This repository explains the steps involved in Whole Genome Imputation using 1000G as reference panel
Imputation provides a higher-resolution view of a genetic region by adding more variants, thereby increasing the chances of identifying a causal variant.
Imputation also helps in meta-analysis by facilitating the combination of results across studies. Different studies often use different genotyping arrays containing different sets of variants. For instance, only 20% of the SNPs included on the Affymetrix 6.0 SNP array are included on the Illumina 660K array. Genotype imputation can generate a common set of variants that can be analyzed across all the studies to boost power.
Another benefit of imputation lies in increasing the power to detect an association signal. When SNPs are genotyped in only a portion of the samples, imputation can increase the effective sample size by filling in the missing genotypes.
Genotype Imputation from Large Reference Panels https://www.annualreviews.org/doi/10.1146/annurev-genom-083117-021602
Tutorial:Produce PCA bi-plot for 1000 Genomes Phase III - Version 2 https://www.biostars.org/p/335605/
Principal Components Analysis of the 1000 Genome Project Phase I Data https://wangjingke.com/2015/08/04/Principal-Components-Analysis-of-the-1000-Genome-Project-Phase-I-Data
Imputation and quality control steps for combining multiple genome-wide datasets https://www.frontiersin.org/articles/10.3389/fgene.2014.00370/full