Pyropia haitanensis, a representative species of intertidal red algae, has significant economic value. However, its traditional breeding cycle is lengthy and inefficient, leading to a shortage of new varieties of P. haitanensis. Genomic selection (GS), an efficient molecular-breeding strategy, holds promise for accelerating the genetic improvement of yield-related traits. To date, no systematic GS study has been reported for red algae. In this study, we used a doubled haploid (DH) population to perform genomic prediction for five yield‑related traits (length, width, fresh weight, days to maturity, and thickness) based on whole-genome sequencing data. We systematically compared the effects of eight statistical models on prediction accuracy and evaluated the effect of different marker densities, ranging from 100 SNPs to the full marker set, under two SNP selection strategies: genome-wide association study (GWAS)-based selection and random selection. The results showed that the heritability of five yield traits ranged from 0.12 (length) to 0.57 (thickness). The random forest (RF) model consistently achieved higher prediction accuracy than the other models across all traits, with accuracies ranging from 0.448 for length to 0.727 for thickness. For all models, prediction accuracy reached a plateau when the number of markers was in the range of 1,000–5,000, with limited gains from further increases in marker density. GWAS-based selection performed better than random selection for most traits at low marker densities (100–1,000 SNPs), while random selection showed higher accuracy for width. In summary, the application of genomic selection of P. haitanensis breeding provides an effective strategy for identifying superior candidate individuals. In practical applications, the optimal model should be selected flexibly according to the genetic architecture of the traits and the available SNP density.
Wheat grain yield is determined by three primary components, one of which is grain number per spike (GPS; or spike fertility). GPS is further determined by spikelet number per spike (StPS) and grain number per spikelet (GPSt). TN28, a pyramidale wheat accession, produces up to five grains per spikelet, substantially exceeding the number in typical cultivars. To elucidate the genetic architecture underlying TN28’s superior traits and determine the optimal breeding strategy for trait introgression, we evaluated yield-related traits in a biparental F2 population derived from TN28 × Langdon across three environments over two years using quantitative trait locus (QTL) mapping and genomic selection (GS). QTL analysis using two mapping approaches detected no stable QTLs other than known flowering genes, indicating that TN28’s yield-related traits cannot be reliably introgressed via single- or few-marker selection. Conversely, GS using five prediction models demonstrated potential for accumulating favorable minor-effect alleles. The ridge regression model exhibited optimal performance for GPSt-related traits, with the top 20
Selection and breeding of rice varieties depend on genetic diversity contained in the primary and secondary gene pools. So far, researches on introduction of total DNA by means of pollen tube pathway method, ovary injection, seed sink method, ion beam mediated transformation method so as to contribute to expansion of gene pool have been reported in rice. In this study, we injected millet DNA into primary leaf-drawn coleoptile surrounding shoot apical meristem so that we could introduce foreign DNA more easily and not to disturb growth of rice. In the following generations, we selected strain MR-4 exhibiting shortened growth period and more tillers. Although MR-4 exhibited short growth period than DNA recipient, Sohaechal 16, its yield was not significantly different from DNA recipient apparently due to more tillers. RM marker analysis indicates apparently homozygous integration of donor millet DNA in MR-4 genome. Total DNA introduction through rice coleoptile could overcome species barrier and expand gene pool rapidly, thus contributing to the development of rice germplasms with increased abiotic stress tolerance and higher yield.
Flowering is a key developmental stage determining reproductive success and yield in olive trees, integrating complex genetic regulation and environmental factors mainly temperatures. Given its environment-dependency, olive flowering phenology is increasingly challenged by ongoing global warming in the Mediterranean region. Therefore, understanding its genetic architecture is essential for developing efficient and climate-resilient breeding programs. Here, we investigated flowering phenology related-traits using a multi-model genome-wide association study (GWAS) framework. Phenological traits, representing first-observation dates of developmental stages for 257 olive Mediterranean varieties, along with more than 555 K high-quality variants, were analyzed. For each trait, Best Linear Unbiased Predictors (BLUPs), Finlay-Wilkinson slopes, and single-year data were examined using both single-locus and multi-loci models. Additionally, a multivariate GWAS was conducted for the traits. Our results revealed high inter-genotype variation and significant genotype-by-environment interactions, with heterogenous responsiveness to environmental changes. The integration of multiple GWAS models provided complementary insights into the genetic architecture of flowering phenology, highlighting both shared and model-specific associations. We identified both stage-specific and pleiotropic associations. Analyses of plasticity slopes identified additional distinct association patterns from BLUP-based results, suggesting a partially independent genetic basis of flowering phenology plasticity in olive trees. Our findings provide a comprehensive genomic dissection of olive flowering phenology and emphasize its complex architecture, involving pleiotropic, stage-specific and plasticity-related mechanisms. Flowering phenology in olive emerges as a combination of pleiotropic, stage-specific and plasticity-related mechanisms, highlighting the importance of considering multiple genetic layers when studying phenological traits.
Grain hardness is a critical quality trait that determines the processing properties and commercial value of wheat, playing an essential role in both food security and the high-quality development of the wheat industry. In this study, we used an association panel of 233 wheat accessions to measure the grain hardness index (HI) across four different environments. Based on the re-sequencing data, we performed a genome-wide association study (GWAS) to identify the genetic loci associated with wheat grain hardness. We detected two environmentally stable QTLs named QHa.hzau-5D and QHa.hzau-6B.1, which explained 11.27
