Acute Exacerbation of Chronic Hepatitis B by Qin Ning

Author:Qin Ning
Language: eng
Format: epub
ISBN: 9789402416060
Publisher: Springer Netherlands

4.3.4.2 Genotype Imputation

Genotype imputation is another commonly used strategy in GWAS. This method mainly focuses on non-typed SNPs of genotyped samples with microarrays. Based on the haplotype structures in reference populations and genotypes of adjacent SNPs, the non-typed SNPs could be predicted. This strategy has been commonly used in GWAS because it expanded the SNPs quantity after imputation [27]. It’s an effective method to improve the coverage and efficacy of GWAS. Genotype imputation is applied to elevate the performance of association analysis, fine mapping of associated loci, integrating genotype data from different genotyping platforms in meta-analysis, and correcting mistake genotypes. Haplotype information of reference population involved in genotype imputation includes HapMap [6] and 1000 genomes [28] databases. Among them, HapMap II database contains populations from European Caucasians (CEU), Yoruba people of Ibadan, Nigeria (YRI), Japanese in Tokyo (JPT) and Han Chinese in Beijing (CHB), comprising 120, 120 and 180 haplotypes respectively. HapMap III database contains haplotypes of 10 or more populations worldwide. The later 1000 genomes database contains more SNPs, making the genetic markers much dense with high resolution. The 1000 genomes data imputation is fit for untyped SNPs with low frequencies. Due to this, 1000 genomes data becomes the finest reference data in GWAS strategy. At present, commonly used imputation tools include IMPUTE, MACH, fast PHASE, BIMBAM, BEAGLE, each has their own statistical models respectively. In this area, Marchini and Howie had reviewed the technical details of some different statistical methods in genotyping imputation, discussed the confounding factors and methods to evaluate the imputation performance, and summarized the methods of association analysis for imputed SNPs [29]. With the development of NGS technology in reads length, data throughput and cost decrease, more haplotype databases will be constructed to provide more reference panel for genotype imputation. On this basis, meta-GWAS will be pushed forward greatly.

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