Estimates of additive and non-additive genetic effects on growth traits in a diallel cross of three strains of pearl oyster (Pinctada fucata)

Author(s):  
Jian Chen ◽  
Hui Luo ◽  
Ziqin Zhai ◽  
Hongchen Wang ◽  
Baosuo Liu ◽  
...  
2010 ◽  
Vol 34 (1) ◽  
pp. 26-31
Author(s):  
Long-chun GU ◽  
Jin-bi LI ◽  
Da-hui YU ◽  
Gui-ju HUANG ◽  
Jian-ye LIU

1998 ◽  
Vol 66 (2) ◽  
pp. 349-355 ◽  
Author(s):  
M. Diop ◽  
L. D. Van Vleck

AbstractEstimates of (co)variance components and genetic parameters were obtained for birth (no. = 3909), weaning (no. = 3425), yearling (no. = 2763), and final weight (no. = 2142) for Gobra cattle at the Centre de Recherches Zootechniques de Dahra (Senegal), using single trait animal models. Data were analysed by restricted maximum likelihood. Four different animal models were fitted for each trait. Model 1 considered the animal as the only random effect. Model 2 included in addition to the additive direct effect of the animal, the environmental effect due to the dam. Model 3 added the maternal additive genetic effects and allowed a covariance between the direct and maternal genetic effects. Model 4 fitted both maternal genetic and permanent environmental effects. Inclusion of both types of maternal effects (genetic and environmental) provided a better fit for birth and weaning weights than models with one maternal effect only. For yearling and final weights, the improvement was not significant. Important maternal effects werefound for all traits. Estimates of direct heritabilities were substantially higher when maternal effects were ignored. Estimates of direct and maternal heritabilities with model 4 were 0·07 (s.e. 0·03) and 0·04 (s.e. 0·02), 0·20 (s.e. 0·05) and 0·21 (s.e. 0.05), 0·24 (s.e. 0·07) and 0·21 (s.e. 0·06), and 0·14 (s.e. 0·06) and 0.16 (s.e. 0·06) for birth, weaning, yearling and final weights, respectively. Correlations between direct and maternal genetic effects were negative for all traits, and large for weaning and yearling weights with estimates of -0·61 (s.e. 0·33) and -0·50 (s.e. 0·31), respectively. There was a significant positive linear phenotypic trend for weaning and yearling weights. Linear trends for additive direct and maternal breeding values were not significant for any trait except maternal breeding value for yearling weight.


1987 ◽  
Vol 40 (1) ◽  
pp. 57 ◽  
Author(s):  
BP Oldroyd ◽  
C Moran

Nine lines of honeybees were used to form a 9 x 9 partial diallel cross. Hamuli number was determined for samples of worker offspring. One set of workers was reared in non-maternal colonies which had been made uniform, as far as possible, with respect to colony strength (number of workers), while another set was sampled directly from the combs of each maternal colony. Combining ability analysis of variance revealed significant additive and non-additive genetic effects for both sets of data, regardless of whether inbred parentals were included or excluded from the analysis. Uniform rearing removed average heterosis and reciprocal effects.


2018 ◽  
Vol 82 (7) ◽  
pp. 1073-1080 ◽  
Author(s):  
Qingheng Wang ◽  
Ruijuan Hao ◽  
Xiaoxia Zhao ◽  
Ronglian Huang ◽  
Zhe Zheng ◽  
...  

Author(s):  
Ankit Magotra ◽  
Yogesh C. Bangar ◽  
Ashish Chauhan ◽  
B.S. Malik ◽  
Z.S. Malik

2013 ◽  
Vol 20 (6) ◽  
pp. 1182-1187
Author(s):  
Huojin LI ◽  
Baosuo LIU ◽  
Hui LUO ◽  
Guiju HUANG ◽  
Mingqiang CHEN ◽  
...  

2020 ◽  
Vol 8 (11) ◽  
pp. 896
Author(s):  
Ruijuan Hao ◽  
Chuchu Mo ◽  
Linda Adzigbli ◽  
Chuangye Yang ◽  
Yuewen Deng ◽  
...  

Fibroblast growth factor 18 (FGF18) plays an important functional role in skeletal growth and development. The FGF18 gene was characterized in pearl oyster Pinctada fucata martensii (PmFGF18) with the full-length sequence containing an open reading frame of 714 bp encoding 237 amino acids. The domain analysis of PmFGF18 showed a distinctive FGF domain, with a high similarity to FGF18 protein sequences from Crassostrea gigas (43.35%) and C. virginica (37.43%). PmFGF18 expression was revealed in all analyzed tissues with a significantly higher expression level in the fast-growing group than the slow-growing group. The analysis of PmFGF18 polymorphism demonstrated 33 SNPs (single nucleotide polymorphisms) in the CDS and promoter region of PmFGF18 sequence. Association analysis revealed 19 SNPs (2 SNPs from CDS and 17 SNPs from the promoter region) associating significantly with growth traits. Among the associated SNPs, one SNP g.50918198 A > C was verified in the other breeding line. Therefore, PmFGF18 can be utilized as a candidate gene for growth, and its related SNPs could be used in selective breeding of P. f. martensii for the improvement of growth traits.


2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 18-19
Author(s):  
Haipeng Yu ◽  
Jaap Milgen ◽  
Egbert Knol ◽  
Rohan Fernando ◽  
Jack C Dekkers

Abstract Genomic prediction has advanced genetic improvement by enabling more accurate estimates of breeding values at an early age. Although genomic prediction is efficient in predicting traits dominated by additive genetic effects within common settings, prediction in the presence of non-additive genetic effects and genotype by environmental interactions (GxE) remains a challenge. Previous studies have attempted to address these challenges by statistical modeling, while the augmentation of statistical models with biological information has received relatively little attention. A pig growth model assumes growth performance is a nonlinear functional interaction between the animal’s genetic potential for underlying latent growth traits and environmental factors and has the potential to capture GxE and non-additive genetic effects. The objective of this study was to integrate a nonlinear stable Gompertz function of three latent growth traits and age into genomic prediction models using Bayesian hierarchical modeling. The three latent growth traits were modeled as a linear combination of systematic environmental, marker, and residual effects. The model was applied to daily body weight data from ~83 to ~186 days of age on 4,039 purebred boars that were genotyped for 24K markers. Bias and prediction accuracy of genomic predictions of selection candidates were assessed by extending the linear regression method of predictions based on part and whole data to a non-linear setting. The accuracy (bias) of genomic predictions was 0.58 (0.82), 0.46 (0.90), 0.54 (0.78), and 0.60 (0.84) for the three latent growth traits and average daily gain derived from integrated nonlinear model, respectively, compared to 0.58 (0.87) for genomic predictions of average daily gain using standard linear models. In subsequent work, the growth model will be extended to include daily feed intake and carcass composition data. Resulting models are expected to substantially advance genetic improvement in pigs across environments. Funded by USDA-NIFA grant # 2020-67015-31031.


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