Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation
Human genetic studies have demonstrated that quantitative human anthropometric and metabolic traits, including body mass index, waist-hip ratio, and plasma concentrations of glucose and insulin, are highly heritable, and are established risk factors for type 2 diabetes and cardiovascular diseases. Although many regions of the genome have been associated with these traits, the specific genes responsible have not yet been identified. By making use of advanced statistical “imputation” techniques applied to more than 87,000 individuals of European ancestry, and publicly available “reference panels” of more than 37 million genetic variants, we have been able to identify novel regions of the genome associated with these glycaemic and obesity-related traits and localise genes within these regions that are most likely to be causal. This improved understanding of the biological mechanisms underlying glycaemic and obesity-related traits is extremely important because it may advance drug development for downstream disease endpoints, ultimately leading to public health benefits.
Vyšlo v časopise:
Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation. PLoS Genet 11(7): e32767. doi:10.1371/journal.pgen.1005230
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1005230
Souhrn
Human genetic studies have demonstrated that quantitative human anthropometric and metabolic traits, including body mass index, waist-hip ratio, and plasma concentrations of glucose and insulin, are highly heritable, and are established risk factors for type 2 diabetes and cardiovascular diseases. Although many regions of the genome have been associated with these traits, the specific genes responsible have not yet been identified. By making use of advanced statistical “imputation” techniques applied to more than 87,000 individuals of European ancestry, and publicly available “reference panels” of more than 37 million genetic variants, we have been able to identify novel regions of the genome associated with these glycaemic and obesity-related traits and localise genes within these regions that are most likely to be causal. This improved understanding of the biological mechanisms underlying glycaemic and obesity-related traits is extremely important because it may advance drug development for downstream disease endpoints, ultimately leading to public health benefits.
Zdroje
1. Rose KM, Newman B, Mayer-Davis EJ, Selby JV (1998) Genetic and behavioural determinants of waist-hip ratio and waist circumference in women twins. Obes Res 6: 383–392. 9845227
2. Poulsen P, Kyvik KO, Vaag A, Beck-Nielsen H (1999) Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance—a population-based twin study. Diabetologia 42: 139–145. 10064092
3. Poulsen P, Levin K, Petersen I, Christensen K, Beck-Nielsen H, et al. (2005) Heritability of insulin secretion, peripheral and hepatic insulin action, and intracellular glucose partitioning in young and old Danish twins. Diabetes 54: 275–283. 15616039
4. Silventoinen K, Rokholm B, Kaprio J, Sørensen TI (2010) The genetic and environmental influences on childhood obesity: a systematic review of twin and adoption studies. Int J Obes 34: 29–40.
5. Van Dongen J, Willemsen G, Chen WW, de Geus EJ, Boomsma DI (2013) Heritability of metabolic syndrome traits in a large population-based sample. J Lipid Res 54: 2914–2923. doi: 10.1194/jlr.P041673 23918046
6. American Diabetes Association (2003) The expert committee on the diagnosis and classification of diabetes mellitus: follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 26: 3160–3167. 14578255
7. Weyer C, Bogardus C, Mott DM, Pratley RE (1999) The natural history of insulin secretory dysfunction and insulin resistance in the pathogenesis of type 2 diabetes mellitus. J Clin Invest 104: 787–794. 10491414
8. DeFronzo RA, Ferrannini E (1991) Insulin resistance: a multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care 14: 173–194. 2044434
9. Lewis CE, McTigue KM, Burke LE, Poirier P, Eckel RH, et al. (2009) Mortality, health outcomes, and body mass index in the overweight range: a science advisory from the American Heart Association. Circulation 119: 3263–3271. doi: 10.1161/CIRCULATIONAHA.109.192574 19506107
10. Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, et al (2008) General and abdominal adiposity and risk of death in Europe. N Engl J Med 359: 2105–2120. doi: 10.1056/NEJMoa0801891 19005195
11. Chambers JC, Elliott P, Zabaneh D, Zhang W, Li Y, et al. (2008) Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet 40: 716–718. doi: 10.1038/ng.156 18454146
12. Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, et al. (2009) Variants in MTNR1B influence fasting glucose levels. Nat Genet 41: 77–81. doi: 10.1038/ng.290 19060907
13. Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, et al. (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 41: 25–34. doi: 10.1038/ng.287 19079261
14. Lindgren CM, Heid IM, Randall JC, Lamina C, Steinthorsdottir V, et al. (2009) Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution. PLoS Genet 5: e1000508. doi: 10.1371/journal.pgen.1000508 19557161
15. Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, et al. (2009) A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41: 527–534. doi: 10.1038/ng.357 19396169
16. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, et al. (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42: 105–116. doi: 10.1038/ng.520 20081858
17. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42: 937–948. doi: 10.1038/ng.686 20935630
18. Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, et al. (2010) Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 42: 949–960. doi: 10.1038/ng.685 20935629
19. Scott RA, Lagou V, Welch RP, Wheeler E, Montasser ME, et al. (2012) Large-scale association analyses identify new loci influencing glycaemic traits and provide insight into the underlying biological pathways. Nat Genet 44: 991–1005. doi: 10.1038/ng.2385 22885924
20. Manning AK, Hivert MF, Scott RA, Grimsby JL, Bouatia-Naji N, et al. (2012) A genome-wide approach accounting for body-mass index identifies genetic variants influencing fasting glycaemic traits and insulin resistance. Nat Genet 44: 659–669. doi: 10.1038/ng.2274 22581228
21. Okada Y, Kubo M, Ohmiya H, Takahashi A, Kumasaka N, et al. (2012) Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations. Nat Genet 44: 302–306. doi: 10.1038/ng.1086 22344221
22. Wen W, Cho YS, Zheng W, Dorajoo R, Kato N, et al. (2012) Meta-analysis identifies common variants associated with body-mass index in east Asians. Nat Genet 44: 307–311. doi: 10.1038/ng.1087 22344219
23. Ng MC, Hester JM, Wing MR, Li J, Xu J, et al. (2012) Genome-wide association of BMI in African Americans. Obesity 20: 622–627. doi: 10.1038/oby.2011.154 21701570
24. Berndt SI, Gustafsson S, Mägi R, Ganna A, Wheeler E, et al. (2013) Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet 45: 501–512. doi: 10.1038/ng.2606 23563607
25. Monda KL, Chen GK, Taylor KC, Palmer C, Edwards TL, et al. (2013) A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat Genet 45:690–696. doi: 10.1038/ng.2608 23583978
26. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, et al. (2014) Genetic studies of body mass index yield new insights for obesity biology. Nature (in press).
27. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, et al. (2014) New genetic loci link adipose and insulin biology to body fat distribution. Nature (in press).
28. Marchini J, Howie B (2010) Genotype imputation for genome-wide association studies. Nat Rev Genet 11 499–511. doi: 10.1038/nrg2796 20517342
29. The International HapMap Consortium (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449: 851–861. 17943122
30. The International HapMap Consortium (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467: 52–58. doi: 10.1038/nature09298 20811451
31. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753. doi: 10.1038/nature08494 19812666
32. Dickson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB (2010) Rare variants create synthetic genome-wide associations. PLoS Biol 26: e1000294.
33. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, et al. (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42: 565–569. doi: 10.1038/ng.608 20562875
34. Pritchard JK (2001) Are rare variants responsible for susceptibility to complex diseases? Am J Hum Genet 69: 124–137. 11404818
35. Barrett JC, Cardon LR (2006) Evaluating coverage of genome-wide association studies. Nat Genet 38: 659–662. 16715099
36. Anderson CA, Pettersson FH, Barrett JC, Zhuang JJ, Ragoussis J, et al. (2008) Evaluating the effects of imputation on the power, coverage and cost-efficiency of genome-wide SNP platforms. Am J Hum Genet 83: 112–119.
37. Jostins L, Morley KI, Barrett JC (2011) Imputation of low-frequency variants using the HapMap3 benefits from large, diverse reference sets. Eur J Hum Genet 19: 662–666. doi: 10.1038/ejhg.2011.10 21364697
38. The 1000 Genomes Project Consortium (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65. doi: 10.1038/nature11632 23128226
39. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44: 955–959. doi: 10.1038/ng.2354 22820512
40. Porcu E, Sanna S, Fuchsberger C, Fritsche LG (2013) Genotype imputation in genome-wide association studies. Curr Protoc Hum Genet: Chapter 1, Unit 1.25.
41. Duan Q, Liu EY, Croteau-Chonka DC, Mohlke KL, Li Y (2013) A comprehensive SNP and indel imputability database. Bioinformatics 29: 528–531.
42. Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5: e1000529. doi: 10.1371/journal.pgen.1000529 19543373
43. Winkler TW, Day FR, Croteau-Chonka DC, Wood AR, Locke AE, et al (2014) Quality control and conduct of genome-wide association meta-analyses. Nat Protoc 9: 1192–1212. doi: 10.1038/nprot.2014.071 24762786
44. Yang J, Ferreira T, Morris AP, Medland SE; Genetic Investigation of ANthropometric Traits (GIANT) Consortium, et al. (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44: 369–375. doi: 10.1038/ng.2213 22426310
45. Maller JB, McVean G, Byrnes J, Vukcevic D, Palin K, et al. (2012) Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat Genet 44: 1294–1301. doi: 10.1038/ng.2435 23104008
46. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè AV, et al. (2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 44: 981–990. doi: 10.1038/ng.2383 22885922
47. Mahajan A, Sim X, Ng HJ, Manning A, Rivas MA, et al. (2014) Identification and functional characterization of G6PC2 coding variants influencing glycaemic traits define an effector transcript at the G6PC2-ABCB11 locus. PLoS Genet 11: e1004876.
48. Pasquali L, Gaulton KJ, Rodríguez-Seguí SA, Mularoni L, Miguel-Escalada I, et al. (2014) Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46: 136–143. doi: 10.1038/ng.2870 24413736
49. Huyghe JR, Jackson AU, Fogarty MP, Buchkovich ML, Stancakova A, et al. (2013) Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat Genet 45: 197–201. doi: 10.1038/ng.2507 23263489
50. Peloso GM, Auer PL, Bis JC, Voorman A, Morrison AC, et al. (2014) Association of low-frequency and rare coding-sequence variants with blood lipids and coronary artery disease in 56,000 whites and blacks. Am J Hum Genet 94: 223–232. doi: 10.1016/j.ajhg.2014.01.009 24507774
51. Holmen OL, Zhang H, Zhou W, Schmidt E, Hovelson DH, et al. (2014) No large-effect low-frequency coding variation found for myocardial infarction. Hum Mol Genet 23: 4721–4728. doi: 10.1093/hmg/ddu175 24728188
52. Moutsianas L, Morris AP (2014) Methodology for the analysis of rare genetic variation in genome-wide association and re-sequencing studies of complex human traits. Brief Funct Genomics 13: 362–370. doi: 10.1093/bfgp/elu012 24916163
53. Chen F, Klein AP, Klein BE, Lee KE, Truitt B, et al. (2014) Exome array analysis identifies CAV1/CAV2 as a susceptibility locus for intraocular pressure. Invest Opthalmol Vis Sci 56: 544–551.
54. Wessel J, Chu AY, Willems SM, Wang S, Yaghootkar H, et al. (2015) Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Comms 6: 5897.
55. Chen JA, Wang Q, Davis-Turak J, Li Y, Karydas AM, et al. (2015) A multiancestral genome-wide exome array study of Alzheimer disease, frontotemporal dementia, and progressive supranuclear palsy. JAMA Meurol (in press).
56. Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55: 997–1004. 11315092
57. Magi R, Morris AP (2010) GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11: 288. doi: 10.1186/1471-2105-11-288 20509871
58. Ioannidis JP, Patsopoulos NA, Evangelou E (2007) Heterogeneity in meta-analyses of genome-wide association investigations. PLoS ONE 2: e841. 17786212
59. Wakefield JA (2007) Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am J Hum Genet 81: 208–227. 17668372
60. Ward LD, Kellis M (2012) HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucl Acids Res 40: D930–934. doi: 10.1093/nar/gkr917 22064851
61. Kumar P, Henikoff S, Ng P (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4: 1073–1081. doi: 10.1038/nprot.2009.86 19561590
62. Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012) Predicting the functional effect of amino acid substitutions and indels. PLoS ONE 7: e46688. doi: 10.1371/journal.pone.0046688 23056405
63. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, et al. (2010) A method and server for predicting damaging missense mutations. Nat Methods 7: 248–249. doi: 10.1038/nmeth0410-248 20354512
64. The ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489: 57–74. doi: 10.1038/nature11247 22955616
65. Pasquali L, Gaulton KJ, Rodríguez-Seguí SA, Mularoni L, Miguel-Escalada I, et al. (2014) Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46: 136–143. doi: 10.1038/ng.2870 24413736
66. Mikkelsen TS, Xu Z, Zhang X, Wang L, Gimble JM, et al. (2010) Comparative epigenomic analysis of murine and human adipogenesis. Cell 143: 156–169. doi: 10.1016/j.cell.2010.09.006 20887899
67. Li H, Durbin R. (2009) Fast and accurate short read alignment with Burrows—Wheeler transform. Bioinformatics 25: 1754–1760. doi: 10.1093/bioinformatics/btp324 19451168
68. Ernst J, Kellis M (2010) Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat Biotechnol 28: 817–825. doi: 10.1038/nbt.1662 20657582
69. Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, et al. (2012) GENCODE: The reference human genome annotation for the ENCODE project. Genome Res 22: 1760–1774. doi: 10.1101/gr.135350.111 22955987
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
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