A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies

By Bryan N. Howie, Peter Donnelly, Jonathan Marchini. PLoS Genetics, 2009

4,153 citations recorded by the source corpus.

Genotype imputation methods are now being widely used in the analysis of genome-wide association studies. Most imputation analyses to date have used the HapMap as a reference dataset, but new reference panels (such as controls genotyped on multiple SNP chips and densely typed samples from the 1,000 Genomes Project) will soon allow a broader range of SNPs to be imputed with higher accuracy, thereby increasing power. We describe a genotype imputation method (IMPUTE version 2) that is designed to address the challenges presented by these new datasets. The main innovation of our approach is a flexible modelling framework that increases accuracy and combines information across multiple reference panels while remaining computationally feasible. We find that IMPUTE v2 attains higher accuracy than other methods when the HapMap provides the sole reference panel, but that the size of the panel constrains the improvements that can be made. We also find that imputation accuracy can be greatly enhanced by expanding the reference panel to contain thousands of chromosomes and that IMPUTE v2 outperforms other methods in this setting at both rare and common SNPs, with overall error rates that are 15%–20% lower than those of the closest competing method. One particularly challenging aspect of next-generation association studies is to integrate information across multiple reference panels genotyped on different sets of SNPs; we show that our approach to this problem has practical advantages over other suggested solutions.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/06778d3e840c40cd65d41e5096d3156febcb5311

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In Genetics

Citations in the corpus

Host genetic variation impacts microbiome composition across human body sites (2015, methodology): “Host SNP genotyping data were fully imputed using IMPUTE version 2[83], and quality checked as previously described [50].”
Gene-Wide Analysis Detects Two New Susceptibility Genes for Alzheimer's Disease (2014, methodology): “Eachofthese datasets was imputed with Impute2[38] or MACH[39] software using the 1000 genomes data (release Dec2010) as a reference panel.”
Multi-Population Classical HLA Type Imputation (2013, background): “However, under other circumstances, for example when SNP coverage in the reference panel varies strongly, not imputing missing SNP data may turn out to be beneficial [6].”
A one penny imputed genome from next generation reference panels (2018, background): “5k – 10M) and target sample sizes (10-1000) considered.”
Genomic basis for RNA alterations in cancer (2020, methodology): “For assembling phased germline genotypes, we used the Sanger 1000G callset 6 , and applied IMPUTE2 98 for phasing of heterozygous germline variants.”
Chapter 11: Genome-Wide Association Studies (2012, methodology): “Popular algorithms for genotype imputation include BimBam [52], IMPUTE [53], MaCH [54],”
ProbABEL package for genome-wide association analysis of imputed data (2010, methodology): “In this work, we describe the ProbABEL package, which was designed to perform genome-wide regression on posterior genotypic probabilities estimated using imputation software, such as MACH[6] or IMPUTE[4,9].”
A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness (2014, methodology): “We recently compared several methods all designed to phase nominally unrelated samples (SHAPEIT2 [2], SHAPEIT1 [5], Beagle [4], HAPI-UR [6], Impute2 [24], MaCH [25], fastPHASE [26]) and found that SHAPEIT2 was the most accurate method in this setting.”
HIBAG – HLA Genotype Imputation with Attribute Bagging (2013, neutral): “12 BEAGLE, an alternative imputation method to the approximate coalescent approach, allows for the prediction of multiallelic loci.”