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.

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.

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.

Relationship between litter size and perinatal and pre-weaning survival in pigs

2002 ◽  
Vol 74 (2) ◽  
pp. 217-222 ◽  
Author(s):  
M. S. Lund ◽  
M. Puonti ◽  
L. Rydhmer ◽  
J. Jensen
Keyword(s):  
Litter Size ◽  
Negative Correlation ◽  
Genetic Effects ◽  
Direct Effects ◽  
Selection For ◽  
Heritability Estimates ◽  
The Relationship ◽  
Piglet Survival

AbstractThe objective of this study was to analyse the relationship between direct and maternal genetic effects on litter size and piglet survival. The analyses were performed on records from 26 564 Landrace litters and 15 103 Yorkshire litters from first parity dams in Finnish herds. The trivariate model fitted total number of piglets born, proportion alive at birth and proportion survived from birth until 3 weeks as traits of the litter. The model included direct genetic and maternal genetic effects for all traits. In Landrace pigs, maternal heritabilities were estimated to be 0·11 for total number born, 0·06 for proportion alive at birth, and 0·08 for proportion survived from birth until 3 weeks. The corresponding estimates for the Yorkshire breed were 0·14, 0·06 and 0·01. All heritability estimates of direct effects were below 0·05. In the Landrace breed, there was a negative correlation of -0·39 between maternal genetic effects on total number born and maternal genetic effects on proportion survived from birth until 3 weeks and a negative correlation of -0·41 between direct and maternal genetic effects on proportion survived from birth until 3 weeks. These correlations were not significant in the Yorkshire breed. The results show that selection for number born in total alone will lead to a deterioration in the maternal ability of sows. Selection for pre-weaning survival could be achieved by selecting on direct and maternal components jointly.

Estimation of Genetic Parameters and Selection Response for Reproductive and Growth Traits in Rideau-Arcott sheep

2020 ◽  
Author(s):  
Mohammed Naser Boareki ◽  
Luiz Brito ◽  
Angela Cánovas ◽  
V.R. Osborne ◽  
Flavio S Schenkel
Keyword(s):  
Litter Size ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Reproductive Traits ◽  
Selection Response ◽  
Direct Selection ◽  
Correlated Response ◽  
Response To Selection ◽  
Weaning Weight ◽  
Selection For

The goal of this study was to estimate genetic parameters and predict direct and correlated response to selection for lamb growth traits and ewe reproductive traits, based on single trait selection or combining multiple traits in an optimum index that targets total litter post-weaning weight in the first lambing as the main selection goal. Heritability estimates ranged from 0.04 to 0.19. Genetic correlations between growth and reproductive traits ranged from -0.24 to 0.15. The indirect response to selection for reproductive traits in later lambings, by selecting on first lambing performance, was 11 to 25% greater than direct selection. The response to indirect selection for composite reproductive traits, i.e. total weaning weight or total post-weaning weight, by selecting on individual lamb weaning weight or post-weaning weight was 1 to 69% greater than direct selection, but it was accompanied by a negative response on litter size. However, combining alternate growth and reproductive traits in optimum selection index resulted in correlated response of up to 96% greater than direct selection response for reproductive traits without a negative response on litter size. Therefore, multiple trait selection using an index of component traits was more effective than direct selection for a composite trait.

scholarly journals Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects

2021 ◽  
Author(s):  
Seema Yadav ◽  
Xianming Wei ◽  
Priya Joyce ◽  
Felicity Atkin ◽  
Emily Deomano ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Complex Traits ◽  
Genetic Variance ◽  
Prediction Models ◽  
Additive Genetic Variance ◽  
Genetic Effects ◽  
Additive Effects ◽  
Genetic Progress ◽  
Accurate Identification ◽  
Additive Genetic Effects

AbstractKey messageNon-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance.AbstractIn the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.

Modelling non-additive genetic effects using ridge regression for an Angus–Nellore crossbred population

2019 ◽  
Vol 59 (5) ◽  
pp. 823 ◽  
Author(s):  
C. D. Bertoli ◽  
J. Braccini Neto ◽  
C. McManus ◽  
J. A. Cobuci ◽  
G. S. Campos ◽  
...  
Keyword(s):  
Ridge Regression ◽  
Genetic Effects ◽  
Additive Models ◽  
Additive Effect ◽  
Ratio Test ◽  
Additive Effects ◽  
Complete Model ◽  
Complementarity Effect ◽  
Scrotal Circumference ◽  
Additive Genetic Effects

Data from 294045 records from a crossbred Angus × Nellore population were used to estimate fixed genetic effects (both additive and non-additive) and to test different non-additive models using ridge regression. The traits studied included weaning gain (WG), postweaning gain (PG), phenotypic scores for weaning (WC) and postweaning (PC) conformation, weaning (WP) and postweaning (PP) precocity, weaning (WM) and postweaning (PM) muscling and scrotal circumference (SC). All models were compared using the likelihood-ratio test. The model including all fixed genetic effects (breed additive and complementarity, heterosis and epistatic loss non-additive effects, both direct and maternal) was the best option to analyse this crossbred population. For the complete model, all effects were statistically significant (P &lt; 0.01) for weaning traits, except the direct breed additive effects for WP and WM; direct complementarity effect for WP, WM, PP and PM and maternal epistatic loss for PG. Direct breed additive effect was positive for weaning traits and negative for postweaning. Maternal breed additive effect was negative for SC and WP. Direct complementarity and heterosis were positive for all traits and maternal complementarity and heterosis were also positive for all traits, except for PG. Direct and maternal epistatic loss effects were negative for all traits. We conclude that the fixed genetic effects are mostly significant. Thus, it is important to include them in the model when evaluating crossbred animals, and the model that included breed additive effects, complementarity, heterosis and epistatic loss differed significantly from all reduced models, allowing to infer that it was the best model. The model with only breed additive and heterosis was parsimonious and could be used when the structure or amount of data does not allow the use of complete model.

scholarly journals Assessing gene action for hypoxia tolerance in cotton (Gossypium hirsutum L.)

2019 ◽  
pp. 51-62
Author(s):  
Altaf Hussain ◽  
Zafar Ullah Zafar ◽  
Habib-Ur-Rehman Athar ◽  
Jehanzeb Farooq ◽  
Saghir Ahmad ◽  
...  
Keyword(s):  
Genetic Effects ◽  
Hypoxia Tolerance ◽  
Narrow Sense Heritability ◽  
Partial Dominance ◽  
Additive Component ◽  
Hypoxic Environment ◽  
High Heritability ◽  
Selection For ◽  
Additive Genetic Effects ◽  
F1 Generation

Introduction. The inheritance studies on hypoxia tolerance in cotton are very scanty. Objective. The objective of this work was to study the inheritance pattern of seed cotton yield, ginning out-turn, and fiber length under normal and hypoxia conditions. Materials and Methods. An 8•8 diallel mating system was employed in F1 generation of cotton. The experiment was conducted during the years 2011-2012 at Cotton Research Institute, Multan, Pakistan. Results. Analysis of variance revealed significant variation (P<0.01) for all the traits under both non-stress and flooding stress conditions. The value of regression coefficient b deviated significantly from zero but not from unity for all the traits which enabled further analysis of the data. The additive component (D) was significant for all the traits and this has more values than the values of dominance components H1 and H2, which clearly indicates the preponderance of additive genetic effects. Under normal and hypoxia conditions the estimates of narrow sense heritability were higher. Conclusion. Predominance of additive genetic effects, high heritability along with partial dominance under both conditions suggested that selection for these traits under hypoxic environment at early generation will be useful.

scholarly journals Transition between vegetative phases in maize: genetic effects and variances and associated markers

2009 ◽  
Vol 147 (5) ◽  
pp. 547-554 ◽  
Author(s):  
B. ORDÁS ◽  
L. SERRANO ◽  
A. ORDÁS ◽  
A. BUTRÓN ◽  
P. REVILLA
Keyword(s):  
Phase Transition ◽  
Late Phase ◽  
Genetic Effects ◽  
Vegetative Phase ◽  
Maize Genetic ◽  
Complex Regulation ◽  
Selection For ◽  
Additive Genetic Effects ◽  
Vegetative Phases ◽  
Simple Sequence

SUMMARYTransition from juvenile to adult vegetative phase in maize is associated with pest and disease resistance. However, reports are not consistent on the significance and relative importance of additive or dominance genetic effects and variances. The purpose of the present research was to elucidate the genetic effects and variances and to identify molecular markers associated with phase transition. Three cycles of divergent selection were carried out in a maize synthetic accelerating and delaying phase transition. Three and four inbred lines were released from the third cycles of late phase transition (LPT) and early phase transition (EPT), respectively. Generation mean analyses were performed from two LPT×EPT crosses in order to calculate genetic effects and variances. Markers associated with vegetative phase transition were identified by contrasting simple sequence repeat (SSR) alleles between LPT and EPT inbreds and selection cycles, and by testing whether drift could explain the allelic changes observed in the respective third cycles of selection. Juvenile traits are mainly regulated by additive genetic effects and variances, whereas adult traits have a complex regulation involving dominance and epistatic effects. Based on error variances, EPT improves phenotypic stability. The SSRs phi028, phi112 and umc1725 were associated with selection for phase transition, suggesting that these genome regions are involved in the regulation of vegetative phase transition of maize, although none of the genes previously associated with phase transition has been located in those regions.

Genetic Variability and Heritability Estimates Based on the F2 Generation from Crosses of Large-Seeded Virginia-Type Peanuts with Lines Resistant to Cylindrocladium Black Rot1

1983 ◽  
Vol 10 (1) ◽  
pp. 47-51 ◽  
Author(s):  
C. C. Green ◽  
J. C. Wynne ◽  
M. K. Beute
Keyword(s):  
Genetic Effects ◽  
Fruit Traits ◽  
Resistant Lines ◽  
The Common ◽  
Low Levels ◽  
Selection For ◽  
Heritability Estimates ◽  
F2 Generation ◽  
Additive Genetic Effects ◽  
Selection Of

Abstract Selection of large-fruited, high-yielding Cylindrocladium black rot — (CBR) resistant lines from two sets of crosses, each set consisting of crosses in F2 generation between a virginia and two CBR-resistant lines, was evaluated to determine the potential for selecting cultivars with these traits. Heritabilities, phenotypic and genotypic correlations and estimates of additive and nonadditive genetic effects were determined for yield and fruit traits at two locations and CBR resistance at one location. Crosses with NC 2 as a parent had higher levels of CBR resistance than those with Florigiant as the common virginia-type parent. Heritability estimates for the necrosis index (CBR resistance) were high while estimates for yield and fruit traits were variable over crosses and locations. The necrosis index was significantly and negatively correlated with all fruit and yield traits for three of the four crosses indicating it should be possible to select high-yielding, largefruited lines with low levels of disease from three crosses. Only significant additive genetic effects were found for CBR resistance. Significant additive and nonadditive genetic effects were found for the yield and fruit traits. Estimates of additive and non-additive genetic effects indicate that early generation selection for CBR resistance should be effective, whereas selection for yield and fruit traits would be more effective in later generations.

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