Wang Xu, Cheng Ching-Yu, Liao Jiemin, Sim Xueling, Liu Jianjun, Chia Kee-Seng, Tai E-Shyong, Little Peter, Khor Chiea-Chuen, Aung Tin, Wong Tien-Yin, Teo Yik-Ying
Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore.
Eur J Hum Genet. 2016 Apr;24(4):592-9. doi: 10.1038/ejhg.2015.150. Epub 2015 Jul 1.
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.
在全基因组关联研究(GWAS)中,识别观察到的关联信号背后的因果变异的成功率有限。1000基因组计划(1KGP)的使用使得通过推断来估计未分型变异的遗传信息成为可能。然而,基因组内长片段的高度连锁不平衡使我们无法区分因果变异和完美替代物,从而限制了我们识别因果变异的能力。跨种族策略已被提议作为减轻这一问题的可能解决方案。然而,这些研究通常依赖于从1KGP推断多个祖先的基因型,而不是针对特定人群的参考面板。在这里,我们使用由来自相同祖先群体的全基因组测序样本生成的特定人群参考面板,对来自不同祖先的三个亚洲队列中与眼睛和血脂性状相关的位点进行了首次跨种族精细定位研究。我们的研究概述了在精细定位过程中面临的几个挑战,在这个过程中,人们只是简单地对已经根据1KGP的参考单倍型推断出的现有GWAS进行荟萃分析。