Use of Allele-Specific FAIRE to Determine Functional Regulatory Polymorphism Using Large-Scale Genotyping Arrays

English version České info

Following the widespread use of genome-wide association studies (GWAS), focus is turning towards identification of causal variants rather than simply genetic markers of diseases and traits. As a step towards a high-throughput method to identify genome-wide, non-coding, functional regulatory variants, we describe the technique of allele-specific FAIRE, utilising large-scale genotyping technology (FAIRE-gen) to determine allelic effects on chromatin accessibility and regulatory potential. FAIRE-gen was explored using lymphoblastoid cells and the 50,000 SNP Illumina CVD BeadChip. The technique identified an allele-specific regulatory polymorphism within NR1H3 (coding for LXR-α), rs7120118, coinciding with a previously GWAS-identified SNP for HDL-C levels. This finding was confirmed using FAIRE-gen with the 200,000 SNP Illumina Metabochip and verified with the established method of TaqMan allelic discrimination. Examination of this SNP in two prospective Caucasian cohorts comprising 15,000 individuals confirmed the association with HDL-C levels (combined beta = 0.016; p = 0.0006), and analysis of gene expression identified an allelic association with LXR-α expression in heart tissue. Using increasingly comprehensive genotyping chips and distinct tissues for examination, FAIRE-gen has the potential to aid the identification of many causal SNPs associated with disease from GWAS.

Vyšlo v časopise: Use of Allele-Specific FAIRE to Determine Functional Regulatory Polymorphism Using Large-Scale Genotyping Arrays. PLoS Genet 8(8): e32767. doi:10.1371/journal.pgen.1002908
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1002908

Souhrn

Zdroje

1. HindorffLA, SethupathyP, JunkinsHA, RamosEM, MehtaJP, et al. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proceedings of the National Academy of Sciences of the United States of America 106: 9362–9367.

2. DimasAS, DermitzakisET (2009) Genetic variation of regulatory systems. Current opinion in genetics & development 19: 586–590.

3. BirneyE, StamatoyannopoulosJA, DuttaA, GuigoR, GingerasTR, et al. (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447: 799–816.

4. ParkPJ (2009) ChIP-seq: advantages and challenges of a maturing technology. Nature reviews Genetics 10: 669–680.

5. MikkelsenTS, KuM, JaffeDB, IssacB, LiebermanE, et al. (2007) Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448: 553–560.

6. CrawfordGE, HoltIE, WhittleJ, WebbBD, TaiD, et al. (2006) Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome research 16: 123–131.

7. GiresiPG, KimJ, McDaniellRM, IyerVR, LiebJD (2007) FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res 17: 877–885.

8. GordonJ, GuyG, WalkerL, NathanP, ExleyR, et al. (1986) Autocrine growth of human B lymphocytes: maintained response to autostimulatory factors is the special feature of immortalization by Epstein-Barr virus–a hypothesis. Med Oncol Tumor Pharmacother 3: 269–273.

9. SaboPJ, HawrylyczM, WallaceJC, HumbertR, YuM, et al. (2004) Discovery of functional noncoding elements by digital analysis of chromatin structure. Proceedings of the National Academy of Sciences of the United States of America 101: 16837–16842.

10. SaboPJ, KuehnMS, ThurmanR, JohnsonBE, JohnsonEM, et al. (2006) Genome-scale mapping of DNase I sensitivity in vivo using tiling DNA microarrays. Nature methods 3: 511–518.

11. GiresiPG, LiebJD (2009) Isolation of active regulatory elements from eukaryotic chromatin using FAIRE (Formaldehyde Assisted Isolation of Regulatory Elements). Methods 48: 233–239.

12. BernsteinBE, KamalM, Lindblad-TohK, BekiranovS, BaileyDK, et al. (2005) Genomic maps and comparative analysis of histone modifications in human and mouse. Cell 120: 169–181.

13. SabattiC, ServiceSK, HartikainenAL, PoutaA, RipattiS, et al. (2009) Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet 41: 35–46.

14. TeslovichTM, MusunuruK, SmithAV, EdmondsonAC, StylianouIM, et al. (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466: 707–713.

15. VeyrierasJB, KudaravalliS, KimSY, DermitzakisET, GiladY, et al. (2008) High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet 4: e1000214 doi:10.1371/journal.pgen.1000214.

16. GaultonKJ, NammoT, PasqualiL, SimonJM, GiresiPG, et al. (2010) A map of open chromatin in human pancreatic islets. Nat Genet 42: 255–259.

17. WangY, MoserAH, ShigenagaJK, GrunfeldC, FeingoldKR (2005) Downregulation of liver X receptor-alpha in mouse kidney and HK-2 proximal tubular cells by LPS and cytokines. J Lipid Res 46: 2377–2387.

18. TalmudPJ, DrenosF, ShahS, ShahT, PalmenJ, et al. (2009) Gene-centric association signals for lipids and apolipoproteins identified via the HumanCVD BeadChip. American journal of human genetics 85: 628–642.

19. KeatingBJ, TischfieldS, MurraySS, BhangaleT, PriceTS, et al. (2008) Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PLoS ONE 3: e3583 doi:10.1371/journal.pone.0003583.

20. Frikke-SchmidtR, NordestgaardBG, SteneMC, SethiAA, RemaleyAT, et al. (2008) Association of loss-of-function mutations in the ABCA1 gene with high-density lipoprotein cholesterol levels and risk of ischemic heart disease. JAMA : the journal of the American Medical Association 299: 2524–2532.

21. StenderS, Frikke-SchmidtR, AnestisA, KardassisD, SethiAA, et al. (2011) Genetic variation in liver X receptor alpha and risk of ischemic vascular disease in the general population. Arteriosclerosis, thrombosis, and vascular biology 31: 2990–2996.

22. FolkersenL, van't HooftF, ChernogubovaE, AgardhHE, HanssonGK, et al. (2010) Association of genetic risk variants with expression of proximal genes identifies novel susceptibility genes for cardiovascular disease. Circulation Cardiovascular genetics 3: 365–373.

23. BlankenbergD, Von KusterG, CoraorN, AnandaG, LazarusR, et al. (2010) Galaxy: a web-based genome analysis tool for experimentalists. Current protocols in molecular biology/edited by Frederick M Ausubel [et al] Chapter 19: Unit 19 10 11–21.

24. GiardineB, RiemerC, HardisonRC, BurhansR, ElnitskiL, et al. (2005) Galaxy: a platform for interactive large-scale genome analysis. Genome research 15: 1451–1455.

25. GoecksJ, NekrutenkoA, TaylorJ (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome biology 11: R86.

26. GentlemanRC, CareyVJ, BatesDM, BolstadB, DettlingM, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome biology 5: R80.

27. PruimRJ, WelchRP, SannaS, TeslovichTM, ChinesPS, et al. (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26: 2336–2337.