Non-additive Genetic Effects Research Articles

Comparison of Single-Trait and Multi-Trait GBLUP Models for Genomic Prediction in Red Clover

Red clover (Trifolium pratense) is a perennial forage legume wildly used in temperate regions, including northern Europe. Its breeders are under increasing pressure to obtain rapid genetic gains to meet the high demand for improved forage yield and quality. One solution to increase genetic gain by reducing time and increasing accuracy is genomic selection. Thus, efficient genomic prediction (GP) models need to be developed, which are unbiased to traits and harvest time points. This study aimed to develop and evaluate single-trait (ST) and multi-trait (MT) models that simultaneously target more than one trait or cut. The target traits were dry matter yield, crude protein content, net energy for lactation, and neutral detergent fiber. The MT models either combined dry matter yield with one forage quality trait, all traits at one cut, or one trait across all cuts. The results show an increase with MT models where the traits had a genetic correlation of 0.5 or above. This study indicates that non-additive genetic effects have significant but varying effects on the predictive ability and reliability of the models. The key conclusion of this study was that these non-additive genetic effects could be better described by incorporating genetically correlated traits or cuts.

Genome-Wide Search for Nonadditive Allele Effects Identifies PSKH2 as Involved in the Variability of Factor V Activity.

Factor V (FV) is a key molecular player in the coagulation cascade. FV plasma levels have been associated with several human diseases, including thrombosis, bleeding, and diabetic complications. So far, 2 genes have been robustly found through genome-wide association analyses to contribute to the inter-individual variability of plasma FV levels: structural F5 gene and PLXDC2. The authors used the underestimated Brown-Forsythe methodology implemented in the QuickTest software to search for non-additive genetic effects that could contribute to the inter-individual variability of FV plasma activity. QUICKTEST was applied to 4 independent genome-wide association studies studies (LURIC [Ludwigshafen RIsk and Cardiovascular Health Study], MARTHA [Marseille Thrombosis Association], MEGA [Multiple Environmental and Genetic Assessment], and RETROVE [Riesgo de Enfermedad Tromboembolica Venosa]) totaling 4505 participants of European ancestry with measured FV plasma levels. Results obtained in the 4 cohorts were meta-analyzed using a fixed-effect model. Additional analyses involved exploring haplotype and gene×gene interactions in downstream investigations. A genome-wide significant signal at the PSKH2 locus on chr8q21.3 with lead variant rs75463553 with no evidence for heterogeneity across cohorts was observed (P=0.518). Although rs75463553 did not show an association with mean FV levels (P=0.49), it demonstrated a robust significant (P=3.38x10-9) association with the variance of FV plasma levels. Further analyses confirmed the reported association of PSKH2 with neutrophil biology and revealed that rs75463553 likely interacts with two loci, GRIN2A and POM121L12, known for their involvement in smoking biology. This comprehensive approach identifies the role of PSKH2 as a novel molecular player in the genetic regulation of FV, shedding light on the contribution of neutrophils to FV biology.

Optimizing purebred selection to improve crossbred performance.

Crossbreeding is a widely adopted practice in the livestock industry, leveraging the advantages of heterosis and breed complementarity. The prediction of Crossbred Performance (CP) often relies on Purebred Performance (PB) due to limited crossbred data availability. However, the effective selection of purebred parents for enhancing CP depends on non-additive genetic effects and environmental factors. These factors are encapsulated in the genetic correlation between crossbred and purebred populations ( ). In this study, a two-way crossbreeding simulation was employed to investigate various strategies for integrating data from purebred and crossbred populations. The goal was to identify optimal models that maximize CP across different levels of . Different scenarios involving the selection of genotyped individuals from purebred and crossbred populations were explored using ssGBLUP (single-step Genomic Best Linear Unbiased Prediction) and ssGBLUP-MF (ssGBLUP with metafounders) models. The findings revealed an increase in prediction accuracy across all scenarios as values increased. Notably, in the scenario incorporating genotypes from both purebred parent breeds and their crossbreds, both ssGBLUP and ssGBLUP-MF models exhibited nearly identical predictive accuracy. This scenario achieved maximum accuracy when was less than 0.5. However, at = 0.8, ssGBLUP, which exclusively included sire breed genotypes in the training set, achieved the highest overall prediction accuracy at 73.2%. In comparison, the BLUP-UPG (BLUP with unknown parent group) model demonstrated lower accuracy than ssGBLUP and ssGBLUP-MF across all levels. Although ssGBLUP and ssGBLUP-MF did not demonstrate a definitive trend in their respective scenarios, the prediction ability for CP increased when incorporating both crossbred and purebred population genotypes at lower levels of . Furthermore, when was high, utilizing paternal genotype for CP predictions emerged as the most effective strategy. Predicted dispersion remained relatively similar in all scenarios, indicating a slight underestimation of breeding values. Overall, the value emerged as a critical factor in predicting CP based on purebred data. However, the optimal model to maximize CP depends on the factors influencing . Consequently, ongoing research aims to develop models that optimize purebred selection, further enhancing CP.

313 Many farmers want a prediction of future performance, not necessarily breeding potential

Abstract High heritability is a characteristic of many performance traits in cattle translating to a relatively strong correlation between an (accurate) estimated breeding value for an animal and its (future) production potential. However, the broad sense heritability (i.e., numerator in the formula includes both additive and non-additive genetic variance) is often larger than the narrow sense heritability (i.e., numerator includes only the additive genetic variance) especially for low heritability traits like reproductive performance. Moreover, the repeatability of a trait can be greater again, the difference between the heritability and repeatability is the inclusion of a permanent environmental variance in the numerator. Accurate and pertinent omic-based animal-level features can contribute to an increase in narrow sense heritability owing to a possible reduction in the residual variance; such data can also provide insights into both non-additive genetic variance and the permanent environmental variance. Even at the level of the contemporary group, omic-level data from the likes of bulk blood/saliva/milk sample, can improve how systematic environmental effects are modeled in the mixed model equations thus also reducing the residual variance. The greater heritability will contribute to a strengthening of the correlation between future phenotypic performance potential and estimated additive genetic merit. Importantly, omic measures for individual animals can help predict the permanent environmental effect of that animal capturing the yet untapped contributor to phenotypic variance. Irrespective, predictions need to be replaced by prescriptions; end users are inundated with data yet crave actionable information. Animal breeders have a lush (pardon the pun!) history of distilling estimates of genetic merit for a plethora of traits into a single value per animal (i.e., breeding indexes); this is the first step in transitioning from predictions to prescriptions. Given the familiarity of end users with breeding indexes, the potential exists to develop production indexes to rank commercial animals on future expected performance taking cognizance of genetic and non-genetic effects. Such production indexes align well with the associated breeding index but where the estimates of genetic merit for the traits in question are replaced by estimates of production merit; these estimated production values are the sum of the additive genetic, non-additive genetic and permanent environmental effects. The weighting factors in the production indexes are more short-term using current costs and prices as opposed to breeding indexes which are longer term. In comparison with the breeding indexes, the production indexes can include ancillary information like gender, age, and month of calving/birth. Like breeding indexes, they are updated in real time. Examples of the application of genome-enabled production indexes are guides for valuing animals for purchase or culling.

Analysis of inbreeding depression in five S1 Cassava families of elite varieties at the seedling nursery and clonal evaluation trial stages.

Background Cassava is an outcrossing, highly heterozygous plant that is reported to suffer from inbreeding depression. However, unraveling recessive traits and exploring additive genes would require a limited level of inbreeding for the genetic improvement of cassava. Method The impact of inbreeding depression (ID) on agronomic and biotic tolerance of cassava was evaluated using the S1 progenies of five African cassava varieties (TMS 30572, TME 419, TMS 98/0505, TMS 01/1371, and TMS 98/0002) at the seedling and clonal evaluation stages. Results At both trial stages, the effects of ID were severe on average performance of fresh root yield and fresh foliage yield; moderate on harvest index, dry matter content, vigor, and tolerance to cassava mosaic disease, cassava bacterial blight, and cassava anthracnose diseases; and less severe on plant height. Further examination of S1 families using molecular markers showed varying levels of heterozygosity at several loci across the genome. In addition, the degree of heterozygosity and ID varied by the S1 family. The TMS 01/1371 family showed the lowest degree of heterozygosity and ID on the average performance of different agronomic attributes, indicating that inbreeding may be strategically explored in this family to increase genetic gain and identify useful recessive traits. Lastly, the observed depression from inbreeding was higher in seedling evaluation than in clonal evaluation trials for fresh root yield, fresh foliage yield, harvest index, and dry matter content. Conclusion S1 individuals showing relatively low heterozygosity based on SNP data were selected as parents to advance genetic gain in cassava. Generally, reduced heterozygosity was prominent in traits with severe ID impacts on average phenotypic performance. Our results highlight the relative importance of exploring non-additive genetic effects and transgressive segregations for favorable allele combinations in cassava improvement.

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions.

Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly.

Machine learning for genomic and pedigree prediction in sugarcane.

Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome prediction (GP) models of crops with highly heterozygous polyploid genomes. This study incorporates non-additive genetic effects and pedigree information using machine learning methods to track sugarcane breeding lines and enhance the prediction by assessing the degree of association between genotypes. This study measured the stalk biomass and sugar content of 297 clones from 87 families within a breeding population used in the Japanese sugarcane breeding program. Subsequently, we conducted analyses based on the marker genotypes of 33,149 single-nucleotide polymorphisms. To validate the accuracy of GP in the population, we first predicted the prediction accuracy of the best linear unbiased prediction (BLUP) based on a genomic relationship matrix. Prediction accuracy was assessed using two different cross-validation methods: repeated 10-fold cross-validation and leave-one-family-out cross-validation. The accuracy of GP of the first and second methods ranged from 0.36 to 0.74 and 0.15 to 0.63, respectively. Next, we compared the prediction accuracy of BLUP and two machine learning methods: random forests and simulation annealing ensemble (SAE), a newly developed machine learning method that explicitly models the interaction between variables. Both pedigree and genomic information were utilized as input in these methods. Through repeated 10-fold cross-validation, we found that the accuracy of the machine learning methods consistently surpassed that of BLUP in most cases. In leave-one-family-out cross-validation, SAE demonstrated the highest accuracy among the methods. These results underscore the effectiveness of GP in Japanese sugarcane breeding and highlight the significant potential of machine learning methods.

Discovering non-additive heritability using additive GWAS summary statistics.

LD score regression (LDSC) is a method to estimate narrow-sense heritability from genome-wide association study (GWAS) summary statistics alone, making it a fast and popular approach. In this work, we present interaction-LD score (i-LDSC) regression: an extension of the original LDSC framework that accounts for interactions between genetic variants. By studying a wide range of generative models in simulations, and by re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals in the UK Biobank and up to 159,095 individuals in BioBank Japan, we show that the inclusion of a cis-interaction score (i.e. interactions between a focal variant and proximal variants) recovers genetic variance that is not captured by LDSC. For each of the 25 traits analyzed in the UK Biobank and BioBank Japan, i-LDSC detects additional variation contributed by genetic interactions. The i-LDSC software and its application to these biobanks represent a step towards resolving further genetic contributions of sources of non-additive genetic effects to complex trait variation.

Simulation of functional additive and non-additive genetic effects using statistical estimates from quantitative genetic models

Stochastic simulation software is commonly used to aid breeders designing cost-effective breeding programs and to validate statistical models used in genetic evaluation. An essential feature of the software is the ability to simulate populations with desired genetic and non-genetic parameters. However, this feature often fails when non-additive effects due to dominance or epistasis are modeled, as the desired properties of simulated populations are estimated from classical quantitative genetic statistical models formulated at the population level. The software simulates underlying functional effects for genotypic values at the individual level, which are not necessarily the same as effects from statistical models in which dominance and epistasis are included. This paper provides the theoretical basis and mathematical formulas for the transformation between functional and statistical effects in such simulations. The transformation is demonstrated with two statistical models analyzing individual phenotypes in a single population (common in animal breeding) and plot phenotypes of three-way hybrids involving two inbred populations (observed in some crop breeding programs). We also describe different methods for the simulation of functional effects for additive genetics, dominance, and epistasis to achieve the desired levels of variance components in classical statistical models used in quantitative genetics.

Studies on Heterozsis Breeding in Brinjal Germplasm for Growth and Yield Traits, Solanum melongena L.

Brinjal, Solanum melongena L. is one of the commercial annual vegetables grown all over India for its edible fruits. Heterosis in vegetable crops helps to exploit the superior hybrids based on its phenotypic superiority over their parents and indicates predominance of non-additive genetic effects. This research was carried out at Department of Vegetable Science, Horticultural College and Research Institute, Periyakulam. The study involves six parents and thirty hybrids laid out in Randomised Block Design with two replications. The cross CO2 × Kothampatti Kathiri, obtained significant positive heterosis (16.53%) for plant height over better parent. Significant heterosis over mid parent was observed for number of branches in Kothampatti Kathiri × Odavai Pachai Kathiri, 33.33%, fruit width in Dharmapuri Oodha Kathiri × Poiyur Purple Kathiri, 25.54%, fruit length in Kothampatti Kathiri × Gobi Pachai Kathiri, 24.33%. Significant negative heterosis over better parent for number of days to 50% flowering was observed in the cross Odavai Pachai Kathiri × Gobi Pachai Kathiri, -9.25%. Significant positive heterosis over standard check was obtained for number of fruit per plant, 35.42% in the cross Dharmapuri Oodha Kathiri × CO2 and fruit yield per plant in Dharmapuri Oodha Kathiri × Odavai Pachai Kathiri, 21.54%. Heterosis exploited could be utilized in further crop improvement program in the development of a superior hybrid.

Detecting directional epistasis and dominance from cross-line analyses in alpine populations of Arabidopsis thaliana.

The contribution of non-additive genetic effects to the genetic architecture of fitness and to the evolutionary potential of populations has been a topic of theoretical and empirical interest for a long time. Yet, the empirical study of these effects in natural populations remains scarce, perhaps because measuring dominance and epistasis relies heavily on experimental line crosses. In this study, we explored the contribution of dominance and epistasis in natural alpine populations of Arabidopsis thaliana for 2 fitness traits, the dry biomass and the estimated number of siliques, measured in a greenhouse. We found that, on average, crosses between inbred lines of A. thaliana led to mid-parent heterosis for dry biomass but outbreeding depression for an estimated number of siliques. While heterosis for dry biomass was due to dominance, we found that outbreeding depression for an estimated number of siliques could be attributed to the breakdown of beneficial epistatic interactions. We simulated and discussed the implication of these results for the adaptive potential of the studied populations, as well as the use of line-cross analyses to detect non-additive genetic effects.

Evaluation of non-additive genetic effects on carcass and meat quality traits in Korean Hanwoo cattle using genomic models

The traditional genetic evaluation methods generally consider additive genetic effects only and often ignore non-additive (dominance and epistasis) effects that may have contributed to genetic variation of complex traits of livestock species. The available dense single nucleotide polymorphisms (SNPs) panels offer to investigate the potential benefits of including non-additive genetic effects in the genomic evaluation models. Data from 16 971 genotyped (Illumina Bovine 50 K SNP chip) Korean Hanwoo cattle were used to estimate genetic variance components and prediction accuracy of genomic breeding values (GEBVs) for four carcass and meat quality traits: carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS). Five different genetic models were evaluated through including additive, dominance and epistatic interactions (additive by additive, A × A; additive by dominance, A × D and dominance by dominance, D × D) successively in the models. The estimates of additive genetic variances and narrow sense heritabilities (ha2) were found similar across the evaluated models and traits except when additive interaction (A × A) was included. The dominance variance estimates relative to phenotypic variance ranged from 1.7–3.4% for CWT and MS traits, whereas, they were close to zero for EMA and BFT traits. The magnitude of A × A epistatic heritability (haa2) ranged between 14.8 and 27.7% in all traits. However, heritability estimates for A × D and D × D epistatic interactions (had2 and hdd2) were quite low compared to haa2 and were contributed only 0.0–9.7% of the total phenotypic variation. In general, broad sense heritability (hG2) estimates were almost twice (ranging between 0.54 and 0.68) the ha2 for all of the investigated traits. The inclusion of dominance effects did not improve the prediction accuracy of GEBV but improved 2.0–3.0% when epistatic effects were included in the model. More importantly, rank correlation revealed that partitioning of variance components considering dominance and epistatic effects in the model would enable to re-rank of top animals with better prediction of GEBV. The present result suggests that dominance and epistatic effects could be included in the genomic evaluation model for better estimates of variance components and more accurate prediction of GEBV for carcass and meat quality traits in Korean Hanwoo cattle.

The Combining Ability and Heterosis Analysis of Sweet-Waxy Corn Hybrids for Yield-Related Traits and Carotenoids.

Improving sweet-waxy corn hybrids enriched in carotenoids via a hybrid breeding approach may provide an alternative cash crop for growers and provide health benefits for consumers. This study estimates the combining ability and heterosis of sweet-waxy corn hybrids for yield-related traits and carotenoids. Eight super sweet corn and three waxy corn lines were crossed to generate 24 F1 hybrids according to the North Carolina Design II scheme, and these hybrids were evaluated across two seasons of 2021/22. The results showed that both additive and non-additive genetic effects were involved in expressing the traits, but the additive genetic effect was more predominant. Most observed traits exhibited moderate to high narrow-sense heritability. Three parental lines, namely the ILS2 and ILS7 females and the ILW1 male, showed the highest positive GCA effects on yield-related traits, making them desirable for developing high-yielding hybrids. Meanwhile, five parental lines, namely the ILS3, ILS5, and ILS7 females and the ILW1 and ILW2 males, were favorable general combiners for high carotenoids. A tested hybrid, ILS2 × ILW1, was a candidate biofortified sweet-waxy corn hybrid possessing high yields and carotenoids. Heterosis and per se performance were more positively correlated with GCAsum than SCA, indicating that GCAsum can predict heterosis for improving biofortified sweet-waxy corn hybrid enriched in carotenoids. The breeding strategies of biofortified sweet-waxy corn hybrids with high yield and carotenoid content are discussed.

Scald resistance in hybrid rye (Secale cereale): genomic prediction and GWAS.

Rye (Secale cereale L.) is an important cereal crop used for food, beverages, and feed, especially in North-Eastern Europe. While rye is generally more tolerant to biotic and abiotic stresses than other cereals, it still can be infected by several diseases, including scald caused by Rhynchosporium secalis. The aims of this study were to investigate the genetic architecture of scald resistance, to identify genetic markers associated with scald resistance, which could be used in breeding of hybrid rye and to develop a model for genomic prediction for scald resistance. Four datasets with records of scald resistance on a population of 251 hybrid winter rye lines grown in 2 years and at 3 locations were used for this study. Four genomic models were used to obtain variance components and heritabilities of scald resistance. All genomic models included additive genetic effects of the parental components of the hybrids and three of the models included additive-by-additive epistasis and/or dominance effects. All models showed moderate to high broad sense heritabilities in the range of 0.31 (SE 0.05) to 0.76 (0.02). The model without non-additive genetic effects and the model with dominance effects had moderate narrow sense heritabilities ranging from 0.24 (0.06) to 0.55 (0.08). None of the models detected significant non-additive genomic variances, likely due to a limited data size. A genome wide association study was conducted to identify markers associated with scald resistance in hybrid winter rye. In three datasets, the study identified a total of twelve markers as being significantly associated with scald resistance. Only one marker was associated with a major quantitative trait locus (QTL) influencing scald resistance. This marker explained 11-12% of the phenotypic variance in two locations. Evidence of genotype-by-environment interactions was found for scald resistance between one location and the other two locations, which suggested that scald resistance was influenced by different QTLs in different environments. Based on the results of the genomic prediction models and GWAS, scald resistance seems to be a quantitative trait controlled by many minor QTL and one major QTL, and to be influenced by genotype-by-environment interactions.

Genomic dissection of additive and non-additive genetic effects and genomic prediction in an open-pollinated family test of Japanese larch

Genomic dissection of genetic effects on desirable traits and the subsequent use of genomic selection hold great promise for accelerating the rate of genetic improvement of forest tree species. In this study, a total of 661 offspring trees from 66 open-pollinated families of Japanese larch (Larix kaempferi (Lam.) Carrière) were sampled at a test site. The contributions of additive and non-additive effects (dominance, imprinting and epistasis) were evaluated for nine valuable traits related to growth, wood physical and chemical properties, and competitive ability using three pedigree-based and four Genomics-based Best Linear Unbiased Predictions (GBLUP) models and used to determine the genetic model. The predictive ability (PA) of two genomic prediction methods, GBLUP and Reproducing Kernel Hilbert Spaces (RKHS), was compared. The traits could be classified into two types based on different quantitative genetic architectures: for type I, including wood chemical properties and Pilodyn penetration, additive effect is the main source of variation (38.20-67.46%); for type II, including growth, competitive ability and acoustic velocity, epistasis plays a significant role (50.76-91.26%). Dominance and imprinting showed low to moderate contributions (< 36.26%). GBLUP was more suitable for traits of type I (PAs = 0.37–0.39 vs. 0.14–0.25), and RKHS was more suitable for traits of type II (PAs = 0.23–0.37 vs. 0.07–0.23). Non-additive effects make no meaningful contribution to the enhancement of PA of GBLUP method for all traits. These findings enhance our current understanding of the architecture of quantitative traits and lay the foundation for the development of genomic selection strategies in Japanese larch.

Evaluation of genomic prediction considering non-additive genetic effects on fatty acid traits of Japanese Black cattle.

Genomic prediction was conducted using 2494 Japanese Black cattle from Hiroshima Prefecture and both single-nucleotide polymorphism information and phenotype data on monounsaturated fatty acid (MUFA) and oleic acid (C18:1) analyzed with gas chromatography. We compared the prediction accuracy for four models (A, additive genetic effects; AD, as for A with dominance genetic effects; ADR, as for AD with the runs of homozygosity (ROH) effects calculated by ROH-based relationship matrix; and ADF, as for AD with the ROH-based inbreeding coefficient of the linear regression). Bayesian methods were used to estimate variance components. The narrow-sense heritability estimates for MUFA and C18:1 were 0.52-0.53 and 0.57, respectively; the corresponding proportions of dominance genetic variance were 0.04-0.07 and 0.04-0.05, and the proportion of ROH variance was 0.02. The deviance information criterion values showed slight differences among the models, and the models provided similar prediction accuracy.

STUDY OF PHENOTYPIC AND GENETIC VARIABILITY IN MAIZE CROSSINGS BETWEEN CYCLE I INBRED LINES AND ELITE LINES (CYCLE II)

Maize inbred lines derived from local populations (Cycle I lines) were tested using cycle II elite inbred lines. In the cross system, hybrids have a wide variability for yield (6601-10571 kg/ha), the highest differences between hybrids are due to the non-additive genetic effects; undesirable traits transmitted by cycle I inbred lines are weak resistance to stalk breaking and fallen plants; for the percentage of unbroken plants, the genetic variability is high, the percentage of unbroken plants for simple hybrids has a range value between 65.02% and 89.70%.There were calculated the correlations coefficients between ”per se” values for the correlation of inbred lines from crossbreeding system and additive genetic effects values calculated in the crossbreeding system ”m x n”. Positive correlations were identified between the values per se and the positive genetic effects for yield, ear weight, ear length, numbers of kernels per ear, TKW, kernel depth. These high positive correlation values indicate the possibility to use phenotypic markers in the process of inbred lines creation.

Investigating the impact of non-additive genetic effects in the estimation of variance components and genomic predictions for heat tolerance and performance traits in crossbred and purebred pig populations

BackgroundNon-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows.ResultsIncluding non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95–69.59% of the total genetic variance.ConclusionsIncluding non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs.

Genetic parameters, reciprocal cross differences, and age-related heterosis of egg-laying performance in chickens

BackgroundEgg-laying performance is economically important in poultry breeding programs. Crossbreeding between indigenous and elite commercial lines to exploit heterosis has been an upward trend in traditional layer breeding for niche markets. The objective of this study was to analyse the genetic background and to estimate the heterosis of longitudinal egg-laying traits in reciprocal crosses between an indigenous Beijing-You and an elite commercial White Leghorn layer line. Egg weights were measured for the first three eggs, monthly from 28 to 76 weeks of age, and at 86 and 100 weeks of age. Egg quality traits were measured at 32, 54, 72, 86, and 100 weeks of age. Egg production traits were measured from the start of lay until 43, 72, and 100 weeks of age. Heritabilities and phenotypic and genetic correlations were estimated. Heterosis was estimated as the percentage difference of performance of a crossbred from that of the parental average. Reciprocal cross differences were estimated as the difference between the reciprocal crossbreds as a percentage of the parental average.ResultsEstimates of heritability of egg weights ranged from 0.29 to 0.75. Estimates of genetic correlations between egg weights at different ages ranged from 0.72 to 1.00. Estimates of heritability for cumulative egg numbers until 43, 72, and 100 weeks of age were around 0.15. Estimates of heterosis for egg weight and cumulative egg number increased with age, ranging from 1.0 to 9.0% and from 1.4 to 11.6%, respectively. From 72 to 100 weeks of age, crossbreds produced more eggs per week than the superior parent White Leghorn (3.5 eggs for White Leghorn, 3.8 and 3.9 eggs for crossbreds). Heterosis for eggshell thickness ranged from 2.7 to 6.6% when using Beijing-You as the sire breed. No significant difference between reciprocal crosses was observed for the investigated traits, except for eggshell strength at 54 weeks of age.ConclusionsThe heterosis was substantial for egg weight and cumulative egg number, and increased with age, suggesting that non-additive genetic effects are important in crossbreds between the indigenous and elite breeds. Generally, the crossbreds performed similar to or even outperformed the commercial White Leghorns for egg production persistency.

A novel open pollinated seed production strategy to exploit both additive and non-additive genetic effects in Eucalyptus dunnii

Eucalyptus dunnii, an important plantation species, is deployed either as clones or open-pollinated (OP) seed. OP seed lots are mainly sourced from seed orchards, where the mothers are known and the pollen is an uncontrolled mix of orchard genotypes. Seed Energy’s current E. dunnii clonal seed orchards have been established using genotypes selected for their general combining ability (GCA), an approach that exploits additive genetic effects. OP progeny trials of the seed orchard parents have been used to increase genetic gain deployed via backward selection. The best parents have demonstrated growth gains of up to 19.3% over unimproved native forest collections. Evaluation of control pollinated (CP) progeny trials, established from crosses of the best seed orchard parents, have shown growth traits to be under a degree of non-additive genetic control. The presence of non-additive variance in these traits suggests that additional genetic gain can be made by specific combinations of mother and pollen parent, known as specific combining ability (SCA). Capturing and deploying the gains from GCA and SCA effects into plantations at commercial scale currently require capital intensive clonal propagation programmes. Here we present a new strategy to exploit both additive and non-additive genetic effects through a novel open-pollinated seed orchard design called micro-orchards. This strategy aims to increase the frequency of the best performing combinations of mother and pollen in an OP environment. These orchards are structured with a central mother genotype tree initially surrounded by ten pollen parent genotypes, which is replicated in a repeated block design. The pollen parents are culled over several years to retain only the best specific combinations with the mother genotype. The culling of pollen parents is based on the results of iterative genetic evaluations of data from the CP trials, which contain families of the mother-pollen combinations present in the micro-orchards. The number of pollen parents culled increases with trial assessment age, concluding with a cull utilising age 6 trial data. The cost and productivity of this strategy allow gains from GCA and SCA effects to be accessible to a significant portion of the Uruguayan market at lower costs and complexity than operating a clonal propagation program.