Genotype Imputation with Thousands of Genomes

By Bryan N. Howie, Jonathan Marchini, Matthew Stephens. G3: Genes, Genomes, Genetics, 2011

1,027 citations recorded by the source corpus.

Genotype imputation is a statistical technique that is often used to increase the power and resolution of genetic association studies. Imputation methods work by using haplotype patterns in a reference panel to predict unobserved genotypes in a study dataset, and a number of approaches have been proposed for choosing subsets of reference haplotypes that will maximize accuracy in a given study population. These panel selection strategies become harder to apply and interpret as sequencing efforts like the 1000 Genomes Project produce larger and more diverse reference sets, which led us to develop an alternative framework. Our approach is built around a new approximation that uses local sequence similarity to choose a custom reference panel for each study haplotype in each region of the genome. This approximation makes it computationally efficient to use all available reference haplotypes, which allows us to bypass the panel selection step and to improve accuracy at low-frequency variants by capturing unexpected allele sharing among populations. Using data from HapMap 3, we show that our framework produces accurate results in a wide range of human populations. We also use data from the Malaria Genetic Epidemiology Network (MalariaGEN) to provide recommendations for imputation-based studies in Africa. We demonstrate that our approximation improves efficiency in large, sequence-based reference panels, and we discuss general computational strategies for modern reference datasets. Genome-wide association studies will soon be able to harness the power of thousands of reference genomes, and our work provides a practical way for investigators to use this rich information. New methodology from this study is implemented in the IMPUTE2 software package.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/fea11c3c9a0042c1f50eab5fe4c1c1430e122918

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Citations in the corpus

An integrated map of genetic variation from 1,092 human genomes (2012, methodology): “A second major use of the 1000 Genomes Project data in medical genetics is imputing genotypes in existing genome-wide association studies (GWAS) 26 .”
Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing (2013, methodology): “Hence, we did not evaluate pipelines that specifically employ imputation or multi-unrelated-sample variant-calling algorithms, such as Thunder [56], IMPUTE2 [57-59], or BEAGLE [60,61], or specific procedures within the software tools used that allow for calling of multiple unrelated sample (such as ”
Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel (2014, methodology): “We used a carefully chosen subset containing K1 = 400 haplotypes that most closely match the haplotypes of the individual being updated [10].”
Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders (2019, methodology): “Next, pre-phasing and imputation were implemented using IMPUTE2 (Howie et al., 2011) and the 1000 Genomes reference panel.”
Allelic Landscape of Human Blood Cell Trait Variation and Links (2016, background): “, the systematic genome-wide statistical inference of unmeasured genotypes using exogenous reference panels of sequenced individuals) (Howie et al., 2011) is fast becoming a viable strategy to”