Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis

Key message Including additive and additive-by-additive epistasis in a NOIA parametrization did not yield orthogonal partitioning of genetic variances, nevertheless, it improved predictive ability in a leave-one-out cross-validation for wheat grain yield. Abstract Additive-by-additive epistasis is the principal non-additive genetic effect in inbred wheat lines and is potentially useful for developing cultivars based on total genetic merit; nevertheless, its practical benefits have been highly debated. In this article, we aimed to (i) evaluate the performance of models including additive and additive-by-additive epistatic effects for variance components (VC) estimation of grain yield in a wheat-breeding population, and (ii) to investigate whether including additive-by-additive epistasis in genomic prediction enhance wheat grain yield predictive ability (PA). In total, 2060 sixth-generation (F6) lines from Nordic Seed A/S breeding company were phenotyped in 21 year-location combinations in Denmark, and genotyped using a 15 K-Illumina-BeadChip. Three models were used to estimate VC and heritability at plot level: (i) “I-model” (baseline), (ii) “I + GA-model”, extending I-model with an additive genomic effect, and (iii) “I + GA + GAA-model”, extending I + GA-model with an additive-by-additive genomic effects. The I + GA-model and I + GA + GAA-model were based on the Natural and Orthogonal Interactions Approach (NOIA) parametrization. The I + GA + GAA-model failed to achieve orthogonal partition of genetic variances, as revealed by a change in estimated additive variance of I + GA-model when epistasis was included in the I + GA + GAA-model. The PA was studied using leave-one-line-out and leave-one-breeding-cycle-out cross-validations. The I + GA + GAA-model increased PA significantly (16.5%) compared to the I + GA-model in leave-one-line-out cross-validation. However, the improvement due to including epistasis was not observed in leave-one-breeding-cycle-out cross-validation. We conclude that epistatic models can be useful to enhance predictions of total genetic merit. However, even though we used the NOIA parameterization, the variance partition into orthogonal genetic effects was not possible.

Genotyping marker density reduction is not an effective approach in long-term prediction-based breeding of cross-pollinated crops

Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computational time-consumption and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles, but not for the long term. Moreover, to the best of our knowledge, no GS models consider the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are an important and unexplored issue in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models (additive, additive-dominant, general (GCA), and specific combining ability (SCA)). Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additive-dominant or SCA for reciprocal.

Genomic Regions Associated with the Position and Number of Hair Whorls in Horses

The position and number of hair whorls have been associated with the behavior, temperament, and laterality of horses. The easy observation of whorls assists in the prediction of reactivity, and thus permits the development of better measures of handling, training, mounting, and riding horses. However, little is known about the genetics involved in the formation of hair whorls. Therefore, the aim of this study was to perform a genome-wide association analysis to identify chromosome regions and candidate genes associated with hair whorl traits. Data from 342 Quarter Horses genotyped for approximately 53,000 SNPs were used in an association study using a single-step procedure. The following traits were analyzed: vertical position of hair whorl on the head, number of whorls on the head, and number of whorls on the left and right sides of the neck. The traits had between one and three genomic windows associated. Each of them explained at least 4% of the additive variance. The windows accounted for 20–80% of additive variance for each trait analyzed. Many of the prospected genes are related to hair follicle growth. Some of these genes exert a pleiotropic effect on neurological and behavioral traits. This is the first indication of biological and physiological activity that might explain the association of hair whorls and temperament.

Significance of Linkage Disequilibrium and Epistasis on the Genetic Variances in Non- Inbred and Inbred Populations

Background The influence of linkage disequilibrium (LD), epistasis, and inbreeding on the genotypic variance continues to be an important area of investigation in genetics and evolution. Although the current knowledge about biological pathways and gene networks imply that epistasis is important in determining quantitative traits, the empirical evidence for a range of species and traits is that the genetic variance is most additive. This is confirmed by some recent theoretical studies. However, because these investigations have assumed linkage equilibrium, only additive effects, or simplified assumptions for the two- and high-order epistatic effects, the objective of this investigation was to provide additional information about the impact of LD and epistasis on the genetic variances in non-inbred and inbred populations, using a simulated data set.Results The epistatic variance in generation 0 corresponded to 1 to 10% of the genotypic variance, with 30% of epistatic genes, but it corresponded to 5 to 45% assuming 100% of epistatic genes. After 10 generations of random cross or selfing the ratio epistatic variance/genotypic variance increased in the range of 15 to 1,079%. The epistatic variances are maximized assuming dominant epistasis, duplicate genes with cumulative effects, and non-epistatic gene interaction. A minimization occurs with complementary, recessive, and dominant and recessive epistasis. In non-inbred populations, the genetic covariances have negligible magnitude compared with the genetic variances. In inbred populations, excepting for duplicate epistasis, the sum of the epistatic covariances was in general negative and with magnitude higher than the non-additive variances, especially under 100% of epistatic genes.Conclusions The LD level for genes, even under a relatively low gene density, has a significant effect on the genetic variances in non-inbred and inbred populations. Assuming digenic epistasis, the additive variance is in general the most important component of the genotypic variance in non-inbred and inbred populations. The ratio epistatic variance/genotypic variance is proportional to the percentage of interacting genes and increases with random cross and selfing. In general, the additive x additive variance is the most important component of the epistatic variance. The maximization of the epistatic variance depends on the allele frequency, LD level, and epistasis type.

DYNAMICS OF HERITABILITY IN DIFFERENT CHARACTERS OF LETTUCE

ABSTRACT The lack of knowledge of the genetic parameters and the type of gene action results in difficulties to obtain varieties that combine agronomic and functional characteristics. The objective of this study was to determine the type of gene action associated with the contents of chlorophyll and agronomic characteristics in lettuce populations. The research was conducted at the Experimental Station of Vegetables, Monte Carmelo, Brazil. To perform the crosses and obtain the generations P1, P2, F1, F2, Bc1 and Bc2 the genotypes UFU-Albina#2, UFU-Albina#3, UFU-Lisa124#2#1, UFU-Crespa199#1#1 and UFU-Lisa217#5#2 were used as parents. The generations were evaluated for three characteristics: number of leaves, plant diameter and chlorophyll content. The parameters: genetic, phenotypic, environmental and additive variance, the dominance in F2, heritability in the broad and narrow-sense, the average degree of dominance based on averages, number of genes, the measure of the dominance deviations, additive effects and all interactions of ‘additive x additive’, ‘additive x dominant’ and ‘dominant x dominant’ type, were evaluated. Chlorophyll content was the most influenced by the environment. Plant diameter had the highest narrow-sense heritability for the cross UFU-Lisa-124#2#1 x UFU-Albina#3 (82.1%). The number of genes for the number of leaves varied from inconclusive to a minimum of 9 genes. The gene interactions for number of leaves were of overdominance type, while for plant diameter and for chlorophyll content the gene interactions were of partial dominance. The characteristics are suggested as being of polygenic or oligogenic nature.

Characterization of Runs of Homozygosity, Heterozygosity-Enriched Regions, and Population Structure in Cattle Populations Selected for Different Breeding Goals

Background: A decline in the level of genetic diversity can result in reduced response to selection, greater incidence of genetic defects, and inbreeding depression. In this context, some metrics have been proposed to assess the levels of populational genetic diversity in selected populations. The main goals of this study were to: 1) investigate the population structure of 16 cattle populations from 15 different pure breeds or composite populations, which have been selected for different breeds goals; and, 2) identify and compare runs of homozygosity (ROH) and heterozygosity-enriched regions (HER) based on different single nucleotide polymorphism (SNP) panels and whole-genome sequence data (WGS), followed by functional genomic analyses. Results: A total of 24,187 ROH were found across all cattle populations, with 55% classified in the 2-4 Mb size group. Fourteen homozygosity islands were found in five populations, where four islands located on BTA1, BTA5, BTA16, and BTA19 overlapped between the Brahman (BRM) and Gyr (GIR) breeds. A functional analysis of the genes found in these islands revealed candidate genes known to play a role in the melanogenesis, prolactin signaling, and calcium signaling pathways. The correlations between inbreeding metrics ranged from 0.02 to 0.95, where the methods based on homozygous genotypes (FHOM), uniting of gametes (FUNI), and genotype additive variance (FGRM) showed strong correlations among them. All methods yielded low to moderate correlations with the inbreeding coefficients based on runs of homozygosity (FROH). For the HER, 3,576 runs and 26 islands, distributed across all autosomal chromosomes, were found in regions containing genes mainly related to the immune system. Although the analyses with WGS did not enable detection of the same island patterns, it unraveled novel regions not captured when using SNP panel data.Conclusions: The cattle populations that showed the largest amount of ROH and HER were Senepol (SEN) and Montana (MON), respectively. Overlapping ROH islands were identified between GIR and BRM breeds, indicating a possible historical connection between the populations. The distribution and pattern of ROH and HER are population specific, indicating that different breeds have experienced divergent selection processes or different genetic processes.

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

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.

Significance of Linkage Disequilibrium and Epistasis on the Genetic Variances in Non-Inbred and Inbred Populations

Background The influence of linkage disequilibrium (LD), epistasis, and inbreeding on the genotypic variance continues to be an important area of investigation in genetics and evolution. Although the current knowledge about biological pathways and gene networks imply that epistasis is important in determining quantitative traits, the empirical evidence for a range of species and traits is that the genetic variance is most additive. This is confirmed by some recent theoretical studies. However, because these investigations have assumed linkage equilibrium, only additive effects, or simplified assumptions for the two- and high-order epistatic effects, the objective of this investigation was to provide additional information about the impact of LD and epistasis on the genetic variances in non-inbred and inbred populations, using a simulated data set.Results The epistatic variance in generation 0 corresponded to 1 to 10% of the genotypic variance, with 30% of epistatic genes, but it corresponded to 5 to 45% assuming 100% of epistatic genes. After 10 generations of random cross or selfing the ratio epistatic variance/genotypic variance increased in the range of 15 to 1,079%. The epistatic variances are maximized assuming dominant epistasis, duplicate genes with cumulative effects, and non-epistatic gene interaction. A minimization occurs with complementary, recessive, and dominant and recessive epistasis. In non-inbred populations, the genetic covariances have negligible magnitude compared with the genetic variances. In inbred populations, excepting for duplicate epistasis, the sum of the epistatic covariances was in general negative and with magnitude higher than the non-additive variances, especially under 100% of epistatic genes.Conclusions The LD level for genes, even under a relatively low gene density, has a significant effect on the genetic variances in non-inbred and inbred populations. Assuming digenic epistasis, the additive variance is in general the most important component of the genotypic variance in non-inbred and inbred populations. The ratio epistatic variance/genotypic variance is proportional to the percentage of interacting genes and increases with random cross and selfing. In general, the additive x additive variance is the most important component of the epistatic variance. The maximization of the epistatic variance depends on the allele frequency, LD level, and epistasis type.

Genetic Variance, Heritability and Number of Effective Genes for Spike Characters in Castor (Ricinus communis L.)

Adequate information on genetic variance and nature of gene actions for yield component characters are very crucial for a successful breeding programme. In this study, genetic variance, heritability and number of effective genes were estimated for Some Spike Characters in Castor. Bi-parental crosses were made among six castor lines at the National Cereals Research Institute (NCRI), Badeggi, Nigeria. Forty five (45) plant samples of parents and F1, 120 F2 and 60 BC1 and BC2 of the three crosses were evaluated for estimation of the genetic variance, heritability and number of effective genes for the traits studied. The results revealed significant variability among the generations for the genetic analysis. The results showed that the magnitude of the environmental variance for all the traits in all the crosses was lesser than both additive and dominance variances except for the number of capsules per raceme in the Cross II. High heritability (h2> 60%), high magnitude of additive variance compared to dominance, high genetic advance (GA > 20%) were observed for total raceme length in all the crosses. Predominant dominance variance, low heritability and moderate average genetic advance (17.69%) were recorded in the crosses for the seed yield per plant. Number of capsules per raceme (CPR) was controlled by 4.47 to 20.96 genes among the crosses. Seed weight (SW) was controlled by a range of 3.13 to 25.16 effective genes. For seed yield (SY), a range from 27.50 to 52.29 effective genes was observed. These findings provide a guide on choice of selection procedures for the improvement of the traits studied.

Gene action, genetic variation, and GWAS: A user-friendly web tool

Fisher’s partitioning of genotypic values and genetic variance is highly relevant in the current era of genome-wide association studies (GWASs). However, despite being more than a century old, a number of persistent misconceptions related to nonadditive genetic effects remain. We developed a user-friendly web tool, the Falconer ShinyApp, to show how the combination of gene action and allele frequencies at causal loci translate to genetic variance and genetic variance components for a complex trait. The app can be used to demonstrate the relationship between a SNP effect size estimated from GWAS and the variation the SNP generates in the population, i.e., how locus-specific effects lead to individual differences in traits. In addition, it can also be used to demonstrate how within and between locus interactions (dominance and epistasis, respectively) usually do not lead to a large amount of nonadditive variance relative to additive variance, and therefore, that these interactions usually do not explain individual differences in a population.