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.

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.

scholarly journals A multivariate analysis with direct additive and inbreeding depression load effects

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Luis Varona ◽  
Juan Altarriba ◽  
Carlos Moreno ◽  
María Martínez-Castillero ◽  
Joaquim Casellas
Keyword(s):  
Beef Cattle ◽  
Inbreeding Depression ◽  
Negative Correlation ◽  
Simulated Data ◽  
Genetic Effects ◽  
Strong Negative Correlation ◽  
Additive Variance ◽  
Inbreeding Coefficients ◽  
Load Effects ◽  
Additive Genetic Effects

Abstract Background Inbreeding is caused by mating between related individuals and its most common consequence is inbreeding depression. Several studies have detected heterogeneity in inbreeding depression among founder individuals, and recently a procedure for predicting hidden inbreeding depression loads associated with founders and the Mendelian sampling of non-founders has been developed. The objectives of our study were to expand this model to predict the inbreeding loads for all individuals in the pedigree and to estimate the covariance between the inbreeding loads and the additive genetic effects for the trait of interest. We tested the proposed approach with simulated data and with two datasets of records on weaning weight from the Spanish Pirenaica and Rubia Gallega beef cattle breeds. Results The posterior estimates of the variance components with the simulated datasets did not differ significantly from the simulation parameters. In addition, the correlation between the predicted and simulated inbreeding loads were always positive and ranged from 0.27 to 0.82. The beef cattle datasets comprised 35,126 and 75,194 records on weights between 170 and 250 days of age, and pedigrees of 308,836 and 384,434 individual-sire-dam entries for the Pirenaica and Rubia Gallega breeds, respectively. The posterior mean estimates of the variance of inbreeding depression loads were 29,967.8 and 28,222.4 for the Pirenaica and Rubia Gallega breeds, respectively. They were larger than those of the additive variance (695.0 and 439.8 for Pirenaica and Rubia Gallega, respectively), because they should be understood as the variance of the inbreeding depression achieved by a fully inbred (100%) descendant. Therefore, the inbreeding loads have to be rescaled for smaller inbreeding coefficients. In addition, a strong negative correlation (− 0.43 ± 0.10) between additive effects and inbreeding loads was detected in the Pirenaica, but not in the Rubia Gallega breed. Conclusions The results of the simulation study confirmed the ability of the proposed procedure to predict inbreeding depression loads for all individuals in the populations. Furthermore, the results obtained from the two real datasets confirmed the variability in the inbreeding depression loads in both breeds and suggested a negative correlation of the inbreeding loads with the additive genetic effects in the Pirenaica breed.

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 Resistance to Spot Blotch in Selected Wheat Genotypes

2018 ◽  
Vol 6 (1) ◽  
pp. 33-38
Author(s):  
Batiseba Tembo ◽  
Julia Sibiya ◽  
Pangirayi Tongoona ◽  
Rob Melis
Keyword(s):  
Recurrent Selection ◽  
Genetic Effect ◽  
Resistance Breeding ◽  
Bipolaris Sorokiniana ◽  
Spot Blotch ◽  
Breeding Strategy ◽  
Genetic Effects ◽  
Selection Strategy ◽  
Wheat Genotypes ◽  
Additive Genetic Effects

Spot blotch disease of wheat (Triticum aestivum L.) caused by Bipolaris sorokiniana (Sacc) Shoemaker causes significant yield and quality losses in warm and humid agro-ecologies of the world. Breeding for resistance is considered to be the most economical and sustainable approach of controlling the disease. The objective of this study was to determine the genetic effect influencing inheritance of resistance to spot blotch in selected wheat genotypes using generation mean analysis to devise a resistance breeding strategy. Populations involving six generations (P1, P2, F1, F2, BCP1 and BCP2) were developed comprising two selected parental lines, i.e., Loerrie II and 19HRWSN6. Test materials were field evaluated for resistance to spot blotch during the 2014/15 cropping season in Zambia. Additive genetic effects were significant and accounted for 94.79% of the total genetic variation for spot blotch resistance in wheat. Dominance and epistatic effects were not detected. The predominance of additive genetic effects suggests that recurrent selection strategy could boost spot blotch resistance in these population to develop pure line wheat cultivars.

PSI-17 Genetic analysis of weaning weight in Hungarian Simmental cattle

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 274-275
Author(s):  
Afees Ajasa ◽  
Barnabás Vágó ◽  
Imre Füller ◽  
István Komlósi ◽  
János Posta
Keyword(s):  
Variance Components ◽  
Random Effect ◽  
Random Effect Model ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Covariance Model ◽  
Genetic Covariance ◽  
Weaning Weight ◽  
Effect Model ◽  
Maternal Heritability

Abstract The aim of the study was to partition the total phenotypic variation in the weaning weight of Hungarian Simmental calves into their various causal components. The data used was provided by the Association of Hungarian Simmental Breeders. The dataset comprised of the weaning weight records of 44,278 calves (sire = 879, dam = 14,811) born from 1975 to 2020. A total of six models were fitted to the weaning weight data. Herd, birth year, calving order and sex were included as fixed effects in the models. Model 1 had direct genetic effect as the only random effect. Model 2 had a permanent maternal environment as an additional random effect. Model 3 had both direct and maternal genetic effects, with their covariance is being zero. Model 4 was similar to Model 3 but with non-zero direct-maternal genetic covariance. Model 5 had direct, maternal genetic and permanent environmental effects and a zero direct-maternal genetic covariance. Model 6 was similar to model 5 but the direct-maternal genetic effect was assumed to be correlated. Variance components and genetic parameters were estimated using restricted maximum likelihood method with the Wombat software. The best fit model was determined using the Log likelihood ratio test. Inclusion of direct maternal genetic covariance increased the variance components estimates dramatically which resulted in a corresponding increase in the direct and maternal heritability estimates. The best fitted model (Model 4) had direct and maternal genetic effects as the only random effects with a non-zero direct-maternal genetic covariance. The direct heritability, maternal heritability and direct-maternal genetic correlation estimate of the best model was 0.57, 0.16 and -0.78, respectively. Our result suggests the problem of (co)sampling variation in the partitioning of additive genetic effect into direct and maternal components.

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.

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.

Relative Importance of Additive Genetic and Maternal Sources of Variation in Early Survival of Young Splake Hybrids (Salvelinus fontinalis × S. namaycush)

1974 ◽  
Vol 31 (9) ◽  
pp. 1499-1502 ◽  
Author(s):  
G. Burton Ayles
Keyword(s):  
Maternal Effects ◽  
Salvelinus Fontinalis ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Additive Genetic Effect ◽  
Relative Importance ◽  
Egg Survival ◽  
Brood Stock ◽  
Sources Of Variation ◽  
Additive Genetic Effects

Additive genetic and maternal effects of survival of uneyed eggs, eyed eggs, and alevins were determined from five series of matings within a splake brood stock. Average values for family h2 (heritability) and family m (maternal variance/total variance) were estimated. There were additive genetic effects in alevin survival (h2 =.41 ±.18) but not in uneyed or eyed egg survival. Maternal effects were greatest within the uneyed stage (m =.78 ±.22), decreased during the eyed egg stage (m =.68 ±.24), and were least within the alevin stage (.40 ±.19). The additive genetic effect on alevin survival was attributable to genetic differences in the resistance of young splake to blue sac disease (h2 =.76 ±.28).

32 A Bayesian Hierarchical Model to Integrate Growth Models into Genomic Evaluation of Pigs

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 18-19
Author(s):  
Haipeng Yu ◽  
Jaap Milgen ◽  
Egbert Knol ◽  
Rohan Fernando ◽  
Jack C Dekkers
Keyword(s):  
Genomic Prediction ◽  
Genetic Improvement ◽  
Growth Traits ◽  
Average Daily Gain ◽  
Genetic Effects ◽  
Latent Growth ◽  
Bayesian Hierarchical ◽  
Additive Genetic Effects ◽  
Daily Gain ◽  
Genomic Predictions

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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