High-throughput field phenotyping reveals that selection in breeding has affected the phenology and temperature response of wheat in the stem elongation phase.

Crop growth and phenology are driven by seasonal changes in environmental variables, with temperature as one important factor. However, knowledge about genotype-specific temperature response and its influence on phenology is limited. Such information is fundamental to improve crop models and adapt selection strategies. We measured the increase in height of 352 European winter wheat varieties in 4 years to quantify phenology, and fitted an asymptotic temperature response model. The model used hourly fluctuations in temperature to parameterize the base temperature (Tmin), the temperature optimum (rmax), and the steepness (lrc) of growth responses. Our results show that higher Tmin and lrc relate to an earlier start and end of stem elongation. A higher rmax relates to an increased final height. Both final height and rmax decreased for varieties originating from the continental east of Europe towards the maritime west. A genome-wide association study (GWAS) indicated a quantitative inheritance and a large degree of independence among loci. Nevertheless, genomic prediction accuracies (GBLUPs) for Tmin and lrc were low (r≤0.32) compared with other traits (r≥0.59). As well as known, major genes related to vernalization, photoperiod, or dwarfing, the GWAS indicated additional, as yet unknown loci that dominate the temperature response.

Multiple-trait genomic prediction for swine meat quality traits using gut microbiome features as a correlated trait.

Traits such as meat quality and composition are becoming valuable in modern pork production; however, they are difficult to include in genetic evaluations because of the high phenotyping costs. Combining genomic information with multiple-trait indirect selection with cheaper indicator traits is an alternative for continued cost-effective genetic improvement. Additionally, gut microbiome information is becoming more affordable to measure using targeted rRNA sequencing, and its applications in animal breeding are becoming relevant. In this paper, we investigated the usefulness of microbial information as a correlated trait in selecting meat quality in swine. This study incorporated phenotypic data encompassing marbling, colour, tenderness, loin muscle and backfat depth, along with the characterization of gut (rectal) microbiota through 16S rRNA sequencing at three distinct time points of the animal's growth curve. Genetic progress estimation and cross-validation were employed to evaluate the utility of utilizing host genomic and gut microbiota information for selecting expensive-to-record traits in crossbred individuals. Initial steps involved variance components estimation using multiple-trait models on a training dataset, where the top 25 associated operational taxonomic units (OTU) for each meat quality trait and time point were included. The second step compared the predictive ability of multiple-trait models incorporating different numbers of OTU with single-trait models in a validation set. Results demonstrated the advantage of including genomic information for some traits, while in some instances, gut microbial information proved advantageous, namely, for marbling and pH. The study suggests further investigation into the shared genetic architecture between microbial features and traits, considering microbial data's compositional and high-dimensional nature. This research proposes a straightforward method to enhance swine breeding programs for improving costly-to-record traits like meat quality by incorporating gut microbiome information.

Multivariate analysis of herd structure and genetic resource indicators in seedstock beef cattle herds.

Genetic, environmental, technological and financial resources are used differently in cattle herds that participate in the same breeding programme. The percentages of calves sired by sires within their own herd or from external herds vary across herds, as do the intensities of use of reproductive biotechnologies. These divergences may be related to differences in the indicators of genetic performance for economic traits. The aim of this study was to determine the factors related to herd structure and genetic resource utilization that exert the greatest influence on the genetic merit of seedstock herds within a Nellore breeding programme. The database comprised 21 factors, along with genomic-enhanced expected progeny differences (GE-EPDs) for growth, reproductive and carcass traits, as well as a selection index of animals from 128 herds. By combining principal component analysis and cluster analysis, we were able to group the herds. We identified statistically significant differences (p < 0.05) in the mean values of the factors, GE-EPDs and genetic trends among the groups of herds. Differences in the percentage of sires from external herds and in sire age between the groups of herds were the factors most associated with differences in mean GE-EPDs and genetic trends. Using young sires from other herds or lineages is an effective strategy in animal breeding. By enhancing genetic variability, this approach does not only improve the genetic quality of herds but also accelerates genetic progress in desired traits over time. Therefore, to ensure the success of this strategy, it is crucial that seedstock herds undergo a thorough selection process aimed at maximizing the genetic potential of future generations of beef cattle.

Eco-Evolutionary Consequences of Dispersal Syndromes during Colonization in a Passerine Bird.

AbstractIn most animal species, dispersing individuals possess phenotypic attributes that mitigate the costs of colonization and/or increase settlement success in new areas (dispersal syndromes). This phenotypic integration likely affects population dynamics and the direction of selection, but data are lacking for natural populations. Using an approach that combines population dynamics, quantitative genetics, and phenotypic selection analyses, we reveal the existence of dispersal syndromes in a pied flycatcher (Ficedula hypoleuca) population in the Netherlands: immigrants were larger, tended to have darker plumage, bred earlier, and produced larger clutches than local recruits, and some of these traits were genetically correlated. Over time, the phenotypic profile of the population gradually changed: each generation advanced arrival and breeding and exhibited longer wings as a result of direct and indirect selection on these correlated traits. Although phenotypic attributes of immigrants were favored by selection during the early phase of colonization, observed phenotypic changes were similar for immigrants and local recruits. We propose that immigrants facilitated initial population establishment but that temporal changes likely resulted from climate change-induced large-scale selection. This study highlights that newly established populations are of nonrandom composition and that phenotypic architecture affects evolutionary population trajectories.

Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize.

BACKGROUND: Success in any genomic prediction platform is directly dependent on establishing a representative training set. This is a complex task, even in single-trait single-environment conditions and tends to be even more intricated wherein additional information from envirotyping and correlated traits are considered. Here, we aimed to design optimized training sets focused on genomic prediction, considering multi-trait multi-environment trials, and how those methods may increase accuracy reducing phenotyping costs. For that, we considered single-trait multi-environment trials and multi-trait multi-environment trials for three traits: grain yield, plant height, and ear height, two datasets, and two cross-validation schemes. Next, two strategies for designing optimized training sets were conceived, first considering only the genomic by environment by trait interaction (GET), while a second including large-scale environmental data (W, enviromics) as genomic by enviromic by trait interaction (GWT). The effective number of individuals (genotypes × environments × traits) was assumed as those that represent at least 98% of each kernel (GET or GWT) variation, in which those individuals were then selected by a genetic algorithm based on prediction error variance criteria to compose an optimized training set for genomic prediction purposes. RESULTS: The combined use of genomic and enviromic data efficiently designs optimized training sets for genomic prediction, improving the response to selection per dollar invested by up to 145% when compared to the model without enviromic data, and even more when compared to cross validation scheme with 70% of training set or pure phenotypic selection. Prediction models that include G × E or enviromic data + G × E yielded better prediction ability. CONCLUSIONS: Our findings indicate that a genomic by enviromic by trait interaction kernel associated with genetic algorithms is efficient and can be proposed as a promising approach to designing optimized training sets for genomic prediction when the variance-covariance matrix of traits is available. Additionally, great improvements in the genetic gains per dollar invested were observed, suggesting that a good allocation of resources can be deployed by using the proposed approach.

Low but significant evolutionary potential for growth, phenology and reproduction traits in European beech.

Local survival of forest tree populations under climate change depends on existing genetic variation and their adaptability to changing environments. Responses to selection were studied in European beech (Fagus sylvatica) under field conditions. A total of 1087 adult trees, seeds, 1-year-old seedlings and established multiyear saplings were genotyped with 16 nuSSRs. Adult trees were assessed for phenotypic traits related to growth, phenology and reproduction. Parentage and paternity analyses were used to estimate effective female and male fecundity as a proxy of fitness and showed that few parents contributed to successful regeneration. Selection gradients were estimated from the relationship between traits and fecundity, while heritability and evolvability were estimated using mixed models and the breeder's equation. Larger trees bearing more fruit and early male flowering had higher total fecundity, while trees with longer growth season had lower total fecundity (directional selection). Stabilizing selection on spring phenology was found for female fecundity, highlighting the role of late frosts as a selection driver. Selection gradients for other traits varied between measurement years and the offspring cohort used to estimate parental fecundity. Compared to other studies in natural populations, we found low to moderate heritability and evolvability for most traits. Response to selection was higher for growth than for budburst, leaf senescence or reproduction traits, reflecting more consistent selection gradients across years and sex functions, and higher phenotypic variability in the population. Our study provides empirical evidence suggesting that populations of long-lived organisms such as forest trees can adapt locally, even at short-time scales.

Functional colour genes and signals of selection in colour-polymorphic salamanders.

Coloration has been associated with multiple biologically relevant traits that drive adaptation and diversification in many taxa. However, despite the great diversity of colour patterns present in amphibians the underlying molecular basis is largely unknown. Here, we use insight from a highly colour-variable lineage of the European fire salamander (Salamandra salamandra bernardezi) to identify functional associations with striking variation in colour morph and pattern. The three focal colour morphs-ancestral black-yellow striped, fully yellow and fully brown-differed in pattern, visible coloration and cellular composition. From population genomic analyses of up to 4,702 loci, we found no correlations of neutral population genetic structure with colour morph. However, we identified 21 loci with genotype-phenotype associations, several of which relate to known colour genes. Furthermore, we inferred response to selection at up to 142 loci between the colour morphs, again including several that relate to coloration genes. By transcriptomic analysis across all different combinations, we found 196 differentially expressed genes between yellow, brown and black skin, 63 of which are candidate genes involved in animal coloration. The concordance across different statistical approaches and 'omic data sets provide several lines of evidence for loci linked to functional differences between colour morphs, including TYR, CAMK1 and PMEL. We found little association between colour morph and the metabolomic profile of its toxic compounds from the skin secretions. Our research suggests that current ecological and evolutionary hypotheses for the origins and maintenance of these striking colour morphs may need to be revisited.

Bayesian estimation of genetic variance and response to selection on linear or ratio traits of feed efficiency in dairy cattle.

This study aimed to estimate genetic parameters of the linear trait genetic residual feed intake (RFI) and the ratio traits feed conversion ratio (FCR) and feed conversion efficiency (FCE) along with dry matter intake (DMI) and energy sink traits such as energy-corrected milk (ECM), body weight (BW), body condition score (BCS), and BW change (BWC) across different weeks in the first lactation of Danish Holstein cows. A second objective was to conduct a Bayesian analysis of direct and correlated superiority of the selected group when selecting on genetic RFI, FCR, or FCE. Feed intake and energy sink traits were recorded during wk 1 to 44 of lactation on 847 primiparous Danish Holstein cows. A Bayesian multivariate random regression animal model was used to analyze DMI, ECM, BW, and BCS in different weeks of lactation. Genetic RFI was obtained by conditioning DMI on ECM, BW, BCS, and BWC using genetic partial regression coefficients. The posterior distribution of the breeding values for FCR and FCE was derived from the posterior distribution of functions of "fixed" environmental effects and random additive genetic effects on DMI and ECM. Genetic superiority of the selected group was defined as the difference in additive genetic mean of the selected top individuals expected to be potential parents, and the total population after integrating genetic trends out of the posterior distribution of selection responses. Posterior means of heritability of genetic RFI ranged from 0.10 to 0.15, genetic variance of FCR and FCE ranged from 2.13 × 10-3 to 3.2 × 10-3 (kg2 DMI/kg2 ECM) and 6.11 × 10-3 to 2.4 × 10-2 (kg2 ECM/kg2 DMI), respectively. Selection against RFI showed a direct response of -1.01 to -2.23 kg/d RFI and correlated responses of -0.031 to -0.056 kg/kg for FCR, 0.104 to 0.160 kg/kg for FCE, and -0.316 to -1.057 kg/d for DMI in different weeks of lactation. Selection against RFI had no significant effect on production traits but selection for ratio traits reduced BW and BCS. Posterior means of genetic correlation between DMI and ratio traits were low. In conclusion, the Bayesian procedure allowed us to estimate genetic RFI without the need for separate multiple regression analysis and considered the non-normal posterior distribution of ratio traits. Selection against genetic RFI might be an effective means to improve feed efficiency compared with ratio traits for feed efficiency in dairy cattle.

Experimental evolution of parasitic host manipulation.

Host manipulation is a parasite-induced alteration of a host's phenotype that increases parasite fitness. However, if genetically encoded in the parasite, it should be under selection in the parasite. Such host manipulation has often been assumed to be energetically costly, which should restrict its evolution. Evidence of such costs, however, remains elusive. The trophically transmitted cestode Schistocephalus solidus manipulates the activity of its first intermediate copepod host to reduce its predation susceptibility before the parasite is ready for transmission. Thereafter, S. solidus increases host activity to facilitate transmission to its subsequent fish host. I selected S. solidus for or against host manipulation over three generations to investigate the evolvability of manipulation and identify potential trade-offs. Host manipulation responded to selection, confirming that this trait is heritable in the parasite and hence can present an extended phenotype. Changes in host manipulation were not restrained by any obvious costs.

Opposite responses to selection and where to find them.

We generally expect traits to evolve in the same direction as selection. However, many organisms possess traits that appear to be costly for individuals, while plant and animal breeding experiments reveal that selection may lead to no response or even negative responses to selection. We formalize both of these instances as cases of "opposite responses to selection." Using quantitative genetic models for the response to selection, we outline when opposite responses to selection should be expected. These typically occur when social selection opposes direct selection, when individuals interact with others less related to them than a random member of the population, and if the genetic covariance between direct and indirect effects is negative. We discuss the likelihood of each of these occurring in nature and therefore summarize how frequent opposite responses to selection are likely to be. This links several evolutionary phenomena within a single framework.

Appropriate selection indices for functional traits in dairy cattle breeding schemes.

The objective of this study was to establish different single or multiple trait selection indices to calculate genetic and economic gains by combining some production, reproduction and udder health traits in a population similar to the overall practical situation in Iran, with and without imposing restrictions on genetic change for some traits. The SelAction software was used to perform the analyses based on selection index theory through a deterministic model. Results indicated that among established indices, the index that showed the highest genetic gain for milk yield did not maximize the total genetic and economic gains. Rather, the index that included all production, reproduction and udder health traits yielded the highest genetic and economic gains. When we placed restriction on the selection indices, the economic gain decreased and the amount of reduction depended on the heritability and the correlation of restricted trait(s) with other traits. Generally, regarding the economic genetic gain per generation, the indices based on records of 200 offspring were 4.819% more efficient than those that used information of 100 offspring.

Genetic evaluation of growth in farmers' flocks of Madras Red sheep under long-term selection in a group breeding scheme.

The Network Project on Sheep Improvement (NWPSI)-Madras Red field unit is a group breeding scheme involving 198 farmers' flocks of Madras Red sheep in which selection for growth traits and rotation of rams have been practised for over two decades. Growth data collected from these flocks were used to evaluate the performance and understand the direct and expected responses to selection based on genetic parameters. The body weight at birth (BW), weaning weight (WW), 6-month weight (6W), 9-month weight (9W), 12-month weight (YW), pre-weaning average daily gain (ADG1, birth to 3 months), post-weaning ADG2 (3-6 months), ADG3 (6-9 months), ADG4 (9-12 months) and ADG5 (3-12 months) were 2.67, 10.05, 14.56, 18.36, 21.36, 80.13, 49.05, 43.00, 34.21 and 41.18 g, respectively. Univariate analyses were carried out using animal and sire models to estimate variance components. Heritability obtained from animal model for BW was 0.36 and the values for other body weight traits were almost unity. Heritability estimate for pre-weaning ADG1 was 0.31. Very high genetic variability was observed in spite of long-term selection and this sustenance of variability is one of the main advantages of a group breeding scheme, combining several flocks of smaller size. An increasing genetic and phenotypic trend was noticed for almost all the traits studied. The expected responses calculated based on genetic parameters also indicated scope for improvement.

Social traits, social networks and evolutionary biology.

The social environment is both an important agent of selection for most organisms, and an emergent property of their interactions. As an aggregation of interactions among members of a population, the social environment is a product of many sets of relationships and so can be represented as a network or matrix. Social network analysis in animals has focused on why these networks possess the structure they do, and whether individuals' network traits, representing some aspect of their social phenotype, relate to their fitness. Meanwhile, quantitative geneticists have demonstrated that traits expressed in a social context can depend on the phenotypes and genotypes of interacting partners, leading to influences of the social environment on the traits and fitness of individuals and the evolutionary trajectories of populations. Therefore, both fields are investigating similar topics, yet have arrived at these points relatively independently. We review how these approaches are diverged, and yet how they retain clear parallelism and so strong potential for complementarity. This demonstrates that, despite separate bodies of theory, advances in one might inform the other. Techniques in network analysis for quantifying social phenotypes, and for identifying community structure, should be useful for those studying the relationship between individual behaviour and group-level phenotypes. Entering social association matrices into quantitative genetic models may also reduce bias in heritability estimates, and allow the estimation of the influence of social connectedness on trait expression. Current methods for measuring natural selection in a social context explicitly account for the fact that a trait is not necessarily the property of a single individual, something the network approaches have not yet considered when relating network metrics to individual fitness. Harnessing evolutionary models that consider traits affected by genes in other individuals (i.e. indirect genetic effects) provides the potential to understand how entire networks of social interactions in populations influence phenotypes and predict how these traits may evolve. By theoretical integration of social network analysis and quantitative genetics, we hope to identify areas of compatibility and incompatibility and to direct research efforts towards the most promising areas. Continuing this synthesis could provide important insights into the evolution of traits expressed in a social context and the evolutionary consequences of complex and nuanced social phenotypes.

Evolutionary response when selection and genetic variation covary across environments.

Although models of evolution usually assume that the strength of selection on a trait and the expression of genetic variation in that trait are independent, whenever the same ecological factor impacts both parameters, a correlation between the two may arise that accelerates trait evolution in some environments and slows it in others. Here, we address the evolutionary consequences and ecological causes of a correlation between selection and expressed genetic variation. Using a simple analytical model, we show that the correlation has a modest effect on the mean evolutionary response and a large effect on its variance, increasing among-population or among-generation variation in the response when positive, and diminishing variation when negative. We performed a literature review to identify the ecological factors that influence selection and expressed genetic variation across traits. We found that some factors - temperature and competition - are unlikely to generate the correlation because they affected one parameter more than the other, and identified others - most notably, environmental novelty - that merit further investigation because little is known about their impact on one of the two parameters. We argue that the correlation between selection and genetic variation deserves attention alongside other factors that promote or constrain evolution in heterogeneous landscapes.

Shared Selective Pressures on Fungal and Human Metabolic Pathways Lead to Divergent yet Analogous Genetic Responses.

Reduced metabolic efficiency, toxic intermediate accumulation, and deficits of molecular building blocks, which all stem from disruptions of flux through metabolic pathways, reduce organismal fitness. Although these represent shared selection pressures across organisms, the genetic signatures of the responses to them may differ. In fungi, a frequently observed signature is the physical linkage of genes from the same metabolic pathway. In contrast, human metabolic genes are rarely tightly linked; rather, they tend to show tissue-specific coexpression. We hypothesized that the physical linkage of fungal metabolic genes and the tissue-specific coexpression of human metabolic genes are divergent yet analogous responses to the range of selective pressures imposed by disruptions of flux. To test this, we examined the degree to which the human homologs of physically linked metabolic genes in fungi (fungal linked homologs or FLOs) are coexpressed across six human tissues. We found that FLOs are significantly more correlated in their expression profiles across human tissues than other metabolic genes. We obtained similar results in analyses of the same six tissues from chimps, gorillas, orangutans, and macaques. We suggest that when selective pressures remain stable across large evolutionary distances, evidence of selection in a given evolutionary lineage can become a highly reliable predictor of the signature of selection in another, even though the specific adaptive response in each lineage is markedly different.

Allelic diversity for neutral markers retains a higher adaptive potential for quantitative traits than expected heterozygosity.

The adaptive potential of a population depends on the amount of additive genetic variance for quantitative traits of evolutionary importance. This variance is a direct function of the expected frequency of heterozygotes for the loci which affect the trait (QTL). It has been argued, but not demonstrated experimentally, that long-term response to selection is more dependent on QTL allelic diversity than on QTL heterozygosity. Conservation programmes, aimed at preserving this variation, usually rely on neutral markers rather than on quantitative traits for making decisions on management. Here, we address, both through simulation analyses and experimental studies with Drosophila melanogaster, the question of whether allelic diversity for neutral markers is a better indicator of a high adaptive potential than expected heterozygosity. In both experimental and simulation studies, we established synthetic populations for which either heterozygosity or allelic diversity was maximized using information from QTL (simulations) or unlinked neutral markers (simulations and experiment). The synthetic populations were selected for the quantitative trait to evaluate the evolutionary potential provided by the two optimization methods. Our results show that maximizing the number of alleles of a low number of markers implies higher responses to selection than maximizing their heterozygosity.

The strength of assortative mating for flowering date and its basis in individual variation in flowering schedule.

Although it has been widely asserted that plants mate assortatively by flowering time, there is virtually no published information on the strength or causes of phenological assortment in natural populations. When strong, assortative mating can accelerate the evolution of plant reproductive phenology through its inflationary effect on genetic variance. We estimated potential assortative mating for flowering date in 31 old-field species in Ontario, Canada. For each species, we constructed a matrix of pairwise mating probabilities from the individual flowering schedules, that is the number of flower deployed on successive dates. The matrix was used to estimate the phenotypic correlation between mates, ρ, for flowering date. We also developed a measure of flowering synchrony within species, S, based upon the eigenstructure of the mating matrix. The mean correlation between pollen recipients and potential donors for flowering date was ρ=0.31 (range: 0.05-0.63). A strong potential for assortative mating was found among species with high variance in flowering date, flowering schedules of short duration and skew towards early flower deployment. Flowering synchrony, S, was negatively correlated with potential assortment (r= -0.49), but we go on to show that although low synchrony is a necessary condition for phenological assortative mating, it may not be sufficient to induce assortment for a given phenological trait. The potential correlation between mates showed no seasonal trend; thus, as climate change imposes selection on phenology through longer growing seasons, spring-flowering species are no more likely to experience an accelerated evolutionary response than summer species.

Prediction of response to truncated selection based on BLUP of breeding values and its prediction accuracy.

Three methods of predicting the response to truncated selection based on BLUP of breeding values (BVs) were compared under conditions in which the phenotypic values for the progenies of selected animals were not available. The following methods were used to predict the response to selection: (1) based on the mean of estimated breeding values (EBV) in the candidate population for selection ( ∆ g 1 $$ \Delta {\mathrm{g}}_1 $$ ), (2) based on the variance of EBV in the candidate population for selection ( ∆ g 2 $$ \Delta {\mathrm{g}}_2 $$ ), and (3) based on diagonal elements of the inverse matrix on the left-hand side of the mixed model equation ( ∆ g 3 $$ \Delta {\mathrm{g}}_3 $$ ). The deviation of the average BV of the selected animals from the average BV of the candidate population for selection was taken as the true response to selection. The pedigree information and phenotypic values used for comparison were generated by Monte Carlo computer simulation. The results showed that ∆ g 1 $$ \Delta {\mathrm{g}}_1 $$ had the smallest absolute mean error and ∆ g 2 $$ \Delta {\mathrm{g}}_2 $$ had the smallest root-mean-square error. We concluded that it is desirable to use ∆ g 1 $$ \Delta {\mathrm{g}}_1 $$ or ∆ g 2 $$ \Delta {\mathrm{g}}_2 $$ to predict the response to truncated selection based on BLUP of BVs. However, in the population where selection is ongoing, the prediction accuracy of selection response is likely to be affected by the distortion of the distribution and the Bulmer effect for ∆ g 2 $$ \Delta {\mathrm{g}}_2 $$ .

Prediction of additive genetic variances of descendants for complex families based on Mendelian sampling variances.

The ability to predict the outcome of selection and mating decisions enables breeders to make strategically better selection decisions. To improve genetic progress, those individuals need to be selected whose offspring can be expected to show high genetic variance next to high breeding values. Previously published approaches enable to predict the variance of descendants of two future generations for up to 4 founding haplotypes, or 2 outbred individuals, based on phased genotypes, allele effects and recombination frequencies. The purpose of this study was to develop a general approach for the analytical calculation of the genetic variance in any future generation. The core development is an equation for the prediction of the variance of double haploid lines, under the assumption of no selection and negligible drift, stemming from an arbitrary number of founder haplotypes. This double haploid variance can be decomposed into gametic Mendelian sampling variances (MSV) of ancestors of the double haploid lines allowing usage for non-double haploid genotypes which enables application in animal breeding programs as well as in plant breeding programs. Together with the breeding values of the founders, the gametic MSV may be used in new selection criteria. We present our idea of such a criterion that describes the genetic level of selected individuals in four generations. Since breeding programs do select, the assumption made for predicting variances is clearly violated which decreases the accuracy of predicted gametic MSV caused by changes in allele frequency and linkage disequilibrium. Despite violating the assumption, we found high predictive correlations of our criterion to the true genetic level which was obtained by means of simulation for the "corn" and "cattle" genome models tested in this study (0.90 and 0.97). In practice, the genotype phases, genetic map and allele effects all need to be estimated meaning inaccuracies in their estimation will lead to inaccurate variance prediction. Investigation of variance prediction accuracy when input parameters are estimated was not part of this study.

Realized Genetic Gain in Rice: Achievements from Breeding Programs.

Genetic improvement is crucial for ensuring food security globally. Indeed, plant breeding has contributed significantly to increasing the productivity of major crops, including rice, over the last century. Evaluating the efficiency of breeding strategies necessitates a quantification of this progress. One approach involves assessing the genetic gain achieved through breeding programs based on quantitative traits. This study aims to provide a theoretical understanding of genetic gain, summarize the major results of genetic gain studies in rice breeding, and suggest ways of improving breeding program strategies and future studies on genetic gain. To achieve this, we present the concept of genetic gain and the essential aspects of its estimation. We also provide an extensive literature review of genetic gain studies in rice (Oryza sativa L.) breeding programs to understand the advances made to date. We reviewed 29 studies conducted between 1999 and 2023, covering different regions, traits, periods, and estimation methods. The genetic gain for grain yield, in particular, showed significant variation, ranging from 1.5 to 167.6 kg/ha/year, with a mean value of 36.3 kg/ha/year. This translated into a rate of genetic gain for grain yield ranging from 0.1% to over 3.0%. The impact of multi-trait selection on grain yield was clarified by studies that reported genetic gains for other traits, such as plant height, days to flowering, and grain quality. These findings reveal that while breeding programs have achieved significant gains, further improvements are necessary to meet the growing demand for rice. We also highlight the limitations of these studies, which hinder accurate estimations of genetic gain. In conclusion, we offer suggestions for improving the estimation of genetic gain based on quantitative genetic principles and computer simulations to optimize rice breeding strategies.