Genotype Imputation

By Yun Li, Cristen J. Willer, Serena Sanna, Gonçalo R. Abecasis. Annual review of genomics and human genetics (Print), 2009

1,061 citations recorded by the source corpus.

Genotype imputation is now an essential tool in the analysis of genomewide association scans. The technique allows geneticists to accurately evaluate the evidence for association at genetic markers that are not directly genotyped. Genotype imputation increases power of genomewide association scans and is particularly useful for combining the association scan results across studies that rely on different genotyping platforms. Here, we review the history and theoretical underpinnings of the technique. To illustrate performance of the approach, we summarize results from several actual gene mapping studies. Finally, we preview the role of genotype imputation in an era when whole genome resequencing is becoming increasingly common.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/015604c3ba76cf247a8010f74ec1386e58ceb530

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In Genetics

Citations in the corpus

PRSice-2: Polygenic Risk Score software for biobank-scale data (2019, neutral): “…be imputed and are usually either recorded as a set of 3 probabilities corresponding to the probability of each of the possible genotypes [19] or, based on these, as the expected genotype (a real number between 0 and 2 known as the “dosage”) [19] or as the “best-guess” (most probable)…”
ProbABEL package for genome-wide association analysis of imputed data (2010, methodology): “Recently, novel statistical tools for genotype imputations [4,6-9] and experimental techniques for highthroughput sequencing were developed.”
Genome-wide association study of prostate cancer in men of African ancestry identifies a susceptibility locus at 17q21 (2011, neutral): “ETO, a target of t(8;21) in acute leukemia, makes distinct”
Multi-Population Classical HLA Type Imputation (2013, result): “These observations are consistent with results from SNP genotype imputation, where using matched and diverse reference panels is also known to have a positive effect on accuracy [11,12,26,27].”
A new approach for efficient genotype imputation using information from relatives (2014, background): “Phasing and imputation methods can be broadly divided into family-based methods, which use linkage information from close relatives, and population-based methods, which use population linkage disequilibrium information [9].”
OME Remote Objects (OMERO): a flexible, model-driven data management system for experimental biology (2012, methodology): “We chose this implementation to solve a critical data-processing bottleneck that occurs when extra-polating experimentally obtained genotyping data 13 to combine and impute missing genotypes for a set of individuals by using information available on their relatives 14 .”
MaCH: Using Sequence and Genotype Data to Estimate Haplotypes and Unobserved Genotypes (2010, background): “…r 2 increased from 0.22 to 0.66 in the set of SNPs whose best genotyped proxy had r 2 o 0.30 and from 0.33 to 0.75 in the set of SNPs whose best genotyped proxy had r 2 o 0.5 [for specific examples of disease susceptibility loci that would be missed without imputation, see Li et al., 2009b].”
The MRC IEU OpenGWAS data infrastructure (2020, methodology): “Results The OpenGWAS infrastructure incorporates four domains - (1) data are sourced and imported before being (2) processed for format and quality control in order to be (3) served publicly and (4) connected to a range of analytical tools (Figure 1).”
LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms (2015, methodology): “Imputing the missing genotypes has been shown to improve the power of methods such as GWA (Li et al. 2009) and so genotype imputation is becoming an increasingly important component of studies into genotype 2 phenotype relationships, especially when data are collected using methods like GBS.…”
Minimac2: Faster Genotype Imputation (2015, methodology): “Genotype imputation is routinely used to increase the power of genome-wide association studies (GWAS; Howie et al. , 2012; Li et al. , 2009; Marchini et al. , 2007).”
Fast and accurate genotype imputation in genome-wide association studies through pre-phasing (2012, background): “Conversely, it is important to realize that imputation accuracy is affected by phasing accuracy in the reference panel and by the GWAS SNPs used to drive imputation 4,5 .”