A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel

A. Kraja, E. W. Daw, P. Lenzini, Lihua Wang, Shiow J. Lin, Christine A. Williams, Alan B. Wells, K. Lunetta, J. Murabito, P. Sebastiani, G. Tosto, S. Barral, R. Minster, A. Yashin, T. Perls, M. Province
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Abstract

This study compares methods of imputing genetic markers, given a typed GWAS scaffold from the Long Life Family Study (LLFS) and latest reference panel of 1000-Genomes. We examined two programs for pre-phasing haplotypes MACH/SHAPEIT2 and MINIMAC/IMPUTE2 for imputation. SHAPEIT2 is advantageous for haplotype pre-phasing. MINIMAC and IMPUTE2 produced similar imputation quality. We used a 4MB region on chromosome 2 of LLFS and in the Supplement, we compared methods using chromosome 19 data from the Genetic Analysis Workshop-19. IMPUTE2 had the advantage of using two references 1000G and a sequence for a subset of subjects. SHAPEIT2 and IMPUTE2 were used to finalise the full LLFS autosome imputation. In LLFS, 44% of ~80M autosomal imputed variants showed good imputation quality (info ≥ 0.30). Low imputation quality was associated with a predominantly low allele frequency in 1000-Genomes. New emerging large-scale sequences and enhanced imputation methodologies will further improve imputation quality.
使用长寿家族研究基因型和序列数据与1000基因组参考面板的遗传插入方法的比较
本研究比较了来自长寿家族研究(Long Life Family study, LLFS)的分型GWAS支架和最新的1000个基因组参考面板的遗传标记输入方法。我们检测了两种预相位单倍型MACH/SHAPEIT2和MINIMAC/IMPUTE2的程序进行了代入。SHAPEIT2有利于单倍型预相位。MINIMAC和IMPUTE2产生相似的输入质量。我们在LLFS的2号染色体上使用了一个4MB的区域,在补充中,我们使用来自遗传分析车间-19的19号染色体数据比较了方法。IMPUTE2的优点是使用了两个参考文献1000G和一个被试子集的序列。使用SHAPEIT2和IMPUTE2完成完整的LLFS自动基因插入。在LLFS中,约80M常染色体归因变异中有44%表现出良好的归因质量(信息≥0.30)。在1000个基因组中,低输入质量主要与低等位基因频率相关。新出现的大规模序列和改进的代入方法将进一步提高代入质量。
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