Imputation of Single-Nucleotide Polymorphisms in Inbred Mice Using Local Phylogeny

By Wang, Jeremy R.; de Villena, Fernando Pardo-Manuel et al. | Genetics, February 2012 | Go to article overview

Imputation of Single-Nucleotide Polymorphisms in Inbred Mice Using Local Phylogeny


Wang, Jeremy R., de Villena, Fernando Pardo-Manuel, Lawson, Heather A., Cheverud, James M., Churchill, Gary A., McMillan, Leonard, Genetics


ABSTRACT We present full-genome genotype imputations for 100 classical laboratory mouse strains, using a novel method. Using genotypes at 549,683 SNP loci obtained with the Mouse Diversity Array, we partitioned the genome of 100 mouse strains into 40,647 intervals that exhibit no evidence of historical recombination. For each of these intervals we inferred a local phylogenetic tree. We combined these data with 12 million loci with sequence variations recently discovered by whole-genome sequencing in a common subset of 12 classical laboratory strains. For each phylogenetic tree we identified strains sharing a leaf node with one or more of the sequenced strains. We then imputed high- and medium-confidence genotypes for each of 88 nonsequenced genomes. Among inbred strains, we imputed 92% of SNPs genome-wide, with 71% in high-confidence regions. Our method produced 977 million new genotypes with an estimated per-SNP error rate of 0.083% in high-confidence regions and 0.37% genome-wide. Our analysis identified which of the 88 nonsequenced strains would be the most informative for improving full-genome imputation, as well as which additional strain sequences will reveal more new genetic variants. Imputed sequences and quality scores can be downloaded and visualized online.

AMONG the many advantages of inbred strains in genetic studies is that each strain needs to be genotyped only once, and that information can be reused in many experiments. Moreover, as more genotype data become available for a given inbred strain, the analysis can be updated. This cycle can continue until, ultimately, all inbred strains are fully sequenced. In the meantime, there is a need to leverage the handful of inbred strains that have been sequenced using robust imputation methods to maximize the value of existing data. High-quality imputed sequence has many potential applications including identification of functional variants and the creation of accurate scaffolds for the analysis of next-generation RNAseq and bisulfite sequencing data. Until affordable deep sequencing becomes a reality, a balanced approach that combines targeted sequencing with accurate imputation offers the best of both worlds: high-quality genomic data today at little additional cost.

A recent sequencing effort by the Wellcome Trust/Sanger Institute has made available dense genome sequences for a set of 17 inbred mouse strains, including 13 common laboratory strains, 3 wild-derived mouse strains from different subspecies of Mus musculus, and a single strain from a different species, M. spretus (Keane et al. 2011). This set of samples is expected to capture much of the variation found in common laboratory mouse strains and, therefore, provides a foundation for sequence imputation. A complementary resource is the recent release of Mouse Diversity Array (MDA) genotypes from 162 mouse strains (Yang et al. 2011). MDA is a high-density DNA microarray designed to assay diversity among commonly used laboratory mice (Yang et al. 2009). The density of SNP genotypes available on the MDA exceeds the density of recombination events accumulated over the development of the classical inbred strains and as such the MDA SNPs can provide a framework for imputation of the underlying whole-genome sequence.

Imputation can be used to increase the effective resolution of a lower-density SNP panel to match that of a higher-density panel when there is a subset of samples common to both sets. Previous imputation methods use variations of a hidden Markov model (HMM) to infer sequence similarities and likely transitions between haplotypes. These methods employ probabilistic models based on local sequence similarity to infer the state of missing genotypes. Missing genotypes arise from two sources. No-calls (N's) can indicate either technical noise or an unexpected sequence variant such as a nearby SNP or an indel that interferes with probe hybridization. A second, and more extensive, source of missing genotypes is due to differences in the density of marker sets between platforms. …

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Imputation of Single-Nucleotide Polymorphisms in Inbred Mice Using Local Phylogeny
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