Association of levels of fasting glucose and insulin with rare variants at the chromosome 11p11.2-madd locus cohorts for heart and aging research in genomic epidemiology (charge) consortium targeted sequencing study

Circulation: Cardiovascular Genetics

Volumn 7, Issue 3, 2014, Pages 374-382

  Cornes, Belinda K ^a,b   Brody, Jennifer A ^c   Nikpoor, Naghmeh ^e   Morrison, Alanna C ^f   Dang, Huan Chu Pham ^e   Ahn, Byung Soo ^e   Wang, Shuai ^g   Dauriz, Marco ^h   Barzilay, Joshua I ⁱ   Dupuis, Josée ^g,j   Florez, Jose C ^a,b,k   Coresh, Josef ^l,m   Gibbs, Richard A ^f   Kao, W H Linda ^m   Liu, Ching Ti ^g   McKnight, Barbara ^c   Muzny, Donna ^a,b,f   Pankow, James S ⁿ   Reid, Jeffrey G ^m   White, Charles C ^g   Johnson, Andrew D ^j   Wong, Tien Y ^o,p   Psaty, Bruce M ^q   Boerwinkle, Eric ^f   Rotter, Jerome I ^r   Siscovick, David S ^c,d   Sladek, Robert ^e   Meigs, James B ^a,b   more..

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c Cardiovascular Health Research Unit   (United States)

^d UNIVERSITY OF WASHINGTON   (United States)

^e MCGILL UNIVERSITY   (Canada)

^f BAYLOR COLLEGE OF MEDICINE   (United States)

^g BOSTON UNIVERSITY   (United States)

^h UNIVERSITY HOSPITAL OF VERONA   (Italy)

ⁱ EMORY UNIVERSITY SCHOOL OF MEDICINE   (United States)

^j FRAMINGHAM HEART STUDY   (United States)

^k BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^l DEPARTMENT OF PATHOLOGY   (United States)

^m JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

ⁿ UNIVERSITY OF MINNESOTA   (United States)

^o SINGAPORE EYE RESEARCH INSTITUTE   (Singapore)

^p NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^q UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

^r Los Angeles Biomedical Research Institute   (United States)

more..

Author keywords

Genetic epidemiology; Glucose; Human genetics; Insulin; Molecular genetics

Indexed keywords

FORKHEAD TRANSCRIPTION FACTOR; GLUCOSE; INSULIN; DEATH DOMAIN RECEPTOR SIGNALING ADAPTOR PROTEIN; GLUCOSE BLOOD LEVEL; GUANINE NUCLEOTIDE EXCHANGE FACTOR; MADD PROTEIN, HUMAN;

ADULT; ARTICLE; BINDING SITE; CHROMOSOME 11P; DIET RESTRICTION; FEMALE; GENE FREQUENCY; GENE LOCUS; GENETIC ASSOCIATION; GENETIC EPIDEMIOLOGY; GENETIC VARIABILITY; GLUCOSE BLOOD LEVEL; HUMAN; INSULIN BLOOD LEVEL; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; SEQUENCE ANALYSIS; AGED; AGING; BLOOD; CHROMOSOME 11; COHORT ANALYSIS; DNA SEQUENCE; GENETICS; GENOMICS; HEART DISEASES; METABOLISM; MIDDLE AGED; NON INSULIN DEPENDENT DIABETES MELLITUS; SINGLE NUCLEOTIDE POLYMORPHISM; VERY ELDERLY;

AGED; AGED, 80 AND OVER; AGING; BLOOD GLUCOSE; CHROMOSOMES, HUMAN, PAIR 11; COHORT STUDIES; DEATH DOMAIN RECEPTOR SIGNALING ADAPTOR PROTEINS; DIABETES MELLITUS, TYPE 2; FASTING; FEMALE; GENE FREQUENCY; GENETIC VARIATION; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; GUANINE NUCLEOTIDE EXCHANGE FACTORS; HEART DISEASES; HUMANS; INSULIN; MALE; MIDDLE AGED; POLYMORPHISM, SINGLE NUCLEOTIDE; SEQUENCE ANALYSIS, DNA;

EID: 84903585424     PISSN: 1942325X     EISSN: 19423268     Source Type: Journal    
DOI: 10.1161/CIRCGENETICS.113.000169     Document Type: Article

References (46)

1. Scott RA, Lagou V, Welch RP, Wheeler E, Montasser ME, Luan J, et al; DIAbetes Genetics Replication and Meta-Analysis (DIAGRAM) Consortium. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet. 2012;44:991-1005.
2. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, et al DIAGRAM Consortium; GIANT Consortium; Global BPgen Consortium; Anders Hamsten on behalf of Procardis Consortium; MAGIC investigators. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42:105-116.
3. Strawbridge RJ, Dupuis J, Prokopenko I, Barker A, Ahlqvist E, Rybin D, et al DIAGRAM Consortium; GIANT Consortium; MuTHER Consortium; CARDIoGRAM Consortium; C4D Consortium. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes. 2011;60:2624-2634.
4. Lumley T, Dupuis J, Rice KM, Barbalic M, Bis JC, Cupples LA, et al. Two-phase subsampling designs for genomic resequencing studies. http:// stattech.wordpress.fos.auckland.ac.nz/2012-2-two-phase-subsamplingdesigns-for- genomic-resequencing-studies/. Accessed May 17, 2014.
5. Lin H, Wang M, Brody JA, Bis JC, Dupuis J, Lumley T, et al. Strategies to design and analyze targeted sequencing data: Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study. Circ Cardiovasc Genet. 2014;7:335-343.
6. Psaty BM, O'Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, Rotter JI, et al CHARGE Consortium. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-Analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2:73-80.
7. Cushman M, Cornell ES, Howard PR, Bovill EG, Tracy RP. Laboratory methods and quality assurance in the Cardiovascular Health Study. Clin Chem. 1995;41:264-270.
8. Meigs JB, Nathan DM, Wilson PW, Cupples LA, Singer DE. Metabolic risk factors worsen continuously across the spectrum of nondiabetic glucose tolerance. The Framingham Offspring Study. Ann Intern Med. 1998;128:524-533.
9. Smith NL, Barzilay JI, Shaffer D, Savage PJ, Heckbert SR, Kuller LH, et al. Fasting and 2-hour postchallenge serum glucose measures and risk of incident cardiovascular events in the elderly: the Cardiovascular Health Study. Arch Intern Med. 2002;162:209-216.
10. Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005;15:1034-1050.
11. Durbin RM, Abecasis GR, Altshuler DL, Auton A, Brooks LD, Gibbs RA, et al. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061-1073.
12. Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, et al. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57-74.
13. Hollenhorst PC, Chandler KJ, Poulsen RL, Johnson WE, Speck NA, Graves BJ. DNA specificity determinants associate with distinct transcription factor functions. PLoS Genet. 2009;5:e1000778.
14. Jolma A, Kivioja T, Toivonen J, Cheng L, Wei G, Enge M, et al. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res. 2010;20:861-873.
15. Verzi MP, Shin H, He HH, Sulahian R, Meyer CA, Montgomery RK, et al. Differentiation-specific histone modifications reveal dynamic chromatin interactions and partners for the intestinal transcription factor CDX2. Dev Cell. 2010;19:713-726.
16. Wei GH, Badis G, Berger MF, Kivioja T, Palin K, Enge M, et al. Genomewide analysis of ETS-family DNA-binding in vitro and in vivo. EMBO J. 2010;29:2147-2160.
17. Lo KA, Bauchmann MK, Baumann AP, Donahue CJ, Thiede MA, Hayes LS, et al. Genome-wide profiling of H3K56 acetylation and transcription factor binding sites in human adipocytes. PLoS One. 2011;6:e19778.
18. Novershtern N, Subramanian A, Lawton LN, Mak RH, Haining WN, Mc-Conkey ME, et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell. 2011;144:296-309.
19. Palii CG, Perez-Iratxeta C, Yao Z, Cao Y, Dai F, Davison J, et al. Differential genomic targeting of the transcription factor TAL1 in alternate haematopoietic lineages. EMBO J. 2011;30:494-509.
20. Yu S, Cui K, Jothi R, Zhao DM, Jing X, Zhao K, et al. GABP controls a critical transcription regulatory module that is essential for maintenance and differentiation of hematopoietic stem/progenitor cells. Blood. 2011;117:2166-2178.
21. Myers AJ, Gibbs JR, Webster JA, Rohrer K, Zhao A, Marlowe L, et al. A survey of genetic human cortical gene expression. Nat Genet. 2007;39:1494-1499.
22. Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, et al. Population genomics of human gene expression. Nat Genet. 2007;39:1217-1224.
23. Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M. Linking disease associations with regulatory information in the human genome. Genome Res. 2012;22:1748-1759.
24. Veyrieras JB, Kudaravalli S, Kim SY, Dermitzakis ET, Gilad Y, Stephens M, et al. High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet. 2008;4:e1000214.
25. Dimas AS, Deutsch S, Stranger BE, Montgomery SB, Borel C, Attar-Cohen H, et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science. 2009;325:1246-1250.
26. Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010;6:e1000952.
27. Montgomery SB, Sammeth M, Gutierrez-Arcelus M, Lach RP, Ingle C, Nisbett J, et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature. 2010;464:773-777.
28. Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010;464:768-772.
29. Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, Castagne R, et al. Genetics and beyond-the transcriptome of human monocytes and disease susceptibility. PLoS One. 2010;5:e10693.
30. Degner JF, Pai AA, Pique-Regi R, Veyrieras JB, Gaffney DJ, Pickrell JK, et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature. 2012;482:390-394.
31. Rhee HS, Pugh BF. Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution. Cell. 2011;147:1408-1419.
32. Zhu X, Ahmad SM, Aboukhalil A, Busser BW, Kim Y, Tansey TR, et al. Differential regulation of mesodermal gene expression by Drosophila cell type-specific Forkhead transcription factors. Development. 2012;139:1457-1466.
33. Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, et al. Architecture of the human regulatory network derived from ENCODE data. Nature. 2012;489:91-100.
34. Cirillo LA, Zaret KS. Specific interactions of the wing domains of FOXA1 transcription factor with DNA. J Mol Biol. 2007;366:720-724.
35. Manning AK, Hivert MF, Scott RA, Grimsby JL, Bouatia-Naji N, Chen H, et al; DIAbetes Genetics Replication And Meta-Analysis (DIAGRAM) Consortium; Multiple Tissue Human Expression Resource (MUTHER) Consortium. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet. 2012;44:659-669.
36. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89:82-93.
37. Chen H, Meigs JB, Dupuis J. Sequence kernel association test for quantitative traits in family samples. Genet Epidemiol. 2013;37:196-204.
38. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-Analysis of genomewide association scans. Bioinformatics. 2010;26:2190-2191.
39. Kaestner KH. The FoxA factors in organogenesis and differentiation. Curr Opin Genet Dev. 2010;20:527-532.
40. Gao N, Le Lay J, Qin W, Doliba N, Schug J, Fox AJ, et al. Foxa1 and Foxa2 maintain the metabolic and secretory features of the mature betacell. Mol Endocrinol. 2010;24:1594-1604.
41. Huyghe JR, Jackson AU, Fogarty MP, Buchkovich ML, Stančá ková A, Stringham HM, et al. Exome array analysis identifies new loci and lowfrequency variants influencing insulin processing and secretion. Nat Genet. 2013;45:197-201.
42. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè AV, Steinthorsdottir V, et al; Wellcome Trust Case Control Consortium; Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; Asian Genetic Epidemiology Network-Type 2 Diabetes (AGEN-T2D) Consortium; South Asian Type 2 Diabetes (SAT2D) Consortium; DIAbetes Genetics Replication And Meta-Analysis (DIAGRAM) Consortium. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44:981-990.
43. Hu C, Zhang R, Wang C, Wang J, Ma X, Hou X, et al. Variants from GIPR, TCF7L2, DGKB, MADD, CRY2, GLIS3, PROX1, SLC30A8 and IGF1 are associated with glucose metabolism in the Chinese. PLoS One. 2010;5:e15542.
44. Krasowski MD, Ni A, Hagey LR, Ekins S. Evolution of promiscuous nuclear hormone receptors: LXR, FXR, VDR, PXR, and CAR. Mol Cell Endocrinol. 2011;334:39-48.
45. Meng ZX, Nie J, Ling JJ, Sun JX, Zhu YX, Gao L, et al. Activation of liver X receptors inhibits pancreatic islet beta cell proliferation through cell cycle arrest. Diabetologia. 2009;52:125-135.
46. Shih DQ, Navas MA, Kuwajima S, Duncan SA, Stoffel M. Impaired glucose homeostasis and neonatal mortality in hepatocyte nuclear factor 3alpha-deficient mice. Proc Natl Acad Sci U S A. 1999;96:10152-10157.