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. 2014 May;197(1):401-4.
doi: 10.1534/genetics.113.158014. Epub 2014 Feb 28.

Efficient Imputation of Missing Markers in Low-Coverage Genotyping-By-Sequencing Data From Multiparental Crosses

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Free PMC article

Efficient Imputation of Missing Markers in Low-Coverage Genotyping-By-Sequencing Data From Multiparental Crosses

B Emma Huang et al. Genetics. .
Free PMC article

Abstract

We consider genomic imputation for low-coverage genotyping-by-sequencing data with high levels of missing data. We compensate for this loss of information by utilizing family relationships in multiparental experimental crosses. This nearly quadruples the number of usable markers when applied to a large rice Multiparent Advanced Generation InterCross (MAGIC) study.

Keywords: GBS; HMM; MAGIC; MPRIL.

Figures

Figure 1
Figure 1
Diagram of process for imputing missing genotypes. We first construct a hidden Markov model (HMM) based on the progeny genotypes to estimate founder haplotype probabilities across the genome. Then at each position, for each missing founder, we audit genotypes among progeny inheriting that founder haplotype. The most common genotype inherited in those progeny is imputed in the founders. The imputed founders are then used as a reference panel to impute missing progeny data by summing HMM probabilities for possible alleles.

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