Comparative study of estimation methods for genomic breeding values

Genome-wide association mapping for yield-related traits in soybean (Glycine max) under well-watered and drought-stressed conditions

Soybean (Glycine max) productivity is significantly reduced by drought stress. Breeders are aiming to improve soybean grain yields both under well-watered (WW) and drought-stressed (DS) conditions, however, little is known about the genetic architecture of yield-related traits. Here, a panel of 188 soybean germplasm was used in a genome wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers linked to yield-related traits including pod number per plant (PN), biomass per plant (BM) and seed weight per plant (SW). The SLAF-seq genotyping was conducted on the population and three phenotype traits were examined in WW and DS conditions in four environments. Based on best linear unbiased prediction (BLUP) data and individual environmental analyses, 39 SNPs were significantly associated with three soybean traits under two conditions, which were tagged to 26 genomic regions by linkage disequilibrium (LD) analysis. Of these, six QTLs qPN-WW19.1, qPN-DS8.8, qBM-WW1, qBM-DS17.4, qSW-WW4 and qSW-DS8 were identified controlling PN, BM and SW of soybean. There were larger proportions of favorable haplotypes for locus qPN-WW19.1 and qSW-WW4 rather than qBM-WW1, qBM-DS17.4, qPN-DS8.8 and qSW-DS8 in both landraces and improved cultivars. In addition, several putative candidate genes such as Glyma.19G211300, Glyma.17G057100 and Glyma.04G124800, encoding E3 ubiquitin-protein ligase BAH1, WRKY transcription factor 11 and protein zinc induced facilitator-like 1, respectively, were predicted. We propose that the further exploration of these locus will facilitate accelerating breeding for high-yield soybean cultivars.

Construction of a genetic linkage map and QTL mapping of fruit quality traits in guava (Psidium guajava L.)

Guava (Psidium guajava L.) is an important fruit crop of the Indian sub-continent, with potential for improvements in quality and yield. The goal of the present study was to construct a genetic linkage map in an intraspecific cross between the elite cultivar 'Allahabad Safeda' and the Purple Guava landrace to identify the genomic regions responsible for important fruit quality traits, viz., total soluble solids, titratable acidity, vitamin C, and sugars. This population was phenotyped in field trials (as a winter crop) for three consecutive years, and showed moderate-to-high values of heterogeneity coefficients along with higher heritability (60.0%-97.0%) and genetic-advance-over-mean values (13.23%-31.17%), suggesting minimal environmental influence on the expression of fruit-quality traits and indicating that these traits can be improved by phenotypic selection methods. Significant correlations and strong associations were also detected among fruit physico-chemical traits in segregating progeny. The constructed linkage map consisted of 195 markers distributed across 11 chromosomes, spanning a length of 1,604.47 cM (average inter-loci distance of 8.80 markers) and with 88.00% coverage of the guava genome. Fifty-eight quantitative trait loci (QTLs) were detected in three environments with best linear unbiased prediction (BLUP) values using the composite interval mapping algorithm of the BIP (biparental populations) module. The QTLs were distributed on seven different chromosomes, explaining 10.95%-17.77% of phenotypic variance, with the highest LOD score being 5.96 for qTSS.AS.pau-6.2. Thirteen QTLs detected across multiple environments with BLUPs indicate stability and utility in a future breeding program for guava. Furthermore, seven QTL clusters with stable or common individual QTLs affecting two or more different traits were located on six linkage groups (LGs), explaining the correlation among fruit-quality traits. Thus, the multiple environmental evaluations conducted here have increased our understanding of the molecular basis of phenotypic variation, providing the basis for future high-resolution fine-mapping and paving the way for marker-assisted breeding of fruit-quality traits.

Genetic Analysis of Heterosis for Yield Influencing Traits in Brassica juncea Using a Doubled Haploid Population and Its Backcross Progenies

The exploitation of heterosis through hybrid breeding is one of the major breeding objectives for productivity increase in crop plants. This research analyzes the genetic basis of heterosis in Brassica juncea by using a doubled haploid (DH) mapping population derived from F1 between two heterotic inbred parents, one belonging to the Indian and the other belonging to the east European gene pool, and their two corresponding sets of backcross hybrids. An Illumina Infinium Brassica 90K SNP array-based genetic map was used to identify yield influencing quantitative trait loci (QTL) related to plant architecture, flowering, and silique- and seed-related traits using five different data sets from multiple trials, allowing the estimation of additive and dominance effects, as well as digenic epistatic interactions. In total, 695 additive QTL were detected for the 14 traits in the three trials using five data sets, with overdominance observed to be the predominant type of effect in determining the expression of heterotic QTL. The results indicated that the design in the present study was efficient for identifying common QTL across multiple trials and populations, which constitute a valuable resource for marker-assisted selection and further research. In addition, a total of 637 epistatic loci were identified, and it was concluded that epistasis among loci without detectable main effects plays an important role in controlling heterosis in yield of B. juncea.