Summary
Hitherto, most quantitative trait loci of maize growth and biomass yield have been identified for a single time point, usually the final harvest stage. Through this approach cumulative ...effects are detected, without considering genetic factors causing phase‐specific differences in growth rates. To assess the genetics of growth dynamics, we employed automated non‐invasive phenotyping to monitor the plant sizes of 252 diverse maize inbred lines at 11 different developmental time points; 50 k SNP array genotype data were used for genome‐wide association mapping and genomic selection. The heritability of biomass was estimated to be over 71%, and the average prediction accuracy amounted to 0.39. Using the individual time point data, 12 main effect marker‐trait associations (MTAs) and six pairs of epistatic interactions were detected that displayed different patterns of expression at various developmental time points. A subset of them also showed significant effects on relative growth rates in different intervals. The detected MTAs jointly explained up to 12% of the total phenotypic variation, decreasing with developmental progression. Using non‐parametric functional mapping and multivariate mapping approaches, four additional marker loci affecting growth dynamics were detected. Our results demonstrate that plant biomass accumulation is a complex trait governed by many small effect loci, most of which act at certain restricted developmental phases. This highlights the need for investigation of stage‐specific growth affecting genes to elucidate important processes operating at different developmental phases.
Significance Statement
Most genetic studies of biomass accumulation or yield in crops have focused on a single growth stage, but agronomic traits are complex and controlled by many genes, each with small effect. Here we use high‐throughput non‐invasive phenotyping to show that genetic effects on maize biomass accumulation differ across developmental phases, that there are complex interactions of loci with developmental progression, that allele effects and epistatic interaction patterns change over time, and that functional mapping can uncover additional genetic factors. Our results indicate that continuous assessment of growth dynamics coupled with transcript profiling will aid in detecting superior stage‐specific genes/alleles and thus provide a powerful tool for crop improvement.
Powdery mildew is an important foliar disease of barley (Hordeum vulgare L.) caused by the biotrophic fungus Blumeria graminis f. sp. hordei (Bgh). The understanding of the resistance mechanism is ...essential for future resistance breeding. In particular, the identification of race-nonspecific resistance genes is important because of their regarded durability and broad-spectrum activity. We assessed the severity of powdery mildew infection on detached seedling leaves of 267 barley accessions using two poly-virulent isolates and performed a genome-wide association study exploiting 201 of these accessions. Two-hundred and fourteen markers, located on six barley chromosomes are associated with potential race-nonspecific Bgh resistance or susceptibility. Initial steps for the functional validation of four promising candidates were performed based on phenotype and transcription data. Specific candidate alleles were analyzed via transient gene silencing as well as transient overexpression. Microarray data of the four selected candidates indicate differential regulation of the transcription in response to Bgh infection. Based on our results, all four candidate genes seem to be involved in the responses to powdery mildew attack. In particular, the transient overexpression of specific alleles of two candidate genes, a potential arabinogalactan protein and the barley homolog of Arabidopsis thaliana's Light-Response Bric-a-Brac/-Tramtrack/-Broad Complex/-POxvirus and Zinc finger (AtLRB1) or AtLRB2, were top candidates of novel powdery mildew susceptibility genes.
Climatic conditions affect the growth, development and final crop production. As wheat is of paramount importance as a staple crop in the human diet, there is a growing need to study its abiotic ...stress adaptation through the performance of key breeding traits. New and complementary approaches, such as genome-wide association studies (GWAS) and genomic selection (GS), are used for the dissection of different agronomic traits. The present study focused on the dissection of agronomic and quality traits of interest (initial agronomic score, yield, gluten index, sedimentation index, specific weight, whole grain protein and yellow colour) assessed in a panel of 179 durum wheat lines (Triticum durum Desf.), grown under rainfed conditions in different Mediterranean environments in Southern Spain (Andalusia). The findings show a total of 37 marker-trait associations (MTAs) which affect phenotype expression for three quality traits (specific weight, gluten and sedimentation indexes). MTAs could be mapped on the A and B durum wheat subgenomes (on chromosomes 1A, 1B, 2A, 2B and 3A) through the recently available bread wheat reference assembly (IWGSC RefSeqv1). Two of the MTAs found for quality traits (gluten index and SDS) corresponded to the known Glu-B1 and Glu-A1 loci, for which candidate genes corresponding to high molecular weight glutenin subunits could be located. The GS prediction ability values obtained from the breeding materials analyzed showed promising results for traits as grain protein content, sedimentation and gluten indexes, which can be used in plant breeding programs.
Key message
Genome-wide association mapping as well as marker- and haplotype-based genome-wide selection unraveled a complex genetic architecture of grain yield with absence of large effect QTL and ...presence of local epistatic effects.
The genetic architecture of grain yield determines to a large extent the optimum design of genomic-assisted wheat breeding programs. The main goal of our study was to examine the potential and limitations to dissect the genetic architecture of grain yield in wheat using a large experimental data set. Our study was based on phenotypic information and genomic data of 13,901 SNPs of a diverse set of 3816 elite wheat lines adapted to Central Europe. We applied genome-wide association mapping based on experimental and simulated data sets and performed marker- and haplotype-based genomic prediction. Computer simulations revealed for our mapping population a high power to detect QTL, even if they individually explained only 2.5% of the genetic variation. Despite this, we found no stable marker–trait associations when validating in independent subsets. A two-dimensional scan for marker–marker interactions indicated presence of local epistasis which was further supported by improved prediction abilities when shifting from marker- to haplotype-based genome-wide prediction approaches. We observed that marker effects estimated using genome-wide prediction approaches strongly varied across years albeit resulting in high prediction abilities. Thus, our results suggested that the prediction accuracy of genomic selection in wheat is mainly driven by relatedness rather than by exploiting knowledge of the genetic architecture.
Root system architecture (RSA) is seldom considered as a selection criterion to improve yield in maize breeding, mainly because of the practical difficulties with their evaluation under field ...conditions. In the present study, phenotypic profiling of 187 advanced-backcross BC
4
F
3
maize lines (Ye478 × Wu312) was conducted at different developmental stages under field conditions at two locations (Dongbeiwang in 2007 and Shangzhuang in 2008) for five quantitative root traits. The aims were to (1) understand the genetic basis of root growth in the field; (2) investigate the contribution of root traits to grain yield (GY); and (3) detect QTLs controlling root traits at the seedling (I), silking (II) and maturation (III) stages. Axial root (AR)-related traits showed higher heritability than lateral root (LR)-related traits, which indicated stronger environmental effects on LR growth. Among the three developmental stages, root establishment at stage I showed the closest relationship with GY (
r
= 0.33–0.43,
P
< 0.001). Thirty QTLs for RSA were detected in the BC
4
F
3
population and only 13.3 % of the QTLs were detected at stage III. Most important QTLs for root traits were located on chromosome 6 near the locus umc1257 (bin 6.02–6.04) at stage I, and chromosome 10 near the locus umc2003 (bin 10.04) for number of AR across all three developmental stages. The regions of chromosome 7 near the locus bnlg339 (bin 7.03) and chromosome 1 near the locus bnlg1556 (bin 1.07) harbored QTLs for both GY- and LR-related traits at stages I and II, respectively. These results help to understand the genetic basis of root development under field conditions and their contribution to grain yield.
Marker-assisted selection (MAS) and genomic selection (GS) based on genome-wide marker data provide powerful tools to predict the genotypic value of selection material in plant breeding. However, ...case-to-case optimization of these approaches is required to achieve maximum accuracy of prediction with reasonable input.
Based on extended field evaluation data for grain yield, plant height, starch content and total pentosan content of elite hybrid rye derived from testcrosses involving two bi-parental populations that were genotyped with 1048 molecular markers, we compared the accuracy of prediction of MAS and GS in a cross-validation approach. MAS delivered generally lower and in addition potentially over-estimated accuracies of prediction than GS by ridge regression best linear unbiased prediction (RR-BLUP). The grade of relatedness of the plant material included in the estimation and test sets clearly affected the accuracy of prediction of GS. Within each of the two bi-parental populations, accuracies differed depending on the relatedness of the respective parental lines. Across populations, accuracy increased when both populations contributed to estimation and test set. In contrast, accuracy of prediction based on an estimation set from one population to a test set from the other population was low despite that the two bi-parental segregating populations under scrutiny shared one parental line. Limiting the number of locations or years in field testing reduced the accuracy of prediction of GS equally, supporting the view that to establish robust GS calibration models a sufficient number of test locations is of similar importance as extended testing for more than one year.
In hybrid rye, genomic selection is superior to marker-assisted selection. However, it achieves high accuracies of prediction only for selection candidates closely related to the plant material evaluated in field trials, resulting in a rather pessimistic prognosis for distantly related material. Both, the numbers of evaluation locations and testing years in trials contribute equally to prediction accuracy.
Globally, wheat (
L.) is a major source of proteins in human nutrition despite its unbalanced amino acid composition. The low lysine content in the protein fraction of wheat can lead to ...protein-energy-malnutrition prominently in developing countries. A promising strategy to overcome this problem is to breed varieties which combine high protein content with high lysine content. Nevertheless, this requires the incorporation of yet undefined donor genotypes into pre-breeding programs. Genebank collections are suspected to harbor the needed genetic diversity. In the 1970s, a large-scale screening of protein traits was conducted for the wheat genebank collection in Gatersleben; however, this data has been poorly mined so far. In the present study, a large historical dataset on protein content and lysine content of 4,971 accessions was curated, strictly corrected for outliers as well as for unreplicated data and consolidated as the corresponding adjusted entry means. Four genomic prediction approaches were compared based on the ability to accurately predict the traits of interest. High-quality phenotypic data of 558 accessions was leveraged by engaging the best performing prediction model, namely EG-BLUP. Finally, this publication incorporates predicted phenotypes of 7,651 accessions of the winter wheat collection. Five accessions were proposed as donor genotypes due to the combination of outstanding high protein content as well as lysine content. Further investigation of the passport data suggested an association of the adjusted lysine content with the elevation of the collecting site. This publicly available information can facilitate future pre-breeding activities.
Genome-wide predictions are a powerful tool for predicting trait performance. Against this backdrop we aimed to evaluate the potential and limitations of genome-wide predictions to inform the barley ...collection of the
with phenotypic data on complex traits including flowering time, plant height, thousand grain weight, as well as on growth habit and row type. We used previously published sequence data, providing information on 306,049 high-quality SNPs for 20,454 barley accessions. The prediction abilities of the two unordered categorical traits row type and growth type as well as the quantitative traits flowering time, plant height and thousand grain weight were investigated using different cross validation scenarios. Our results demonstrate that the unordered categorical traits can be predicted with high precision. In this way genome-wide prediction can be routinely deployed to extract information pertinent to the taxonomic status of gene bank accessions. In addition, the three quantitative traits were also predicted with high precision, thereby increasing the amount of information available for genotyped but not phenotyped accessions. Deeply phenotyped core collections, such as the barley 1,000 core set of the IPK Gatersleben, are a promising training population to calibrate genome-wide prediction models. Consequently, genome-wide predictions can substantially contribute to increase the attractiveness of gene bank collections and help evolve gene banks into bio-digital resource centers.
Abstract Climate change and population growth are putting increasing pressure on global food security. The development of high-yielding varieties for important crops such as wheat is crucial to meet ...these challenges. The basis for this is extensive exploitation of beneficial genetic variation resting in genebanks around the world. Selecting suitable donor genotypes from the vast number of wheat accessions stored in genebanks is a difficult task and depends critically on the density of information on the performance of individual accessions. Therefore, this study aimed to access phenotypic data from the Czech genebank, storing over 13,000 wheat accessions. We curated and analyzed data on heading date, plant height, and thousand grain weight for more than one-third of all available accessions regenerated across 70 years. The data underwent analysis using a linear mixed model, revealing high quality of curated data with heritability reaching 99%. The raw data, but also derived data such as the best linear unbiased estimations, are now available for the wheat collection of the Czech genebank for research and breeding.
Key message
The portfolio of available
Reduced height
loci (
Rht
-
B1
,
Rht
-
D1
, and
Rht24
) can be exploited for hybrid wheat breeding to achieve the desired heights in the female and male ...parents, as well as in the hybrids, without adverse effects on other traits relevant for hybrid seed production.
Plant height is an important trait in wheat line breeding, but is of even greater importance in hybrid wheat breeding. Here, the height of the female and male parental lines must be controlled and adjusted relative to each other to maximize hybrid seed production. In addition, the height of the resulting hybrids must be fine-tuned to meet the specific requirements of the farmers in the target regions. Moreover, this must be achieved without adversely impacting traits relevant for hybrid seed production. In this study, we explored
Reduced height
(
Rht
) loci effective in elite wheat and exploited their utilization for hybrid wheat breeding. We performed association mapping in a panel of 1705 wheat hybrids and their 225 parental lines, which besides the
Rht
-
B1
and
Rht
-
D1
loci revealed
Rht24
as a major QTL for plant height. Furthermore, we found that the
Rht
-
1
loci also reduce anther extrusion and thus cross-pollination ability, whereas
Rht24
appeared to have no adverse effect on this trait. Our results suggest different haplotypes of the three
Rht
loci to be used in the female or male pool of a hybrid breeding program, but also show that in general, plant height is a quantitative trait controlled by numerous small-effect QTL. Consequently, marker-assisted selection for the major
Rht
loci must be complemented by phenotypic selection to achieve the desired height in the female and male parents as well as in the wheat hybrids.
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