The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits
Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the “Metabochip,” a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.
Vyšlo v časopise:
The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits. PLoS Genet 8(8): e32767. doi:10.1371/journal.pgen.1002793
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1002793
Souhrn
Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the “Metabochip,” a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.
Zdroje
1. YangJ, BenyaminB, McEvoyBP, GordonS, HendersAK, et al. (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42: 565–569.
2. YangJ, ManolioTA, PasqualeLR, BoerwinkleE, CaporasoN, et al. (2011) Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 43: 519–525.
3. AllenHL, EstradaK, LettreG, BerndtSI, WeedonMN, et al. (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467: 832–838.
4. TeslovichTM, MusunuruK, SmithAV, EdmondsonAC, StylianouIM, et al. (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466: 707–713.
5. MusunuruK, StrongA, Frank-KamenetskyM, LeeNA, AhfeldtT, et al. (2010) From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466: 714–719.
6. FrazerKA, BallingerDG, CoxDR, HindsDA, StuveLL, et al. (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449: 851–861.
7. The 1000 Genomes Project Consortium (2010) A map of human genome variation from population-scale sequencing. Nature 467: 1061–1073.
8. GundersonKL (2006) SteemersFJ (2006) RenH (2006) NgP (2006) ZhouL (2006) Whole-genome genotyping. Methods Enzymol 410: 359–376.
9. KilpeläinenTO, ZillikensMC, StančákovaA, FinucaneFM, RiedJS, et al. (2011) Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat Genet 43: 753–760.
10. SchunkertH, KönigIR, KathiresanS, ReillyMP, AssimesTL, et al. (2011) Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet 43: 333–338.
11. VoightBF, ScottLJ, SteinthorsdottirV, MorrisAP, DinaC, et al. (2010) Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 42: 579–589.
12. HeidIM, JacksonAU, RandallJC, WinklerTW, QiL, 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.
13. SpeliotesEK, WillerCJ, BerndtSI, MondaKL, ThorleifssonG, et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42: 937–948.
14. SoranzoN, SpectorTD, ManginoM, KühnelB, RendonA, et al. (2009) A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat Genet 41: 1182–1190.
15. EhretGB, MunroePM, RiceKM, BochudM, JohnsonAD, et al. (2011) Genetic variants in novel pathways influence blood pressure and coronary artery disease risk. Nature 478: 103–109.
16. DupuisJ, LangenbergC, ProkopenkoI, SaxenaR, SoranzoN, et al. (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42: 105–116.
17. SoranzoN, SannaS, WheelerE, GiegerC, RadkeD, et al. (2010) Common variants at 10 genomic loci influence hemoglobin A1(C) levels via glycemic and nonglycemic pathways. Diabetes 59: 3229–3239.
18. SaxenaR, HivertMF, LangenbergC, TanakaT, PankowJS, et al. (2010) Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet 42: 142–148.
19. Newton-ChehC, EijgelsheimM, RiceKM, de BakkerPI, YinX, et al. (2009) Common variants at ten loci influence QT interval duration in the QTGEN Study. Nat Genet 41: 399–406.
20. PfeuferA, SannaS, ArkingDE, MüllerM, GatevaV, et al. (2009) Common variants at ten loci modulate the QT interval duration in the QTSCD Study. Nat Genet 41: 407–414.
21. HindorffLA, SethupathyP, JunkinsHA, RamosEM, MehtaJP, et al. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 106: 9362–9367.
22. de BakkerPI, McVeanG, SabetiPC, MirettiMM, GreenT, et al. (2006) A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC. Nat Genet 38: 1166–1172.
23. SaxenaR, deBakkerPI, SingerK, MoothaV, BurttN, et al. (2006) Comprehensive association testing of common mitochondrial DNA variation in metabolic disease. Am J Hum Genet 79: 54–61.
24. Van den OuwelandJM, LemkesHH, RuitenbeekW, SandkuijlLA, de VijlderMF, et al. (1992) Mutation in mitochondrial tRNA(Leu)(UUR) gene in a large pedigree with maternally transmitted type 2 diabetes mellitus and deafness. Nat Genet 1: 368–371.
25. PoultonL, LuanJ, MacaulayV, HenningsS, MitchellJ, et al. (2002) Type 2 diabetes is associated with a common mitochondrial variant: evidence from a population-based case-control study. Hum Mol Genet 11: 1581–1583.
26. SchwarzPEH, TowersGW, FischerS, GovindarajaluS, SchulzeJ, et al. (2006) Hypoadiponectinemia is associated with progression toward type 2 diabetes and genetic variation in the ADIPOQ gene promoter. Diabetes Care 29: 1645–1650.
27. ScottLJ, MohlkeKL, BonnycastleLL, WillerCJ, LiY, et al. (2007) A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316: 1341–1345.
28. StancákováA, JavorskýM, LuulasmaaT, HaffnerSM, KuusistoL, et al. (2009) Changes in insulin sensitivity and insulin release in relation to glycemia and glucose tolerance in 6416 Finnish men. Diabetes 58: 1212–1221.
29. HertelJK, JohanssonS, SonestedtE, JohssonA, LieRT, et al. (2011) FTO, type 2 diabetes, and weight gain throughout adult life: a meta-analysis of 41,504 subjects from the Scandinavian HUNT, MDC, and MPP studies. Diabetes 60: 1637–1644.
30. JacobsenBK, EggenAE, MathiesenEB, WilsgaardT, NjolstadI (2011) Cohort profile: The Tromso Study. Int J Epidemiol DOI doi: 10.1093/ije/dyr049.
31. PiliaG (2006) ChenWM (2006) ScuteriA (2006) OrrúM (2006) AlbaiG, et al. (2006) Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet 2: e132.
32. SannaS, LiB, MulasA, SidoreC, KangHM, et al. (2011) Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PloS Genet DOI:10.1371/journal.pgen.1002198.
33. BakhtadzeE, CervinC, LindholmE, BorgH, NilssonP, et al. (2008) Common variants in the TCF7L2 gene help to differentiate autoimmune from non-autoimmune diabetes in young (15–34 years) but not in middle aged (40–59 years) diabetic patients. Diabetologia 51: 2224–2232.
34. CervinC, LyssenkoV, BakhtadzeE, LindholmE, NilssonP, et al. (2008) Genetic similarities between latent autoimmune diabetes in adults, type 1 diabetes, and type 2 diabetes. Diabetes 57: 1433–1437.
35. LyssenkoV, JonssonA, AlmgrenP, PulizziN, IsomaaB, et al. (2008) Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med 359: 2220–2232.
36. KornJM, KuruvillaFG, McCarrollSA, WysokerA, NemeshJ, et al. (2008) Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat Genet 40: 1253–1260.
37. DevlinB, RoederK (1999) Genomic control for association studies. Biometrics 55: 997–1004.
38. MilliganGB (2003) Maximum-likelihood estimation of relatedness. Genetics 163: 1153–1167.
39. PurcellS, NealeB, Todd-BrownK, ThomasL, FerreiraMA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575.
40. BaldingDJ, NicholsRA (1995) A method for quantifying differentiation between populations at multi-allelic loci and its implications for investigating identify and paternity. Genetics 96: 3–12.
41. KangHM, SulJH, ServiceSK, ZaitlenNA, KongSY, et al. (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42: 348–354.
42. ScuteriA, SannaS, ChenWM, UdaM, AlbaiG, et al. (2007) Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PloS Genet 3 doi:10.1371/journal.pgen.0030115.
43. HowieB, FuchsbergerC, StephensM, MarchiniJ, AbecasisGR (2011) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Submitted
44. GiannoulatouE, YauC, ColellaS, RagoussisJ, HolmesCC (2008) GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population. Bioinformatics 24: 2209–2214.
45. The International HapMap 3 Consortium (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467: 52–58.
46. WillerCJ, SannaS, JacksonAU, ScuteriA, BonnycastleLL, et al. (2008) Newly-identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 40: 161–169.
47. LoosRJ, LindgrenCM, LiS, WheelerE, ZhaoJH, et al. (2008) Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 40: 768–75.
48. MorrisAP, VoightBF, TeslovichTM, FerreiraT, SegreAV, et al. (2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes, submitted for publication.
49. CortesA, BrownMA (2011) Promise and pitfalls of the Immunochip. Arthritis Res Ther 13: 101.
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
2012 Číslo 8
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