Evidence map›Paper›PMID 31625012›Full record

ArticleTropical animal health and production2020

Estimation of additive and non-additive genetic variance component for growth traits in Adani goats.

Seyed Abu Taleb Sadeghi, Mohammad Rokouei, Mehdi Vafaye Valleh, Mokhtar Ali Abbasi, Hadi Faraji-Arough

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Article in Tropical animal health and production, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Seyed Abu Taleb SadeghiDepartment of Animal Sciences, Faculty of Agriculture, University of Zabol, Zabol, Iran.
Mohammad RokoueiDepartment of Animal Sciences, Faculty of Agriculture, University of Zabol, Zabol, Iran. Rokouei@uoz.ac.ir.
Mehdi Vafaye VallehDepartment of Animal Sciences, Faculty of Agriculture, University of Zabol, Zabol, Iran.
Mokhtar Ali AbbasiAnimal Science Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
Hadi Faraji-AroughResearch Center of Special Domestic Animals, University of Zabol, Zabol, Iran.
Zabol University · IRAgricultural Research & Education Organization · IRBushehr University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-additive genetic effects are important to increase the accuracy of estimating genetic parameters for growth traits. The aim of this study was to estimate genetic parameters and variance components, specially dominance and epistasis genetic effects, for growth traits (birth weight (BW), weaning weight (WW), 3 (W3), 6 (W6), 9 (W9), and 12 (W12) month weight) in Adani goats. Analyses were carried out using Bayesian method via Gibbs sampler animal model by fitting of 18 different models. All fixed effects (sex, type of birth, age of dam, and year) showed significant effects on BW, WW, W3, and W6, whereas the type of birth and age of dam were not significant on W9 and W12. With the best model, direct heritability estimates were 0.347, 0.178, 0.158, 0.359, 0.278, and 0.281 for BW, WW, W3, W6, W9, and W12 traits, respectively. Maternal permanent environmental effect was significant for BW and WW, but maternal genetic effect was significant only for W3. Dominance and epitasis effects were significant almost for all traits and as a proportion of phenotypic variance were ranged from 0.115 to 0.258 and 0.107 to 0.218, respectively. The range of accuracy of breeding values estimated for growth traits with appropriate evaluation models was from 0.521 to 0.652, 0.616 to 0.694, and 0.548 to 0.684 for the all animals, 10% of the best males and 50% of the best females, respectively. When dominance and epistasis effects added to models, the error variance was reduced and the accuracy of estimated breeding values increased. The accuracy of the best model showed a significant difference with the accuracy of other models (p < 0.01). The result of the present study suggests that non-additive genetic effects should be in genetic evaluation models for goat growth traits because of its effect on accuracy of estimated breeding values.

Indexed as

AnimalsBayes TheoremBirth WeightBody WeightBreedingClimateEpistasis, GeneticFemaleGoatsLeast-Squares AnalysisMaleMaternal InheritancePhenotypePregnancySex FactorsWeaningAccuracyCorrelationDominanceEpistasisHeritability

Identifiers

PMID31625012
OpenAlexW2980580249

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.