Background/Objective The 16q12.2 locus in the first intron of FTO has been robustly associated with body mass index (BMI) and type 2 diabetes in genome-wide association studies (GWAS). To improve the resolution of fine-scale mapping at FTO, we performed a systematic approach consisting of two parts. Methods The first part is to partition the associated variants into linkage disequilibrium (LD) clusters, followed by conditional and haplotype analyses. The second part is to filter the list of potential causal variants through trans-ethnic comparison. Results We first examined the LD relationship between FTO SNPs showing significant association with type 2 diabetes in Japanese GWAS and between those previously reported in European GWAS. We could partition all the assayed or imputed SNPs showing significant association in the target FTO region into 7 LD clusters. Assaying 9 selected SNPs in 4 Asian-descent populations—Japanese, Vietnamese, Sri Lankan and Chinese (n≤26,109 for BMI association and n≤24,079 for type 2 diabetes association), we identified a responsible haplotype tagged by a cluster of SNPs and successfully narrowed the list of potential causal variants to 25 SNPs, which are the smallest in number among the studies conducted to date for FTO. Conclusions Our data support that the power to resolve the causal variants from those in strong LD increases consistently when three distant populations—Europeans, Asians and Africans—are included in the follow-up study. It has to be noted that this fine-mapping approach has the advantage of applicability to the existing GWAS data set in combination with direct genotyping of selected variants.
References
[1]
Edwards SL, Beesley J, French JD, Dunning AM (2013) Beyond GWASs: illuminating the dark road from association to function. Am J Hum Genet 93: 779–797. doi: 10.1016/j.ajhg.2013.10.012
[2]
Dunning AM, Durocher F, Healey CS, Teare MD, McBride SE, et al. (2000) The extent of linkage disequilibrium in four populations with distinct demographic histories. Am J Hum Genet 67: 1544–1554. doi: 10.1086/316906
[3]
Zaitlen N, Pa？aniuc B, Gur T, Ziv E, Halperin E (2010) Leveraging genetic variability across populations for the identification of causal variants. Am J Hum Genet 86: 23–33. doi: 10.1016/j.ajhg.2009.11.016
[4]
Teo YY, Ong RT, Sim X, Tai ES, Chia KS (2010) Identifying candidate causal variants via trans-population fine-mapping. Genet Epidemiol 34: 653–664. doi: 10.1002/gepi.20522
[5]
Morris AP (2011) Transethnic meta-analysis of genomewide association studies. Genet Epidemiol 35: 809–822. doi: 10.1002/gepi.20630
[6]
Hassanein MT, Lyon HN, Nguyen TT, Akylbekova EL, Waters K, et al. (2010) Fine mapping of the association with obesity at the FTO locus in African-derived populations. Hum Mol Genet 19: 2907–2916. doi: 10.1093/hmg/ddq178
[7]
Peters U, North KE, Sethupathy P, Buyske S, Haessler J, et al. (2013) A systematic mapping approach of 16q12.2/FTO and BMI in more than 20,000 African Americans narrows in on the underlying functional variation: results from the Population Architecture using Genomics and Epidemiology (PAGE) study. PLoS Genet 9: e1003171. doi: 10.1371/journal.pgen.1003171
[8]
Tsuchihashi-Makaya M, Serizawa M, Yanai K, Katsuya T, Takeuchi F, et al. (2009) Gene-environmental interaction regarding alcohol-metabolizing enzymes in the Japanese general population. Hypertens Res 32: 207–213. doi: 10.1038/hr.2009.3
[9]
Nanri A, Yoshida D, Yamaji T, Mizoue T, Takayanagi R, et al. (2008) Dietary patterns and C-reactive protein in Japanese men and women. Am J Clin Nutr 87: 1488–496. doi: 10.2337/dc08-0297
[10]
Takeuchi F, Serizawa M, Yamamoto K, Fujisawa T, Nakashima E, et al. (2009) Confirmation of multiple risk Loci and genetic impacts by a genome-wide association study of type 2 diabetes in the Japanese population. Diabetes 58: 1690–1699. doi: 10.2337/db08-1494
[11]
Takeuchi F, Katsuya T, Chakrewarthy S, Yamamoto K, Fujioka A, et al. (2010) Common variants at the GCK, GCKR, G6PC2-ABCB11 and MTNR1B loci are associated with fasting glucose in two Asian populations. Diabetologia 53: 299–308. doi: 10.1007/s00125-009-1595-1
[12]
Pinidiyapathirage MJ, Dassanayake AS, Rajindrajith S, Kalubowila U, Kato N, et al. (2011) Non-alcoholic fatty liver disease in a rural, physically active, low income population in Sri Lanka. BMC Res Notes 4: 513. doi: 10.1186/1756-0500-4-513
[13]
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
[14]
Shu XO, Long J, Cai Q, Xiang YB, Cho YS, et al. (2010) Identification of new genetic risk variants for type 2 diabetes. PLoS Genet 6: e1001127. doi: 10.1371/journal.pgen.1001127
[15]
Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, et al. (2007) A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316: 889–894. doi: 10.1126/science.1141634
[16]
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575. doi: 10.1086/519795
[17]
Stephens M, Smith NJ, Donnelly P (2001) A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 68: 978–989. doi: 10.1086/319501
[18]
Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21: 263–265. doi: 10.1093/bioinformatics/bth457
[19]
Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, et al. (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40: 638–645. doi: 10.1038/ng.120
[20]
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.
[21]
Meyer KB, O'Reilly M, Michailidou K, Carlebur S, Edwards SL, et al. (2013) Fine-scale mapping of the FGFR2 breast cancer risk locus: putative functional variants differentially bind FOXA1 and E2F1. Am J Hum Genet 93: 1046–1060.
[22]
Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, et al. (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22: 1790–1797. doi: 10.1101/gr.137323.112
[23]
Ward LD, Kellis M (2012) HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 40: D930–934. doi: 10.1093/nar/gkr917
[24]
Smemo S, Tena JJ, Kim KH, Gamazon ER, Sakabe NJ, et al. (2014) Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507: 371–375. doi: 10.1038/nature13138
[25]
Gorkin DU, Ren B (2014) Genetics: Closing the distance on obesity culprits. Nature 507: 309–310. doi: 10.1038/nature13212
