Adunola, P. | Search bioRxiv

bioRxiv2025

Genome-wide association study reveals candidate loci for resistance to anthracnose in blueberry

Anthracnose, caused by Colletotrichum gloeosporioides, poses a significant threat to blueberries, necessitating a deeper understanding of the genetic mechanisms underlying resistance to develop efficient breeding strategies. Here, we conducted a genome-wide association study on two distinct populations, comprising 355 advanced selections from the University of Florida Blueberry Breeding and Genomics Program. Visual scores and image analyses were used for assessing disease severity. The population was genotyped using Capture-Seq, detecting 38,379 single nucleotide polymorphisms. The study revealed a moderate narrow-sense heritability estimate ([~]0.5) for anthracnose resistance in blueberries. Minor additive loci contributing to anthracnose resistance were identified on chromosomes 2, 3, 5, 6, 8, 9, 10, 11 and 12, spanning different populations. Image analyses demonstrated heightened sensitivity, detecting more associations within both populations compared to the visual approach. Candidate gene mining flanking significant associations unveiled key defense-related proteins, such as serine/threonine protein kinases, pentatricopeptide repeat-containing proteins, E3 ubiquitin ligases that have been well-known for their roles in plant defense signaling pathways. The dissection of speculated defense-related proteins into distinct layers offers further understanding into the intricate defense responses against C. gloeosporioides. Our findings highlight the complex and quantitative resistance mechanism for anthracnose in blueberry, providing insights for breeding strategies and sustainable disease management.

genomics↗

bioRxiv2024

Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcanes yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (GxE) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6 x 186 = 1,116 phenotypes). Our study employed three predictive models, including environment and genotype as main effects, as well as the GxE interaction to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for GxE pattern prediction. Results demonstrate that maximum accuracy for SA ({rho} = 0.611) and for TCH ({rho} = 0.341) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72 = 12 x 6) being the most convenient cost-benefit combination.

genomics↗