Genotype imputation for genome-wide association studies

By Jonathan Marchini, Bryan N. Howie. Nature reviews genetics, 2010

1,730 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/28c3f90fe9f38b34cbd1d6706517c64c12e80dbe

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In Genetics

Citations in the corpus

Genome-wide genetic data on ~500,000 UK Biobank participants (2017, background): “Genotype imputation [44] is the process of predicting genotypes that are not directly assayed in a sample of individuals.”
Imputing Amino Acid Polymorphisms in Human Leukocyte Antigens (2013, result): “Accuracy is generally lower for low-frequency or rare alleles, which is similar to experience with imputation methods outside of the MHC region [21].”
A new approach for efficient genotype imputation using information from relatives (2014, background): “Sporadically missing genotypes can also be imputed in order to improve the genotype call rate [8].”
Stalk Bending Strength is Strongly Associated with Maize Stalk Lodging Incidence Across Multiple Environments (2019, background): “, 2007; van Buuren, 2018v, genetics (Marchini and Howie, 2010; Dahl et al., 2016), engineering (Mockus, 2008), and medical research (Janssen et al.”
Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique (2016, background): “HapMap(26) and 1000 Genomes(27) projects) from more densely genotyped samples as a reference panel.(28) Therefore, in recent GWAS reports the summary results made available are almost always in reference to the forward strand (Figure 2) as a consequence of imputation to a common reference panel.”
Dissecting the genetics of complex traits using summary association statistics (2016, background): “A standard approach to boost association power in GWAS is to leverage LD information from a population reference panel to impute genotypes at variants not typed in the study 11 .”
Computational pan-genomics: status, promises and challenges (2016, background): “One of the primary applications of the resources provided by the HapMap [56] and 1000 Genomes [8] (1KG) projects was LD-based imputation [57], allowing GWAS to test many more variants than typically pre-sent on a single microarray chip.”
Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. (2012, methodology): “In this study, we explore imputation of genotypes (e.g., Browning and Browning, 2009; Marchini and Howie, 2010) as an efficient strategy to derive a large reference set with 800K genotypes.”
TASSEL-GBS: A High Capacity Genotyping by Sequencing Analysis Pipeline (2014, positive): “Numerous, general purpose imputation tools are already available [55].”
Robust inference with knockoffs (2018, methodology): “Returning to our genetic example, it has been shown that the joint distribution of SNPs may be accurately modeled by hidden Markov models (see [14, 16, 12, 10] for some early formulations), and there certainly is an abundance of genotype data to estimate the various model parameters; compare for ins”
Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel (2014, methodology): “We also used the CG samples not included in Phase 1 to assess the quality of the estimated haplotypes when used as a reference panel for GWAS imputation [5, 10].”
Extremely low-coverage sequencing and imputation increases power for genome-wide association studies (2012, methodology): “Imputing genotypes from sequencing data—Genotypes can be inferred from sequencing data by either (1) inferring genotypes independently at each SNP in each individual, (2) making use of allele frequencies inferred from all sequenced individuals, (3) making use of linkage disequilibrium (LD) patterns ”
Genotype Imputation with Millions of Reference Samples. (2016, methodology): “Genotype imputation methods use genotype data in a panel of reference samples to infer ungenotyped variants in target samples.(1,2) With existing reference panels, it is possible to accurately impute millions of genetic variants,(3) and there are millions of additional low-frequency variants that ar”
Genotype Imputation with Thousands of Genomes (2011, background): “KEYWORDS GWAS reference panel haplotype linkage disequilibrium human Genotype imputation is a well-established statistical technique for estimating unobserved genotypes in association studies (Browning 2008; Li et al. 2009; Marchini and Howie 2010).”