Genetic variance components and genetic effects among eleven diverse upland cotton lines and their F2 hybrids

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.

Download Full-text

Estimation of additive and dominance genetic variance components for female fertility traits in Iranian Holstein cows

The Journal of Agricultural Science ◽

10.1017/s0021859618000497 ◽

2018 ◽

Vol 156 (4) ◽

pp. 565-569

Author(s):

H. Ghiasi ◽

R. Abdollahi-Arpanahi ◽

M. Razmkabir ◽

M. Khaldari ◽

R. Taherkhani

Keyword(s):

Dairy Cows ◽

Variance Components ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Genetic Effects ◽

Information Criteria ◽

Phenotypic Variance ◽

Dominance Variance ◽

Dominance Genetic Variance ◽

Estimated Heritability

AbstractThe aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike's information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman's rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.

Download Full-text

Estimation of Additive and Dominance Genetic Effects on Body Weight, Carcass and Ham Quality Traits in Heavy Pigs

Animals ◽

10.3390/ani11020481 ◽

2021 ◽

Vol 11 (2) ◽

pp. 481

Author(s):

Valentina Bonfatti ◽

Roberta Rostellato ◽

Paolo Carnier

Keyword(s):

Variance Components ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Model Fitting ◽

Genetic Effects ◽

Phenotypic Variance ◽

Genetic Response ◽

Dominance Variance ◽

Dry Cured Ham ◽

Genetic Evaluations

Neglecting dominance effects in genetic evaluations may overestimate the predicted genetic response achievable by a breeding program. Additive and dominance genetic effects were estimated by pedigree-based models for growth, carcass, fresh ham and dry-cured ham seasoning traits in 13,295 crossbred heavy pigs. Variance components estimated by models including litter effects, dominance effects, or both, were compared. Across traits, dominance variance contributed up to 26% of the phenotypic variance and was, on average, 22% of the additive genetic variance. The inclusion of litter, dominance, or both these effects in models reduced the estimated heritability by 9% on average. Confounding was observed among litter, additive genetic and dominance effects. Model fitting improved for models including either the litter or dominance effects, but it did not benefit from the inclusion of both. For 15 traits, model fitting slightly improved when dominance effects were included in place of litter effects, but no effects on animal ranking and accuracy of breeding values were detected. Accounting for litter effects in the models for genetic evaluations would be sufficient to prevent the overestimation of the genetic variance while ensuring computational efficiency.

Download Full-text

SNP-based mate allocation strategies to maximize total genetic value in pigs

Genetics Selection Evolution ◽

10.1186/s12711-019-0498-y ◽

2019 ◽

Vol 51 (1) ◽

Cited By ~ 1

Author(s):

David González-Diéguez ◽

Llibertat Tusell ◽

Céline Carillier-Jacquin ◽

Alban Bouquet ◽

Zulma G. Vitezica

Keyword(s):

Inbreeding Depression ◽

Genetic Gain ◽

Variance Components ◽

Genetic Variance ◽

Additive Genetic Variance ◽

Genetic Effects ◽

Breeding Values ◽

Genetic Merit ◽

Additive Genetic Effects ◽

Allocation Strategies

Abstract Background Mate allocation strategies that account for non-additive genetic effects can be used to maximize the overall genetic merit of future offspring. Accounting for dominance effects in genetic evaluations is easier in a genomic context, than in a classical pedigree-based context because the combinations of alleles at loci are known. The objective of our study was two-fold. First, dominance variance components were estimated for age at 100 kg (AGE), backfat depth (BD) at 140 days, and for average piglet weight at birth within litter (APWL). Second, the efficiency of mate allocation strategies that account for dominance and inbreeding depression to maximize the overall genetic merit of future offspring was explored. Results Genetic variance components were estimated using genomic models that included inbreeding depression with and without non-additive genetic effects (dominance). Models that included dominance effects did not fit the data better than the genomic additive model. Estimates of dominance variances, expressed as a percentage of additive genetic variance, were 20, 11, and 12% for AGE, BD, and APWL, respectively. Estimates of additive and dominance single nucleotide polymorphism effects were retrieved from the genetic variance component estimates and used to predict the outcome of matings in terms of total genetic and breeding values. Maximizing total genetic values instead of breeding values in matings gave the progeny an average advantage of − 0.79 days, − 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE. Conclusions Genomic mate allocation accounting for non-additive genetic effects is a feasible and potential strategy to improve the performance of the offspring without dramatically compromising additive genetic gain.

Download Full-text

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

BMC Genomics ◽

10.1186/s12864-021-07792-y ◽

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.

Download Full-text

Genetic effects of introgression genomic components from Sea Island cotton (Gossypium barbadense L.) on fiber related traits in upland cotton (G. hirsutum L.)

Euphytica ◽

10.1007/s10681-011-0378-1 ◽

2011 ◽

Vol 181 (1) ◽

pp. 41-53 ◽

Cited By ~ 24

Author(s):

Furong Wang ◽

Yongchao Gong ◽

Chuanyun Zhang ◽

Guodong Liu ◽

Liuming Wang ◽

...

Keyword(s):

Upland Cotton ◽

Gossypium Barbadense ◽

Genetic Effects

Download Full-text

Genetic variance components for female fertility in Iranian Holstein cows

Livestock Science ◽

10.1016/j.livsci.2011.01.020 ◽

2011 ◽

Vol 139 (3) ◽

pp. 277-280 ◽

Cited By ~ 23

Author(s):

Heydar Ghiasi ◽

Abbas Pakdel ◽

Ardeshir Nejati-Javaremi ◽

Hassan Mehrabani-Yeganeh ◽

Mahmood Honarvar ◽

...

Keyword(s):

Variance Components ◽

Genetic Variance ◽

Female Fertility ◽

Holstein Cows

Download Full-text

Genetic analysis of spike traits in two- and multi-rowed barley crosses

Genetika ◽

10.2298/gensr1203475m ◽

2012 ◽

Vol 44 (3) ◽

pp. 475-482

Author(s):

Milomirka Madic ◽

Desimir Knezevic ◽

Aleksandar Paunovic ◽

Dragan Djurovic

Keyword(s):

Variance Components ◽

Genetic Variance ◽

Grain Weight ◽

Spike Length ◽

Grain Number ◽

Partial Dominance ◽

Grain Number Per Spike ◽

Diallel Crossing ◽

Barley Genotypes ◽

Mode Of Inheritance

Mode of inheritance and genetic variance components for spike length, grain number per spike and grain weight per spike were evaluated in four parental genotypes and their F1 and F2 hybrids obtained through incomplete diallel crossing. Multi-rowed barley genotypes HVW-247 and Partizan and two-rowed barley genotypes KG-15 and NS-293 were selected for the crossing based on the trait concept with the parents being divergent for spike length, grain number per spike and grain weight per spike. The average values for spike length in F1 and F2 generations were intermediate or close to those of the parent having longer spikes, with the mode of inheritance being partial dominance, dominance or overdominance, depending on the crossing combination. The six-rowed x six-rowed and two-rowed x two-rowed crossing combinations showed dominance of increased grain number per spike and increased grain weight per spike in the inheritance of grain number per spike and grain weight per spike, respectively, whereas the six-rowed x two-rowed cross was predominated by partial dominance.

Download Full-text

Quantitative genetics and the evolution of ontogeny: I. Ontogenetic changes in quantitative genetic variance components in randombred mice

Genetics Research ◽

10.1017/s0016672300021492 ◽

1983 ◽

Vol 42 (1) ◽

pp. 65-75 ◽

Cited By ~ 33

Author(s):

James M. Cheverud ◽

Larry J. Leamy ◽

William R. Atchley ◽

J. J. Rutledge

Keyword(s):

Maternal Effects ◽

Environmental Effects ◽

Variance Components ◽

Genetic Variance ◽

Tail Length ◽

Head Length ◽

Ontogenetic Changes ◽

Trunk Length ◽

Quantitative Genetic ◽

Significant Difference

SUMMARYWe report the results of an ontogenetic analysis of quantitative genetic variance components with two replicates drawn from the randombred ICR strain of mice. A total of 432 mice from 108 full-sib families raised in a cross-fostering design were used to estimate direct effects heritability, maternal effects, and environmental effects for weight, head length, trunk length, trunk circumference, and tail length at 17, 24, 31, 38, 45, 52, 59, and 66 days of age. There was no significant difference in heritability between the replicates. Heritabilities either stayed more or less constant with age at about 0·30 (weight, trunk length, trunk circumference) or increased slightly with age (head length, tail length). Maternal effects decreased with age from a maximum of about 0·50 at weaning to about 0·15 at age 66 when growth was nearly complete. Environmental effects increased in relative importance during ontogeny.

Download Full-text