Estimation of genetic variability and heritability for biofuel feedstock yield in several populations of switchgrass

Information on heritability and predicted gains from selection for increased biomass yield for ethanol production in switchgrass is limited and may vary among breeding populations. The purpose of this study was to estimate heritability and predicted gains from selection for higher biomass yield within a lowland ecotype switchgrass population, Southern Lowland 93 (SL‐93), and two upland ecotype switchgrass populations, Southern Upland Northern Upland Early Maturing (SNU‐EM) and Southern Upland Northern Upland Late Maturing (SNU‐LM). Narrow‐sense heritabilities (h_n²) for biomass yield in each of the three populations were estimated via progeny–parent regression analysis. Half‐sib (HS) progeny families from 130 randomly selected plants from the SL‐93 population were evaluated for biomass yield in replicated trials in 2002 and 2003. Clonal parent plants were evaluated for biomass yield in separate environments to provide unbiased h_n² estimates from progeny–parent regression. Yield differences were highly significant among SL‐93 HS progenies within and over years. For the SL‐93 population, h_n² estimates were 0.13 and 0.12 based on individual plant and phenotypic family mean (PFM) selection, respectively. Predicted genetic gains (ΔG) per selection cycle were 0.15 kg dry matter (dm) plant^?1 and 0.10 kg dm plant^?1 for PFM and individual plant selection methods, respectively. For the SNU‐EM and SNU‐LM populations, year and year × HS family effects were highly significant (P < 0.01) and the HS family effect over years was nonsignificant (P < 0.05). However, HS family effects were highly significant within respective years (P < 0.01). Estimates of h_n² for the SNU‐EM and SNU‐LM populations based on PFM and individual plant selection were similar, ranging from 0.44 to 0.47; ΔG per selection cycle ranged from 0.22 to 0.33 kg dm plant^?1. The magnitudes of the estimates of additive genetic variation suggest that selection for higher biomass yield should be possible. The substantial effect of environment on biomass yields in the upland populations and the failure of families to respond similarly over years stress the importance of adequately testing biomass yield over years to assess yield.     

Hybrid maize breeding with doubled haploids: V. Selection strategies for testcross performance with variable sizes of crosses and S1 families

In hybrid maize (Zea mays L.) breeding, doubled haploids (DH) are increasingly replacing inbreds developed by recurrent selfing. Doubled haploids may be developed directly from S₀ plants in the parental cross or via S₁ families. In both these breeding schemes, we examined 2 two-stage selecting strategies, i.e., considering or ignoring cross and family structure while selection among and within parental crosses and S₁ families. We examined the optimum allocation of resources to maximize the selection gain ΔG and the probability P(q) of identifying the q% best genotypes. Our specific objectives were to (1) determine the optimum number and size of crosses and S₁ families, as well as the optimum number of test environments and (2) identify the superior selection strategy. Selection was based on the evaluation of testcross progenies of (1) DH lines in both stages (DHTC) and (2) S₁ families in the first stage and of DH lines within S₁ families in the second stage (S₁TC-DHTC) with uniform and variable sizes of crosses and S₁ families. We developed and employed simulation programs for selection with variable sizes of crosses and S₁ families within crosses. The breeding schemes and selection strategies showed similar relative efficiency for both optimization criteria ΔG and P (0.1%). As compared with DHTC, S₁TC-DHTC had larger ΔG and P (0.1%), but a higher standard deviation of ΔG. The superiority of S₁TC-DHTC was increased when the selection was done among all DH lines ignoring their cross and family structure and using variable sizes of crosses and S₁ families. In DHTC, the best selection strategy was to ignore cross structures and use uniform size of crosses.     

Genetic and physical characterization of theLR1 leaf rust resistance locus in wheat (Triticum aestivum L.)

The objective of this study was to characterize the leaf rust resistance locusLr1 in wheat. Restriction fragment length polymorphism (RELP) analysis was performed on the resistant lineLr1/6^*Thatcher and the susceptible varieties Thatcher and Frisal, as well as on the segregating F₂ populations. Seventeen out of 37 RFLP probes mapping to group 5 chromosomes showed polymorphism betweenLr1/6^*Thatcher and Frisal, whereas 11 probes were polymorphic between the near-isogenic lines (NILs)Lr1/6^*Thatcher and Thatcher. Three of these probes were linked to the resistance gene in the segregating F₂ populations. One probe (pTAG621) showed very tight linkage toLr1 and mapped to a single-copy region on chromosome 5D. The map location of pTAG621 at the end of the long arm of chromosome 5D was confirmed by the absence of the band in the nulli-tetrasomic line N5DT5B of Chinese Spring and a set of deletion lines of Chinese Spring lacking the distal part of 5DL. Twenty-seven breeding lines containing theLr1 resistance gene in different genetic backgrounds showed the same band asLr1/6^*Thatcher when hybridized with pTAG621. The RFLP marker was converted to a sequence-tagged-site marker using polymerase chain reaction (PCR) amplification. Sequencing of the specific fragment amplified from both NILs revealed point mutations as well as small insertion/deletion events. These were used to design primers that allowed amplification of a specific product only from the resistant lineLr1/6^*Thatcher. This STS, specific for theLr1 resistance gene, will allow efficient selection for the disease resistance gene in wheat breeding programmes. In addition, the identification of a D-genome-specific probe tightly linked toLr1 should ultimately provide the basis for positional cloning of the gene.     

Parental selection,number of breeding populations,and size of each population in inbred development

Some breeders select inbreds from many F₂ or backcross breeding populations, each with relatively few progenies. Other breeders select inbreds from only a few breeding populations, each with many progenies. My objectives were to: (1) determine the relative importance of parental selection, number of breeding populations, and size of each population, and (2) find optimum combinations between number and size of breeding populations. I assumed that a breeder has resources to test a total of 2,000 recombinant inbreds for a quantitative trait that was controlled by 100 additive loci and had a heritability of 0.20, 0.60, or 1.0. The parental inbreds had an inherent pedigree structure due to advanced cycle breeding. The parental inbreds were ranked according to their mean performance, and breeding populations were made among all parents, the top 25% of parents, and the top 10% of parents. I found that the issue of number versus size of breeding populations was only secondary compared with the ability to identify, prior to making the crosses, the breeding populations with the highest mean performance. For a given level of effectiveness of parental selection, the selection response was largest when the maximum number of breeding populations was used. The effect of the number of breeding populations was minor, however, when selection was practiced among the parents or when heritability was less than 1.0. The results suggested that, in practice, large selection responses could be obtained with a wide range of combinations between number and size of breeding populations.Communicated by H.C. Becker     

Designing artificial selection experiments for specific objectives

The observed genetic gain (ΔP) from selection in a finite population is the possible expected genetic gain E(Δ G) minus the difference in inbreeding depression effects in the selected and control lines. The inbreeding depression can be avoided by crossing the control and selected ♂ and ♀ parents to unrelated mates and summing the observed gains. The possible expected gain will be reduced by an amount D from the predicted gain because of the effects of the genetic limit and random genetic drift, the magnitude of which is a function of effective population size, N. The expected value of D is zero in unselected control populations and in the first generation for selected populations. Therefore, this source of bias can be reduced by increasing N in the selected populations and can be avoided by selecting for a single generation. To obtain observed responses which are unbiased estimates of the predicted response from which to estimate the realized heritability (or regression) in the zero generation, or to test genetic theory based on infinite population size, single-generation selection with many replications would be most efficient. To measure the "total" effect or genetic efficiency of a selection criterion or method, including the effect of different selection intensities, effective population sizes, and space requirements, more than one generation of selection is required to estimate the expected response in breeding values. The efficiency, in the sense of minimum variance, of estimating the expected breeding values at any generation t will decline as the number of generations t increases. The variance of either the estimated mean gain or the regression of gain on selection differential can be reduced more by increasing the number of replicates K than by increasing the number of generations t. Also the general pattern of the response over t can be estimated if the N's are known. Therefore, two- or not more than three-generation selection experiments with many replications would be most efficient.     

Hybrid maize breeding with doubled haploids. IV. Number versus size of crosses and importance of parental selection in two-stage selection for testcross performance

Parental selection influences the gain from selection and the optimum allocation of test resources in breeding programs. We compared two hybrid maize (Zea mays L.) breeding schemes with evaluation of testcross progenies: (a) doubled haploid (DH) lines in both stages (DHTC) and (b) S₁ families in the first stage and DH lines within S₁ families in the second stage (S₁TC-DHTC). Our objectives were to (1) determine the optimum allocation regarding the number of crosses, S₁ families, DH lines, and test locations, (2) investigate the impact of parental selection on the optimum allocation and selection gain (ΔG), and (3) compare the maximum ΔG achievable with each breeding scheme. Selection gain was calculated by numerical integration. Different assumptions were made regarding the budget, variance components, correlation between the mean phenotypic performance of the parents and the mean genotypic value of the testcross performance of their progenies (ρ _P ), and the composition of the finally selected test candidates. In comparison with randomly chosen crosses, maximum ΔG was largely increased with parental selection in both breeding schemes. With an increasing correlation ρ _P , this superiority increased strongly, while the optimum number of crosses decreased in favor of an increased number of test candidates within crosses. Thus, concentration on few crosses among the best parental lines might be a promising approach for short-term success in advanced cycle breeding. Breeding scheme S₁TC-DHTC led to a larger ΔG but had a longer cycle length than DHTC. However, with further improvements in the DH technique and the realization of more than two generations per year, early testing of S₁ families prior to production of DH lines would become very attractive in hybrid maize breeding. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. T. Wegenast and C. F. H. Longin contributed equally to this work.     

Expected gain and status number following restricted individual and combined-index selection.

Imposition of restrictions on number of individuals selected from a family and number of families from which superior individuals are selected could markedly alter the consequences of individual and combined-index selection. Predicted genetic gain and diversity measured as status number following selection were studied to draw general conclusions. Selection and its prediction were applied to two sets of real-life data. Theoretical prediction gave results close to those from factual selection. Gain and status number varied with initial family number and size, sib type, heritability, selection proportion, restriction type and intensity, and selection criteria. Proper restriction on the number of individuals selected can control the reduction of status number to an acceptable level, particularly when breeding values are used as the selection criterion. Restriction on the number of families selected would effectively improve the gain efficiency of selection based on phenotypic values. Choosing combinations of both restrictions might produce higher gain without the loss of status number. Given constant population size, family number should be large enough to ensure that restricted selection will yield higher gain and status number.     

The effect of trait type and strength of selection on heritability and evolvability in an island bird population

The heritability (h²) of fitness traits is often low. Although this has been attributed to directional selection having eroded genetic variation in direct proportion to the strength of selection, heritability does not necessarily reflect a trait's additive genetic variance and evolutionary potential (“evolvability”). Recent studies suggest that the low h² of fitness traits in wild populations is caused not by a paucity of additive genetic variance (V_A) but by greater environmental or nonadditive genetic variance (V_R). We examined the relationship between h² and variance‐standardized selection intensities (i or β_σ), and between evolvability (I_A:V_A divided by squared phenotypic trait mean) and mean‐standardized selection gradients (β_μ). Using 24 years of data from an island population of Savannah sparrows, we show that, across diverse traits, h² declines with the strength of selection, whereas I_A and I_R (V_R divided by squared trait mean) are independent of the strength of selection. Within trait types (morphological, reproductive, life‐history), h², I_A, and I_R are all independent of the strength of selection. This indicates that certain traits have low heritability because of increased residual variance due to the age at which they are expressed or the multiple factors influencing their expression, rather than their association with fitness.     

Genomic selection in forest tree breeding

Genomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most quantitative trait loci (QTL) for the target trait(s). GS is revolutionizing breeding practice in domestic animals. The same approach and concepts can be readily applied to forest tree breeding where long generation times and late expressing complex traits are also a challenge. GS in forest trees would have additional advantages: large training populations can be easily assembled and accurately phenotyped for several traits, and the extent of linkage disequilibrium (LD) can be high in elite populations with small effective population size (N _e) frequently used in advanced forest tree breeding programs. Deterministic equations were used to assess the impact of LD (modeled by N _e and intermarker distance), the size of the training set, trait heritability, and the number of QTL on the predicted accuracy of GS. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP selection is reached by GS even at a marker density ~2 markers/cM when N _e ≤ 30, while up to 20 markers/cM are necessary for larger N _e. Shortening the breeding cycle by 50% with GS provides an increase ≥100% in selection efficiency. With the rapid technological advances and declining costs of genotyping, our cautiously optimistic outlook is that GS has great potential to accelerate tree breeding. However, further simulation studies and proof-of-concept experiments of GS are needed before recommending it for operational implementation.     

Genetics of resistance to Cry1C toxin from Bacillus thuringiensis in Spodoptera litura (Fab.)

The present study was undertaken to determine the genetics of Cry1C resistance in Spodoptera litura. Selection of S. litura (Fab.) with Cry1C was done for eight generations to develop resistance. Reciprocal crosses between resistant and susceptible populations were made to understand the population genetics of Cry1C resistance in S. litura. Generation wise selection with Cry1C was evaluated for resistance development in S. litura. The LC₅₀ of Cry1C was 0.14 µg/cm² for the first selected generation and it increased to 23.98 µg/cm² after eight selected generations, which is a 285.47-fold increase in resistance compared with the susceptible strain. The estimated realized heritability (h²) after eight generations of selection with Cry1C insecticidal protein was 0.44. The number of generations required for the tenfold increase in LC₅₀ (1/R) was estimated to be 3.33. Response to Cry1C selection in S. litura was 0.30, the estimated selection differential was 0.69 and the pheonotypic standard deviation (dP) was 0.24. Reciprocal crosses between Cry1C resistant and susceptible strain of S. litura showed autosomal resistance.     

Evidence for ecological divergence across a mosaic of soil types in an Amazonian tropical tree: Protium subserratum (Burseraceae)

Soil heterogeneity is an important driver of divergent natural selection in plants. Neotropical forests have the highest tree diversity on earth, and frequently, soil specialist congeners are distributed parapatrically. While the role of edaphic heterogeneity in the origin and maintenance of tropical tree diversity is unknown, it has been posited that natural selection across the patchwork of soils in the Amazon rainforest is important in driving and maintaining tree diversity. We examined genetic and morphological differentiation among populations of the tropical tree Protium subserratum growing parapatrically on the mosaic of white‐sand, brown‐sand and clay soils found throughout western Amazonia. Nuclear microsatellites and leaf morphology were used to (i) quantify the extent of phenotypic and genetic divergence across habitat types, (ii) assess the importance of natural selection vs. drift in population divergence, (iii) determine the extent of hybridization and introgression across habitat types, (iv) estimate migration rates among populations. We found significant morphological variation correlated with soil type. Higher levels of genetic differentiation and lower migration rates were observed between adjacent populations found on different soil types than between geographically distant populations on the same soil type. P_ST‐F_ST comparisons indicate a role for natural selection in population divergence among soil types. A small number of hybrids were detected suggesting that gene flow among soil specialist populations may occur at low frequencies. Our results suggest that edaphic specialization has occurred multiple times in P. subserratum and that divergent natural selection across edaphic boundaries may be a general mechanism promoting and maintaining Amazonian tree diversity.     

Long-term effects of selection based on the animal model BLUP in a finite population

Monte Carlo simulation was used to assess the long-term effects of truncation selection within small populations using indices (I=f+m) combining mid-parent [f=(a _i+a _d)/2] and Mendelian-sampling (m=a-f) evaluations provided by an animal model BLUP (a=f+m). Phenotypic values of panmictic populations were generated for 30 discrete generations. Assuming a purely additive polygenic model, heritability (h ₂) values were 0.10, 0.25 or 0.50. Two population sizes were considered: five males and 25 females selected out of 50 candidates of each sex (small populations, S) and 50 males and 250 females selected out of 500 candidates in each sex (large populations, L). Selection was carried out on the index defined above with = 1 (animal model BLUP), =1/2, or =0 (selection on within-family deviations). Mass selection was also considered. Selection based on the animal model BLUP (=1) maximized the cumulative genetic gain in L populations. In S populations, selection using = 1/2 and mass selection were more efficient than selection under an animal model (+ 3 to + 7% and + 1 to + 4% respectively, depending on h ²). Selection on within-family deviations always led to the lowest gains. In most cases, the variance of response to selection between replicates did not depend on the selection method. The within-replicate genetic variance and the average coefficient of inbreeding (F) were highly affected by selection with =1 or 1/2, especially in populations of size S. As expected, selection based on within-family deviations was less detrimental in that respect. The number of copies of founder neutral genes at a separate locus, and the probability vector of origin of the genes in reference to the founder animals, were also observed in addition to F values. The conclusion was that selection procedures placing less emphasis on family information might be interesting alternatives to selection based on animal model BLUP, especially for small populations with long-term selection objectives.     

Integration of genomic information into sport horse breeding programs for optimization of accuracy of selection

Reliable selection criteria are required for young riding horses to increase genetic gain by increasing accuracy of selection and decreasing generation intervals. In this study, selection strategies incorporating genomic breeding values (GEBVs) were evaluated. Relevant stages of selection in sport horse breeding programs were analyzed by applying selection index theory. Results in terms of accuracies of indices (r_TI) and relative selection response indicated that information on single nucleotide polymorphism (SNP) genotypes considerably increases the accuracy of breeding values estimated for young horses without own or progeny performance. In a first scenario, the correlation between the breeding value estimated from the SNP genotype and the true breeding value (= accuracy of GEBV) was fixed to a relatively low value of r_mg = 0.5. For a low heritability trait (h² = 0.15), and an index for a young horse based only on information from both parents, additional genomic information doubles r_TI from 0.27 to 0.54. Including the conventional information source ‘own performance’ into the before mentioned index, additional SNP information increases r_TI by 40%. Thus, particularly with regard to traits of low heritability, genomic information can provide a tool for well-founded selection decisions early in life. In a further approach, different sources of breeding values (e.g. GEBV and estimated breeding values (EBVs) from different countries) were combined into an overall index when altering accuracies of EBVs and correlations between traits. In summary, we showed that genomic selection strategies have the potential to contribute to a substantial reduction in generation intervals in horse breeding programs.     

Genotyping strategies for genomic selection in small dairy cattle populations

This study evaluated different female-selective genotyping strategies to increase the predictive accuracy of genomic breeding values (GBVs) in populations that have a limited number of sires with a large number of progeny. A simulated dairy population was utilized to address the aims of the study. The following selection strategies were used: random selection, two-tailed selection by yield deviations, two-tailed selection by breeding value, top yield deviation selection and top breeding value selection. For comparison, two other strategies, genotyping of sires and pedigree indexes from traditional genetic evaluation, were included in the analysis. Two scenarios were simulated, low heritability (h² = 0.10) and medium heritability (h² = 0.30). GBVs were estimated using the Bayesian Lasso. The accuracy of predicted GBVs using the two-tailed strategies was better than the accuracy obtained using other strategies (0.50 and 0.63 for the two-tailed selection by yield deviations strategy and 0.48 and 0.63 for the two-tailed selection by breeding values strategy in low- and medium-heritability scenarios, respectively, using 1000 genotyped cows). When 996 genotyped bulls were used as the training population, the sire’ strategy led to accuracies of 0.48 and 0.55 for low- and medium-heritability traits, respectively. The Random strategies required larger training populations to outperform the accuracies of the pedigree index; however, selecting females from the top of the yield deviations or breeding values of the population did not improve accuracy relative to that of the pedigree index. Bias was found for all genotyping strategies considered, although the Top strategies produced the most biased predictions. Strategies that involve genotyping cows can be implemented in breeding programs that have a limited number of sires with a reliable progeny test. The results of this study showed that females that exhibited upper and lower extreme values within the distribution of yield deviations may be included as training population to increase reliability in small reference populations. The strategies that selected only the females that had high estimated breeding values or yield deviations produced suboptimal results.     

Accuracy of breeding values of 'unrelated' individuals predicted by dense SNP genotyping

Background

Recent developments in SNP discovery and high throughput genotyping technology have made the use of high-density SNP markers to predict breeding values feasible. This involves estimation of the SNP effects in a training data set, and use of these estimates to evaluate the breeding values of other ''evaluation'' individuals. Simulation studies have shown that these predictions of breeding values can be accurate, when training and evaluation individuals are (closely) related. However, many general applications of genomic selection require the prediction of breeding values of ''unrelated'' individuals, i.e. individuals from the same population, but not particularly closely related to the training individuals.

Methods

Accuracy of selection was investigated by computer simulation of small populations. Using scaling arguments, the results were extended to different populations, training data sets and genome sizes, and different trait heritabilities.

Results

Prediction of breeding values of unrelated individuals required a substantially higher marker density and number of training records than when prediction individuals were offspring of training individuals. However, when the number of records was 2*N_e*L and the number of markers was 10*N_e*L, the breeding values of unrelated individuals could be predicted with accuracies of 0.88 – 0.93, where N_e is the effective population size and L the genome size in Morgan. Reducing this requirement to 1*N_e*L individuals, reduced prediction accuracies to 0.73–0.83.

Conclusion

For livestock populations, 1N_eL requires about ~30,000 training records, but this may be reduced if training and evaluation animals are related. A prediction equation is presented, that predicts accuracy when training and evaluation individuals are related. For humans, 1N_eL requires ~350,000 individuals, which means that human disease risk prediction is possible only for diseases that are determined by a limited number of genes. Otherwise, genotyping and phenotypic recording need to become very common in the future.     

Allozyme diversity in pheasants (<Emphasis Type="Italic">Phasianus</Emphasis> spp.) from breeding stations in Serbia

The pheasant breeds are widely used for restocking of natural populations depleted by hunting. The pheasant population number decline was detected during the 1970s in many hunting areas of Europe. One of its possible reasons might be the loss of adaptability in populations originating from breeding stations, which was caused by inbreeding depression. The aim of this paper was the analysis of genetic variability in pheasant populations from three breeding stations in Vojvodina province (Serbia) by means of allozyme diversity detection. The allozyme variability analysis of pheasants from all three breeding stations revealed polymorphisms at nine loci: Ldh-1, Mor-1, Mor-2, Es-1, Mod-2, Pgd, Gpi-2, Odh, and Sod. The analysis of individuals from three different breeding stations showed mean values of observed heterozygosity of H _o=0.137, polymorphism P _95%=30%, and H/P ratio H/P=0.430, which indicate a normal level of genetic variability for bird populations. Comparative analysis of three pheasant populations showed a high level of interpopulation differentiation.     

Managing genetic gain and diversity in clonal deployment of white spruce in New Brunswick,Canada

When selecting a clonal mixture for clonal forestry, a common practice is to specify a minimum acceptable genetic diversity for the mixture, and under that constraint, to maximize its genetic gain. Three selection methods—combined index selection (CIS), clonal mean selection (GMS), and family plus within-family clonal selection (FWFGS)—together with various restrictions on family contributions (family restrictions) were used to estimate gain at a given effective population size (N _e ) of clonal mixtures selected from a clonally replicated genetic test of white spruce. The designated target trait for improvement was individual tree volume at age 14 after planting. Regardless of selection method, all genetic gains were >30% at given N _e from five to 20, suggesting that implementing clonal forestry was a very effective deployment strategy. Genetic gain at a specified N _e could be enhanced by using appropriate selection methods: CIS was the most effective, followed by GMS and FWFGS. With an N _e of 15, CIS resulted in an average gain of 43.2%, which corresponded to an increase by 8.1% and 17.3% relative to GMS and FWFGS, respectively. Imposing family restriction increased gain at an N _e . Compared with the respective unrestricted selection, family restrictions increase genetic gain by 3.3%, 7.7%, and 54% for CIS, GMS, and FWFGS, respectively. The optimal family restriction level for each selection method varied with the specified N _e .     

Breeding Enrichment of Genetic Variation of Grain Yield and Its Attributes in Bread Wheat under Drought Stress and Well Irrigation

Drought stress (DS) is one of the most critical environmental abiotic stresses for wheat production in the arid environments. Selection of high-yielding genotypes tolerant to DS can play a significant role in mitigation the negative impacts associated with DS. In the present study, generation means analysis (GMA) was used to study the performance of two crosses under well irrigation (WI) and deficit irrigation [cross I (Line 44 × Shandweel-1) and cross II (Line 20 × Sakha 93)]. Significant differences were observed for days to heading (DH), days to maturity (DM), plant height (PH), spike length (SL), number of spikes per plant (NS/P), number of grains per spike (NG/S), thousand-grain weight (TGW), grain yield per plant (GY/P), and proline content (PC) in the six populations of the two crosses within each irrigation level. Cross II had early maturity and the highest PC, NS/P, TGW, and GY/P regardless of the irrigation level. Cross I showed positive significant relative heterosis and heterobeltiosis for GY/P under the two irrigation levels. The inheritance of characters of cross I revealed additive, dominant, and epistatic effects, which varied with trait and stress. Additive genetic effects predominated in DH, SL, and PC, while non- additive were found in DM, NS/P, NG/S, and GY/P. Narrow-sense heritability estimates (h²_n) were high for DH and PC, moderate to high for PH and SL, moderate for DM, NG/S, NS/P, and TGW, and low for GY/P. Based on different drought indices the populations BC₁, BC₂, F₁, and P₁ of cross II and BC1 of cross I were more tolerant to drought stress. Therefore, PC, TGW and DH can be used as selection indicators to improve wheat for drought tolerance in early generations and other yield components traits in late generations. The second cross (Line 20 × Sakha 93) shows promise and is of interest to a drought tolerance breeding program, where wheat breeders can use recombinant breeding strategies to construct desirable drought stress genes. Correlation and path coefficient revealed that TGW and PC were the main contributor in grain yield in both environments.     

Gene stacking strategies with doubled haploids derived from biparental crosses: theory and simulations assuming a finite number of loci

Recent progress in genotyping and doubled haploid (DH) techniques has created new opportunities for development of improved selection methods in numerous crops. Assuming a finite number of unlinked loci (ℓ) and a given total number (n) of individuals to be genotyped, we compared, by theory and simulations, three methods of marker-assisted selection (MAS) for gene stacking in DH lines derived from biparental crosses: (1) MAS for high values of the marker score (T, corresponding to the total number of target alleles) in the F₂ generation and subsequently among DH lines derived from the selected F₂ individual (Method 1), (2) MAS for augmented F₂ enrichment and subsequently for T among DH lines from the best carrier F₂ individual (Method 2), and (3) MAS for T among DH lines derived from the F₁ generation (Method 3). Our objectives were to (a) determine the optimum allocation of resources to the F₂ ( n₁^* \, n_{1}^{*} ) and DH generations (n - n₁^* ) (n - n_{1}^{*} ) for Methods 1 and 2 by simulations, (b) compare the efficiency of all three methods for gene stacking by simulations, and (c) develop theory to explain the general effect of selection on the segregation variance and interpret our simulation results. By theory, we proved that for smaller values of ℓ, the segregation variance of T among DH lines derived from F₂ individuals, selected for high values of T, can be much smaller than expected in the absence of selection. This explained our simulation results, showing that for Method 1, it is best to genotype more F₂ individuals than DH lines ($ n_{1}^{*} :n > 0.5 $ n_{1}^{*} :n > 0.5 ), whereas under Method 2, the optimal ratio n₁^* :n n_{1}^{*} :n was close to 0.5. However, for ratios deviating moderately from the optimum, the mean [`(X)] \overline{X} of T in the finally selected DH line ( T<sub>\textDH</sub><sup>*</sup> T_{\text{DH}}^{*} ) was hardly reduced. Method 3 had always the lowest mean [`(X)] \overline{X} of T_\textDH^* T_{\text{DH}}^{*} except for small numbers of loci (ℓ = 4) and is favorable only if a small number of loci are to be stacked in one genotype and/or saving one generation is of crucial importance in cultivar development. Method 2 is under most circumstances the superior method, because it generally showed the highest mean [`(X)] \overline{X} and lowest SD of T_\textDH^* T_{\text{DH}}^{*} for the finally selected DH.