Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 541005
Title Accurate genotype imputation in multiparental populations from low-coverage sequence
Author(s) Zheng, Chaozhi; Boer, Martin P.; Eeuwijk, Fred A. van
Source Genetics 210 (2018)1. - ISSN 0016-6731 - p. 71 - 82.
Department(s) Biometris
Mathematical and Statistical Methods - Biometris
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) Cross-pollinated (CP) population - Genotype imputation - Genotyping by sequencing - Hidden Markov model - MPP - Multiparent advanced generation inter-cross (MAGIC) - Multiparental populations

Many different types of multiparental populations have recently been produced to increase genetic diversity and resolution in QTL mapping. Low-coverage, genotyping-by-sequencing (GBS) technology has become a cost-effective tool in these populations, despite large amounts of missing data in offspring and founders. In this work, we present a general statistical framework for genotype imputation in such experimental crosses from low-coverage GBS data. Generalizing a previously developed hidden Markov model for calculating ancestral origins of offspring DNA, we present an imputation algorithm that does not require parental data and that is applicable to bi-and multiparental populations. Our imputation algorithm allows heterozygosity of parents and offspring as well as error correction in observed genotypes. Further, our approach can combine imputation and genotype calling from sequencing reads, and it also applies to called genotypes from SNP array data. We evaluate our imputation algorithm by simulated and real data sets in four different types of populations: the F2, the advanced intercross recombinant inbred lines, the multiparent advanced generation intercross, and the cross-pollinated population. Because our approach uses marker data and population design information efficiently, the comparisons with previous approaches show that our imputation is accurate at even very low (< 1 ×) sequencing depth, in addition to having accurate genotype phasing and error detection.

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