Biology subjects

Wasson, A. P.

Publications and source records attributed to Wasson, A. P..

3 recordsLinked to original sources

bioRxiv2024

Phenotyping the hidden half: Combining UAV phenotyping and machine learning to predict barley root traits in the field

Improving crop root systems for enhanced adaptation and productivity remains challenging due to limitations in scalable non-destructive phenotyping approaches, inconsistent translation of root phenotypes from controlled environment to the field, and a lack of understanding of the genetic controls. This study serves as a proof of concept, evaluating a panel of Australian barley breeding lines and cultivars (Hordeum vulgare L) in two field experiments. Integrated ground-based root and shoot phenotyping was performed at key growth stages. UAV-captured vegetation indices (VIs) were explored for their potential to predict root distribution and above-ground biomass. Machine learning models, trained on a subset of 20 diverse lines, with the most accurate model applied to predict traits across a broader panel of 395 lines. Unlike previous studies focusing on above-ground traits or indirect proxies, this research directly predicts root traits in field conditions using VIs, machine learning and root phenotyping. Root trait predictions for the broader panel enabled genomic analysis using a haplotype-based approach, identifying key genetic drivers, including EGT1 and EGT2 which regulate root gravitropism. This approach offers the potential to advance root research across various crops and integrate root traits into breeding programs, fostering the development of varieties adapted to future environments. HighlightIntegrating UAV phenotyping and machine learning can be used to predict RSA traits non-destructively and offers a new approach to support root research and crop improvement.

physiology↗

bioRxiv2020

Genetics of phosphorus use efficiency in a MAGIC wheat population grown in the field

Phosphorus (P) is an essential plant nutrient and regular applications are essential in most farming systems to maintain high yields. Yet the P fertilizers applied to crops and pastures are derived from non-renewable resources. It is therefore important to find agronomic and genetic strategies for using this resource efficiently, especially since only a proportion of the applied P is absorbed by crops. The aim of this study was to identify Quantitative Trait Loci (QTL) for P use efficiency (PUE) in wheat using a Multiparent Advanced Generation InterCross (MAGIC) population grown in the field. The 357 genotypes were arranged in paired plots with and without P fertilization. Yield and biomass were measured and PUE was calculated as either the performance of the genotype relative to the average response to fertilization, or the performance of the genotype relative to the average resilience in the absence of fertilization. Five trials were conducted over three years in Australia at three sites with contrasting clay and sandy soil types. Genotypic variation for response and resilience were identified in all trials with moderate to strong correlation with productivity with and without P between trials. Multiparent Whole Genome Average Interval Mapping (WGAIM) QTL analyses were conducted on the four traits (Biomass / Yield x P Response / Resilience) across the five trials and identified 130 QTL in total. QTL within 10 cM of each other were clustered into 56 groups that were likely to represent identical or linked loci. Of the clusters 27 (48%) contained only a single QTL but 17 (31%) contained 3 or more in different trials or traits. This suggests multiple biological mechanisms driving PUE in different environments. Eight of the 56 groups collocated with QTL for seedling root hair length identified in the same MAGIC population in an earlier study. HighlightIdentification of genetic loci for phosphorus use efficiency in a multigenic population of Australian wheats grown on contrasting soils.

plant biology↗

bioRxiv2016

Rooting density differentiates wheat genotypes through Bayesian modeling

Wheat pre-breeders use soil coring and core-break counts to phenotype root architecture traits, with data collected on rooting density for hundreds of genotypes in small increments of depth. The measured densities are both large datasets and highly variable even within the same genotype, hence, any rigorous, comprehensive statistical analysis of such complex field data would be technically challenging. Traditionally, most attributes of the field data are therefore discarded in favor of simple numerical summary descriptors which retain much of the high variability exhibited by the raw data. This poses practical challenges: although plant scientists have established that root traits do drive resource capture in crops, traits that are more randomly (rather than genetically) determined are difficult to breed for. In this paper we develop a Bayesian hierarchical nonlinear modeling approach that utilizes the complete field data for wheat genotypes to fit an idealized relative intensity function for the root distribution over depth. Our approach was used to determine heritability: how much of the variation between field samples was purely random versus being mechanistically driven by the plant genetics? Based on the genotypic intensity functions, the overall heritability estimate was 0.62 (95% Bayesian confidence interval was 0.52 to 0.71). Despite root count profiles that were statistically very noisy, our Bayesian analysis led to denoised profiles which exhibited rigorously discernible phenotypic traits. The profile-specific traits could be representative of a genotype and thus can be used as a quantitative tool to associate phenotypic traits with specific genotypes.

plant biology↗