A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel
by Aldi T. Kraja; E. Warwick Daw; Petra Lenzini; Lihua Wang; Shiow J. Lin; Christine A. Williams; Alan B. Wells; Kathryn L. Lunetta; Joanne M. Murabito; Paola Sebastiani; Giuseppe Tosto; Sandra Barral; Ryan L. Minster; Anatoly Yashin; Thomas Perls; Michael A. Province
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 16, No. 1, 2020

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.

Online publication date: Wed, 05-Feb-2020

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