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1. Erratum: Meta-analysis of dense genecentric association studies reveals common and uncommon variants associated with height ((The American Journal of Human Genetics (2010) 88 (6-18))

Author

Lanktree, MB, Guo, Y, Murtaza, M, Glessner, JT, Bailey, SD, Onland-Moret, NC, Lettre, G, Ongen, H, Rajagopalan, R, Johnson, T, Shen, H, Nelson, CP, Klopp, N, Baumert, J, Padmanabhan, S, Pankratz, N, Pankow, JS, Shah, S, Taylor, K, Barnard, J, Peters, BJ, Maloney, CM, Lobmeyer, MT, Stanton, A, Zafarmand, MH, Romaine, SPR, Mehta, A, Van Iperen, EPA, Gong, Y, Price, TS, Smith, EN, Kim, CE, Li, YR, Asselbergs, FW, Atwood, LD, Bailey, KM, Bhatt, D, Bauer, F, Behr, ER, Bhangale, T, Boer, JMA, Boehm, BO, Bradfield, JP, Brown, M, Braund, PS, Burton, PR, Carty, C, Chandrupatla, HR, Chen, W, Connell, J, Dalgeorgou, C, De Boer, A, Drenos, F, Elbers, CC, Fang, JC, Fox, CS, Frackelton, EC, Fuchs, B, Furlong, CE, Gibson, Q, Gieger, C, Goel, A, Grobbee, DE, Hastie, C, Howard, PJ, Huang, G-H, Johnson, WC, Li, Q, Kleber, ME, Klein, BEK, Klein, R, Kooperberg, C, Ky, B, Lacroix, A, Lanken, P, Lathrop, M, Li, M, Marshall, V, Melander, O, Mentch, FD, Meyer, NJ, Monda, KL, Montpetit, A, Murugesan, G, Nakayama, K, Nondahl, D, Onipinla, A, Rafelt, S, Newhouse, SJ, Otieno, FG, Patel, SR, Putt, ME, Rodriguez, S, Safa, RN, Sawyer, DB, Schreiner, PJ, Simpson, C, Sivapalaratnam, S, Srinivasan, SR, Suver, C, Swergold, G, Sweitzer, NK, Thomas, KA, Thorand, B, Timpson, NJ, Tischfield, S, Tobin, M, Tomaszewski, M, Verschuren, WMM, Wallace, C, Winkelmann, B, Zhang, H, Zheng, D, Zhang, L, Zmuda, JM, Clarke, R, Balmforth, AJ, Danesh, J, Day, IN, Schork, NJ, De Bakker, PIW, Delles, C, Duggan, D, Hingorani, AD, Hirschhorn, JN, Hofker, MH, Humphries, SE, Kivimaki, M, Lawlor, DA, Kottke-Marchant, K, Mega, JL, Mitchell, BD, Morrow, DA, Palmen, J, Redline, S, Shields, DC, Shuldiner, AR, Sleiman, PM, Smith, GD, Farrall, M, Jamshidi, Y, Christiani, DC, Casas, JP, Hall, AS, Doevendans, PA, Christie, JD, Berenson, GS, Murray, SS, Illig, T, Dorn, GW, Cappola, TP, Boerwinkle, E, Sever, P, Rader, DJ, Reilly, MP, Caulfield, M, Talmud, PJ, Topol, E, Engert, JC, Wang, K, Dominiczak, A, Hamsten, A, Curtis, SP, Silverstein, RL, Lange, LA, Sabatine, MS, Trip, M, Saleheen, D, Peden, JF, Cruickshanks, KJ, März, W, O'Connell, JR, Klungel, OH, Wijmenga, C, Maitland-Van Der Zee, AH, Schadt, EE, Johnson, JA, Jarvik, GP, Papanicolaou, GJ, Grant, SFA, Munroe, PB, North, KE, Samani, NJ, Koenig, W, Gaunt, TR, Anand, SS, Van Der Schouw, YT, Soranzo, N, Fitzgerald, GA, Reiner, A, Hegele, RA, Hakonarson, H, and Keating, BJ

Published
2012

2. Erratum: Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci (American Journal of Human Genetics (2012) 90 (410-425))

Author

Saxena, R, Elbers, CC, Guo, Y, Peter, I, Gaunt, TR, Mega, JL, Lanktree, MB, Tare, A, Castillo, BA, Li, YR, Johnson, T, Bruinenberg, M, Gilbert-Diamond, D, Rajagopalan, R, Voight, BF, Balasubramanyam, A, Barnard, J, Bauer, F, Baumert, J, Bhangale, T, Böhm, BO, Braund, PS, Burton, PR, Chandrupatla, HR, Clarke, R, Cooper-Dehoff, RM, Crook, ED, Davey-Smith, G, Day, IN, De Boer, A, De Groot, MCH, Drenos, F, Ferguson, J, Fox, CS, Furlong, CE, Gibson, Q, Gieger, C, Gilhuijs-Pederson, LA, Glessner, JT, Goel, A, Gong, Y, Grant, SFA, Grobbee, DE, Hastie, C, Humphries, SE, Kim, CE, Kivimaki, M, Kleber, M, Meisinger, C, Kumari, M, Langaee, TY, Lawlor, DA, Li, M, Lobmeyer, MT, Maitland-Van Der Zee, A-H, Meijs, MFL, Molony, CM, Morrow, DA, Murugesan, G, Musani, SK, Nelson, CP, Newhouse, SJ, O'Connell, JR, Padmanabhan, S, Palmen, J, Patel, SR, Pepine, CJ, Pettinger, M, Price, TS, Rafelt, S, Ranchalis, J, Rasheed, A, Rosenthal, E, Ruczinski, I, Shah, S, Shen, H, Silbernagel, G, Smith, EN, Spijkerman, AWM, Stanton, A, Steffes, MW, Thorand, B, Trip, M, Van Der Harst, P, Van Der A, DL, Van Iperen, EPA, Van Setten, J, Van Vliet-Ostaptchouk, JV, Verweij, N, Wolffenbuttel, BHR, Young, T, Hadi Zafarmand, M, Zmuda, JM, Boehnke, M, Altshuler, D, McCarthy, M, Linda Kao, WH, Pankow, JS, Cappola, TP, Sever, P, Poulter, N, Caulfield, M, Dominiczak, A, Shields, DC, Bhatt, DL, Zhang, L, Curtis, SP, Danesh, J, Casas, JP, Van Der Schouw, YT, Onland-Moret, NC, Doevendans, PA, Dorn II, GW, Farrall, M, Fitzgerald, GA, Robert Hegele, AH, Hingorani, AD, Hofker, MH, Huggins, GS, Illig, T, Jarvik, GP, Johnson, JA, Klungel, OH, Knowler, WC, Koenig, W, März, W, Meigs, JB, Melander, O, Munroe, PB, Mitchell, BD, Bielinski, SJ, Rader, DJ, Reilly, MP, Rich, SS, Rotter, JI, Saleheen, D, Samani, NJ, Schadt, EE, Shuldiner, AR, Silverstein, R, Kottke-Marchant, K, Talmud, PJ, Watkins, H, Asselbergs, FW, De Bakker, PIW, McCaffery, J, Wijmenga, C, Sabatine, MS, Wilson, JG, Reiner, A, Bowden, DW, Hakonarson, H, Siscovick, DS, and Keating, BJ

Published
2012

3. Design and implementation of the international genetics and translational research in transplantation network

Author

Keating, BJ, Van Setten, J, Jacobson, PA, Holmes, MV, Verma, SS, Chandrupatla, HR, Nair, N, Gao, H, Li, YR, Chang, BL, Wong, C, Phillips, R, Cole, BS, Mukhtar, E, Zhang, W, Cao, H, Mohebnasab, M, Hou, C, Lee, T, Steel, L, Shaked, O, Garifallou, J, Miller, MB, Karczewski, KJ, Akdere, A, Gonzalez, A, Lloyd, KM, McGinn, D, Michaud, Z, Colasacco, A, Lek, M, Fu, Y, Pawashe, M, Guettouche, T, Himes, A, Perez, L, Guan, W, Wu, B, Schladt, D, Menon, M, Zhang, Z, Tragante, V, De Jonge, N, Otten, HG, De Weger, RA, Van De Graaf, EA, Baan, CC, Manintveld, OC, De Vlaminck, I, Piening, BD, Strehl, C, Shaw, M, Snieder, H, Klintmalm, GB, O'Leary, JG, Amaral, S, Goldfarb, S, Rand, E, Rossano, JW, Kohli, U, Heeger, P, Stahl, E, Christie, JD, Fuentes, MH, Levine, JE, Aplenc, R, Schadt, EE, Stranger, BE, Kluin, J, Potena, L, Zuckermann, A, Khush, K, Alzahrani, AJ, Al-Muhanna, FA, Al-Ali, AK, Al-Ali, R, Al-Rubaish, AM, Al-Mueilo, S, Byrne, EM, Miller, D, Alexander, SI, Onengut-Gumuscu, S, Rich, SS, Suthanthiran, M, Tedesco, H, Saw, CL, Ragoussis, J, Kfoury, AG, Horne, B, Carlquist, J, Gerstein, MB, Reindl-Schwaighofer, R, Oberbauer, R, Wijmenga, C, Palmer, S, Pereira, AC, Segovia, J, Alonso-Pulpon, LA, Comez-Bueno, M, Vilches, C, Keating, BJ, Van Setten, J, Jacobson, PA, Holmes, MV, Verma, SS, Chandrupatla, HR, Nair, N, Gao, H, Li, YR, Chang, BL, Wong, C, Phillips, R, Cole, BS, Mukhtar, E, Zhang, W, Cao, H, Mohebnasab, M, Hou, C, Lee, T, Steel, L, Shaked, O, Garifallou, J, Miller, MB, Karczewski, KJ, Akdere, A, Gonzalez, A, Lloyd, KM, McGinn, D, Michaud, Z, Colasacco, A, Lek, M, Fu, Y, Pawashe, M, Guettouche, T, Himes, A, Perez, L, Guan, W, Wu, B, Schladt, D, Menon, M, Zhang, Z, Tragante, V, De Jonge, N, Otten, HG, De Weger, RA, Van De Graaf, EA, Baan, CC, Manintveld, OC, De Vlaminck, I, Piening, BD, Strehl, C, Shaw, M, Snieder, H, Klintmalm, GB, O'Leary, JG, Amaral, S, Goldfarb, S, Rand, E, Rossano, JW, Kohli, U, Heeger, P, Stahl, E, Christie, JD, Fuentes, MH, Levine, JE, Aplenc, R, Schadt, EE, Stranger, BE, Kluin, J, Potena, L, Zuckermann, A, Khush, K, Alzahrani, AJ, Al-Muhanna, FA, Al-Ali, AK, Al-Ali, R, Al-Rubaish, AM, Al-Mueilo, S, Byrne, EM, Miller, D, Alexander, SI, Onengut-Gumuscu, S, Rich, SS, Suthanthiran, M, Tedesco, H, Saw, CL, Ragoussis, J, Kfoury, AG, Horne, B, Carlquist, J, Gerstein, MB, Reindl-Schwaighofer, R, Oberbauer, R, Wijmenga, C, Palmer, S, Pereira, AC, Segovia, J, Alonso-Pulpon, LA, Comez-Bueno, M, and Vilches, C

Abstract

Background. Genetic association studies of transplantation outcomes have been hampered by small samples and highly complex multifactorial phenotypes, hindering investigations of the genetic architecture of a range of comorbidities which significantly impact graft and recipient life expectancy. We describe here the rationale and design of the International Genetics & Translational Research in Transplantation Network. The network comprises 22 studies to date, including 16 494 transplant recipients and 11 669 donors, of whom more than 5000 are of non-European ancestry, all of whom have existing genomewide genotype data sets. Methods. We describe the rich genetic and phenotypic information available in this consortium comprising heart, kidney, liver, and lung transplant cohorts. Results. We demonstrate significant power in International Genetics & Translational Research in Transplantation Network to detect main effect association signals across regions such as the MHC region as well as genomewide for transplant outcomes that span all solid organs, such as graft survival, acute rejection, new onset of diabetes after transplantation, and for delayed graft function in kidney only. Conclusions. This consortium is designed and statistically powered to deliver pioneering insights into the genetic architecture of transplant-related outcomes across a range of different solid-organ transplant studies. The study design allows a spectrum of analyses to be performed including recipient-only analyses, donor-recipient HLA mismatches with focus on loss-of-function variants and nonsynonymous single nucleotide polymorphisms.

Published
2015

4. Causal effects of body mass index on cardiometabolic traits and events: a Mendelian randomization analysis.

Author

Holmes MV, Lange LA, Palmer T, Lanktree MB, North KE, Almoguera B, Buxbaum S, Chandrupatla HR, Elbers CC, Guo Y, Hoogeveen RC, Li J, Li YR, Swerdlow DI, Cushman M, Price TS, Curtis SP, Fornage M, Hakonarson H, Patel SR, Redline S, Siscovick DS, Tsai MY, Wilson JG, van der Schouw YT, FitzGerald GA, Hingorani AD, Casas JP, de Bakker PI, Rich SS, Schadt EE, Asselbergs FW, Reiner AP, and Keating BJ

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Blood Glucose metabolism, Blood Pressure, Cholesterol, HDL blood, Cholesterol, LDL blood, Coronary Disease blood, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 genetics, Fasting, Female, Genetic Association Studies, Humans, Insulin blood, Interleukin-6 blood, Longitudinal Studies, Male, Meta-Analysis as Topic, Middle Aged, Odds Ratio, Phenotype, Polymorphism, Single Nucleotide, Prospective Studies, Risk Factors, Selection, Genetic, Sensitivity and Specificity, Stroke blood, White People genetics, Young Adult, Body Mass Index, Coronary Disease genetics, Mendelian Randomization Analysis, Stroke genetics
Abstract

Elevated body mass index (BMI) associates with cardiometabolic traits on observational analysis, yet the underlying causal relationships remain unclear. We conducted Mendelian randomization analyses by using a genetic score (GS) comprising 14 BMI-associated SNPs from a recent discovery analysis to investigate the causal role of BMI in cardiometabolic traits and events. We used eight population-based cohorts, including 34,538 European-descent individuals (4,407 type 2 diabetes (T2D), 6,073 coronary heart disease (CHD), and 3,813 stroke cases). A 1 kg/m(2) genetically elevated BMI increased fasting glucose (0.18 mmol/l; 95% confidence interval (CI) = 0.12-0.24), fasting insulin (8.5%; 95% CI = 5.9-11.1), interleukin-6 (7.0%; 95% CI = 4.0-10.1), and systolic blood pressure (0.70 mmHg; 95% CI = 0.24-1.16) and reduced high-density lipoprotein cholesterol (-0.02 mmol/l; 95% CI = -0.03 to -0.01) and low-density lipoprotein cholesterol (LDL-C; -0.04 mmol/l; 95% CI = -0.07 to -0.01). Observational and causal estimates were directionally concordant, except for LDL-C. A 1 kg/m(2) genetically elevated BMI increased the odds of T2D (odds ratio [OR] = 1.27; 95% CI = 1.18-1.36) but did not alter risk of CHD (OR 1.01; 95% CI = 0.94-1.08) or stroke (OR = 1.03; 95% CI = 0.95-1.12). A meta-analysis incorporating published studies reporting 27,465 CHD events in 219,423 individuals yielded a pooled OR of 1.04 (95% CI = 0.97-1.12) per 1 kg/m(2) increase in BMI. In conclusion, we identified causal effects of BMI on several cardiometabolic traits; however, whether BMI causally impacts CHD risk requires further evidence., (Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Published
2014
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5. Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci.

Author

Saxena R, Elbers CC, Guo Y, Peter I, Gaunt TR, Mega JL, Lanktree MB, Tare A, Castillo BA, Li YR, Johnson T, Bruinenberg M, Gilbert-Diamond D, Rajagopalan R, Voight BF, Balasubramanyam A, Barnard J, Bauer F, Baumert J, Bhangale T, Böhm BO, Braund PS, Burton PR, Chandrupatla HR, Clarke R, Cooper-DeHoff RM, Crook ED, Davey-Smith G, Day IN, de Boer A, de Groot MC, Drenos F, Ferguson J, Fox CS, Furlong CE, Gibson Q, Gieger C, Gilhuijs-Pederson LA, Glessner JT, Goel A, Gong Y, Grant SF, Grobbee DE, Hastie C, Humphries SE, Kim CE, Kivimaki M, Kleber M, Meisinger C, Kumari M, Langaee TY, Lawlor DA, Li M, Lobmeyer MT, Maitland-van der Zee AH, Meijs MF, Molony CM, Morrow DA, Murugesan G, Musani SK, Nelson CP, Newhouse SJ, O'Connell JR, Padmanabhan S, Palmen J, Patel SR, Pepine CJ, Pettinger M, Price TS, Rafelt S, Ranchalis J, Rasheed A, Rosenthal E, Ruczinski I, Shah S, Shen H, Silbernagel G, Smith EN, Spijkerman AW, Stanton A, Steffes MW, Thorand B, Trip M, van der Harst P, van der A DL, van Iperen EP, van Setten J, van Vliet-Ostaptchouk JV, Verweij N, Wolffenbuttel BH, Young T, Zafarmand MH, Zmuda JM, Boehnke M, Altshuler D, McCarthy M, Kao WH, Pankow JS, Cappola TP, Sever P, Poulter N, Caulfield M, Dominiczak A, Shields DC, Bhatt DL, Zhang L, Curtis SP, Danesh J, Casas JP, van der Schouw YT, Onland-Moret NC, Doevendans PA, Dorn GW 2nd, Farrall M, FitzGerald GA, Hamsten A, Hegele R, Hingorani AD, Hofker MH, Huggins GS, Illig T, Jarvik GP, Johnson JA, Klungel OH, Knowler WC, Koenig W, März W, Meigs JB, Melander O, Munroe PB, Mitchell BD, Bielinski SJ, Rader DJ, Reilly MP, Rich SS, Rotter JI, Saleheen D, Samani NJ, Schadt EE, Shuldiner AR, Silverstein R, Kottke-Marchant K, Talmud PJ, Watkins H, Asselbergs FW, de Bakker PI, McCaffery J, Wijmenga C, Sabatine MS, Wilson JG, Reiner A, Bowden DW, Hakonarson H, Siscovick DS, and Keating BJ

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Case-Control Studies, Diabetes Mellitus, Type 2 ethnology, Ethnicity, Female, Follow-Up Studies, Genetic Predisposition to Disease, Genome-Wide Association Study methods, Genotype, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Young Adult, Diabetes Mellitus, Type 2 genetics, Genetic Loci
Abstract

To identify genetic factors contributing to type 2 diabetes (T2D), we performed large-scale meta-analyses by using a custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) with ∼2000 candidate genes in 39 multiethnic population-based studies, case-control studies, and clinical trials totaling 17,418 cases and 70,298 controls. First, meta-analysis of 25 studies comprising 14,073 cases and 57,489 controls of European descent confirmed eight established T2D loci at genome-wide significance. In silico follow-up analysis of putative association signals found in independent genome-wide association studies (including 8,130 cases and 38,987 controls) performed by the DIAGRAM consortium identified a T2D locus at genome-wide significance (GATAD2A/CILP2/PBX4; p = 5.7 × 10(-9)) and two loci exceeding study-wide significance (SREBF1, and TH/INS; p < 2.4 × 10(-6)). Second, meta-analyses of 1,986 cases and 7,695 controls from eight African-American studies identified study-wide-significant (p = 2.4 × 10(-7)) variants in HMGA2 and replicated variants in TCF7L2 (p = 5.1 × 10(-15)). Third, conditional analysis revealed multiple known and novel independent signals within five T2D-associated genes in samples of European ancestry and within HMGA2 in African-American samples. Fourth, a multiethnic meta-analysis of all 39 studies identified T2D-associated variants in BCL2 (p = 2.1 × 10(-8)). Finally, a composite genetic score of SNPs from new and established T2D signals was significantly associated with increased risk of diabetes in African-American, Hispanic, and Asian populations. In summary, large-scale meta-analysis involving a dense gene-centric approach has uncovered additional loci and variants that contribute to T2D risk and suggests substantial overlap of T2D association signals across multiple ethnic groups., (Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Published
2012
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6. Meta-analysis of Dense Genecentric Association Studies Reveals Common and Uncommon Variants Associated with Height.

Author

Lanktree MB, Guo Y, Murtaza M, Glessner JT, Bailey SD, Onland-Moret NC, Lettre G, Ongen H, Rajagopalan R, Johnson T, Shen H, Nelson CP, Klopp N, Baumert J, Padmanabhan S, Pankratz N, Pankow JS, Shah S, Taylor K, Barnard J, Peters BJ, Maloney CM, Lobmeyer MT, Stanton A, Zafarmand MH, Romaine SP, Mehta A, van Iperen EP, Gong Y, Price TS, Smith EN, Kim CE, Li YR, Asselbergs FW, Atwood LD, Bailey KM, Bhatt D, Bauer F, Behr ER, Bhangale T, Boer JM, Boehm BO, Bradfield JP, Brown M, Braund PS, Burton PR, Carty C, Chandrupatla HR, Chen W, Connell J, Dalgeorgou C, Boer Ad, Drenos F, Elbers CC, Fang JC, Fox CS, Frackelton EC, Fuchs B, Furlong CE, Gibson Q, Gieger C, Goel A, Grobbee DE, Hastie C, Howard PJ, Huang GH, Johnson WC, Li Q, Kleber ME, Klein BE, Klein R, Kooperberg C, Ky B, Lacroix A, Lanken P, Lathrop M, Li M, Marshall V, Melander O, Mentch FD, Meyer NJ, Monda KL, Montpetit A, Murugesan G, Nakayama K, Nondahl D, Onipinla A, Rafelt S, Newhouse SJ, Otieno FG, Patel SR, Putt ME, Rodriguez S, Safa RN, Sawyer DB, Schreiner PJ, Simpson C, Sivapalaratnam S, Srinivasan SR, Suver C, Swergold G, Sweitzer NK, Thomas KA, Thorand B, Timpson NJ, Tischfield S, Tobin M, Tomaszewski M, Verschuren WM, Wallace C, Winkelmann B, Zhang H, Zheng D, Zhang L, Zmuda JM, Clarke R, Balmforth AJ, Danesh J, Day IN, Schork NJ, de Bakker PI, Delles C, Duggan D, Hingorani AD, Hirschhorn JN, Hofker MH, Humphries SE, Kivimaki M, Lawlor DA, Kottke-Marchant K, Mega JL, Mitchell BD, Morrow DA, Palmen J, Redline S, Shields DC, Shuldiner AR, Sleiman PM, Smith GD, Farrall M, Jamshidi Y, Christiani DC, Casas JP, Hall AS, Doevendans PA, Christie JD, Berenson GS, Murray SS, Illig T, Dorn GW 2nd, Cappola TP, Boerwinkle E, Sever P, Rader DJ, Reilly MP, Caulfield M, Talmud PJ, Topol E, Engert JC, Wang K, Dominiczak A, Hamsten A, Curtis SP, Silverstein RL, Lange LA, Sabatine MS, Trip M, Saleheen D, Peden JF, Cruickshanks KJ, März W, O'Connell JR, Klungel OH, Wijmenga C, Maitland-van der Zee AH, Schadt EE, Johnson JA, Jarvik GP, Papanicolaou GJ, Grant SF, Munroe PB, North KE, Samani NJ, Koenig W, Gaunt TR, Anand SS, van der Schouw YT, Soranzo N, Fitzgerald GA, Reiner A, Hegele RA, Hakonarson H, and Keating BJ

Subjects
Adult, Black or African American genetics, Asian People genetics, Female, Gene Frequency, Genome-Wide Association Study, Hispanic or Latino genetics, Humans, Interleukin-11 genetics, Male, Smad3 Protein genetics, White People genetics, Body Height genetics, Cardiovascular System, Genetic Heterogeneity, Genetic Loci, Polymorphism, Single Nucleotide
Abstract

Height is a classic complex trait with common variants in a growing list of genes known to contribute to the phenotype. Using a genecentric genotyping array targeted toward cardiovascular-related loci, comprising 49,320 SNPs across approximately 2000 loci, we evaluated the association of common and uncommon SNPs with adult height in 114,223 individuals from 47 studies and six ethnicities. A total of 64 loci contained a SNP associated with height at array-wide significance (p < 2.4 × 10(-6)), with 42 loci surpassing the conventional genome-wide significance threshold (p < 5 × 10(-8)). Common variants with minor allele frequencies greater than 5% were observed to be associated with height in 37 previously reported loci. In individuals of European ancestry, uncommon SNPs in IL11 and SMAD3, which would not be genotyped with the use of standard genome-wide genotyping arrays, were strongly associated with height (p < 3 × 10(-11)). Conditional analysis within associated regions revealed five additional variants associated with height independent of lead SNPs within the locus, suggesting allelic heterogeneity. Although underpowered to replicate findings from individuals of European ancestry, the direction of effect of associated variants was largely consistent in African American, South Asian, and Hispanic populations. Overall, we show that dense coverage of genes for uncommon SNPs, coupled with large-scale meta-analysis, can successfully identify additional variants associated with a common complex trait.

Published
2011
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7. Evidence for coregulation of myocardial gene expression by MEF2 and NFAT in human heart failure.

Author

Putt ME, Hannenhalli S, Lu Y, Haines P, Chandrupatla HR, Morrisey EE, Margulies KB, and Cappola TP

Subjects
Databases, Factual, Heart Failure metabolism, Humans, Myogenic Regulatory Factors genetics, NFATC Transcription Factors genetics, Odds Ratio, Oligonucleotide Array Sequence Analysis, Promoter Regions, Genetic, Gene Expression Regulation, Heart Failure genetics, Myocardium metabolism, Myogenic Regulatory Factors metabolism, NFATC Transcription Factors metabolism
Abstract

Background: Pathological stresses induce heart failure in animal models through activation of multiple cardiac transcription factors (TFs) working cooperatively. However, interactions among TFs in human heart failure are less understood. Here, we use genomic data to examine the evidence that 5 candidate TF families coregulate gene expression in human heart failure., Methods and Results: RNA isolates from failing (n=86) and nonfailing (n=16) human hearts were hybridized with Affymetrix HU133A arrays. For each gene on the array, we determined conserved MEF2, NFAT, NKX , GATA , and FOX binding motifs within the -1-kb promoter region using human-murine sequence alignments and the TRANSFAC database. Across 9076 genes expressed in the heart, TF-binding motifs tended to cluster together in nonrandom patterns within promoters of specific genes (P values ranging from 10(-2) to 10(-21)), suggesting coregulation. We then modeled differential expression as a function of TF combinations present in promoter regions. Several combinations predicted increased odds of differential expression in the failing heart, with the highest odds ratios noted for genes containing both MEF2 and NFAT binding motifs together in the same promoter region (peak odds ratio, 3.47; P=0.005)., Conclusions: These findings provide genomic evidence for coregulation of myocardial gene expression by MEF2 and NFAT in human heart failure. In doing so, they extend the paradigm of combinatorial regulation of gene expression to the human heart and identify new target genes for mechanistic study. More broadly, we demonstrate how integrating diverse sources of genomic data yields novel insight into human cardiovascular disorders.

Published
2009
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8. Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies.

Author

Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, Glessner JT, Galver L, Barrett JC, Grant SF, Farlow DN, Chandrupatla HR, Hansen M, Ajmal S, Papanicolaou GJ, Guo Y, Li M, Derohannessian S, de Bakker PI, Bailey SD, Montpetit A, Edmondson AC, Taylor K, Gai X, Wang SS, Fornage M, Shaikh T, Groop L, Boehnke M, Hall AS, Hattersley AT, Frackelton E, Patterson N, Chiang CW, Kim CE, Fabsitz RR, Ouwehand W, Price AL, Munroe P, Caulfield M, Drake T, Boerwinkle E, Reich D, Whitehead AS, Cappola TP, Samani NJ, Lusis AJ, Schadt E, Wilson JG, Koenig W, McCarthy MI, Kathiresan S, Gabriel SB, Hakonarson H, Anand SS, Reilly M, Engert JC, Nickerson DA, Rader DJ, Hirschhorn JN, and Fitzgerald GA

Subjects
Cardiovascular Diseases ethnology, Concept Formation, Gene Frequency, Genome-Wide Association Study instrumentation, Genotype, Humans, Population Groups genetics, Quality Control, Research Design, Cardiovascular Diseases genetics, Genome-Wide Association Study methods, Oligonucleotide Array Sequence Analysis methods, Polymorphism, Single Nucleotide
Abstract

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a "cosmopolitan" tagging approach to capture the genetic diversity across approximately 2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

Published
2008
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