Multi-Level Genetic Variation and Selection Strategy of Neolamarckia cadamba in Successive Years

Neolamarckia cadamba (Roxb.) Bosser is a tropical evergreen broadleaf tree species that could play an important role in meeting the increasing demand for wood products. However, multi-level genetic variation and selection efficiency for growth traits in N. cadamba is poorly characterized. We therefore investigated the efficiency of early selection in N. cadamba by monitoring the height (HT), diameter at breast height (DBH), and tree volume (V) in 39 half-sib families from 11 provenances at ages 2, 3, 4, 5, and 6 years in a progeny test. Age-related trends in growth rate, genetic parameters in multi-level, efficiency of early selection, and realized gain in multi-level for growth traits were analyzed. The result showed that genetic variation among families within provenances was higher than that among provenances. The estimated individual heritability values for the growth traits ranged from 0.05 to 0.26, indicating that the variation of growth traits in N. cadamba was subject to weak or intermediate genetic control. The age–age genetic correlations for growth traits were always positive and high (0.51–0.99), and the relationships between the genetic/phenotypic correlations and the logarithm of the age ratio (LAR) were described well by linear models (R2 > 0.85, except the fitting coefficient of genetic correlation and LAR for HT was 0.35). On the basis of an early selection efficiency analysis, we found that it is the best time to perform early selection for N. cadamba at age 5 before half-rotation, and the selection efficiencies were 157.28%, 151.56%, and 127.08% for V, DBH, and HT, respectively. Higher realized gain can be obtained by selecting superior trees from superior families. These results can be expected to provide theoretical guidance and materials for breeding programs in N. cadamba and can even be a reference for breeding strategies of other fast-growing tree species.

PSXI-27 Comparison of live weight, backfat depth, and loin eye muscle measurements in ewe and ram lambs

Application of ultrasound carcass trait scanning in meat animals enhances the selection efficiency and accuracy. One hundred and sixty-two mixed age Katahdin ewes were selected based on ultrasound carcass traits and bred in a high lean muscle selection (n=81) and control (n=81) flocks, respectively in December 2019. Carcass traits including loin eye area (LEA), loin eye muscle width (LEW) and depth (LED), and back-fat depth (BFD) were measured for sires, dams, and their progeny lambs. Birth weight (BW), rearing rank, sex, and weaning weight (WW=90 d) and post weaning weight (PW=120 d) were recorded for progeny. Post-weaning live weight, LEA and BFD values were calculated for deriving an expected progeny difference lists and ewe replacements. Retaining ram lambs (25% male progeny) were recorded for live weight, carcass trait scanning, and breeding values estimate at six-month old as breeding sires. There was no difference between selection and control progeny for WW, PW, BFD and LEA measurements. Means for BW, WW, PW, LEA, LEW, LED, and FBD in progeny were 3.77 ±0.56, 22.13 ±2.98, 24.48 ±3.08 kg, 5.9 ±0.1cm², 4.4 ±0.5 cm, 1.9 ±0.2 cm and 2.9 ±2.6 mm. However, the WW, PW, LEA, and LEW were measured significantly (P < 0.05) greater for ram lambs (20.6 kg, 25.5 kg, 6.1 cm², 4.5 cm) over ewe lambs (19.5 kg, 23.8 kg, 5.7 cm², 4.3 cm). Single born lambs were significantly (P < 0.01) heavier at birth, weaning and post-weaning, and measured greater value for LEA and LED than twin birth or reared lambs. The statistical analysis showed WW was significantly (P < 0.01) correlated with PW (r=0.71) and both WW and PW correlated with LEA (r=0.5) but not with BFD. The result indicates that lean animal selection using ultrasound carcass trait scanning will improve early age selection efficiency and accuracy.

Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs.

Community diversity determines the evolution of synthetic bacterial communities under artificial selection

Artificial selection can be conducted at the community level in the laboratory through a differential propagation of the communities according to their level of expression of a targeted function (i.e. community phenotype). Working with communities instead of individuals as selection units brings in additional sources of variation in the considered phenotype that can arise through changes in community structure and influence the outcome of the artificial selection. These sources of variation could even be increased by manipulating species diversity. In this study, we wanted to assess the effect of manipulating initial community richness on artificial selection efficiency, defined as the change in the targeted function over time as compared to a control treatment without artificial selection. We applied artificial selection for a high productivity on synthetic bacterial communities varying for their initial richness level (from one to 16 strains). Our results showed that, overall, the communities that were artificially selected were 16% more productive than the control communities. Community richness positively influenced community productivity and metabolic capacities and was a strong determinant of the dynamics of community evolution. Our results suggested that community richness could influence artificial selection efficiency but a convergence of the community composition might have limited the effect of diversity on artificial selection efficiency. We propose that applying artificial selection on communities varying for their diversity could allow to find communities differing for their level of expression of a function but also for their responsiveness to artificial selection, provided that their initial composition is different enough.

Genomic selection and genetic gain for nut yield in an Australian macadamia breeding population

Background Improving yield prediction and selection efficiency is critical for tree breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia breeding program with comparison to traditional breeding methods. This study evaluated the prediction accuracy of GS in a macadamia breeding population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8 years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8 years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related populations and prediction across unrelated populations. Results Narrow-sense heritability of yield and yield stability was low (h2 = 0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related populations and 0.14 when predicted across unrelated populations. Accuracy of prediction of yield stability was high (r = 0.79) for predictions across related populations. Predicted genetic gain of yield using GS in related populations was 474 g/year, more than double that of traditional breeding methods (226 g/year), due to the halving of generation length from 8 to 4 years. Conclusions The results of this study indicate that the incorporation of GS for yield into the Australian macadamia breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present.

Gene co-expression network reveals highly conserved, well-regulated anti-ageing mechanisms in old ant queens

Evolutionary theories of ageing predict a reduction in selection efficiency with age, a so-called ‘selection shadow’, due to extrinsic mortality decreasing effective population size with age. Classic symptoms of ageing include a deterioration in transcriptional regulation and protein homeostasis. Understanding how ant queens defy the trade-off between fecundity and lifespan remains a major challenge for the evolutionary theory of ageing. It has often been discussed that the low extrinsic mortality of ant queens, that are generally well protected within the nest by workers and soldiers, should reduce the selection shadow acting on old queens. We tested this by comparing strength of selection acting on genes upregulated in young and old queens of the ant, Cardiocondyla obscurior. In support of a reduced selection shadow, we find old-biased genes to be under strong purifying selection. We also analysed a gene co-expression network (GCN) with the aim to detect signs of ageing in the form of deteriorating regulation and proteostasis. We find no evidence for ageing. In fact, we detect higher connectivity in old queens indicating increased transcriptional regulation with age. Within the GCN, we discover five highly correlated modules that are upregulated with age. These old-biased modules regulate several anti-ageing mechanisms such as maintenance of proteostasis, transcriptional regulation and stress response. We observe stronger purifying selection on central hub genes of these old-biased modules compared to young-biased modules. These results indicate a lack of transcriptional ageing in old C. obscurior queens possibly facilitated by strong selection at old age and well-regulated anti-ageing mechanisms.

EFFICIENCY BASED ON MARKER ASSISTED SELECTION FOR F2 POPULATION WITH ONE LOCUS MODEL: A SIMULATION STUDY

Simulation study was done to evaluate QTL mapping and selection efficiency of molecular markers utilisation in the F2 population. The simulation study started with formulating genetic configuration which consists of chromosome maps and genetic models. Genetic model for diploid individuals is a model which consists two alleles for each locus. Genetic model that used is a mathematical model consists additive, dominance, and interactions with different effects at each locus, with maximum interaction occurs between two loci (digenic). QTL mapping was constructed by using single locus model, two loci model and multiple loci model. the effect of sample size, heritability, and marker density was observed. Three model was used to analyse QTL position, i.e. marker regression, interval mapping (IM) and composite interval mapping (CIM). Several parameters were specified in this study: genetic variability coefficient (GVC=15%), population mean (μ=10), epistasis and genetic variance ratio (f=0.1), dominance and additive variance ratio (r=0.25), the ratio of AA:AD:DD is 3:2:1 with additive and dominance gene action, and its interaction. The first and last marker were located at each edge of 150 cM chromosome for each chromosome. The interval distance between markers were equal. Haldane’s map function was used in this simulation. The simulation was performed by using the QTL Package on “R” software. With a heritability 0.2, the required sample size to indicate the interval markers associated with QTL were 50 for single locus model. The level of selection efficiency using molecular markers was higher than the phenotypic screening on. Efficiency level of selection based on molecular markers (Em) is a function of the distance between the markers to QTL (d) which follows “reciprocal quadratic” function. Efficiency level of selection based on phenotype (Ef) is a function of heritability favourable traits which follows “reciprocal quadratic” function.Keywords: efficiency, markers, QTL, simulation

Improving Selection Efficiency of Crop Breeding with a Genomic Prediction Aided Partial Phenotyping Strategy

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a partial phenotyping strategy that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the partial genomic phenotyping strategy in a wheat dataset. Whether the partial phenotyping strategy resulted in more selection response depended on the correlations of phenotypes between environments. The partial phenotyping strategy consistently showed statistically significant higher simulated responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. Our results indicate that genomics-based partial phenotyping can improve selection response at middle stages of crop breeding programs.