Genome-Wide Association Study Reveals Novel Genetic Loci for Quantitative Resistance to Septoria Tritici Blotch in Wheat (Triticum aestivum L.)

Septoria tritici blotch, caused by the fungus Zymoseptoria titici, poses serious and persistent challenges to wheat cultivation in Ethiopia and worldwide. Deploying resistant cultivars is a major component of controlling septoria tritici blotch (STB). Thus, the objective of this study was to elucidate the genomic architecture of STB resistance in an association panel of 178 bread wheat genotypes. The association panel was phenotyped for STB resistance, phenology, yield, and yield-related traits in three locations for 2 years. The panel was also genotyped for single nucleotide polymorphism (SNP) markers using the genotyping-by-sequencing (GBS) method, and a total of 7,776 polymorphic SNPs were used in the subsequent analyses. Marker-trait associations were also computed using a genome association and prediction integrated tool (GAPIT). The study then found that the broad-sense heritability for STB resistance ranged from 0.58 to 0.97 and 0.72 to 0.81 at the individual and across-environment levels, respectively, indicating the presence of STB resistance alleles in the association panel. Population structure and principal component analyses detected two sub-groups with greater degrees of admixture. A linkage disequilibrium (LD) analysis in 338,125 marker pairs also detected the existence of significant (p ≤ 0.01) linkage in 27.6% of the marker pairs. Specifically, in all chromosomes, the LD between SNPs declined within 2.26–105.62 Mbp, with an overall mean of 31.44 Mbp. Furthermore, the association analysis identified 53 loci that were significantly (false discovery rate, FDR, <0.05) associated with STB resistance, further pointing to 33 putative quantitative trait loci (QTLs). Most of these shared similar chromosomes with already published Septoria resistance genes, which were distributed across chromosomes 1B, 1D, 2A, 2B, 2D, 3A,3 B, 3D, 4A, 5A, 5B, 6A, 7A, 7B, and 7D. However, five of the putative QTLs identified on chromosomes 1A, 5D, and 6B appeared to be novel. Dissecting the detected loci on IWGSC RefSeq Annotation v2.1 revealed the existence of disease resistance-associated genes in the identified QTL regions that are involved in plant defense responses. These putative QTLs explained 2.7–13.2% of the total phenotypic variation. Seven of the QTLs (R2 = 2.7–10.8%) for STB resistance also co-localized with marker-trait associations (MTAs) for agronomic traits. Overall, this analysis reported on putative QTLs for adult plant resistance to STB and some important agronomic traits. The reported and novel QTLs have been identified previously, indicating the potential to improve STB resistance by pyramiding QTLs by marker-assisted selection.

Development of a SNP linkage map and genome-wide association study for resistance to Aeromonas hydrophila in pacu (Piaractus mesopotamicus)

Background Pacu ( Piaractus mesopotamicus ) is one of the most important neotropical aquaculture species in South America. Disease outbreaks triggered by the bacterium Aeromonas hydrophila have resulted in significant losses to pacu production. Selective breeding using genomic information is a powerful strategy to improve disease resistance in fish species. This study aimed to investigate the genetic architecture of resistance to A. hydrophila in pacu via a Genome-Wide Association Study (GWAS) to identify Quantitative Trait Loci (QTLs) and putative genes associated with this trait in 14 pacu full-sib families. An experimental challenge was performed in order to assess the levels of genetic variation for resistance against the bacteria using the following trait definition: binary test survival (TS) and time of death (TD). Results The analysis of genetic parameters identified moderate heritability values for both resistance traits: h 2 = 0.20 (± 0.09) for TS and h 2 = 0.35 (± 0.15) for TD. To enable the GWAS, a pacu linkage map was developed using genotype data derived from Restriction Site Associated DNA Sequencing (RAD-Seq), resulting in 27 linkage groups consisting of 17,453 Single Nucleotide Polymorphisms (SNPs). The length of linkage groups varied from 79.95 (LG14) to 137.01 (LG1) cM, with a total integrated map length of 2,755.60 cM. GWAS analysis identified 22 putative QTLs associated to A. hydrophila resistance (defined as genomic regions explaining > 1% of the additive genetic variance for the trait), distributed in 17 linkage groups. Several candidate genes related to immune response were located close to the putative QTLs, such as tbk1 , trim16 , Il12rb2 and lyz2 . Conclusion The outputs of this study include the first medium density linkage map for pacu, which can be used as a framework to study different traits relevant to the production of this species. In addition, resistance to A. hydrophila was found to be moderately heritable but with a polygenic architecture suggesting that genomic selection, instead of marker assisted selection, might be tested for efficiently improving resistance to one of the most problematic diseases that affects the South American aquaculture.

Genome-enabled prediction models for black tea (Camellia sisnesnsis) quality and drought tolerance traits

SummaryGenomic selection in tea (Camellia sinensis) breeding has the potential to accelerate efficiency of choosing parents with desirable traits at the seedling stage.The study evaluated different genome-enabled prediction models for black tea quality and drought tolerance traits in discovery and validation populations. The discovery population comprised of two segregating tea populations (TRFK St. 504 and TRFK St. 524) with 255 F1 progenies and 56 individual tea cultivars in validation population genotyped using 1 421 DArTseq markers.Two-fold cross-validation was used for training the prediction models in discovery population, and the best prediction models were consequently, fitted to the validation population.Of all the four based prediction approaches, putative QTLs (Quantitative Trait Loci) + annotated proteins + KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway-based prediction approach, showed robustness and usefulness in prediction of phenotypes.Extreme Learning Machine model had better prediction ability for catechin, astringency, brightness, briskness, and colour based on putative QTLs + annotated proteins + KEGG pathway approach.The percent variables of importance of putatively annotated proteins and KEGG pathways were associated with the phenotypic traits. The findings has for the first time opened up a new avenue for future application of genomic selection in tea breeding.

Combined linkage and association mapping of putative QTLs controlling black tea quality and drought tolerance traits

The advancements in genotyping have opened new approaches for identification and precise mapping of Quantitative Trait Loci (QTLs) in plants, particularly by combining linkage and association mapping (AM) analysis. In this study, a combination of linkage and the AM approach was used to identify and authenticate putative QTLs associated with black tea quality traits and percent relative water content (%RWC). The population structure analysis clustered two parents and their respective 261 F1 progenies from the two reciprocal crosses into two clusters with 141 tea accessions in cluster one and 122 tea accessions in cluster two. The two clusters were of mixed origin with tea accessions in population TRFK St. 504 clustering together with tea accessions in population TRFK St. 524. A total of 71 putative QTLs linked to black tea quality traits and %RWC were detected in interval mapping (IM) method and were used as cofactors in multiple QTL model (MQM) mapping where 46 putative QTLs were detected. The phenotypic variance for each QTL ranged from 2.8–23.3% in IM and 4.1–23% in MQM mapping. Using Q-model and Q+K-model in AM, a total of 49 DArTseq markers were associated with 16 phenotypic traits. Significant marker-trait association in AM were similar to those obtained in IM, and MQM mapping except for six more putative QTLs detected in AM which are involved in biosynthesis of secondary metabolites, carbon fixation and abiotic stress. The combined linkage and AM approach appears to have great potential to improve the selection of desirable traits in tea breeding.

Genetics of physical wood properties and early growth in a tropical pine hybrid

Quantitative trait locus (QTL) detection was carried out for physical wood properties and early growth traits in an interspecific hybrid between Pinus elliottii var. elliottii Engelm. and Pinus caribaea var. hondurensis (Sénécl) Barr. et Golf. A pseudo-testcross QTL detection strategy was used to identify genome regions that influenced wood density, secondary growth, and dry wood mass index on each genetic map for the parents of a single F1 family (n = 133). Traits were measured for annual ring and earlywood and latewood components and were based on both individual and average ring values from 1996 to 1999. A total of 12 significant putative QTLs were identified that clustered into four genomic regions in the P. elliottii var. elliottii parent and a single region in the P. caribaea var. hondurensis parent. The P. elliottii var. elliottii parent largely contributed putative QTLs for diameter growth and wood density, whereas the P. caribaea var. hondurensis parent contributed a putative QTL for earlywood formed in 1997. Putative QTLs that influenced density and ring width did not colocate, suggesting independent inheritance of these characters. This was consistent with the lack of genetic correlation between wood density and diameter growth observed in quantitative studies in hybrid pines.

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.