Biology subjects

Ariza-Suarez, D.

Publications and source records attributed to Ariza-Suarez, D..

3 recordsLinked to original sources

bioRxiv2025

Candidate genes for stem rust resistance in Italian ryegrass revealed by nested association mapping

Stem rust, caused by Puccinia graminis ssp. graminicola, is a major disease affecting the outcrossing species Italian ryegrass (Lolium multiflorum Lam.), leading to substantial reductions in seed yield. Until now, knowledge on the genetic control of stem rust resistance in Italian ryegrass has been limited to a few quantitative trait loci identified in bi-parental mapping populations. To discover novel resistance sources for use in breeding programs, appropriate plant populations, reliable phenotyping methods and advanced genomic tools are essential. In this study, we utilized a previously established F2 nested association mapping (NAM) population comprising 708 individuals derived from 24 founder plants exhibiting high variation in stem rust resistance. Phenotypic evaluation was conducted under natural inoculation in three location-by-year combinations. By integrating reduced-representation sequencing of the NAM population with whole-genome sequencing of the founder plants, we identified 3,199,253 SNP markers for association mapping. The high SNP marker density, together with the strong detection power of the NAM population, enabled the identification of four novel candidate genes. Two of these genes, located on chromosomes 6 and 7, encode receptor-like serine/threonine kinases that are known to play a role in stem rust resistance in other crops. Within the serine/threonine kinase gene Chr7.32208, two superior haplotypes were identified that can be directly implemented as selection criteria in breeding programs. The novel stem rust resistance candidate genes reported here provide promising targets for functional validation and the improvement of stem rust resistance in Italian ryegrass breeding. Key messageField phenotyping of a previously established NAM population for stem rust resistance, combined with high-density genotyping, enabled the identification of novel sources of stem rust resistance in Italian ryegrass.

plant biology↗

bioRxiv2025

The first nested association mapping (NAM) population for outbreeding Italian ryegrass reveals candidate genes for seed shattering and related traits

Nested association mapping (NAM) populations are a powerful tool for investigating the genetic control of agronomically important traits and have been successfully used in many inbreeding crops. Here, we present the first NAM population established in an outcrossing forage grass species, Italian ryegrass (Lolium multiflorum Lam.), to dissect the genetic control of seed shattering. The NAM population was based on 23 diverse and one common founder plants and consisted of 708 F2 individuals. Reduced-representation sequencing (ddRAD) of the 708 F2 individuals, combined with whole genome sequencing data of the 24 founder plants, yielded a total of 3,199,253 SNPs that were used for population structure analysis, parentship analysis and genome-wide association studies. Phenotypic data for seed shattering and seed yield-related traits, collected in three year x location environments, showed high phenotypic variance within the NAM population. A total of seven QTL were identified for seed shattering, seed yield, spike length, flag leaf length and flowering time. Within these QTL regions, one candidate gene for seed shattering and three candidate genes for flowering time were identified. For seed shattering, the significant SNP association within the gene chr7.26897, known to be in involved in ripening-related pathways, explained 10.03% of the phenotypic variance. These candidate genes identified provide valuable targets for functional validation and demonstrate the effectiveness of NAM populations for elucidating the genetic architecture of complex traits in outcrossing forage grasses.

plant biology↗

bioRxiv2024

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winners strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

genetics↗