Abstract
There has been limited success in identifying causal variants underlying association signals observed in genome-wide association studies (GWAS). The use of 1000 Genomes Project (1KGP) allows the imputation to estimate the genetic information at untyped variants. However, long stretches of high linkage disequilibrium within the genome prevent us from differentiating between causal variants and perfect surrogates, thus limiting our ability to identify causal variants. Transethnic strategies have been proposed as a possible solution to mitigate this. However, these studies generally rely on imputing genotypes from multiple ancestries from 1KGP but not against population-specific reference panels. Here, we perform the first transethnic fine-mapping study across three Asian cohorts from diverse ancestries at the loci implicated with eye and blood lipid traits, using population-specific reference panels that have been generated by whole-genome sequencing samples from the same ancestry groups. Our study outlines several challenges faced in a fine-mapping exercise where one simply aims to meta-analyse existing GWAS that have been imputed against reference haplotypes from the 1KGP.
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References
McCarthy MI, Abecasis GR, Cardon LR et al: Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008; 9: 356–369.
Manolio TA, Collins FS, Cox NJ et al: Finding the missing heritability of complex diseases. Nature 2009; 461: 747–753.
Abecasis GR, Auton A, Brooks LD et al: An integrated map of genetic variation from 1,092 human genomes. Nature 2012; 491: 56–65.
Durbin RM, Abecasis GR, Altshuler DL et al: A map of human genome variation from population-scale sequencing. Nature 2010; 467: 1061–1073.
Jallow M, Teo YY, Small KS et al: Genome-wide and fine-resolution association analysis of malaria in West Africa. Nat Genet 2009; 41: 657–665.
Teo YY, Small KS, Kwiatkowski DP : Methodological challenges of genome-wide association analysis in Africa. Nat Rev Genet 2010; 11: 149–160.
Rosenberg NA, Huang L, Jewett EM, Szpiech ZA, Jankovic I, Boehnke M : Genome-wide association studies in diverse populations. Nat Rev Genet 2010; 11: 356–366.
Twee-Hee Ong R, Wang X, Liu X, Teo YY : Efficiency of trans-ethnic genome-wide meta-analysis and fine-mapping. Eur J Hum Genet 2012; 20: 1300–1307.
Teo YY, Ong RT, Sim X, Tai ES, Chia KS : Identifying candidate causal variants via trans-population fine-mapping. Genet Epidemiol 2010; 34: 653–664.
Morris AP : Transethnic meta-analysis of genomewide association studies. Genet Epidemiol 2011; 35: 809–822.
Mahajan A, Go MJ, Zhang W et al: Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 2014; 46: 234–244.
Howie B, Marchini J, Stephens M : Genotype imputation with thousands of genomes. G3 (Bethesda, MD) 2011; 1: 457–470.
Marchini J, Howie B : Genotype imputation for genome-wide association studies. Nat Rev Genet 2010; 11: 499–511.
Howie BN, Donnelly P, Marchini J : A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 2009; 5: e1000529.
Marchini J, Howie B, Myers S, McVean G, Donnelly P : A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 2007; 39: 906–913.
Wong LP, Ong RT, Poh WT et al: Deep whole-genome sequencing of 100 southeast Asian Malays. Am J Hum Genet 2013; 92: 52–66.
Wong LP, Lai JK, Saw WY et al: Insights into the genetic structure and diversity of 38 South Asian Indians from deep whole-genome sequencing. PLoS Genet 2014; 10: e1004377.
Hindorff LA, Mehta J, Wise A et alEuropean Bioinformatics Institute.: A catalog of Published Genome-Wide Association Studies; Available at: www.genome.gov/gwastudies (last access date September 2014).
Lopes MC, Hysi PG, Verhoeven VJ et al: Identification of a candidate gene for astigmatism. Invest Ophthalmol Vis Sci 2013; 54: 1260–1267.
Fan Q, Zhou X, Khor CC et al: Genome-wide meta-analysis of five Asian cohorts identifies PDGFRA as a susceptibility locus for corneal astigmatism. PLoS Genet 2011; 7: e1002402.
Khor CC, Ramdas WD, Vithana EN et al: Genome-wide association studies in Asians confirm the involvement of ATOH7 and TGFBR3, and further identify CARD10 as a novel locus influencing optic disc area. Hum Mol Genet 2011; 20: 1864–1872.
Ramdas WD, van Koolwijk LM, Ikram MK et al: A genome-wide association study of optic disc parameters. PLoS Genet 2010; 6: e1000978.
Guggenheim JA, McMahon G, Kemp JP et al: A genome-wide association study for corneal curvature identifies the platelet-derived growth factor receptor alpha gene as a quantitative trait locus for eye size in white Europeans. Mol Vis 2013; 19: 243–253.
Mishra A, Yazar S, Hewitt AW et al: Genetic variants near PDGFRA are associated with corneal curvature in Australians. Invest Ophthalmol Vis Sci 2012; 53: 7131–7136.
Han S, Chen P, Fan Q et al: Association of variants in FRAP1 and PDGFRA with corneal curvature in Asian populations from Singapore. Hum Mol Genet 2011; 20: 3693–3698.
Kathiresan S, Melander O, Guiducci C et al: Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 2008; 40: 189–197.
Willer CJ, Sanna S, Jackson AU et al: Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 2008; 40: 161–169.
Aulchenko YS, Ripatti S, Lindqvist I et al: Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet 2009; 41: 47–55.
Kathiresan S, Willer CJ, Peloso GM et al: Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet 2009; 41: 56–65.
Teslovich TM, Musunuru K, Smith AV et al: Biological, clinical and population relevance of 95 loci for blood lipids. Nature 2010; 466: 707–713.
Spencer CC, Su Z, Donnelly P, Marchini J : Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet 2009; 5: e1000477.
Frazer KA, Ballinger DG, Cox DR et al: A second generation human haplotype map of over 3.1 million SNPs. Nature 2007; 449: 851–861.
Cho YS, Chen CH, Hu C et al: Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet 2012; 44: 67–72.
Sim X, Ong RT, Suo C et al: Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia. PLoS Genet 2011; 7: e1001363.
de Bakker PI, Ferreira MA, Jia X, Neale BM, Raychaudhuri S, Voight BF : Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet 2008; 17: R122–R128.
Zeggini E, Scott LJ, Saxena R et al: Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 2008; 40: 638–645.
Vithana EN, Aung T, Khor CC et al: Collagen-related genes influence the glaucoma risk factor, central corneal thickness. Hum Mol Genet 2011; 20: 649–658.
Wang X, Chua HX, Chen P et al: Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies. Hum Mol Genet 2013; 22: 2303–2311.
Franceschini N, van Rooij FJ, Prins BP et al: Discovery and fine mapping of serum protein loci through transethnic meta-analysis. Am J Hum Genet 2012; 91: 744–753.
Rasmussen-Torvik LJ, Pacheco JA, Wilke RA et al: High density GWAS for LDL cholesterol in African Americans using electronic medical records reveals a strong protective variant in APOE. Clin Transl Sci 2012; 5: 394–399.
Conrad DF, Jakobsson M, Coop G et al: A worldwide survey of haplotype variation and linkage disequilibrium in the human genome. Nat Genet 2006; 38: 1251–1260.
Hanchard N, Elzein A, Trafford C et al: Classical sickle beta-globin haplotypes exhibit a high degree of long-range haplotype similarity in African and Afro-Caribbean populations. BMC Genet 2007; 8: 52.
Deelen P, Menelaou A, van Leeuwen EM et al: Improved imputation quality of low-frequency and rare variants in European samples using the 'Genome of The Netherlands'. Eur J Hum Genet 2014; 22: 1321–1326.
Gao X, Haritunians T, Marjoram P et al: Genotype Imputation for Latinos Using the HapMap and 1000 Genomes Project Reference Panels. Front Genet 2012; 3: 117.
Huang L, Li Y, Singleton AB et al: Genotype-imputation accuracy across worldwide human populations. Am J Hum Genet 2009; 84: 235–250.
Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR : MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010; 34: 816–834.
Acknowledgements
This project acknowledges the support of the Saw Swee Hock School of Public Health from the National University of Singapore. The Singapore Malay Eye Study (SiMES) was funded by the National Medical Research Council (NMRC 0796/2003 and NMRC/STaR/0003/2008) and Biomedical Research Council (BMRC, 09/1/3/19/616). The Singapore Indian Eye Study (SINDI) was funded by grants from Biomedical Research Council of Singapore (BMRC 09/1/35/19/616 and BMRC 08/1/35/19/550) and National Medical Research Council of Singapore (NMRC/STaR/0003/2008). Y-YT and XW acknowledge the support from the Singapore National Research Foundation, NRF-RF-2010-05.
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Wang, X., Cheng, CY., Liao, J. et al. Evaluation of transethnic fine mapping with population-specific and cosmopolitan imputation reference panels in diverse Asian populations. Eur J Hum Genet 24, 592–599 (2016). https://doi.org/10.1038/ejhg.2015.150
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DOI: https://doi.org/10.1038/ejhg.2015.150
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