2019
DOI: 10.1111/jbg.12382
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Parent‐of‐origin effects on carcass traits in Japanese Black cattle

Abstract: Variances caused by the differential expression of paternally and maternally imprinted genes controlling carcass traits in Japanese Black cattle were estimated in this study. Data on marbling score (BMS), carcass weight, rib thickness, rib-eye area (REA) and subcutaneous fat thickness (SFT) were collected from a total of 13,115 feedlot steers and heifers in a commercial population. A sire-maternal grandsire model was used to analyse the data, and then, imprinting parameters were derived by replacing the geneti… Show more

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Cited by 13 publications
(33 citation statements)
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“…Significant imprinting variances and largest PRIVs and RIVs of carcass traits in our study were found for BMS (Tables 2–4) in Models 1 and 2. These parental contributions for BMS were caused by maternal gametic effects, i.e., they were paternally imprinted as described by Okamoto et al (2019) that showed largest RIV for BMS (35.17%) of the carcass traits, and a larger maternal (75.15%) than paternal contribution (24.85%) to the imprinting variance in Japanese Black cattle. For fat‐related traits, the variance ratio for maternal gametic effects on BMS in the present study was larger than that reported in previous studies using European beef breeds.…”
Section: Discussionmentioning
confidence: 76%
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“…Significant imprinting variances and largest PRIVs and RIVs of carcass traits in our study were found for BMS (Tables 2–4) in Models 1 and 2. These parental contributions for BMS were caused by maternal gametic effects, i.e., they were paternally imprinted as described by Okamoto et al (2019) that showed largest RIV for BMS (35.17%) of the carcass traits, and a larger maternal (75.15%) than paternal contribution (24.85%) to the imprinting variance in Japanese Black cattle. For fat‐related traits, the variance ratio for maternal gametic effects on BMS in the present study was larger than that reported in previous studies using European beef breeds.…”
Section: Discussionmentioning
confidence: 76%
“…Expected variances that are the same as those from Model 1 can be derived from the variance components of the sire–MGS model, and the equations given by Okamoto et al. (2019) are as follows:σnormalA2=2σsire2+8σmgs2σnormali2=2σsire2+8σmgs28σsire,mgsσnormalM2=8σsire,mgsσgs2=2σsire24σsire,mgsσgd2=8σmgs24σsire,mgs…”
Section: Methodsmentioning
confidence: 99%
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