Quantitative Genetic Information Research Articles

Sorghum Hybrids for Low‐Input Farming Systems in West Africa: Quantitative Genetic Parameters to Guide Hybrid Breeding

ABSTRACTThe development of sorghum [Sorghum bicolor (L.) Moench] hybrids with Guinea‐race parents is a promising approach to increase yields in West Africa (WA). There is heretofore no quantitative genetic information about the genetic value of new hybrids and their parents, or about the efficiency of alternative selection methods for targeting yield performance under the predominantly low‐input, P‐deficient production conditions. This study aims to estimate the heterosis levels attainable by new Guinea‐race hybrids and the combining abilities of the current suite of parents with partial‐ to full‐Guinea‐race backgrounds of contrasting geographic origins, and to determine the effectiveness of direct selection under low‐P (LP) relative to indirect selection under high‐P (HP) conditions. Single‐cross hybrids were evaluated in 2015 and 2016 for yield under both LP and HP conditions at two locations in Mali. The hybrid yields were substantially superior to farmers’ local Guinea‐race varieties, with 20 to 80% higher means in both LP and HP environments. Average midparent and better‐parent heterosis estimates were, respectively, 78 and 48% under HP, and 75 and 42% under LP. Direct selection for yield under LP was predicted to be 20 to 60% more effective than indirect selection under HP conditions. The combining ability estimates provided initial insights into the potential benefit of germplasm from more eastern and humid regions of WA for developing a restorer pool distinct and complementary to the female pool from Mali and surrounding countries. Substantial yield superiorities of hybrids over local varieties suggested that a hybrid breeding strategy based on Guinea germplasm could help improve WA smallholder farmers’ livelihood.

An 8-Year Breeding Program for Asian Seabass Lates calcarifer: Genetic Evaluation, Experiences, and Challenges.

Selective breeding for marine finfish is challenging due to difficulties in reproduction, larval rearing, and on-growth in captive environments. The farming of Asian seabass (Lates calcarifer) has all these problems and our knowledge of the quantitative genetic information (heritability and correlations) of traits necessary for commercial exploitation is poor. The present study was conducted to address this knowledge gap and to provide information that can be applied to sea bass and other aquaculture species. We carried out a comprehensive genetic evaluation for three traits (body weight, total length, and survival) collected from a breeding population for Asian seabass over an eight-year period from 2010 to 2017. Statistical analysis was carried out on 4,567 adult fish at 105, 180, 270, 360, 450, and 570 days post-hatch (dph). The heritabilities (h2) estimated for body weight and length using linear mixed model were moderate to high (0.12 to 0.78 and 0.41 to 0.85, respectively) and they differed between the measurement periods. Survival during grow-out phase was analyzed using threshold logistic and probit models. The heritability estimates for survival rate on the underlying liability scale () varied from 0.05 to 0.21. When the observed heritability obtained from the linear mixed model was back-transformed to the liability scale, they were similar but not significant. In addition, we examined effects of genotype by environment (G × E) interaction on body traits. The genetic correlation for body weight between tank and sea cage cultures were high (0.91–0.94) in the first and second rearing periods (180 and 270 dph) but the correlation was decreased to 0.59 ± 0.33 at 360 dph. This suggests that the genotype by environment interaction is important for body traits in this population. Furthermore, the genetic correlations of body weights between different measurement periods were moderate but different from one. This suggests that body weights measured at different time points may be different traits and selection for improved early weight may not capture all genetic expressions in subsequent rearing periods in Asian seabass. Selection of the nucleus in sea cages may produce genotypes that do not perform equally well in tanks, although this deserves further studies to determine a suitable selection environment and optimize the breeding program. This paper discusses challenges encountered during implementation of the selection program for L. calcarifer.

Ridge, Lasso and Bayesian additive-dominance genomic models.

BackgroundA complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes).ResultsG-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close.ConclusionsAmongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.

Half‐Sib Matting and Genetic Analysis of Agronomic, Morphological, and Physiological Traits in Sainfoin under Nonstressed versus Water‐Deficit Conditions

ABSTRACTHalf‐sib matting, including polycross and openpollination, are powerful methods to obtain quantitative genetic information in breeding of forage plants. In sainfoin (Onobrychis viciifolia Scop).no prior efforts have been made to estimate the genetic parameters of traits and comparing these methods. Thirty half‐sib families (16 polycross and 14 open‐pollinated families) were evaluated under two moisture environments (nonstressed and water deficit) during 2010 to 2012. Water deficit decreased plant height (20.3%), plant density (51.8%), dry matter yield (45–53%), relative water content (RWC) (18.9%) and increased carotenoid content (23.9%), proline content (65.7%), specific catalase (74.4%) and ascorbate peroxidase activity (52.7%). High genotypic variation was observed for most of the measured traits. Narrow‐sense heritability (h2PFM) estimates for agro‐morphological traits ranged from 0.42 (leaf to stem ratio) to 0.95 (flowering date). The highest and lowest h2PFM among physiological traits were estimated for carotenoid content (0.50) and RWC (0.83), respectively. Moderate to high heritability for most traits showed the contribution of additive genetic effects suggesting phenotypic selection could be successful. The best agromorphological and physiological traits (such as plant height, plant density, dry matter yield, and RWC) under water deficit belonged to families 16 and 27. Dry matter yield had significant and positive correlation with plant height and plant density indicating indirect selection would also be effective to improve forage yield. Principal component analysis separated polycross and open‐pollinated families into distinct groups. Open‐pollinated families had higher mean values and general combining ability for most of the traits while polycross families had higher heritability and genetic gain from selection. This may indicate a more effective selection and a higher genetic advance in polycross families.

Sub-THz Vibrational Spectroscopy of Bacterial Cells and Molecular Components

In this work, sub-terahertz (THz) spectroscopy is applied to characterize lyophilized and in vitro cultured bacterial cells of non-pathogenic species of Escherichia coli (E. coli) and Bacillus subtilis (BG), spores of BG and DNA fro m E. coli. One of the goals of this research is to demonstrate that Fourier Transform (FT) spectroscopy in the frequency region of 10- 25 cm -1 is sensitive enough to reveal characteristic spectral features fro m bio-cells and spores in different environment, to verify the d ifferences between species, and to show the response of spores to vacuum and response of cultured cells to heat. The experimental technique was significantly improved for sensitivity and reliab ility. Observed spectra taken with a spectral resolution of 0.25 cm -1 using FT spectrometer with a detector operating at 1.7 K are rich in well resolved features having spectral widths of ~0.5-1 cm -1 . The reproducibility of experimental results was verified and confirmed. Measured spectra from E. coli DNA and from the entire cell have many similarit ies, thus demonstrating that the cellu lar co mponents might contribute to the v ibrational spectrum of the cell. The results of this work confirm that observed spectroscopic features are caused by fundamental physical mechanism of interaction between THz rad iation and biological macro-mo lecules. Particu larly, the analysis of results indicates that the spectroscopic signatures of microorganis ms originate fro m the co mbination of low frequency vibrational modes or group of modes at close frequencies (vibrational bands) within mo lecular co mponents of bacterial cells/spores, with the significant contribution from the DNA. The significance of this study is justified by necessity for a fast and effective, label free and reagent free optical technology to protect against environmental and other biological threats, as well as for general medical research. The obtained results show that THz v ibrational spectroscopy promises to add quantitative genetic information to the characteristic signatures of biological objects, increasing the detection accuracy and selectivity when appropriate spectral resolution is used.

A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes.

Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype-phenotype model, we present here a three-dimensional functional-structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed.

QUANTITATIVE GENETICS OF SHAPE IN CRICKET WINGS: DEVELOPMENTAL INTEGRATION IN A FUNCTIONAL STRUCTURE

The role of developmental and genetic integration for evolution is contentious. One hypothesis states that integration acts as a constraint on evolution, whereas an alternative is that developmental and genetic systems evolve to match the functional modularity of organisms. This study examined a morphological structure, the cricket wing, where developmental and functional modules are discordant, making it possible to distinguish the two alternatives. Wing shape was characterized with geometric morphometrics, quantitative genetic information was extracted using a full-sibling breeding design, and patterns of developmental integration were inferred from fluctuating asymmetry of wing shape. The patterns of genetic, phenotypic, and developmental integration were clearly similar, but not identical. Heritabilities for different shape variables varied widely, but no shape variables were devoid of genetic variation. Simulated selection for specific shape changes produced predicted responses with marked deflections due to the genetic covariance structure. Three hypotheses of modularity according to the wing structures involved in sound production were inconsistent with the genetic, phenotypic, or developmental covariance structure. Instead, there appears to be strong integration throughout the wing. The hypothesis that genetic and developmental integration evolve to match functional modularity can therefore be rejected for this example.

Multivariate Quantitative Genetics of Anthropometric Traits from the Boas Data

The use of multivariate quantitative trait information to address questions of population relationships and evolutionary issues has a longstanding history in human anthropometry. Previous analyses have usually rested on a number of explicit or implicit assumptions that allow phenotypic information to be used as a proxy for quantitative genetic information. One (usually implicit) assumption is that the additive genetic variance-covariance matrix (G) among traits is proportional to the phenotypic variance-covariance matrix (P). In this study we discuss the implications of this assumption, demonstrating that if it is true that G = h 2 P, where h 2 is some constant of proportionality, then (1) the biological (phenotypic) Mahalanobis distance will be proportional to genetic distance, (2) phenotypic and genetic allometry coefficients will be equal, and (3) evolutionary models will become simplified. We then use a multivariate quantitative genetic analysis of 12 anthropometric traits in 5 tribes to demonstrate that G = h 2 P for at least a portion of the Boas data.

Multivariate Quantitative Genetics of Anthropometric Traits from the Boas Data

The use of multivariate quantitative trait information to address questions of population relationships and evolutionary issues has a long-standing history in human anthropometry. Previous analyses have usually rested on a number of explicit or implicit assumptions that allow phenotypic information to be used as a proxy for quantitative genetic information. One (usually implicit) assumption is that the additive genetic variance-covariance matrix (G) among traits is proportional to the phenotypic variance-covariance matrix (P). In this study we discuss the implications of this assumption, demonstrating that if it is true that G = h2P, where h2 is some constant of proportionality, then (1) the biological (phenotypic) Mahalanobis distance will be proportional to genetic distance, (2) phenotypic and genetic allometry coefficients will be equal, and (3) evolutionary models will become simplified. We then use a multivariate quantitative genetic analysis of 12 anthropometric traits in 5 tribes to demonstrate that G = h2P for at least a portion of the Boas data.

Pedigree-free animal models: the relatedness matrix reloaded

Animal models typically require a known genetic pedigree to estimate quantitative genetic parameters. Here we test whether animal models can alternatively be based on estimates of relatedness derived entirely from molecular marker data. Our case study is the morphology of a wild bird population, for which we report estimates of the genetic variance-covariance matrices (G) of six morphological traits using three methods: the traditional animal model; a molecular marker-based approach to estimate heritability based on Ritland's pairwise regression method; and a new approach using a molecular genealogy arranged in a relatedness matrix (R) to replace the pedigree in an animal model. Using the traditional animal model, we found significant genetic variance for all six traits and positive genetic covariance among traits. The pairwise regression method did not return reliable estimates of quantitative genetic parameters in this population, with estimates of genetic variance and covariance typically being very small or negative. In contrast, we found mixed evidence for the use of the pedigree-free animal model. Similar to the pairwise regression method, the pedigree-free approach performed poorly when the full-rank R matrix based on the molecular genealogy was employed. However, performance improved substantially when we reduced the dimensionality of the R matrix in order to maximize the signal to noise ratio. Using reduced-rank R matrices generated estimates of genetic variance that were much closer to those from the traditional model. Nevertheless, this method was less reliable at estimating covariances, which were often estimated to be negative. Taken together, these results suggest that pedigree-free animal models can recover quantitative genetic information, although the signal remains relatively weak. It remains to be determined whether this problem can be overcome by the use of a more powerful battery of molecular markers and improved methods for reconstructing genealogies.

THE CONSTANCY OF THE G MATRIX THROUGH SPECIES DIVERGENCE AND THE EFFECTS OF QUANTITATIVE GENETIC CONSTRAINTS ON PHENOTYPIC EVOLUTION: A CASE STUDY IN CRICKETS

Long-term phenotypic evolution can be modeled using the response-to-selection equation of quantitative genetics, which incorporates information about genetic constraints (the G matrix). However, little is known about the evolution of G and about its long-term importance in constraining phenotypic evolution. We first investigated the degree of conservation of the G matrix across three species of crickets and qualitatively compared the pattern of variation of G to the phylogeny of the group. Second, we investigated the effect of G on phenotypic evolution by comparing the direction of greatest quantitative genetic variation within species (gmax) to the direction of phenotypic divergence between species (Δz̄). Each species, Gryllus veletis, G. firmus, and G. pennsylvanicus, was reared in the laboratory using a full-sib breeding design to extract quantitative genetic information. Five morphological traits related to size were measured. G matrices were compared using three statistical approaches: the T method, the Flury hierarchy, and the MANOVA method. Results revealed that the differences between matrices were small and mostly caused by differences in the magnitude of the genetic variation, not by differences in principal component structure. This suggested that the G matrix structure of this group of species was preserved, despite significant phenotypic divergence across species. The small observed differences in G matrices across species were qualitatively consistent with genetic distances, whereas ecological information did not provide a good prediction of G matrix variation. The comparison of gmax and Δz̄ revealed that the angle between these two vectors was small in two of three species comparisons, whereas the larger angle corresponding to the third species comparison was caused in large part by one of the five traits. This suggests that multivariate phenotypic divergence occurred mostly in a direction predicted by the direction of greatest genetic variation, although it was not possible to demonstrate the causal relationship from G to Δz̄. Overall, this study provided some support for the validity of the predictive power of quantitative genetics over evolutionary time scales.

Development of elite families in oil palm (Elaeis guineensis Jacq.)

Data from 29 dura female parents, 14 pisifera male parents and their offspring were assembled to obtain quantitative genetic information on optimizing selection of elite families. Phenotypic values of the offspring were partitioned into general combining ability (GCA) of the parents and their specific combining ability (SCA). Parental GCA-values for leaf-Mg content (LMG), height increment (HT), leaf area ratio (LAR), leaf area (LA), frond production (FPR), vegetative dry matter production (VDM), the rate of increase in leaf area against age (k) and maximum leaf area (Lm) were entered in a multiple regression analysis as predictors of oil-and-kernel yield and Harvest Index (HI), i.e. the proportion of dry matter used for oil and kernel production, of the offspring. The analysis revealed that both selection objectives are positively influenced by indirect selection for high LAR and LMG, and for reduced VDM with FPR as main component. But palms with high values for LA, Lm and HT should not be considered to avoid selecting palms partly for their ability to compete with neighbouring palms for light. The contribution of SCA was relatively small for most of the characters studied, except HT, FPR and HI. From the standard deviations for GCA and SCA of the dura and pisifera parents, i.e. σGCA and σSCA, it was extrapolated that selection can potentially enhance bunch yield (by 24%), oil-to-bunch ratio (19%), kernel-to-bunch ratio (29%), oil-and-kernel yield (31%) and HI (56%) above the mean phenotypic value obtained for the offspring of the present investigation. Heritabilities, calculated as the square of the correlation of the GCA-and phenotypic values of the female parents, were low for bunch yield, oil-and-kernel yield and HI, but moderate for LAR, LMG and VDM; levels were high for oil and kernel extraction rate. These outcomes were confirmed by the correlations between values of the female parent and their offspring. Among the unbalanced incomplete mating designs studied, the rectangular lattice and the alpha design are most suitable to compare GCA-values of pairs of parents. A breeding strategy adapting the recently introduced practical possibility of clonal reproduction is discussed.

Theory and application of half-sib matings in forage grass breeding

Half-sib (HS) matings, including polycross, topcross, and open-pollination, are useful in the breeding of cross-pollinated sexual perennial forage grasses to evaluate general combining ability of parental clones for synthetic cultivar development, recombine selected entries in recurrent selection programs, and obtain quantitative genetic information. The objective of this paper is to review uses of HS matings in breeding of these forage grasses with emphasis on theoretical aspects related to quantitative genetic analysis.Polycross mating with adequate replications and sufficient isolation is recommended over topcross and open-pollinated mating schemes in generating HS families for quantitative genetic studies. For the estimates of many genetic parameters to be valid, the parents must be a random sample from a random mating population in linkage equilibrium. Precision of the estimates depends on adequante sampling of the population of genotypes and environments used for evaluation.Analyses of variance on HS families and parental clones, and analysis of covariance between parent and offspring provide useful information on additivity of genetic effects and on genotype × environment interactions. Classical, narrow-sense heritability on an individual plant basis can be estimated and used to predict genetic gain from individual (mass) selection, providing that within family variance is estimable. If the forage breeder uses family selection, heritability should be estimated according to the proposed unit of selection. The selection unit must be specified in terms of numbers of replications, years, and locations. Polycross HS family selection can be readily adapted to a population improvement program in forage grass breeding.Narrow-sense heritability can also be estimated by doubling the linear regression coefficient of HS prog eny means on parental means. When HS families and parents are evaluated together in replicated experiments under similar environments, covariance analysis is recommended to remove the genotype × environment interaction covariance and environmental error covariance between parent and offspring, since these nongenetic covariances may result in inflated heritability estimates and misleading expected genetic gains from selection.