Issues and challenges of imputation techniques in genome wide association studies (GWAS): A review Banerjee Rahul1,*, Bharti1, Begum Shbana2, Das Pankaj1, Ahmad Tauqueer1 1ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa-110 012, New Delhi, India 2ICAR-National Institute for Plant Biotechnology, LBS Centre, Pusa-110 012, New Delhi, India *Corresponding Author: Rahul Banerjee, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa-110 012, New Delhi, India, Email: rahuliasri@gmail.com
Online published on 21 November, 2023. Abstract A genome-wide association study (GWAS) rapidly scans DNA markers in many individuals to find genetic links to diseases. New findings aid in disease detection, treatment and prevention. Imputation predicts untyped genotypes in genetic studies when data is missing due to quality, cost, or design issues. It's a proven statistical technique for estimating unobserved genotypes by borrowing haplotype segments from a densely genotyped reference panel. This allows estimation and testing of associations at unassayed variants. Genotype imputation is vital in analyzing genome-wide association scans, helping geneticists evaluate evidence for association at untyped genetic markers. This summary outlines missing data issues and various imputation methods. Top Keywords BEAGLE, Fastphase, Genome wide association studies, Imputation methods, IMPUTE, MACH, Missing mechanisms. Top |