Genetic Control Of Quantitative Traits Research Articles

Genome-wide association identifies key loci controlling blackberry postharvest quality.

Blackberry (Rubus subgenus Rubus) is a soft-fruited specialty crop that often suffers economic losses due to degradation in the shipping process. During transportation, fresh-market blackberries commonly leak, decay, deform, or become discolored through a disorder known as red drupelet reversion (RDR). Over the past 50 years, breeding programs have achieved better fruit firmness and postharvest quality through traditional selection methods, but the underlying genetic variation is poorly understood. We conducted a genome-wide association of fruit firmness and RDRmeasured in 300 tetraploid fresh-market blackberry genotypes from 2019-2021 with 65,995 SNPs concentrated in genic regions of the R. argutus reference genome. Fruit firmness and RDR had entry-mean broad sense heritabilities of 68% and 34%, respectively. Three variants on homologs of polygalacturonase (PG), pectin methylesterase (PME), and glucan endo-1,3-β-glucosidase explained 27% of variance in fruit firmness and were located on chromosomes Ra06, Ra01, and Ra02, respectively. Another PG homolog variant on chromosome Ra02 explained 8% of variance in RDR, but it was in strong linkage disequilibrium with 212 other RDR-associated SNPs across a 23 Mb region. A large cluster of six PME and PME inhibitor homologs was located near the fruit firmness quantitative trait locus (QTL) identified on Ra01. RDR and fruit firmness shared a significant negative correlation (r = -0.28) and overlapping QTL regions on Ra02 in this study. Our work demonstrates the complex nature of postharvest quality traits in blackberry, which are likely controlled by many small-effect QTLs. This study is the first large-scale effort to map the genetic control of quantitative traits in blackberry and provides a strong framework for future GWAS. Phenotypic and genotypic datasets may be used to train genomic selection models that target the improvement of postharvest quality.

A multi-reference parent nested-association mapping population to dissect the genetics of quantitative traits in durum wheat

Durum wheat (Triticum durum Desf.) breeding programs face many challenges surrounding the development of stable varieties with high quality and yield. Therefore, researchers and breeders are focused on deciphering the genetic architecture of biotic and abiotic traits with the aim of pyramiding desirable traits. These efforts require access to diverse genetic resources, including wild relatives, germplasm collections and mapping populations. Advances in accelerated generation technologies have enabled the rapid development of mapping populations with significant genetic diversity. Here, we describe the development of a durum Nested Association Mapping (dNAM) population, which represents a valuable genetic resource for mapping the effects of different alleles on trait performance. We created this population to understand the quantitative nature of drought-adaptive traits in durum wheat. We developed 920 F6 lines in only 18 months using speed breeding technology, including the F4 generation in the field. Large variation in above- and below-ground traits was observed, which could be harnessed using genetic mapping and breeding approaches. We genotyped the population using 13,393 DArTseq markers. Quality control resulted in 6,785 high-quality polymorphic markers used for structure analysis, linkage disequilibrium decay, and marker-trait association analyses. To demonstrate the effectiveness of dNAM as a resource for elucidating the genetic control of quantitative traits, we took a genome-wide mapping approach using the FarmCPU method for plant height and days to flowering. These results highlight the power of using dNAM as a tool to dissect the genetics of durum wheat traits, supporting the development of varieties with improved adaptation and yield.

Genetic yield control in sugar beet hybrids obtained by diallel crosses

Purpose. The aim of our study was to determine the combination ability of the studied pollinators and identify the genetic control of the yield sign in interline diallel hybrids of sugar beet.
 Methods. The source material was homozygous as a result self-pollination. The combination ability and genetic control of quantitative traits were determined by the Hayman model.
 Results. Genetic analysis revealed that sugar beet pollinator line demonstrated genetic control of yield in diallel sugar beet hybrids by 14 genes and gene groups. Lines (BZ 1 and BZ 4) with high general combination ability demonstrated a significant additive effect of genes were selected. The effects of specific combination ability, which significantly affected the yield of heterosis hybrids, were revealed. Their share of influence was 36.4 and 23.8%, respectively. High-yielding hybrid combinations of parent genotypes were selected. They are transferred to reproduction and testing for ecological plasticity.
 Conclusions. Genetic control of the yield sign in diallel hybrids is found based on the Hаyman model. The influence of the combination ability of sugar beet pollinators was determined and the best parent genotype pairs were selected. According to the effects of specific gene interaction, the best combinations have been identified that can be used as sources of economically valuable traits.

UNBRANCHED3 Expression and Inflorescence Development is Mediated by UNBRANCHED2 and the Distal Enhancer, KRN4, in Maize.

Enhancers are cis-acting DNA segments with the ability to increase target gene expression. They show high sensitivity to DNase and contain specific DNA elements in an open chromatin state that allows the binding of transcription factors (TFs). While numerous enhancers are annotated in the maize genome, few have been characterized genetically. KERNEL ROW NUMBER4 (KRN4), an intergenic quantitative trait locus for kernel row number, is assumed to be a cis-regulatory element of UNBRANCHED3 (UB3), a key inflorescence gene. However, the mechanism by which KRN4 controls UB3 expression remains unclear. Here, we found that KRN4 exhibits an open chromatin state, harboring sequences that showed high enhancer activity toward the 35S and UB3 promoters. KRN4 is bound by UB2-centered transcription complexes and interacts with the UB3 promoter by three duplex interactions to affect UB3 expression. Sequence variation at KRN4 enhances ub2 and ub3 mutant ear fasciation. Therefore, we suggest that KRN4 functions as a distal enhancer of the UB3 promoter via chromatin interactions and recruitment of UB2-centered transcription complexes for the fine-tuning of UB3 expression in meristems of ear inflorescences. These results provide evidence that an intergenic region helps to finely tune gene expression, providing a new perspective on the genetic control of quantitative traits.

Mapping Agronomic and Quality Traits in Elite Durum Wheat Lines under Differing Water Regimes

Final grain production and quality in durum wheat are affected by biotic and abiotic stresses. The association mapping (AM) approach is useful for dissecting the genetic control of quantitative traits, with the aim of increasing final wheat production under stress conditions. In this study, we used AM analyses to detect quantitative trait loci (QTL) underlying agronomic and quality traits in a collection of 294 elite durum wheat lines from CIMMYT (International Maize and Wheat Improvement Center), grown under different water regimes over four growing seasons. Thirty-seven significant marker-trait associations (MTAs) were detected for sedimentation volume (SV) and thousand kernel weight (TKW), located on chromosomes 1B and 2A, respectively. The QTL loci found were then confirmed with several AM analyses, which revealed 12 sedimentation index (SDS) MTAs and two additional loci for SV (4A) and yellow rust (1B). A candidate gene analysis of the identified genomic regions detected a cluster of 25 genes encoding blue copper proteins in chromosome 1B, with homoeologs in the two durum wheat subgenomes, and an ubiquinone biosynthesis O-methyltransferase gene. On chromosome 2A, several genes related to photosynthetic processes and metabolic pathways were found in proximity to the markers associated with TKW. These results are of potential use for subsequent application in marker-assisted durum wheat-breeding programs.

Heritability of morpho-agronomic traits in ornamental pepper

Abstract This study was conducted to identify the allelic interactions and genetic effects involved in genetic control of quantitative traits in Capsicum annuum. Six generations (P1, P2, F1, F2, BC1 and BC2) were used to study the variances, means and allelic interactions of 16 quantitative traits evaluated. The additive part of the phenotypic variance found for plant height, number of peppers per plant, days to flowering and days to fruiting lead to the conclusion that phenotypic selection in the F2 generation are likely effective in obtaining genetic gains. According to the reduced model, the traits of canopy width, fruit width and number of seeds per fruit were not influenced by epistatic effects, but only by additive and dominant effects, which allows selection of superior individuals in segregating generations or improvement of these traits by means of hybridization.

Potential of a tomato MAGIC population to decipher the genetic control of quantitative traits and detect causal variants in the resequencing era.

Identification of the polymorphisms controlling quantitative traits remains a challenge for plant geneticists. Multiparent advanced generation intercross (MAGIC) populations offer an alternative to traditional linkage or association mapping populations by increasing the precision of quantitative trait loci (QTL) mapping. Here, we present the first tomato MAGIC population and highlight its potential for the valorization of intraspecific variation, QTL mapping and causal polymorphism identification. The population was developed by crossing eight founder lines, selected to include a wide range of genetic diversity, whose genomes have been previously resequenced. We selected 1536 SNPs among the 4 million available to enhance haplotype prediction and recombination detection in the population. The linkage map obtained showed an 87% increase in recombination frequencies compared to biparental populations. The prediction of the haplotype origin was possible for 89% of the MAGIC line genomes, allowing QTL detection at the haplotype level. We grew the population in two greenhouse trials and detected QTLs for fruit weight. We mapped three stable QTLs and six specific of a location. Finally, we showed the potential of the MAGIC population when coupled with whole genome sequencing of founder lines to detect candidate SNPs underlying the QTLs. For a previously cloned QTL on chromosome 3, we used the predicted allelic effect of each founder and their genome sequences to select putative causal polymorphisms in the supporting interval. The number of candidate polymorphisms was reduced from 12284 (in 800 genes) to 96 (in 54 genes), including the actual causal polymorphism. This population represents a new permanent resource for the tomato genetics community.

Genetic Parameters and Combining Ability Effects of Parents for Seed Yield and other Quantitative Traits in Black Gram [Vigna mungo (L.) Hepper

Line x tester analysis was carried out in black gram [Vigna mungo (L.) Hepper], an edible legume, to estimate the gca (general combining ability) effects of parents (3 lines and 3 testers) and the SCA (specific combining ability) effects of 9 crosses for seed yield and other eleven quantitative traits. Though additive and nonadditive gene actions governed the expression of quantitative traits, the magnitude of nonadditive gene action was higher than that of additive gene action for each quantitative trait. Two parents viz. ‘UG157’ and ‘DPU915’ were good general combiners. Two crosses namely ‘PDB 88-31’/’DPU 915’ and ‘PLU 277’/’KAU7’ had high per se performance along with positive significant SCA effect for seed yield/plant. The degree of dominance revealed overdominance for all the traits except clusters/plant with partial dominance. The predictability ratio also revealed the predominant role of nonadditive gene action in the genetic control of quantitative traits. Narrow sense heritability was also low for each trait. Recurrent selection or biparental mating followed by selection which can exploit both additive and nonadditive gene actions would be of interest for yield improvement in black gram. Due to presence of high magnitude of nonadditive gene action, heterosis breeding could also be attempted to develop low cost hybrid variety using genetic male sterility system in black gram.

Genetic control of quantitative traits and hybrid breeding strategies for cashew improvement

Knowledge about genetic parameters and trait control is fundamental for the success of any breeding program. This study aimed to estimate genetic parameters in an interpopulation of cashew using the REML/BLUP methods for vegetative growth, yield and nut quality traits. Results showed that plant height, canopy diameter, kernel weight and nut weight are under strong additive genetic control and do not present heterosis. The heritability of nut number and yield were higher in the broad than in the narrow sense. This indicates dominance and heterosis of these traits that should be adequately exploited in cashew breeding programs. Therefore, the best-suited breeding strategy to exploit heterosis is reciprocal recurrent selection using individual crosses between parents with high mean genotypic performance and high specific combining ability.

Gene actions of QTLs affecting several agronomic traits resolved in a recombinant inbred rice population and two testcross populations.

To understand the types of gene action controlling seven quantitative traits in rice, QTL mapping was performed to dissect the main effect (M-QTLs) and digenic epistatic (E-QTLs) QTLs responsible for the trait performance of 254 recombinant inbred lines (RILs) of "Lemont/Teqing", and two testcross (TC) F(1) populations derived from these RILs. The correlation analyses reveal a general pattern, i.e. trait heritability in the RILs was negatively correlated to trait heterosis in the TC hybrids. A large number of M-QTLs and E-QTLs affecting seven traits, including heading date (HD), plant height (PH), flag leaf length (FLL), flag leaf width (FLW), panicle length (PL), spikelet number per panicle (SN) and spikelet fertility (SF), were identified and could be classified into two predominant groups, additive QTLs detected primarily in the RILs, and overdominant QTLs identified exclusively in the TC populations. There is little overlap between QTLs identified in the RILs and in the TC populations. This result implied that additive gene action is largely independent from non-additive gene action in the genetic control of quantitative traits of rice. The detected E-QTLs collectively explained a much greater portion of the total phenotypic variation than the M-QTLs, supporting prior findings that epistasis has played an important role in the genetic control of quantitative traits in rice. The implications of these results to the development of inbred and hybrid cultivars were discussed.

Improving Physiological Assumptions Of Simulation Models By Using Gene‐Based Approaches

Application of crop models to plant breeding has been limited, in part due to the restricted capabilities of models to accurately represent genetic differences and genotype‐induced crop responses. A gene‐based model, GeneGro, was developed to simulate the effects of seven genes on growth and developmental processes in common bean (Phaseolus vulgaris L.) and was published in 1996. The objective of this paper is to describe the improvements that were made in GeneGro to incorporate the effect of the Tip gene. Presence of the Tip gene in photoperiod‐sensitive cultivars reduces the inhibitory effect of low temperature on photoperiod sensitivity of flowering. A mechanistic approach, further guided by information on two other genes affecting photoperiod response, i.e., Ppd and Hr, was used to incorporate the effect of the Tip gene. In the modified GeneGro, this inhibitory effect is reduced under cooler temperatures ranging from 15 to 20°C for the mean daily temperature. However, in the presence of Tip, no such reduction occurs. For the calibration data, GeneGro explained 75% of the variation in days to flower vs. 61% for the original model. For an extensive evaluation data set, the modification explained 72% of the variation in days to flower vs. 70% for the original version while days to maturity, seed yield, canopy dry mass, and harvest index showed no improvement. These results reflect two major constraints to effective use of gene‐based approaches in crop modeling. The first is the lack of reliable characterizations of cultivars for genes used in the model. The second is the scarcity of data from conditions where phenotypic differences between the Tip and tip gene would be expected. It can be concluded that understanding the genetic control of quantitative traits can guide improvements to simulation models.

Improving Physiological Assumptions Of Simulation Models By Using Gene-Based Approaches

Application of crop models to plant breeding has been limited, in part due to the restricted capabilities of models to accurately represent genetic differences and genotype-induced crop responses. A gene-based model, GeneGro, was developed to simulate the effects of seven genes on growth and developmental processes in common bean (Phaseolus vulgaris L.) and was published in 1996. The objective of this paper is to describe the improvements that were made in GeneGro to incorporate the effect of the Tip gene. Presence of the Tip gene in photoperiod-sensitive cultivars reduces the inhibitory effect of low temperature on photoperiod sensitivity of flowering. A mechanistic approach, further guided by information on two other genes affecting photoperiod response, i.e., Ppd and Hr, was used to incorporate the effect of the Tip gene. In the modified GeneGro, this inhibitory effect is reduced under cooler temperatures ranging from 15 to 20°C for the mean daily temperature. However, in the presence of Tip, no such reduction occurs. For the calibration data, GeneGro explained 75% of the variation in days to flower vs. 61% for the original model. For an extensive evaluation data set, the modification explained 72% of the variation in days to flower vs. 70% for the original version while days to maturity, seed yield, canopy dry mass, and harvest index showed no improvement. These results reflect two major constraints to effective use of gene-based approaches in crop modeling. The first is the lack of reliable characterizations of cultivars for genes used in the model. The second is the scarcity of data from conditions where phenotypic differences between the Tip and tip gene would be expected. It can be concluded that understanding the genetic control of quantitative traits can guide improvements to simulation models.

Genetic Complexity of the Malt Extract Trait in Barley Suggested by QTL Analysis

Many economically important traits of crop species are complexly inherited quantitative traits (QT). Trait expression tends to be continuous due to control by multiple genes (G), the environment (E), and G × G and G × E interactions. The development of comprehensive genome maps and quantitative trait locus (QTL) analysis procedures allowed the first significant examination of the genetic control of QTs. Malt extract is a good example of a QT in barley. Heretofore, little has been known about the genetic control of malt extract. The objective of this study was to elucidate the genetic characteristics of malt extract primarily from QTL analyses of the Steptoe x Morex cross. QTL analysis of data from the Steptoe x Morex F1-derived double haploid mapping population has revealed the location of five malt extract QTLs on three of barley's seven chromosomes. Most of the chromosome regions involved also contain QTLs for other related malting quality traits including α-amylase activity, diastatic power, barley and malt β-glucan content, β-glucanase activity, and/or seed dormancy. The overlapping QTLs indicate either linked genes or pleitropy or both. Analyses also indicated additive and G × E interactions for malt extract. Fine mapping of a critical region of chromosome 1 is in progress to elucidate the overlapping QTL situation among malt extract and the other quality traits listed above. Location of specific genes for malt extract will facilitate more precise breeding for the improvement of this important trait in barley through molecular marker assisted selection.

Toward an integrated linkage map of common bean. III. Mapping genetic factors controlling host-bacteria interactions.

Restriction fragment length polymorphism (RFLP)-based genetic linkage maps allow us to dissect the genetic control of quantitative traits (QT) by locating individual quantitative trait loci (QTLs) on the linkage map and determining their type of gene action and the magnitude of their contribution to the phenotype of the QT. We have performed such an analysis for two traits in common bean, involving interactions between the plant host and bacteria, namely Rhizobium nodule number (NN) and resistance to common bacterial blight (CBB) caused by Xanthomonas campestris pv. phaseoli. Analyses were conducted in the progeny of a cross between BAT93 (fewer nodules; moderately resistant to CBB) and Jalo EEP558 (more nodules; susceptible to CBB). An RFLP-based linkage map for common bean based on 152 markers had previously been derived in the F2 of this cross. Seventy F2-derived F3 families were inoculated in separate greenhouse experiments with Rhizobium tropici strain UMR1899 or X. c. pv. phaseoli isolate isolate W18. Regression and interval mapping analyses were used to identify genomic regions involved in the genetic control of these traits. These two methods identified the same genomic regions for each trait, with a few exceptions. For each trait, at least four putative QTLs were identified, which accounted for approximately 50% and 75% of the phenotypic variation in NN and CBB resistance, respectively. A chromosome region on linkage group D7 carried factor(s) influencing both traits. In all other cases, the putative QTLs affecting NN and CBB were located in different linkage groups or in the same linkage group, but far apart (more than 50 cM). Both BAT93 and Jalo EEP558 contributed alleles associated with higher NN, whereas CBB resistance was always associated with BAT93 alleles. Further investigations are needed to determine whether the QTLs for NN and CBB on linkage group D7 represent linked genes or the same gene with pleiotropic effects. Identification of the QTLs raises the possibility of initiating map-based cloning and marker-assisted selection for these traits.

Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action.

Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17-20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800-1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.