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Background: Septoria tritici blotch (STB) disease causes yield losses of up to 50 per cent in susceptible wheat cultivars and can reduce wheat production. In this study, genomic architecture for adult-plant STB resistance in a Septoria Association Mapping Panel (SAMP) having 181 accessions and genomic regions governing STB resistance in a South Asian wheat panel were looked for. Results: Field experiments during the period from 2019 to 2021 revealed those certain accessions, namely BGD52 (CHIR7/ANB//CHIR1), BGD54 (CHIR7/ANB//CHIR1), IND92 (WH 1218), IND8 (DBW 168), and IND75 (PBW 800), exhibited a high level of resistance. Genetic analysis revealed the presence of 21 stable quantitative trait nucleotides (QTNs) associated with resistance to STB (Septoria tritici blotch) on all wheat chromosomes, except for 2D, 3A, 3D, 4A, 4D, 5D, 6B, 6D, and 7A. These QTNs were predominantly located in chromosome regions previously identified as associated with STB resistance. Three Quantitative Trait Loci (QTNs) were found to have significant phenotypic effects in field evaluations. These QTNs are Q.STB.5A.1, Q.STB.5B.1, and Q.STB.5B.3. Furthermore, it is possible that the QTNs located on chromosomes 1A (Q.STB.1A.1), 2A (Q.STB_DH.2A.1, Q.STB.2A.3), 2B (Q.STB.2B.4), 5A (Q.STB.5A.1, Q.STB.5A.2), and 7B (Q.STB.7B.2) could potentially be new genetic regions associated with resistance. Conclusion: Our findings demonstrate the importance of Asian bread wheat as a source of STB resistance alleles and novel stable QTNs for wheat breeding programs aiming to develop long-lasting and wide-ranging resistance to Zymoseptoria tritici in wheat cultivars. [ABSTRACT FROM AUTHOR]

Increasing SNP density by incorporating sequence information only marginally increases prediction accuracies of breeding values in livestock. To find out why, we used statistical models and simulations to investigate the shape of distribution of estimated SNP effects (a profile) around quantitative trait nucleotides (QTNs) in populations with a small effective population size (Ne). A QTN profile created by averaging SNP effects around each QTN was similar to the shape of expected pairwise linkage disequilibrium (PLD) based on Ne and genetic distance between SNP, with a distinct peak for the QTN. Populations with smaller Ne showed lower but wider QTN profiles. However, adding more genotyped individuals with phenotypes dragged the profile closer to the QTN. The QTN profile was higher and narrower for populations with larger compared to smaller Ne. Assuming the PLD curve for the QTN profile, 80% of the additive genetic variance explained by each QTN was contained in ± 1/Ne Morgan interval around the QTN, corresponding to 2 Mb in cattle and 5 Mb in pigs and chickens. With such large intervals, identifying QTN is difficult even if all of them are in the data and the assumed genetic architecture is simplistic. Additional complexity in QTN detection arises from confounding of QTN profiles with signals due to relationships, overlapping profiles with closely spaced QTN, and spurious signals. However, small Ne allows for accurate predictions with large data even without QTN identification because QTNs are accounted for by QTN profiles if SNP density is sufficient to saturate the segments. [ABSTRACT FROM AUTHOR]

Background: Salt stress significantly reduces soybean yield. To improve salt tolerance in soybean, it is important to mine the genes associated with salt tolerance traits. Results: Salt tolerance traits of 286 soybean accessions were measured four times between 2009 and 2015. The results were associated with 740,754 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) using three-variance-component multi-locus random-SNP-effect mixed linear model (3VmrMLM). As a result, eight salt tolerance genes (GmCHX1, GsPRX9, Gm5PTase8, GmWRKY, GmCHX20a, GmNHX1, GmSK1, and GmLEA2-1) near 179 significant and 79 suggested QTNs and two salt tolerance genes (GmWRKY49 and GmSK1) near 45 significant and 14 suggested QEIs were associated with salt tolerance index traits in previous studies. Six candidate genes and three gene-by-environment interactions (GEIs) were predicted to be associated with these index traits. Analysis of four salt tolerance related traits under control and salt treatments revealed six genes associated with salt tolerance (GmHDA13, GmPHO1, GmERF5, GmNAC06, GmbZIP132, and GmHsp90s) around 166 QEIs were verified in previous studies. Five candidate GEIs were confirmed to be associated with salt stress by at least one haplotype analysis. The elite molecular modules of seven candidate genes with selection signs were extracted from wild soybean, and these genes could be applied to soybean molecular breeding. Two of these genes, Glyma06g04840 and Glyma07g18150, were confirmed by qRT-PCR and are expected to be key players in responding to salt stress. Conclusions: Around the QTNs and QEIs identified in this study, 16 known genes, 6 candidate genes, and 8 candidate GEIs were found to be associated with soybean salt tolerance, of which Glyma07g18150 was further confirmed by qRT-PCR. [ABSTRACT FROM AUTHOR]

Soybean hundred seed weight (HSW) is a complex quantitative trait affected by multiple genes and environmental factors. To date, a large number of quantitative trait nucleotides (QTNs) have been reported, but less information on QTN-by-environment interactions (QEIs) and QTN-QTN interaction (QQIs) for soybean HSW is available. Mapping without QEIs and QQIs result in missing some important QTNs that are significantly related to HSW. Therefore, the present study conducted genome-wide association analysis to map main QTNs, QEIs and QQIs for HSW in a panel with 573 diverse soybean lines tested in three independent environments (E1, E2 and E3) with Mean- and best linear unbiased value (BLUP)- phenotype. In all, 147 main effect QTNs, 11 QEIs, and 24 pairs of QQIs were detected in the Mean-phenotype, and 138 main effect QTNs, 13 QEIs, and 27 pairs of QQIs in the BLUP-phenotype. The total phenotypic variation explained by the main effect QTNs, QEIs, and QQIs were 35.31–39.71, 8.52–8.89 and 34.77–35.09%, respectively, indicating an important role of non-additive effects on HSW. Out of these, 33 QTNs were considered as stable with 23 colocalized with previously known loci, while 10 were novel QTNs. In addition, 10 pairs stable QQIs were simultaneously detected in the two phenotypes. Based on homolog search in Arabidopsis thaliana and in silico transcriptome data, seven genes (Glyma13g42310, Glyma13g42320, Glyma08g19580, Glyma13g44020, Glyma13g43800, Glyma17g16620 and Glyma07g08950) from some main-QTNs and two genes (Glyma06g19000 and Glyma17g09110) of QQIs were identified as potential candidate genes, however their functional role warrant further screening and functional validation. Our results shed light on the involvement of QEIs and QQIs in regulating HSW in soybean, and these together with candidate genes identified would be valuable genomic resources in developing soybean cultivars with desirable seed weight. [ABSTRACT FROM AUTHOR]

Common bean is one of the widely consumed food security crop in Africa, Asia and South America. It is a rich source of protein, minerals and micronutrients. High genotype by environment interaction is one of the main challenge in breeding for high grain micronutrient concentration. The objective of this study was to estimate SNP markers associated with grain Fe and Zn concentration using 289 common bean genotypes and 11,480 SNP markers. The study revealed that 43 quantitative trait loci (QTLs) were associated with grain Fe and Zn concentration. Five quantitative trait nucleotides (QTNs), that is, QTN Fe_1.1, QTN Fe_6.3, QTN Fe_6.5, QTN Fe_10.3 and QTN Fe_11.6 were detected both at Haramaya and Melkassa locations. Two of the markers, that is, QTN Fe_6.3 and QTN Fe_6.5, were located on chromosome 06 while QTN Fe_1.1, QTN Fe_10.3 and QTN Fe_ 11.6 were residing on chromosomes 01, 10 and 11, respectively. Among these, QTN Fe_11.6 had a large and positive consistent effect across locations. The five stable QTNs along with the potential candidate genes could be used for Fe biofortification through marker assisted selection. [ABSTRACT FROM AUTHOR]

Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits. [ABSTRACT FROM AUTHOR]

Although genome-wide association studies are widely used to mine genes for quantitative traits, the effects to be estimated are confounded, and the methodologies for detecting interactions are imperfect. To address these issues, the mixed model proposed here first estimates the genotypic effects for AA, Aa, and aa, and the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model. This strategy was further expanded to cover QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) using the same mixed-model framework. Thus, a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model (mrMLM) method to establish a new methodological framework, 3VmrMLM, that detects all types of loci and estimates their effects. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and a low false positive rate. In re-analyses of 10 traits in 1439 rice hybrids, detection of 269 known genes, 45 known gene-by-environment interactions, and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor-allele-frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%) and more dominance loci. In addition, a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs, and variable selection under a polygenic background was proposed for QQI detection. This study provides a new approach for revealing the genetic architecture of quantitative traits. A compressed mixed model in which a parameter vector replaces all the effects of interest and its polygenic background replaces all the polygenic backgrounds is integrated with mrMLM to establish 3VmrMLM, which detects QTNs, QEIs, and QQIs and estimates their effects. In re-analyses of 10 rice traits, detection of 269 known genes, 45 known GEIs, and 20 known GGIs validated the 3VmrMLM approach. [ABSTRACT FROM AUTHOR]

As the major source of edible protein and oil, the global demand for soybean (Glycine max (L.) Merr.) is increasing. Plant height is closely related to yield; therefore, understanding the genetic basis of plant height will help to improve soybean plant type and increase seed yield. In this study, quantitative trait loci (QTLs) and nucleotides (QTNs) for soybean plant height were detected by linkage analysis and association analysis. A high-density map containing 2225 bin markers was constructed by using 108 342 SNPs of a recombinant inbred line population (named RIL3613) of 120 lines for linkage analysis. In total, 39 QTLs were detected, including 16 QTLs that were repeatedly detected in multiple environments. Association analysis was performed by using 63 306 SNPs from a germplasm population of 455 natural soybean accessions. In total, 62 QTNs were detected, and 26 QTNs were repeatedly detected by multiple methods. Fourteen QTNs were distributed in the intervals of six multiple-environment QTLs by comparing the results of association analysis and linkage analysis. With pathway analysis, six candidate genes were identified as being associated with plant height. These results contribute to analysis of the genetic basis of plant height and will promote marker-assisted selection for breeding ideal plant shape. Linkage analysis and association analysis are important methods for studying the inheritance of quantitative traits in soybean. Using the RIL3613 population and a germplasm population, we constructed a high-density bin map and identified 16 multi-environment QTLs and 26 multi-method QTNs and six candidate genes for plant height. These results are helpful for analysing the genetic basis of plant height. [ABSTRACT FROM AUTHOR]

• Extensive and continuous variations for leaf angle related traits were observed among 200 cut chrysanthemum accessions across two years. • 100 stable quantitative trait nucleotides (QTNs) and 75 QTN×environments interactions (QEIs) were identified to be associated with leaf angle using multi-locus genome-wide association study via 3VmrMLM. • 26 known and 32 novel genes mined here will contribute to the molecular breeding program of chrysanthemum plant architecture. • Haplotype analysis of ARF16 revealed four accessions with elite leaf angle as potential donors for improving chrysanthemum leaf angle. Chrysanthemum (Chrysanthemum morifolium Ramat.), one of the four most popular cut flowers in the world, occupies a large share of global floriculture trade. Leaf angle (LA) is an important factor affecting plant architecture, photosynthesis and yields of cut chrysanthemum. However, the genetic basis and related genes underlying LA remain largely unknown. In this research, phenotypes of five LA-related traits in 200 cut chrysanthemum accessions were investigated across two consecutive years (Y2020 and Y2021), and their broad-sense genetic heritabilities ranged from 68.91% to 84.70%. Leaf orientation value (LOV) exhibited the maximum phenotypic coefficient of variation of 35.66% in Y2020 and 32.34% in Y2021. A new methodological framework named 3VmrMLM was applied for multi-locus genome-wide association study (GWAS) based on a set of 330,710 single nucleotide polymorphisms (SNPs) to explore the genetic loci and candidate genes of LA. As a result, 303 quantitative trait nucleotides (QTNs) in single-environment, 120 QTNs and 75 QTN × environments interactions (QEIs) in multiple-environments were identified. Among the genes around these QTNs and QEIs, 26 known genes were considered to be associated with LA, including ARF16, IBH1, AN3 , etc. Furthermore, the haplotype analysis of ARF16 revealed four accessions with elite LA. Our findings provide a fundamental understanding to the genetic architecture of LA and contribute to further gene cloning and molecular improvement of chrysanthemum plant architecture. [ABSTRACT FROM AUTHOR]

Background: High-resolution mapping of the loci (QTN) responsible for genetic variation in quantitative traits is essential for positional cloning of candidate genes, and for effective marker assisted selection. The confidence interval (QTL) flanking the point estimate of QTN-location is proportional to the number of individuals in the mapping population carrying chromosomes recombinant in the given interval. Consequently, many designs for high resolution QTN mapping are based on increasing the proportion of recombinants in the mapping population. The "Targeted Recombinant Progeny" (TRP) design is a new design for high resolution mapping of a target QTN in crosses between pure, or inbred lines. It is a three-generation procedure generating a large number of recombinant individuals within a QTL previously shown to contain a QTN. This is achieved by having individuals that carry chromosomes recombinant across the target QTL interval as parents of a large mapping population; most of whom will therefore carry recombinant chromosomes targeted to the given QTL. The TRP design is particularly useful for high resolution mapping of QTN that differentiate inbred or pure lines, and hence are not amenable to high resolution mapping by genome-wide association tests. Results: In the absence of residual polygenic variation, population sizes required for achieving given mapping resolution by the TRP-F2 design relative to a standard F2 design ranged from 0.289 for a QTN with standardized allele substitution effect = 0.2, mapped to an initial QTL of 0.2 Morgan to 0.041 for equivalent QTN mapped to an initial QTL of 0.02 M. In the presence of residual polygenic variation, the relative effectiveness of the TRP design ranges from 1.068 to 0.151 for the same initial QTL intervals and QTN effect. Thus even in the presence of polygenic variation, the TRP can still provide major savings. Simulation showed that mapping by TRP should be based on 30-50 markers spanning the initial interval; and on at least 50 or more G2 families representing this number of recombination points,. Conclusions: The TRP design can be an effective procedure for achieving high and ultra-high mapping resolution of a target QTN previously mapped to a known confidence interval (QTL). [ABSTRACT FROM AUTHOR]

Most association studies for pinpointing trait-associated variants are performed within breed. The availability of sequence data from key ancestors of several cattle breeds now enables immediate assessment of the frequency of trait-associated variants in populations different from the mapping population and their imputation into large validation populations. The objective of this study was to validate the effects of 4 putatively causative variants on milk production traits, male fertility, and stature in German Fleckvieh and Holstein-Friesian animals using targeted sequence imputation. We used whole-genome sequence data of 456 animals to impute 4 missense mutations in DGAT1, GHR, PRLR, and PROP1 into 10,363 Fleckvieh and 8,812 Holstein animals. The accuracy of the imputed genotypes exceeded 95% for all variants. Association testing with imputed variants revealed consistent antagonistic effects of the DGAT1 p.A232K and GHR p.F279Y variants on milk yield and protein and fat contents, respectively, in both breeds. The allele frequency of both polymorphisms has changed considerably in the past 20 yr, indicating that they were targets of recent selection for milk production traits. The PRLR p.S18N variant was associated with yield traits in Fleckvieh but not in Holstein, suggesting that it may be in linkage disequilibrium with a mutation affecting yield traits rather than being causal. The reported effects of the PROP1 p.H173R variant on milk production, male fertility, and stature could not be confirmed. Our results demonstrate that population-wide imputation of candidate causal variants from sequence data is feasible, enabling their rapid validation in large independent populations. [ABSTRACT FROM AUTHOR]

Background: High-resolution mapping of the loci (QTN) responsible for genetic variation in quantitative traits is essential for positional cloning of candidate genes, and for effective marker assisted selection. The confidence interval (QTL) flanking the point estimate of QTN-location is proportional to the number of individuals in the mapping population carrying chromosomes recombinant in the given interval. Consequently, many designs for high resolution QTN mapping are based on increasing the proportion of recombinants in the mapping population. The "Targeted Recombinant Progeny" (TRP) design is a new design for high resolution mapping of a target QTN in crosses between pure, or inbred lines. It is a three-generation procedure generating a large number of recombinant individuals within a QTL previously shown to contain a QTN. This is achieved by having individuals that carry chromosomes recombinant across the target QTL interval as parents of a large mapping population; most of whom will therefore carry recombinant chromosomes targeted to the given QTL. The TRP design is particularly useful for high resolution mapping of QTN that differentiate inbred or pure lines, and hence are not amenable to high resolution mapping by genome-wide association tests. Results: In the absence of residual polygenic variation, population sizes required for achieving given mapping resolution by the TRP-F2 design relative to a standard F2 design ranged from 0.289 for a QTN with standardized allele substitution effect = 0.2, mapped to an initial QTL of 0.2 Morgan to 0.041 for equivalent QTN mapped to an initial QTL of 0.02 M. In the presence of residual polygenic variation, the relative effectiveness of the TRP design ranges from 1.068 to 0.151 for the same initial QTL intervals and QTN effect. Thus even in the presence of polygenic variation, the TRP can still provide major savings. Simulation showed that mapping by TRP should be based on 30-50 markers spanning the initial interval; and on at least 50 or more G2 families representing this number of recombination points,. Conclusions: The TRP design can be an effective procedure for achieving high and ultra-high mapping resolution of a target QTN previously mapped to a known confidence interval (QTL). [ABSTRACT FROM AUTHOR]

Evolutionary genetics has entered an unprecedented era of discovery, catalyzed in large part by the development of technologies that provide information about genome sequence and function. An important benefit is the ability to move beyond a handful of model organisms in lab settings to identify the genetic basis for evolutionarily interesting traits in many organisms in natural settings. Other benefits are the abilities to identify causal mutations and validate their phenotypic consequences more readily and in many more species. Genomic technologies have reinvigorated interest in some of the most fundamental and persistent questions in evolutionary genetics, revealed previously unsuspected evolutionary phenomena, and opened the door to a wide range of new questions. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the allele frequency distribution at the DGAT1 p.K232A polymorphic site in seven Italian dairy and dual purpose cattle breeds. On the whole, 651 animals belonging to Italian Holstein (116), Italian Brown (105), Italian Simmental (95), Valdostana Red Pied (95), Rendena (62), Reggiana (128) and Modenese (50) breeds were genotyped by PCR-RFLP. Sequencing was carried out to confirm results of the genotyping protocol. The DGAT1 p.232K allele was identified in Italian Holstein (25.4%), Reggiana (17.2%), and with very low frequency in Italian Simmental, Valdostana Red Pied and Rendena (<1%). In Italian Brown and Modenese, this allele was not detected. These results indicated that this polymorphic site can be considered for association studies only in Italian Holstein and Reggiana breeds. Deviation from Hardy- Weinberg equilibrium was observed in the Reggiana breed (P<0.01) in which there was an excess of heterozygous sires and absence of animals with the p.232KK genotype. This result should be further evaluated because the analysed sires represented almost all bulls available for artificial insemination in this breed. Comparison of allele frequencies at the DGAT1 locus with several other Holstein populations showed a wide range of variability, probably due to different selection strategies adopted. [ABSTRACT FROM AUTHOR]

The downstream consequences of a single quantitative trait polymorphism can provide important insight into the molecular basis of a trait. However, the molecular consequences of a polymorphism may be complex and only a subset of these may influence the trait of interest. In natural isolates of Saccharomyces cerevisiae, a nonsynonymous polymorphism in cystathione β-synthase ( CYS4) causes a deficiency in both cysteine and glutathione that results in rust-colored colonies and drug-dependent growth defects. Using a single-nucleotide allele replacement, we characterized the effects of this polymorphism on gene expression levels across the genome. To determine whether any of the differentially expressed genes are necessary for the production of rust-colored colonies, we screened the yeast deletion collection for genes that enhance or suppress rust coloration. We found that genes in the sulfur assimilation pathway are required for the production of rust color but not the drug-sensitivity phenotype. Our results show that a single quantitative trait polymorphism can generate a complex set of downstream changes, providing a molecular basis for pleiotropy. [ABSTRACT FROM AUTHOR]

Abstract: Early quantitative genetic theories emphasize the distribution of gene effects at individual loci or the distribution of mutant effects in maintaining quantitative genetic variation, but infrequently stress the distribution of gene effects among loci. In this study, we analyse the effects of the distribution of quantitative trait nucleotides (QTN) effects among sites under artificial and stabilizing selection. Wright''s formula is applied to describing the density distribution of allele frequencies of multiple diallelic QTN at the equilibrium of mutation-drift-selection. Our results demonstrate that the distribution of QTN effects among sites can affect additive genetic variation in terms of total additive variance, average gene diversity, per-class contribution of QTN effects and per-QTN contribution. When the distribution of QTN effects among sites is changed from L-shaped to bell-shaped or to be a flatter, both the total additive variance and the average gene diversity are changed. Per-class and per-QTN contributions exhibit different distribution patterns. The L-shaped distribution indicates the predominant role of the aggregative effects from the QTN of small finite effects. The bell-shaped or flatter distributions indicate the predominance of the QTN of intermediate and large effects. These predictions highlight the significance of the distribution of QTN effects among sites in interpreting the maintenance of quantitative genetic variation at the fine genome scale. [Copyright &y& Elsevier]

Wheat quality parameters are complex traits predominantly influenced by the genetic background of varieties. A total of 802 spring bread wheat breeding lines were tested at Marchouch station, Morocco in 2018 aimed at mining invaluable QTNs for grain protein content (PR), gluten content (GL), alveograph strength (W) and zeleny sedimentation test (Z). A single-locus (MLM) and multi-locus (FarmCPU) GWAS models were implemented using 5,095 SNP markers generated from the genotyping-by-sequencing (GBS) platform. A total of 36 and 92 QTNs were detected for the four quality traits from the MLM and FarmCPU GWAS models, respectively. The FarmCPU model detected markers on homoeologous chromosome group one having highly significant QTNs with PR, GL, W and Z traits. The two implemented GWAS models detected significant QTNs on the short arm of homoeologous chromosome groups 2 and 6 for GL, PR, W and Z traits. FarmCPU model detected SNP_615 302 marker on chromosome 3B with highly significant MTAs and concurrent effect for PR, GL and W traits. Moreover, significant MTAs were detected on chromosomes 3D, 4A, 4B, 5A, 5B, 7A, 7B and 7D for the four studied quality traits across the two GWAS models. The in-silico gene annotation for QTNs using the high confidence gene models detected genes involved in numerous functions including regulating plant growth, development and tolerance to biotic and abiotic stresses. The identified markers could potentially be useful tool for quality improvement through marker-assisted selection in future breeding programs. [Display omitted] • Homoeologous chromosome groups 2 and 6 revealed QTLs for protein and dough quality traits. • SNP_615,302 on chromosome 3B had association with concurrent effect for quality traits. • In-silico gene annotations revealed genes involved in plant growth, development and stress tolerance. [ABSTRACT FROM AUTHOR]