | Garner, Stephen F. | Kuhnel, Brigitte | Mangino, Massimo | Oostra, Ben A. | Thein, Swee Lay | Coresh, Josef | Wichmann, H.-Erich | Menzel, Stephan | Lin, JingPing | Pistis, Giorgio | Uitterlinden, André G. | Spector, Tim D. | Teumer, Alexander | Eiriksdottir, Gudny | Gudnason, Vilmundur | Bandinelli, Stefania | Frayling, Timothy M. | Chakravarti, Aravinda | van Duijn, Cornelia M. | Melzer, David | Ouwehand, Willem H. | Levy, Daniel | Boerwinkle, Eric | Singleton, Andrew B. | Hernandez, Dena G. | Longo, Dan L. | Soranzo, Nicole | Witteman, Jacqueline C. M. | Psaty, Bruce M. | Ferrucci, Luigi | Harris, Tamara B. | O'Donnell, Christopher J. | Ganesh, Santhi K.
PLoS Genetics  2011;7(6):e1002113.
White blood cell (WBC) count is a common clinical measure from complete blood count assays, and it varies widely among healthy individuals. Total WBC count and its constituent subtypes have been shown to be moderately heritable, with the heritability estimates varying across cell types. We studied 19,509 subjects from seven cohorts in a discovery analysis, and 11,823 subjects from ten cohorts for replication analyses, to determine genetic factors influencing variability within the normal hematological range for total WBC count and five WBC subtype measures. Cohort specific data was supplied by the CHARGE, HeamGen, and INGI consortia, as well as independent collaborative studies. We identified and replicated ten associations with total WBC count and five WBC subtypes at seven different genomic loci (total WBC count—6p21 in the HLA region, 17q21 near ORMDL3, and CSF3; neutrophil count—17q21; basophil count- 3p21 near RPN1 and C3orf27; lymphocyte count—6p21, 19p13 at EPS15L1; monocyte count—2q31 at ITGA4, 3q21, 8q24 an intergenic region, 9q31 near EDG2), including three previously reported associations and seven novel associations. To investigate functional relationships among variants contributing to variability in the six WBC traits, we utilized gene expression- and pathways-based analyses. We implemented gene-clustering algorithms to evaluate functional connectivity among implicated loci and showed functional relationships across cell types. Gene expression data from whole blood was utilized to show that significant biological consequences can be extracted from our genome-wide analyses, with effect estimates for significant loci from the meta-analyses being highly corellated with the proximal gene expression. In addition, collaborative efforts between the groups contributing to this study and related studies conducted by the COGENT and RIKEN groups allowed for the examination of effect homogeneity for genome-wide significant associations across populations of diverse ancestral backgrounds.
Author Summary
WBC traits are highly variable, moderately heritable, and commonly assayed as part of clinical complete blood count (CBC) examinations. The counts of constituent cell subtypes comprising the WBC count measure are assayed as part of a standard clinical WBC differential test. In this study we employed meta-analytic techniques and identified ten associations with WBC measures at seven genomic loci in a large sample set of over 31,000 participants. Cohort specific data was supplied by the CHARGE, HeamGen, and INGI consortia, as well as independent collaborative studies. We confirm previous associations of WBC traits with three loci and identified seven novel loci. We also utilize a number of additional analytic methods to infer the functional relatedness of independently implicated loci across WBC phenotypes, as well as investigate direct functional consequences of these loci through analyses of genomic variation affecting the expression of proximal genes in samples of whole blood. In addition, subsequent collaborative efforts with studies of WBC traits in African-American and Japanese cohorts allowed for the investigation of the effects of these genomic variants across populations of diverse continental ancestries.
PMCID: PMC3128114  PMID: 21738480
6.  Optimizing the Tracking of Falls in Studies of Older Participants: Comparison of Quarterly Telephone Recall With Monthly Falls Calendars in the MOBILIZE Boston Study 
American Journal of Epidemiology  2010;171(9):1031-1036.
Tracking falls among elders is challenging. In this reliability study, which took place between October 2007 and February 2008, the authors compared participants’ daily recordings of falls on calendars with a telephone survey of recall of falls over the previous 3 months within the population-based MOBILIZE Boston Study cohort, a cohort of 765 elders. From the cohort, 218 participants were randomly selected. Falls were tracked prospectively on daily calendars (mailed back monthly). Telephone recalls of falls over the previous 3 months were conducted in January and February 2008. Agreement, sensitivity, and specificity were calculated to compare the occurrence of falls as determined by 3-month recall with falls recorded by daily calendar (gold standard) during the same 3-month period. Results showed good agreement between recall and calendars: 27 persons reported a fall by both methods. However, while the 3-month recall correctly classified persons who did not fall (164 persons by both methods), it missed 25% of participants who fell (of 36 participants with a calendar-reported fall, 9 did not report a fall by telephone recall). Kappa was 0.74 (95% confidence interval: 0.68, 0.80), sensitivity was 75%, and specificity was 96%. Retrospective 3-month recall of falls resulted in underreporting of falls by as much as 25% compared with daily calendars. Calendars should be considered the preferred method of ascertaining falls in longitudinal studies.
PMCID: PMC2877474  PMID: 20360242
accidental falls; aged; cohort studies; data collection; epidemiologic methods; frail elderly; mental recall; reproducibility of results
7.  Design of the Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Study 
Recent genome-wide association studies (GWAS) of myocardial infarction (MI) and other forms of coronary artery disease (CAD) have led to the discovery of at least 13 genetic loci. In addition to the effect size, power to detect associations is largely driven by sample size. Therefore, to maximize the chance of finding novel susceptibility loci for CAD and MI, the Coronary ARtery DIsease Genome-wide Replication And Meta-analysis (CARDIoGRAM) consortium was formed.
Methods and Results
CARDIoGRAM combines data from all published and several unpublished GWAS in individuals with European ancestry; includes >22 000 cases with CAD, MI, or both and >60 000 controls; and unifies samples from the Atherosclerotic Disease VAscular functioN and genetiC Epidemiology study, CADomics, Cohorts for Heart and Aging Research in Genomic Epidemiology, deCODE, the German Myocardial Infarction Family Studies I, II, and III, Ludwigshafen Risk and Cardiovascular Heath Study/AtheroRemo, MedStar, Myocardial Infarction Genetics Consortium, Ottawa Heart Genomics Study, PennCath, and the Wellcome Trust Case Control Consortium. Genotyping was carried out on Affymetrix or Illumina platforms followed by imputation of genotypes in most studies. On average, 2.2 million single nucleotide polymorphisms were generated per study. The results from each study are combined using meta-analysis. As proof of principle, we meta-analyzed risk variants at 9p21 and found that rs1333049 confers a 29% increase in risk for MI per copy (P=2×10−20).
CARDIoGRAM is poised to contribute to our understanding of the role of common genetic variation on risk for CAD and MI.
PMCID: PMC3070269  PMID: 20923989
coronary artery disease; myocardial infarction; meta-analysis; genetics
8.  Ethnic Differences in Glucose Homeostasis Markers between the Kyushu-Okinawa Population Study and the Framingham Offspring Study 
Scientific Reports  2016;6:36725.
We compared markers of glucose homeostasis and their association with diabetes and impaired fasting glucose (IFG) in Fukuoka, Japanese subjects (n = 1108) and age-, gender- and menopausal status-matched participants in the Framingham Offspring Study (n = 1096). The markers examined included fasting glucose, insulin, adiponectin, and glycated albumin, as well as body mass index (BMI), use of medications, and history of diabetes. The results showed that IFG prevalence in Japanese men (15.9%) and women (7.4%) were 50% less than those observed in Framingham men (34.5%) and women (21.4%) (P < 0.001). However, the diabetes prevalence in Japanese men at 13.3% was twice as high (P < 0.01) as the rate in Framingham men at 6.5%, while these rates were similar in women. Median insulin levels in Japanese men (4.6 μIU/mL) and women (4.3 μIU/mL) were about 50% lower (P < 0.001) than those in Framingham men (10.8 μIU/mL) and women (9.9 μIU/mL), as were insulin resistance values (P < 0.001). These population differences were also observed after subjects were stratified by glucose levels. In conclusion, our data indicate that there is significantly less IFG, lower insulin levels, and insulin resistance, but higher diabetes prevalence in Fukuoka men than in Framingham men, indicating that insulin deficiency may be an important cause of diabetes in Japan.
PMCID: PMC5103215  PMID: 27830830
9.  A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study 
Typical survival studies follow individuals to an event and measure explanatory variables for that event, sometimes repeatedly over the course of follow up. The Cox regression model has been used widely in the analyses of time to diagnosis or death from disease. The associations between the survival outcome and time dependent measures may be biased unless they are modeled appropriately.
In this paper we explore the Time Dependent Cox Regression Model (TDCM), which quantifies the effect of repeated measures of covariates in the analysis of time to event data. This model is commonly used in biomedical research but sometimes does not explicitly adjust for the times at which time dependent explanatory variables are measured. This approach can yield different estimates of association compared to a model that adjusts for these times. In order to address the question of how different these estimates are from a statistical perspective, we compare the TDCM to Pooled Logistic Regression (PLR) and Cross Sectional Pooling (CSP), considering models that adjust and do not adjust for time in PLR and CSP.
In a series of simulations we found that time adjusted CSP provided identical results to the TDCM while the PLR showed larger parameter estimates compared to the time adjusted CSP and the TDCM in scenarios with high event rates. We also observed upwardly biased estimates in the unadjusted CSP and unadjusted PLR methods. The time adjusted PLR had a positive bias in the time dependent Age effect with reduced bias when the event rate is low. The PLR methods showed a negative bias in the Sex effect, a subject level covariate, when compared to the other methods. The Cox models yielded reliable estimates for the Sex effect in all scenarios considered.
We conclude that survival analyses that explicitly account in the statistical model for the times at which time dependent covariates are measured provide more reliable estimates compared to unadjusted analyses. We present results from the Framingham Heart Study in which lipid measurements and myocardial infarction data events were collected over a period of 26 years.
Electronic supplementary material
The online version of this article (doi:10.1186/s12874-016-0248-6) contains supplementary material, which is available to authorized users.
PMCID: PMC5094095  PMID: 27809784
Time dependent covariate model (TDCM); Cross sectional pooling (CSP); Pooled logistic regression (PLR); Longitudinal and survival data
10.  Imputing rare variants in families using a two-stage approach 
BMC Proceedings  2016;10(Suppl 7):209-214.
Recent focus on studying rare variants makes imputation accuracy of rare variants an important issue. Many approaches have been proposed to increase imputation accuracy among rare variants, from reference panel selection to combinations of existing methods to multistage analyses. We aimed to bring the strengths of these new approaches together with our proposed two-stage imputation for family data.
Our imputation methods were tested on the region from 46.75Mb to 49.25Mb on chromosome 3. We did quality control based on the proportion of missing genotypes per variant and individual, leaving 495 individuals with 761 genome-wide association studies (GWAS) variants only, 45 with 14,077 sequence variants only, and 419 with both GWAS and sequencing data. All data were prephased using SHAPEIT2 with a duo hidden Markov model algorithm prior to performing imputation. Imputations were performed 100 times, each time masking the sequence data for 1 individual and imputing it from the GWAS data. We used well-imputed genotypes, defined as a probability of greater than 0.9, above 2 different minor allele frequency cutoffs—0.01 and 0.05—from Impute2 as input for Merlin, and compared these results to Impute2 and Merlin separately. The imputed results were evaluated using correlation measurement and the imputation quality score.
Our method improved imputation accuracy, measured by imputation quality score, for variants with minor allele frequency between 0.01 and 0.40, but failed to improve accuracy for variants with minor allele frequency less than 0.01 when we used a minor allele frequency cutoff of 0.01 for the Impute2 results. In contrast, our 2-stage approach with a minor allele frequency cutoff of 0.05 performed the worst of all methods for variants with minor allele frequency between 0.01 and 0.40.
This method gave promising results, but may be further improved by changing the inclusion criteria of Impute2 variants. More analyses are needed on a larger region with different inclusion thresholds to assess the accuracy of this approach.
PMCID: PMC5133481  PMID: 27980638
11.  Strategies to Design and Analyze Targeted Sequencing Data: The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Targeted Sequencing Study 
Genome-wide association studies (GWAS) have identified thousands of genetic variants that influence a variety of diseases and health-related quantitative traits. However, the causal variants underlying the majority of genetic associations remain unknown. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Targeted Sequencing Study aims to follow up GWAS signals and identify novel associations of the allelic spectrum of identified variants with cardiovascular related traits.
Methods and Results
The study included 4,231 participants from three CHARGE cohorts: the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, and the Framingham Heart Study. We used a case-cohort design in which we selected both a random sample of participants and participants with extreme phenotypes for each of 14 traits. We sequenced and analyzed 77 genomic loci, which had previously been associated with one or more of 14 phenotypes. A total of 52,736 variants were characterized by sequencing and passed our stringent quality control criteria. For common variants (minor allele frequency ≥1%), we performed unweighted regression analyses to obtain p-values for associations and weighted regression analyses to obtain effect estimates that accounted for the sampling design. For rare variants, we applied two approaches: collapsed aggregate statistics and joint analysis of variants using the Sequence Kernel Association Test.
We sequenced 77 genomic loci in participants from three cohorts. We established a set of filters to identify high-quality variants, and implemented statistical and bioinformatics strategies to analyze the sequence data, and identify potentially functional variants within GWAS loci.
PMCID: PMC4176824  PMID: 24951659
genetics; epidemiology; CHARGE; sampling; targeted sequencing
12.  A Randomized Non-inferiority Trial of Condensed Protocols for Genetic Risk Disclosure of Alzheimer’s Disease 
Conventional multi-session genetic counseling is currently recommended when disclosing APOE genotype for risk of Alzheimer’s disease (AD) in cognitively normal individuals.
To evaluate the safety of brief disclosure protocols for disclosing APOE genotype for risk of Alzheimer’s disease (AD).
A randomized, multicenter non-inferiority trial was conducted at 4 sites. Participants were asymptomatic adults having a first-degree relative with AD. A standard disclosure protocol by genetic counselors (SP-GC) was compared to condensed protocols, with disclosures by genetic counselors (CP-GC) and by physicians (CP-MD). Pre-planned co-primary outcomes were anxiety and depression scales 12 months after disclosure.
343 adults (mean age 58.3, range 33–86 years, 71% female, 23% African American) were randomly assigned to the SP-GC protocol (n= 115), CP-GC protocol (n=116) or CP-MD protocol (n=112). Mean post-disclosure scores on all outcomes were well below cut-offs for clinical concern across protocols. Comparing CP-GC to SP-GC, the 97.5% upper confidence limits at 12 months after disclosure on co-primary outcomes of anxiety and depression ranged from a difference of 1.2 to 2.0 in means (all p<0.001 on non-inferiority tests), establishing non-inferiority for condensed protocols. Results were similar between European Americans and African Americans.
These data support the safety of condensed protocols for APOE disclosure for those free of severe anxiety or depression who are actively seeking such information.
PMCID: PMC4461546  PMID: 25499536
Alzheimer; APOE; genetics; genomics; risk assessment; personalized medicine
13.  Whole Exome Sequencing in Atrial Fibrillation 
PLoS Genetics  2016;12(9):e1006284.
Atrial fibrillation (AF) is a morbid and heritable arrhythmia. Over 35 genes have been reported to underlie AF, most of which were described in small candidate gene association studies. Replication remains lacking for most, and therefore the contribution of coding variation to AF susceptibility remains poorly understood. We examined whole exome sequencing data in a large community-based sample of 1,734 individuals with and 9,423 without AF from the Framingham Heart Study, Cardiovascular Health Study, Atherosclerosis Risk in Communities Study, and NHLBI-GO Exome Sequencing Project and meta-analyzed the results. We also examined whether genetic variation was enriched in suspected AF genes (N = 37) in AF cases versus controls. The mean age ranged from 59 to 73 years; 8,656 (78%) were of European ancestry. None of the 99,404 common variants evaluated was significantly associated after adjusting for multiple testing. Among the most significantly associated variants was a common (allele frequency = 86%) missense variant in SYNPO2L (rs3812629, p.Pro707Leu, [odds ratio 1.27, 95% confidence interval 1.13–1.43, P = 6.6x10-5]) which lies at a known AF susceptibility locus and is in linkage disequilibrium with a top marker from prior analyses at the locus. We did not observe significant associations between rare variants and AF in gene-based tests. Individuals with AF did not display any statistically significant enrichment for common or rare coding variation in previously implicated AF genes. In conclusion, we did not observe associations between coding genetic variants and AF, suggesting that large-effect coding variation is not the predominant mechanism underlying AF. A coding variant in SYNPO2L requires further evaluation to determine whether it is causally related to AF. Efforts to identify biologically meaningful coding variation underlying AF may require large sample sizes or populations enriched for large genetic effects.
Author Summary
Atrial fibrillation is a common and morbid cardiac arrhythmia. Atrial fibrillation is heritable, and numerous genome-wide susceptibility loci have been identified, predominantly in non-coding regions. Over 35 genes also have been implicated in atrial fibrillation pathogenesis mostly through prior smaller scale candidate gene association studies, which generally did not have robust replication to support the associations. Therefore, the role of coding variation in the biology of atrial fibrillation is unclear. We examined whole exome sequencing data from 1,734 individuals with and 9,423 without atrial fibrillation, and did not observe any significant associations between coding variation and the arrhythmia. Furthermore, we did not observe any enrichment for association in previously implicated atrial fibrillation genes. In aggregate, our findings suggest that large effect coding variation is unlikely to be a predominant mechanism of common forms of atrial fibrillation encountered in the community.
PMCID: PMC5010214  PMID: 27589061
14.  Influence of smoking status and intensity on discovery of blood pressure loci through gene-smoking interactions 
Genetic epidemiology  2015;39(6):480-488.
Genetic variation accounts for approximately 30% of blood pressure (BP) variability but most of that variability hasn't been attributed to specific variants. Interactions between genes and BP-associated factors may explain some ‘missing heritability.’ Cigarette smoking increases BP after short-term exposure and decreases BP with longer exposure. Gene-smoking interactions have discovered novel BP loci, but the contribution of smoking status and intensity to gene discovery is unknown.
We analyzed gene-smoking intensity interactions for association with systolic BP (SBP) in three subgroups from the Framingham Heart Study: current smokers only (N = 1,057), current and former smokers (‘ever smokers’, N = 3,374), and all subjects (N = 6,710). We used three smoking intensity variables defined at cutoffs of 10, 15, and 20 cigarettes per day (CPD). We evaluated the 1 degree-of-freedom (df) interaction and 2df joint test using generalized estimating equations.
Analysis of current smokers using a CPD cutoff of 10 produced two loci associated with SBP. The rs9399633 minor allele was associated with increased SBP (5 mmHg) in heavy smokers (CPD>10) but decreased SBP (7 mmHg) in light smokers (CPD≤10). The rs11717948 minor allele was associated with decreased SBP (8 mmHg) in light smokers but decreased SBP (2 mmHg) in heavy smokers. Across all nine analyses, 19 additional loci reached p < 1×10−6.
Analysis of current smokers may have the highest power to detect gene-smoking interactions, despite the reduced sample size. Associations of loci near SASH1 and KLHL6/KLHL24 with SBP may be modulated by tobacco smoking.
PMCID: PMC4543586  PMID: 25940791
Cardiovascular genetics; gene interactions; smoking; blood pressure
15.  Association of total protein intake with bone mineral density (BMD) and bone loss in men and women from the Framingham Offspring Study 
Public health nutrition  2013;17(11):2570-2576.
To examine the association of % of total energy from protein (protein%) with bone mineral density (BMD, g/cm2) and bone loss at the femoral neck (FN), trochanter (TR); L2–L4 spine (LS). To examine calcium as an effect modifier.
The Framingham Offspring Study.
1,280 men and 1,639 women completed an FFQ in 1992–95 or 1995–98 and baseline DXA-BMD measurement in 1996–2000. 495 men and 680 women had follow-up BMD measured in 2002–2005.
Cohort study using multivariable regression to examine the association of protein% with each BMD, adjusting for covariates. Statistical interaction between protein% and calcium (total, dietary; supplemental) intake was examined.
The mean age at baseline was 61y(±9). In the cross-sectional analyses, protein% was positively associated with all BMD sites (P:0.02–0.04) in women but not in men. Significant interactions were observed with total calcium intake (<800 vs. ≥800 mg/d) in women at all bone sites (P:0.002–0.02). Upon stratification, protein% was positively associated with all BMD sites (P:0.04–0.10) in women with low calcium intakes but not with high calcium intakes. In the longitudinal analyses, in men, higher protein% was associated with more TR-bone loss (P=0.01) while no associations were seen in women, regardless of calcium intake.
This suggests that greater protein intake benefits women especially those with lower calcium intakes. However, protein effects are not significant for short term changes in bone density. Contrastingly, in men, higher protein intakes lead to greater TR-bone loss. Longer follow-up is required to examine the impact of protein on bone loss.
PMCID: PMC4103961  PMID: 24168918
Protein; bone mineral density; bone loss; diet; calcium
16.  Disclosing Pleiotropic Effects during Genetic Risk Assessment for Alzheimer’s Disease: A Randomized, Controlled Trial 
Annals of internal medicine  2016;164(3):155-163.
Increasing use of genetic testing raises questions about disclosing secondary findings, including pleiotropic information.
To determine the safety and behavioral impact of disclosing modest associations between APOE genotype and coronary artery disease (CAD) risk during APOE-based genetic risk assessments for Alzheimer’s disease (AD).
Randomized, multicenter equivalence clinical trial
Four teaching hospitals
257 asymptomatic adults enrolled, 69% with one AD-affected first degree relative
Disclosing AD and CAD genetic risk information (AD+CAD) versus disclosing only AD genetic risk (AD-only)
Co-primary outcomes were Beck Anxiety Index (BAI) and Center for Epidemiologic Studies Depression Scale (CES-D) scores at 12 months. Secondary outcomes included test-related distress at 12 months, all measures at 6 weeks and 6 months, and health behavior changes at 12 months.
12 months after disclosure, mean BAI scores were 3.5 and 3.5 in AD-only and AD+CAD arms (Δ=0.0, 95%CI: −1.0 to 1.0), and mean CES-D scores were 6.4 and 7.1 in AD-only and AD+CAD arms (Δ=0.7, 95%CI: −1.0 to 2.4). Both confidence bounds fell within the equivalence margin of +/−5 points. Among ε4-positive participants, distress was lower in AD+CAD arms than AD-only arms (Δ=−4.8, 95%CI: −8.6 to −1.0) (p=0.031 for disclosure arm x APOE genotype). AD+CAD participants also reported more health behavior changes, regardless of APOE genotype.
Outcomes were self-reported from volunteers without severe anxiety, severe depression, or cognitive problems. Analyses omitted 33 randomized participants.
Disclosing pleiotropic information did not increase anxiety or depression, and may have decreased distress among those at increased risk for two conditions. Providing risk modification information regarding CAD improved health behaviors. Findings highlight potential benefits of secondary genetic findings disclosure when options exist for decreasing risk.
PMCID: PMC4979546  PMID: 26810768
genetics; genomics; pleiotropy; risk assessment; personalized medicine; secondary findings; Alzheimer; APOE
17.  Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels 
van Leeuwen, Elisabeth M | Sabo, Aniko | Bis, Joshua C | Huffman, Jennifer E | Manichaikul, Ani | Smith, Albert V | Feitosa, Mary F | Demissie, Serkalem | Joshi, Peter K | Duan, Qing | Marten, Jonathan | van Klinken, Jan B | Surakka, Ida | Nolte, Ilja M | Zhang, Weihua | Mbarek, Hamdi | Li-Gao, Ruifang | Trompet, Stella | Verweij, Niek | Evangelou, Evangelos | Lyytikäinen, Leo-Pekka | Tayo, Bamidele O | Deelen, Joris | van der Most, Peter J | van der Laan, Sander W | Arking, Dan E | Morrison, Alanna | Dehghan, Abbas | Franco, Oscar H | Hofman, Albert | Rivadeneira, Fernando | Sijbrands, Eric J | Uitterlinden, Andre G | Mychaleckyj, Josyf C | Campbell, Archie | Hocking, Lynne J | Padmanabhan, Sandosh | Brody, Jennifer A | Rice, Kenneth M | White, Charles C | Harris, Tamara | Isaacs, Aaron | Campbell, Harry | Lange, Leslie A | Rudan, Igor | Kolcic, Ivana | Navarro, Pau | Zemunik, Tatijana | Salomaa, Veikko | Kooner, Angad S | Kooner, Jaspal S | Lehne, Benjamin | Scott, William R | Tan, Sian-Tsung | de Geus, Eco J | Milaneschi, Yuri | Penninx, Brenda W J H | Willemsen, Gonneke | de Mutsert, Renée | Ford, Ian | Gansevoort, Ron T | Segura-Lepe, Marcelo P | Raitakari, Olli T | Viikari, Jorma S | Nikus, Kjell | Forrester, Terrence | McKenzie, Colin A | de Craen, Anton J M | de Ruijter, Hester M | Pasterkamp, Gerard | Snieder, Harold | Oldehinkel, Albertine J | Slagboom, P Eline | Cooper, Richard S | Kähönen, Mika | Lehtimäki, Terho | Elliott, Paul | van der Harst, Pim | Jukema, J Wouter | Mook-Kanamori, Dennis O | Boomsma, Dorret I | Chambers, John C | Swertz, Morris | Ripatti, Samuli | Willems van Dijk, Ko | Vitart, Veronique | Polasek, Ozren | Hayward, Caroline | Wilson, James G | Wilson, James F | Gudnason, Vilmundur | Rich, Stephen S | Psaty, Bruce M | Borecki, Ingrid B | Boerwinkle, Eric | Rotter, Jerome I | Cupples, L Adrienne | van Duijn, Cornelia M
Journal of Medical Genetics  2016;53(7):441-449.
So far, more than 170 loci have been associated with circulating lipid levels through genome-wide association studies (GWAS). These associations are largely driven by common variants, their function is often not known, and many are likely to be markers for the causal variants. In this study we aimed to identify more new rare and low-frequency functional variants associated with circulating lipid levels.
We used the 1000 Genomes Project as a reference panel for the imputations of GWAS data from ∼60 000 individuals in the discovery stage and ∼90 000 samples in the replication stage.
Our study resulted in the identification of five new associations with circulating lipid levels at four loci. All four loci are within genes that can be linked biologically to lipid metabolism. One of the variants, rs116843064, is a damaging missense variant within the ANGPTL4 gene.
This study illustrates that GWAS with high-scale imputation may still help us unravel the biological mechanism behind circulating lipid levels.
PMCID: PMC4941146  PMID: 27036123
Complex traits; Epidemiology; Genetics; Genome-wide; circulating lipid levels
18.  Correction: The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study 
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 J. M. | Bis, Joshua C. | Bonnefond, Amélie | Boucher, Gabrielle | Cadby, Gemma | Cheng, Yu-Ching | Chiang, Charleston W. K. | 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 A. F. | 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 | Tachmazidou, Ioanna | Tanaka, Toshiko | Thorleifsson, Gudmar | Trompet, Stella | Pervjakova, Natalia | Tyrer, Jonathan P. | Vandenput, Liesbeth | van der Laan, Sander W | van der Velde, Nathalie | van Setten, Jessica | van Vliet-Ostaptchouk, Jana V. | Verweij, Niek | Vlachopoulou, Efthymia | Waite, Lindsay L. | Wang, Sophie R. | Wang, Zhaoming | Wild, Sarah H. | Willenborg, Christina | Wilson, James F. | Wong, Andrew | Yang, Jian | Yengo, Loïc | Yerges-Armstrong, Laura M. | Yu, Lei | Zhang, Weihua | Zhao, Jing Hua | Andersson, Ehm A. | Bakker, Stephan J. L. | Baldassarre, Damiano | Banasik, Karina | Barcella, Matteo | Barlassina, Cristina | Bellis, Claire | Benaglio, Paola | Blangero, John | Blüher, Matthias | Bonnet, Fabrice | Bonnycastle, Lori L. | Boyd, Heather A. | Bruinenberg, Marcel | Buchman, Aron S | Campbell, Harry | Chen, Yii-Der Ida | Chines, Peter S. | Claudi-Boehm, Simone | Cole, John | Collins, Francis S. | de Geus, Eco J. C. | de Groot, Lisette C. P. G. M. | Dimitriou, Maria | Duan, Jubao | Enroth, Stefan | Eury, Elodie | Farmaki, Aliki-Eleni | Forouhi, Nita G. | Friedrich, Nele | Gejman, Pablo V. | Gigante, Bruna | Glorioso, Nicola | Go, Alan S. | Gottesman, Omri | Gräßler, Jürgen | Grallert, Harald | Grarup, Niels | Gu, Yu-Mei | Broer, Linda | Ham, Annelies C. | Hansen, Torben | Harris, Tamara B. | Hartman, Catharina A. | Hassinen, Maija | Hastie, Nicholas | Hattersley, Andrew T. | Heath, Andrew C. | Henders, Anjali K. | Hernandez, Dena | Hillege, Hans | Holmen, Oddgeir | Hovingh, Kees G | Hui, Jennie | Husemoen, Lise L. | Hutri-Kähönen, Nina | Hysi, Pirro G. | Illig, Thomas | De Jager, Philip L. | Jalilzadeh, Shapour | Jørgensen, Torben | Jukema, J. Wouter | Juonala, Markus | Kanoni, Stavroula | Karaleftheri, Maria | Khaw, Kay Tee | Kinnunen, Leena | Kittner, Steven J. | Koenig, Wolfgang | Kolcic, Ivana | Kovacs, Peter | Krarup, Nikolaj T. | Kratzer, Wolfgang | Krüger, Janine | Kuh, Diana | Kumari, Meena | Kyriakou, Theodosios | Langenberg, Claudia | Lannfelt, Lars | Lanzani, Chiara | Lotay, Vaneet | Launer, Lenore J. | Leander, Karin | Lindström, Jaana | Linneberg, Allan | Liu, Yan-Ping | Lobbens, Stéphane | Luben, Robert | Lyssenko, Valeriya | Männistö, Satu | Magnusson, Patrik K. | McArdle, Wendy L. | Menni, Cristina | Merger, Sigrun | Milani, Lili | Montgomery, Grant W. | Morris, Andrew P. | Narisu, Narisu | Nelis, Mari | Ong, Ken K. | Palotie, Aarno | Pérusse, Louis | Pichler, Irene | Pilia, Maria G. | Pouta, Anneli | Rheinberger, Myriam | Ribel-Madsen, Rasmus | Richards, Marcus | Rice, Kenneth M. | Rice, Treva K. | Rivolta, Carlo | Salomaa, Veikko | Sanders, Alan R. | Sarzynski, Mark A. | Scholtens, Salome | Scott, Robert A. | Scott, William R. | Sebert, Sylvain | Sengupta, Sebanti | Sennblad, Bengt | Seufferlein, Thomas | Silveira, Angela | Slagboom, P. Eline | Smit, Jan H. | Sparsø, Thomas H. | Stirrups, Kathleen | Stolk, Ronald P. | Stringham, Heather M. | Swertz, Morris A | Swift, Amy J. | Syvänen, Ann-Christine | Tan, Sian-Tsung | Thorand, Barbara | Tönjes, Anke | Tremblay, Angelo | Tsafantakis, Emmanouil | van der Most, Peter J. | Völker, Uwe | Vohl, Marie-Claude | Vonk, Judith M. | Waldenberger, Melanie | Walker, Ryan W. | Wennauer, Roman | Widén, Elisabeth | Willemsen, Gonneke | Wilsgaard, Tom | Wright, Alan F. | Zillikens, M. Carola | van Dijk, Suzanne C. | van Schoor, Natasja M. | Asselbergs, Folkert W. | de Bakker, Paul I. W. | Beckmann, Jacques S. | Beilby, John | Bennett, David A. | Bergman, Richard N. | Bergmann, Sven | Böger, Carsten A. | Boehm, Bernhard O. | Boerwinkle, Eric | Boomsma, Dorret I. | Bornstein, Stefan R. | Bottinger, Erwin P. | Bouchard, Claude | Chambers, John C. | Chanock, Stephen J. | Chasman, Daniel I. | Cucca, Francesco | Cusi, Daniele | Dedoussis, George | Erdmann, Jeanette | Eriksson, Johan G. | Evans, Denis A. | de Faire, Ulf | Farrall, Martin | Ferrucci, Luigi | Ford, Ian | Franke, Lude | Franks, Paul W. | Froguel, Philippe | Gansevoort, Ron T. | Gieger, Christian | Grönberg, Henrik | Gudnason, Vilmundur | Gyllensten, Ulf | Hall, Per | Hamsten, Anders | van der Harst, Pim | Hayward, Caroline | Heliövaara, Markku | Hengstenberg, Christian | Hicks, Andrew A | Hingorani, Aroon | Hofman, Albert | Hu, Frank | Huikuri, Heikki V. | Hveem, Kristian | James, Alan L. | Jordan, Joanne M. | Jula, Antti | Kähönen, Mika | Kajantie, Eero | Kathiresan, Sekar | Kiemeney, Lambertus A. L. M. | Kivimaki, Mika | Knekt, Paul B. | Koistinen, Heikki A. | Kooner, Jaspal S. | Koskinen, Seppo | Kuusisto, Johanna | Maerz, Winfried | Martin, Nicholas G | Laakso, Markku | Lakka, Timo A. | Lehtimäki, Terho | Lettre, Guillaume | Levinson, Douglas F. | Lind, Lars | Lokki, Marja-Liisa | Mäntyselkä, Pekka | Melbye, Mads | Metspalu, Andres | Mitchell, Braxton D. | Moll, Frans L. | Murray, Jeffrey C. | Musk, Arthur W. | Nieminen, Markku S. | Njølstad, Inger | Ohlsson, Claes | Oldehinkel, Albertine J. | Oostra, Ben A. | Palmer, Lyle J | Pankow, James S. | Pasterkamp, Gerard | Pedersen, Nancy L. | Pedersen, Oluf | Penninx, Brenda W. | Perola, Markus | Peters, Annette | Polašek, Ozren | Pramstaller, Peter P. | Psaty, Bruce M. | Qi, Lu | Quertermous, Thomas | Raitakari, Olli T. | Rankinen, Tuomo | Rauramaa, Rainer | Ridker, Paul M. | Rioux, John D. | Rivadeneira, Fernando | Rotter, Jerome I. | Rudan, Igor | den Ruijter, Hester M. | Saltevo, Juha | Sattar, Naveed | Schunkert, Heribert | Schwarz, Peter E. H. | Shuldiner, Alan R. | Sinisalo, Juha | Snieder, Harold | Sørensen, Thorkild I. A. | Spector, Tim D. | Staessen, Jan A. | Stefania, Bandinelli | Thorsteinsdottir, Unnur | Stumvoll, Michael | Tardif, Jean-Claude | Tremoli, Elena | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | Verbeek, André L. M. | Vermeulen, Sita H. | Viikari, Jorma S. | Vitart, Veronique | Völzke, Henry | Vollenweider, Peter | Waeber, Gérard | Walker, Mark | Wallaschofski, Henri | Wareham, Nicholas J. | Watkins, Hugh | Zeggini, Eleftheria | Chakravarti, Aravinda | Clegg, Deborah J. | Cupples, L. Adrienne | Gordon-Larsen, Penny | Jaquish, Cashell E. | Rao, D. C. | Abecasis, Goncalo R. | Assimes, Themistocles L. | Barroso, Inês | Berndt, Sonja I. | Boehnke, Michael | Deloukas, Panos | Fox, Caroline S. | Groop, Leif C. | Hunter, David J. | Ingelsson, Erik | Kaplan, Robert C. | McCarthy, Mark I. | Mohlke, Karen L. | O'Connell, Jeffrey R. | Schlessinger, David | Strachan, David P. | Stefansson, Kari | van Duijn, Cornelia M. | Hirschhorn, Joel N. | Lindgren, Cecilia M. | Heid, Iris M. | North, Kari E. | Borecki, Ingrid B. | Kutalik, Zoltán | Loos, Ruth J. F.
PLoS Genetics  2016;12(6):e1006166.
PMCID: PMC4927064  PMID: 27355579
19.  Assessment of Gene-by-Sex Interaction Effect on Bone Mineral Density 
Liu, Ching-Ti | Estrada, Karol | Yerges-Armstrong, Laura M. | Amin, Najaf | Evangelou, Evangelos | Li, Guo | Minster, Ryan L. | Carless, Melanie A. | Kammerer, Candace M. | Oei, Ling | Zhou, Yanhua | Alonso, Nerea | Dailiana, Zoe | Eriksson, Joel | García-Giralt, Natalia | Giroux, Sylvie | Husted, Lise Bjerre | Khusainova, Rita I. | Koromila, Theodora | Kung, Annie WaiChee | Lewis, Joshua R. | Masi, Laura | Mencej-Bedrac, Simona | Nogues, Xavier | Patel, Millan S. | Prezelj, Janez | Richards, J Brent | Sham, Pak Chung | Spector, Timothy | Vandenput, Liesbeth | Xiao, Su-Mei | Zheng, Hou-Feng | Zhu, Kun | Balcells, Susana | Brandi, Maria Luisa | Frost, Morten | Goltzman, David | González-Macías, Jesús | Karlsson, Magnus | Khusnutdinova, Elza K. | Kollia, Panagoula | Langdahl, Bente Lomholt | Ljunggren, Östen | Lorentzon, Mattias | Marc, Janja | Mellström, Dan | Ohlsson, Claes | Olmos, José M. | Ralston, Stuart H. | Riancho, José A. | Rousseau, François | Urreizti, Roser | Van Hul, Wim | Zarrabeitia, María T. | Castano-Betancourt, Martha | Demissie, Serkalem | Grundberg, Elin | Herrera, Lizbeth | Kwan, Tony | Medina-Gómez, Carolina | Pastinen, Tomi | Sigurdsson, Gunnar | Thorleifsson, Gudmar | vanMeurs, Joyce B.J. | Blangero, John | Hofman, Albert | Liu, Yongmei | Mitchell, Braxton D. | O’Connell, Jeffrey R. | Oostra, Ben A. | Rotter, Jerome I | Stefansson, Kari | Streeten, Elizabeth A. | Styrkarsdottir, Unnur | Thorsteinsdottir, Unnur | Tylavsky, Frances A. | Uitterlinden, Andre | Cauley, Jane A. | Harris, Tamara B. | Ioannidis, John P.A. | Psaty, Bruce M. | Robbins, John A | Zillikens, M. Carola | vanDuijn, Cornelia M. | Prince, Richard L. | Karasik, David | Rivadeneira, Fernando | Kiel, Douglas P. | Cupples, L. Adrienne | Hsu, Yi-Hsiang
Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome-wide, and performed eQTL analysis and bioinformatics network analysis.
We conducted an autosomal genome-wide meta-analysis of gene-by-sex interaction on lumbar spine (LS-) and femoral neck (FN-) BMD, in 25,353 individuals from eight cohorts. In a second stage, we followed up the 12 top SNPs (P<1×10−5) in an additional set of 24,763 individuals. Gene-by-sex interaction and sex-specific effects were examined in these 12 SNPs.
We detected one novel genome-wide significant interaction associated with LS-BMD at the Chr3p26.1-p25.1 locus, near the GRM7 gene (male effect = 0.02 & p-value = 3.0×10−5; female effect = −0.007 & p-value=3.3×10−2) and eleven suggestive loci associated with either FN- or LS-BMD in discovery cohorts. However, there was no evidence for genome-wide significant (P<5×10−8) gene-by-sex interaction in the joint analysis of discovery and replication cohorts.
Despite the large collaborative effort, no genome-wide significant evidence for gene-by-sex interaction was found influencing BMD variation in this screen of autosomal markers. If they exist, gene-by-sex interactions for BMD probably have weak effects, accounting for less than 0.08% of the variation in these traits per implicated SNP.
PMCID: PMC3447125  PMID: 22692763
gene-by-sex; interaction; BMD; association; aging
21.  Discovery of Genetic Variation on Chromosome 5q22 Associated with Mortality in Heart Failure 
PLoS Genetics  2016;12(5):e1006034.
Failure of the human heart to maintain sufficient output of blood for the demands of the body, heart failure, is a common condition with high mortality even with modern therapeutic alternatives. To identify molecular determinants of mortality in patients with new-onset heart failure, we performed a meta-analysis of genome-wide association studies and follow-up genotyping in independent populations. We identified and replicated an association for a genetic variant on chromosome 5q22 with 36% increased risk of death in subjects with heart failure (rs9885413, P = 2.7x10-9). We provide evidence from reporter gene assays, computational predictions and epigenomic marks that this polymorphism increases activity of an enhancer region active in multiple human tissues. The polymorphism was further reproducibly associated with a DNA methylation signature in whole blood (P = 4.5x10-40) that also associated with allergic sensitization and expression in blood of the cytokine TSLP (P = 1.1x10-4). Knockdown of the transcription factor predicted to bind the enhancer region (NHLH1) in a human cell line (HEK293) expressing NHLH1 resulted in lower TSLP expression. In addition, we observed evidence of recent positive selection acting on the risk allele in populations of African descent. Our findings provide novel genetic leads to factors that influence mortality in patients with heart failure.
Author Summary
In this study, we applied a genome-wide mapping approach to study molecular determinants of mortality in subjects with heart failure. We identified a genetic variant on chromosome 5q22 that was associated with mortality in this group and observed that this variant conferred increased function of an enhancer region active in multiple tissues. We further observed association of the genetic variant with a DNA methylation signature in blood that in turn is associated with allergy and expression of the gene TSLP (Thymic stromal lymphoprotein) in blood. Knockdown of the transcription factor predicted to bind the enhancer region also resulted in lower TSLP expression. The TSLP gene encodes a cytokine that induces release of T-cell attracting chemokines from monocytes, promotes T helper type 2 cell responses, enhances maturation of dendritic cells and activates mast cells. Development of TSLP inhibiting therapeutics are underway and currently in phase III clinical trials for asthma and allergy. These findings provide novel genetic leads to factors that influence mortality in patients with heart failure and in the longer term may result in novel therapies.
PMCID: PMC4858216  PMID: 27149122
22.  Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption 
Cornelis, Marilyn C | Byrne, Enda M | Esko, Tõnu | Nalls, Michael A | Ganna, Andrea | Paynter, Nina | Monda, Keri L | Amin, Najaf | Fischer, Krista | Renstrom, Frida | Ngwa, Julius S | Huikari, Ville | Cavadino, Alana | Nolte, Ilja M | Teumer, Alexander | Yu, Kai | Marques-Vidal, Pedro | Rawal, Rajesh | Manichaikul, Ani | Wojczynski, Mary K | Vink, Jacqueline M | Zhao, Jing Hua | Burlutsky, George | Lahti, Jari | Mikkilä, Vera | Lemaitre, Rozenn N | Eriksson, Joel | Musani, Solomon K | Tanaka, Toshiko | Geller, Frank | Luan, Jian’an | Hui, Jennie | Mägi, Reedik | Dimitriou, Maria | Garcia, Melissa E | Ho, Weang-Kee | Wright, Margaret J | Rose, Lynda M | Magnusson, Patrik KE | Pedersen, Nancy L | Couper, David | Oostra, Ben A | Hofman, Albert | Ikram, Mohammad Arfan | Tiemeier, Henning W | Uitterlinden, Andre G | van Rooij, Frank JA | Barroso, Inês | Johansson, Ingegerd | Xue, Luting | Kaakinen, Marika | Milani, Lili | Power, Chris | Snieder, Harold | Stolk, Ronald P | Baumeister, Sebastian E | Biffar, Reiner | Gu, Fangyi | Bastardot, François | Kutalik, Zoltán | Jacobs, David R | Forouhi, Nita G | Mihailov, Evelin | Lind, Lars | Lindgren, Cecilia | Michaëlsson, Karl | Morris, Andrew | Jensen, Majken | Khaw, Kay-Tee | Luben, Robert N | Wang, Jie Jin | Männistö, Satu | Perälä, Mia-Maria | Kähönen, Mika | Lehtimäki, Terho | Viikari, Jorma | Mozaffarian, Dariush | Mukamal, Kenneth | Psaty, Bruce M | Döring, Angela | Heath, Andrew C | Montgomery, Grant W | Dahmen, Norbert | Carithers, Teresa | Tucker, Katherine L | Ferrucci, Luigi | Boyd, Heather A | Melbye, Mads | Treur, Jorien L | Mellström, Dan | Hottenga, Jouke Jan | Prokopenko, Inga | Tönjes, Anke | Deloukas, Panos | Kanoni, Stavroula | Lorentzon, Mattias | Houston, Denise K | Liu, Yongmei | Danesh, John | Rasheed, Asif | Mason, Marc A | Zonderman, Alan B | Franke, Lude | Kristal, Bruce S | Karjalainen, Juha | Reed, Danielle R | Westra, Harm-Jan | Evans, Michele K | Saleheen, Danish | Harris, Tamara B | Dedoussis, George | Curhan, Gary | Stumvoll, Michael | Beilby, John | Pasquale, Louis R | Feenstra, Bjarke | Bandinelli, Stefania | Ordovas, Jose M | Chan, Andrew T | Peters, Ulrike | Ohlsson, Claes | Gieger, Christian | Martin, Nicholas G | Waldenberger, Melanie | Siscovick, David S | Raitakari, Olli | Eriksson, Johan G | Mitchell, Paul | Hunter, David J | Kraft, Peter | Rimm, Eric B | Boomsma, Dorret I | Borecki, Ingrid B | Loos, Ruth JF | Wareham, Nicholas J | Vollenweider, Peter | Caporaso, Neil | Grabe, Hans Jörgen | Neuhouser, Marian L | Wolffenbuttel, Bruce HR | Hu, Frank B | Hyppönen, Elina | Järvelin, Marjo-Riitta | Cupples, L Adrienne | Franks, Paul W | Ridker, Paul M | van Duijn, Cornelia M | Heiss, Gerardo | Metspalu, Andres | North, Kari E | Ingelsson, Erik | Nettleton, Jennifer A | van Dam, Rob M | Chasman, Daniel I
Molecular psychiatry  2014;20(5):647-656.
PMCID: PMC4388784  PMID: 25288136
23.  Association of TTR polymorphisms with hippocampal atrophy in Alzheimer disease families 
Neurobiology of aging  2009;32(2):249-256.
In vitro and animal model studies suggest that transthyretin (TTR) inhibits the production of the amyloid β protein, a major contributor to Alzheimer disease (AD) pathogenesis. We evaluated the association of 16 TTR single nucleotide polymorphisms (SNPs) with AD risk in 158 African American and 469 Caucasian discordant sibships from the MIRAGE Study. There was no evidence for association of TTR with AD in either population sample. To examine the possibility that TTR SNPs affect specific components of the AD process, we tested association of these SNPs with four measures of neurodegeneration and cerebrovascular disease defined by magnetic resonance imaging (MRI) in a subset of 48 African American and 265 Caucasian sibships. Five of seven common SNPs and several haplotypes were significantly associated with hippocampal atrophy in the Caucasian sample. Two of these SNPs also showed marginal evidence for association in the African American sample. Results for the other MRI traits were unremarkable. This study highlights the potential value of neuroimaging endophenotypes as a tool for finding genes influencing AD pathogenesis.
PMCID: PMC2930090  PMID: 19328595
24.  Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture 
Zheng, Hou-Feng | Forgetta, Vincenzo | Hsu, Yi-Hsiang | Estrada, Karol | Rosello-Diez, Alberto | Leo, Paul J | Dahia, Chitra L | Park-Min, Kyung Hyun | Tobias, Jonathan H | Kooperberg, Charles | Kleinman, Aaron | Styrkarsdottir, Unnur | Liu, Ching-Ti | Uggla, Charlotta | Evans, Daniel S | Nielson, Carrie M | Walter, Klaudia | Pettersson-Kymmer, Ulrika | McCarthy, Shane | Eriksson, Joel | Kwan, Tony | Jhamai, Mila | Trajanoska, Katerina | Memari, Yasin | Min, Josine | Huang, Jie | Danecek, Petr | Wilmot, Beth | Li, Rui | Chou, Wen-Chi | Mokry, Lauren E | Moayyeri, Alireza | Claussnitzer, Melina | Cheng, Chia-Ho | Cheung, Warren | Medina-Gómez, Carolina | Ge, Bing | Chen, Shu-Huang | Choi, Kwangbom | Oei, Ling | Fraser, James | Kraaij, Robert | Hibbs, Matthew A | Gregson, Celia L | Paquette, Denis | Hofman, Albert | Wibom, Carl | Tranah, Gregory J | Marshall, Mhairi | Gardiner, Brooke B | Cremin, Katie | Auer, Paul | Hsu, Li | Ring, Sue | Tung, Joyce Y | Thorleifsson, Gudmar | Enneman, Anke W | van Schoor, Natasja M | de Groot, Lisette C.P.G.M. | van der Velde, Nathalie | Melin, Beatrice | Kemp, John P | Christiansen, Claus | Sayers, Adrian | Zhou, Yanhua | Calderari, Sophie | van Rooij, Jeroen | Carlson, Chris | Peters, Ulrike | Berlivet, Soizik | Dostie, Josée | Uitterlinden, Andre G | Williams, Stephen R. | Farber, Charles | Grinberg, Daniel | LaCroix, Andrea Z | Haessler, Jeff | Chasman, Daniel I | Giulianini, Franco | Rose, Lynda M | Ridker, Paul M | Eisman, John A | Nguyen, Tuan V | Center, Jacqueline R | Nogues, Xavier | Garcia-Giralt, Natalia | Launer, Lenore L | Gudnason, Vilmunder | Mellström, Dan | Vandenput, Liesbeth | Karlsson, Magnus K | Ljunggren, Östen | Svensson, Olle | Hallmans, Göran | Rousseau, François | Giroux, Sylvie | Bussière, Johanne | Arp, Pascal P | Koromani, Fjorda | Prince, Richard L | Lewis, Joshua R | Langdahl, Bente L | Hermann, A Pernille | Jensen, Jens-Erik B | Kaptoge, Stephen | Khaw, Kay-Tee | Reeve, Jonathan | Formosa, Melissa M | Xuereb-Anastasi, Angela | Åkesson, Kristina | McGuigan, Fiona E | Garg, Gaurav | Olmos, Jose M | Zarrabeitia, Maria T | Riancho, Jose A | Ralston, Stuart H | Alonso, Nerea | Jiang, Xi | Goltzman, David | Pastinen, Tomi | Grundberg, Elin | Gauguier, Dominique | Orwoll, Eric S | Karasik, David | Davey-Smith, George | Smith, Albert V | Siggeirsdottir, Kristin | Harris, Tamara B | Zillikens, M Carola | van Meurs, Joyce BJ | Thorsteinsdottir, Unnur | Maurano, Matthew T | Timpson, Nicholas J | Soranzo, Nicole | Durbin, Richard | Wilson, Scott G | Ntzani, Evangelia E | Brown, Matthew A | Stefansson, Kari | Hinds, David A | Spector, Tim | Cupples, L Adrienne | Ohlsson, Claes | Greenwood, Celia MT | Jackson, Rebecca D | Rowe, David W | Loomis, Cynthia A | Evans, David M | Ackert-Bicknell, Cheryl L | Joyner, Alexandra L | Duncan, Emma L | Kiel, Douglas P | Rivadeneira, Fernando | Richards, J Brent
Nature  2015;526(7571):112-117.
The extent to which low-frequency (minor allele frequency [MAF] between 1–5%) and rare (MAF ≤ 1%) variants contribute to complex traits and disease in the general population is largely unknown. Bone mineral density (BMD) is highly heritable, is a major predictor of osteoporotic fractures and has been previously associated with common genetic variants1–8, and rare, population-specific, coding variants9. Here we identify novel non-coding genetic variants with large effects on BMD (ntotal = 53,236) and fracture (ntotal = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n=2,882 from UK10K), whole-exome sequencing (n= 3,549), deep imputation of genotyped samples using a combined UK10K/1000Genomes reference panel (n=26,534), and de-novo replication genotyping (n= 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size 4-fold larger than the mean of previously reported common variants for lumbar spine BMD8 (rs11692564[T], MAF = 1.7%, replication effect size = +0.20 standard deviations [SD], Pmeta = 2×10−14), which was also associated with a decreased risk of fracture (OR = 0.85; P = 2×10−11; ncases = 98,742 and ncontrols = 409,511). Using an En1Cre/flox mouse model, we observed that conditional loss of En1 results in low bone mass, likely as a consequence of high bone turn-over. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817[T], MAF = 1.1%, replication effect size = +0.39 SD, Pmeta = 1×10−11). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population.
PMCID: PMC4755714  PMID: 26367794 CAMSID: cams5439
25.  A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease 
Nikpay, Majid | Goel, Anuj | Won, Hong-Hee | Hall, Leanne M | Willenborg, Christina | Kanoni, Stavroula | Saleheen, Danish | Kyriakou, Theodosios | Nelson, Christopher P | Hopewell, Jemma C | Webb, Thomas R | Zeng, Lingyao | Dehghan, Abbas | Alver, Maris | Armasu, Sebastian M | Auro, Kirsi | Bjonnes, Andrew | Chasman, Daniel I | Chen, Shufeng | Ford, Ian | Franceschini, Nora | Gieger, Christian | Grace, Christopher | Gustafsson, Stefan | Huang, Jie | Hwang, Shih-Jen | Kim, Yun Kyoung | Kleber, Marcus E | Lau, King Wai | Lu, Xiangfeng | Lu, Yingchang | Lyytikäinen, Leo-Pekka | Mihailov, Evelin | Morrison, Alanna C | Pervjakova, Natalia | Qu, Liming | Rose, Lynda M | Salfati, Elias | Saxena, Richa | Scholz, Markus | Smith, Albert V | Tikkanen, Emmi | Uitterlinden, Andre | Yang, Xueli | Zhang, Weihua | Zhao, Wei | de Andrade, Mariza | de Vries, Paul S | van Zuydam, Natalie R | Anand, Sonia S | Bertram, Lars | Beutner, Frank | Dedoussis, George | Frossard, Philippe | Gauguier, Dominique | Goodall, Alison H | Gottesman, Omri | Haber, Marc | Han, Bok-Ghee | Huang, Jianfeng | Jalilzadeh, Shapour | Kessler, Thorsten | König, Inke R | Lannfelt, Lars | Lieb, Wolfgang | Lind, Lars | Lindgren, Cecilia M | Lokki, Marja-Liisa | Magnusson, Patrik K | Mallick, Nadeem H | Mehra, Narinder | Meitinger, Thomas | Memon, Fazal-ur-Rehman | Morris, Andrew P | Nieminen, Markku S | Pedersen, Nancy L | Peters, Annette | Rallidis, Loukianos S | Rasheed, Asif | Samuel, Maria | Shah, Svati H | Sinisalo, Juha | Stirrups, Kathleen E | Trompet, Stella | Wang, Laiyuan | Zaman, Khan S | Ardissino, Diego | Boerwinkle, Eric | Borecki, Ingrid B | Bottinger, Erwin P | Buring, Julie E | Chambers, John C | Collins, Rory | Cupples, L Adrienne | Danesh, John | Demuth, Ilja | Elosua, Roberto | Epstein, Stephen E | Esko, Tõnu | Feitosa, Mary F | Franco, Oscar H | Franzosi, Maria Grazia | Granger, Christopher B | Gu, Dongfeng | Gudnason, Vilmundur | Hall, Alistair S | Hamsten, Anders | Harris, Tamara B | Hazen, Stanley L | Hengstenberg, Christian | Hofman, Albert | Ingelsson, Erik | Iribarren, Carlos | Jukema, J Wouter | Karhunen, Pekka J | Kim, Bong-Jo | Kooner, Jaspal S | Kullo, Iftikhar J | Lehtimäki, Terho | Loos, Ruth J F | Melander, Olle | Metspalu, Andres | März, Winfried | Palmer, Colin N | Perola, Markus | Quertermous, Thomas | Rader, Daniel J | Ridker, Paul M | Ripatti, Samuli | Roberts, Robert | Salomaa, Veikko | Sanghera, Dharambir K | Schwartz, Stephen M | Seedorf, Udo | Stewart, Alexandre F | Stott, David J | Thiery, Joachim | Zalloua, Pierre A | O’Donnell, Christopher J | Reilly, Muredach P | Assimes, Themistocles L | Thompson, John R | Erdmann, Jeanette | Clarke, Robert | Watkins, Hugh | Kathiresan, Sekar | McPherson, Ruth | Deloukas, Panos | Schunkert, Heribert | Samani, Nilesh J | Farrall, Martin
Nature genetics  2015;47(10):1121-1130.
Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association studies (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185 thousand CAD cases and controls, interrogating 6.7 million common (MAF>0.05) as well as 2.7 million low frequency (0.005
PMCID: PMC4589895  PMID: 26343387

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