Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study

As optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (<b>A</b> matrix) and the genomic relationship matrix (<b&g...

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Main Authors: Shinichiro Ogawa, Atsushi Zoda, Rino Kagawa, Rui Obinata
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Animals
Subjects:
Online Access:https://www.mdpi.com/2076-2615/13/4/638
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author Shinichiro Ogawa
Atsushi Zoda
Rino Kagawa
Rui Obinata
author_facet Shinichiro Ogawa
Atsushi Zoda
Rino Kagawa
Rui Obinata
author_sort Shinichiro Ogawa
collection DOAJ
description As optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (<b>A</b> matrix) and the genomic relationship matrix (<b>G</b> matrix), respectively, have been proposed. We assessed the performance of MCA and MCG methods using 575 Japanese Black cows. Pedigree data were provided to trace back up to five generations to construct the <b>A</b> matrix with changing the pedigree depth from 1 to 5 (five MCA methods). Genotype information on 36,426 single-nucleotide polymorphisms was used to calculate the <b>G</b> matrix based on VanRaden’s methods 1 and 2 (two MCG methods). The MCG always selected one cow per iteration, while MCA sometimes selected multiple cows. The number of commonly selected cows between the MCA and MCG methods was generally lower than that between different MCA methods or between different MCG methods. For the studied population, MCG appeared to be more reasonable than MCA in selecting cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study.
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spelling doaj.art-921c1ef3aff5459dae8e84fcae0d802b2023-11-16T18:39:26ZengMDPI AGAnimals2076-26152023-02-0113463810.3390/ani13040638Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case StudyShinichiro Ogawa0Atsushi Zoda1Rino Kagawa2Rui Obinata3Division of Meat Animal and Poultry Research, Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0901, JapanResearch and Development Group, Zen-Noh Embryo Transfer Center, Kamishihoro 080-1407, JapanResearch and Development Group, Zen-Noh Embryo Transfer Center, Kamishihoro 080-1407, JapanResearch and Development Group, Zen-Noh Embryo Transfer Center, Kamishihoro 080-1407, JapanAs optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (<b>A</b> matrix) and the genomic relationship matrix (<b>G</b> matrix), respectively, have been proposed. We assessed the performance of MCA and MCG methods using 575 Japanese Black cows. Pedigree data were provided to trace back up to five generations to construct the <b>A</b> matrix with changing the pedigree depth from 1 to 5 (five MCA methods). Genotype information on 36,426 single-nucleotide polymorphisms was used to calculate the <b>G</b> matrix based on VanRaden’s methods 1 and 2 (two MCG methods). The MCG always selected one cow per iteration, while MCA sometimes selected multiple cows. The number of commonly selected cows between the MCA and MCG methods was generally lower than that between different MCA methods or between different MCG methods. For the studied population, MCG appeared to be more reasonable than MCA in selecting cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study.https://www.mdpi.com/2076-2615/13/4/638high-density genotypingimputationJapanese Black cattlepedigreereference populationsingle-nucleotide polymorphism
spellingShingle Shinichiro Ogawa
Atsushi Zoda
Rino Kagawa
Rui Obinata
Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study
Animals
high-density genotyping
imputation
Japanese Black cattle
pedigree
reference population
single-nucleotide polymorphism
title Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study
title_full Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study
title_fullStr Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study
title_full_unstemmed Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study
title_short Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study
title_sort comparing methods to select candidates for re genotyping to impute higher density genotype data in a japanese black cattle population a case study
topic high-density genotyping
imputation
Japanese Black cattle
pedigree
reference population
single-nucleotide polymorphism
url https://www.mdpi.com/2076-2615/13/4/638
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