Age-regulated expression of genetic and environmental variation in fitness traits. 2. Genetic effects and variances for viability among crosses from a factorial mating of six selected Leghorn strains

1999 ◽  
Vol 79 (3) ◽  
pp. 269-276 ◽  
Author(s):  
R. W. Fairfull ◽  
L.-E. Liljedahl ◽  
R. S. Gowe
Keyword(s):  
Egg Production ◽  
Environmental Variation ◽  
Genetic Effects ◽  
Residual Effects ◽  
Additive Effects ◽  
Key Words Ageing ◽  
Fitness Traits ◽  
Regulated Expression ◽  
Factorial Mating ◽  
Genetic Responses

White Leghorn strains were crossed reciprocally in a complete factorial mating producing 6 pure strains and 30 strain-crosses, which were kept for two laying cycles: 133–496 d of age and 547–909 d. Hens were housed for lay individually in four two-tiered batteries of cages. Strain additive effects (Ai), strain sex-linked effects (Zi), strain-cross heterotic effects (hij) and residual effects were calculated using regression. Viability was high in the first cycle of egg production with only 1 to 3% mortality in each of the four 11-wk periods, but lower in the second cycle decreasing with age. There was significant variation among strains in additive autosomal and sex-linked genetic effects and strain-cross heterotic effects, which increased with age in the second cycle. Heterosis for viability was positive in some strain-crosses and negative in others with considerable changes with age. The magnitude of heterotic effects was generally greater than the magnitude of additive or sex-linked genetic effects for viability. These results imply that different genotypes mount subtly different genetic responses to the problems of viability with advancing age and that more than one theory of ageing could apply. The results are discussed in relation to the theoretical aspects of ageing genetics. Key words: Ageing, fitness, viability, genetic effects, genetic variation, environmental variation

Age-regulated expression of genetic and environmental variation in fitness traits. 1. Genetic effects and variances for egg production in a factorial mating of six selected Leghorn strains

1999 ◽  
Vol 79 (3) ◽  
pp. 253-267 ◽  
Author(s):  
L.-E. Liljedahl ◽  
R. W. Fairfull ◽  
R. S. Gowe
Keyword(s):  
Egg Production ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Genetic Effects ◽  
The Other ◽  
Additive Genetic Effect ◽  
Phenotypic Variance ◽  
Age Changes ◽  
Genotypic Variance ◽  
Factorial Mating

White Leghorn strains were crossed reciprocally in a complete factorial mating system producing 6 pure strains and 30 strain-crosses, which were kept in individual cages for two laying cycles, 133–496 and 547–909 d of age. The egg production in the second cycle (C2) of the various genotypes started about 10 – 20% lower and had a more linear and less persistent course than in the first cycle (C1). Strains exhibited very different patterns of age changes in both additive and non-additive genetic effects as well as in cytoplasmic effects. The additive autosomal and sex-linked genes (Ai and Zi) active in one laying cycle were quite different from those active in the other laying cycle as shown by low strain genetic correlations between their effects in C1 and C2. Further, the sets of Ai and Zi genes responded with effects quite opposite to each other in both C1 and C2 as indicated by highly negative strain genetic correlations between the Ai and Zi effects. The average non-additive genetic effect of sire strain i or dam strain j over all its crosses with other strains (hi) and the non-additive genetic effect due to the specific combination of genes occurring in each of the two reciprocal crosses between strain i and strain j (sij), showed very divergent patterns of age changes with a conspicuously greater divergence as age advanced. The overall non-additive genetic effect (mean heterosis) increased significantly with age across the two cycles. The strain crosses that most successfully maintained their rate of lay until the end of C2, also most successfully developed a rising age trend for total heterosis. The non-additive genes active in one laying cycle were significantly different from those active in the other laying cycle as shown by the moderately low strain genetic correlations between their effects in C1 and C2. The genotypic variance and its various components increased markedly with age, however, with a tendency to reach a plateau towards the end of both the first and the second laying cycle. The environmental variance increased parallel to the genotypic variance. Consequently, the phenotypic variance followed the same pattern of age changes. The results are discussed in relation to the theoretical aspects of ageing genetics. A model compatible with all the age trends of the genetic and environmental effects and variances is set up, assuming that ageing is composed of two main opposing forces. Finally, the results are briefly discussed from the animal breeding point of view. Key words: Ageing, fitness, laying hens, genetic effects, variation, expression

scholarly journals Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Akio Onogi ◽  
Toshio Watanabe ◽  
Atsushi Ogino ◽  
Kazuhito Kurogi ◽  
Kenji Togashi
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Variance Components ◽  
Variance Component ◽  
Predictive Accuracy ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Additive Effects ◽  
Additive Variance ◽  
Additive Genetic Effects

Abstract Background Genomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes. Results Records of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6–19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size. Conclusions The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.

An Analysis of Polygenes Affecting Wing Shape on Chromosome 2 inDrosophila melanogaster

Genetics ◽  
2001 ◽  
Vol 159 (3) ◽  
pp. 1045-1057 ◽  
Author(s):  
Kenneth Weber ◽  
Robert Eisman ◽  
Shawn Higgins ◽  
Lisa Morey ◽  
April Patty ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Transposable Element ◽  
Genetic Effects ◽  
Wing Shape ◽  
Pleiotropic Effects ◽  
Phenotypic Variance ◽  
Chromosome 2 ◽  
Additive Effects ◽  
Chromosome Segments ◽  
Small Additive

AbstractGenetic effects on an index of wing shape on chromosome 2 of Drosophila melanogaster were mapped using isogenic recombinants with transposable element markers. At least 10 genes with small additive effects are dispersed evenly along the chromosome. Many interactions exist, with only small net effects in homozygous recombinants and little effect on phenotypic variance. Heterozygous chromosome segments show almost no dominance. Pleiotropic effects on leg shape are only minor. At first view, wing shape genes form a rather homogeneous class, but certain complexities remain unresolved.

scholarly journals Heritability of egg production in laying hens under cumulative, multitrait and repeated measurement animal models

2008 ◽  
Vol 52 (No. 8) ◽  
pp. 254-260 ◽  
Author(s):  
A. Wolc ◽  
M. Lisowski ◽  
T. Szwaczkowski
Keyword(s):  
Animal Models ◽  
Variance Components ◽  
Egg Production ◽  
Genetic Parameters ◽  
Laying Hens ◽  
Genetic Effects ◽  
Repeated Measurement ◽  
Repeated Measurements ◽  
The Other ◽  
Highly Correlated

Six generations of three layer lines (13 770 recorded individuals of A22 line, 13 950 of A88, 9 351 of K66) were used to estimate genetic effects on egg production under cumulative, multitrait and repeatability models. Variance components were estimated by the AI-REML algorithm. The heritability of cumulative records ranged from 0.08 to 0.1. For the repeated measurements model the following genetic parameters were obtained: heritability 0.02–0.03, repeatability 0.04–0.38. The first two months of egg production were found to differ from the other periods: heritability was relatively high (<i>h</i><sup>2</sup> > 0.35) and low or negative correlations with the other periods were found. Heritability was low (<i>h</i><sup>2</sup> < 0.1) from the peak production until the end of recording and the consecutive periods were highly correlated. Further studies on monthly records are suggested.

ADDO: a comprehensive toolkit to detect, classify and visualize additive and non-additive quantitative trait loci

2019 ◽  
Vol 36 (5) ◽  
pp. 1517-1521
Author(s):  
Leilei Cui ◽  
Bin Yang ◽  
Nikolas Pontikos ◽  
Richard Mott ◽  
Lusheng Huang
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genetic Effects ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Additive Effects ◽  
A Genome ◽  
Trait Loci ◽  
Additive Genetic Effects

Abstract Motivation During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. Results We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO’s performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. Availability and implementation ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Analysis of conditional genetic effects and variance components in developmental genetics.

Genetics ◽  
1995 ◽  
Vol 141 (4) ◽  
pp. 1633-1639 ◽  
Author(s):  
J Zhu
Keyword(s):  
Variance Components ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Genetic Model ◽  
Genetic Effects ◽  
Unbiased Estimation ◽  
Developmental Genetics ◽  
Additive Effects ◽  
Genotype X Environment ◽  
Worked Example

Abstract A genetic model with additive-dominance effects and genotype x environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t-1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects.

scholarly journals Genetic effects of aging on egg production traits in the first laying cycle of White Leghorn strains and strain crosses

2000 ◽  
Vol 79 (3) ◽  
pp. 296-304 ◽  
Author(s):  
M.C. Ledur ◽  
R.W. Fairfull ◽  
I. McMillan ◽  
L. Asseltine
Keyword(s):  
Egg Production ◽  
Genetic Effects ◽  
Production Traits ◽  
White Leghorn ◽  
Effects Of Aging

scholarly journals Genetic effects on fitness of the mutant sugary1 in wild-type maize

2011 ◽  
Vol 150 (5) ◽  
pp. 603-609 ◽  
Author(s):  
A. DJEMEL ◽  
B. ORDÁS ◽  
L. KHELIFI ◽  
A. ORDÁS ◽  
P. REVILLA
Keyword(s):  
Zea Mays ◽  
Genetic Control ◽  
Sweet Corn ◽  
Genetic Regulation ◽  
Seed Viability ◽  
Genetic Effects ◽  
Additive Effects ◽  
Wild Type ◽  
Evolutionary Potential ◽  
Breeding Programmes

SUMMARYKnowing the genetic regulation of fitness is crucial for using mutants in breeding programmes, particularly when the mutant is deleterious in some genetic backgrounds, as it happens with the sweet corn mutant sugary1 (su1) in maize (Zea mays L.). The fitness and genetic effects of maize mutant su1 were monitored through five successive selfing generations in two separated mean-generation designs. The first involved two inbreds with similar genetic backgrounds, while unrelated inbreds were used for the second design. Parents, F1s, F2s, and backcrosses were crossed to P39 as the donor of su1 and the 12 crosses were successively self-pollinated for 5 years. The su1 frequency decreased linearly across selfing generations in both designs. Additive effects were significant for su1 seed viability. However, dominance effects were of higher magnitude than additive effects, even though the dominance effects were not significant. Genetic effects depended on genotypes and environments. Therefore, the fitness of su1 is under genetic control, with significant additive effects due to minor contributions of multiple genes. The fitness of su1 is strongly affected by maize genotypic background and environment. It is hypothesized that genotypes could have evolutionary potential for modulating the fitness of single mutations.

scholarly journals Additive Genetic Effects of RNF4, RBP4, and IGF2 Polymorphisms on Litter Size in Landrace and Yorkshire Sows

2019 ◽  
Vol 2 (1) ◽  
pp. 314-320 ◽  
Author(s):  
Vinh Thi Nguyen ◽  
Luc Duc Do ◽  
Thinh Hoang Nguyen ◽  
Bo Xuan Ha ◽  
Mai Ngoc Hoang ◽  
...  
Keyword(s):  
Litter Size ◽  
Reproductive Traits ◽  
Genetic Effects ◽  
Additive Effects ◽  
Selection For ◽  
Additive Genetic Effects

The association of the RNF4, RBP4, and IGF2 genotypes and their additive genetic effects with litter size in purebred Landrace and Yorkshire sows were studied. The results revealed significant associations between the RNF4 and RBP4 genotypes with the total number of piglets born (TNB) and number of piglets born alive (NBA) traits (P <0.05). The RNF4 CC genotype had greater TNB and NBA than the TT genotype in both breeds. The RBP4 BB genotype had greater TNB and NBA than the AA genotype in the Landrace breed. Significant additive effects of the RNF4 and RBP4 genes on the TNB and NBA were detected (P <0.05). No significant associations of the IGF2 genotypes and their additive effects with any reproductive traits were observed in both Landrace and Yorkshire sows (P >0.05). The results suggested that the RNF4 and RBP4 genes could be useful in selection for increasing TNB and NBA traits in pigs.

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