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Bevan, M.

Publications and source records attributed to Bevan, M..

2 recordsLinked to original sources

Epigenomic variation across a polyploid wheat diversity collection

Wheat has been domesticated into a large number of agricultural environments and has a remarkable ability to adapt to diverse environments. To understand this process, we survey genotype, repeat content and DNA methylation across a bread wheat landrace collection representing global genetic diversity. We identify independent variation in methylation, genotype and transposon copy number. We show that these, so far unexploited, sources of variation have had a massive impact on the wheat genome and that ancestral methylation states become preferentially hard coded as SNPs via 5-methylcytosine deamination. These mechanisms also drive local adaption, impacting important traits such as heading date and salt tolerance. Methylation and transposon diversity could therefore be used alongside single nucleotide polymorphism (SNP) based markers for breeding.

genomics

CropQuant: An automated and scalable field phenotyping platform for crop monitoring and trait measurements to facilitate breeding and digital agriculture

Automated phenotyping technologies are capable of providing continuous and precise measurements of traits that are key to todays crop research, breeding and agronomic practices. In additional to monitoring developmental changes, high-frequency and high-precision phenotypic analysis can enable both accurate delineation of the genotype-to-phenotype pathway and the identification of genetic variation influencing environmental adaptation and yield potential. Here, we present an automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments. To manage infield experiments and crop-climate data collection, we have also developed a web-based control system called CropMonitor to provide a unified graphical user interface (GUI) to enable realtime interactions between users and their experiments. Furthermore, we established a high-throughput trait analysis pipeline for phenotypic analyses so that lightweight machine-learning modelling can be executed on CropQuant workstations to study the dynamic interactions between genotypes (G), phenotypes (P), and environmental factors (E). We have used these technologies since 2015 and reported results generated in 2015 and 2016 field experiments, including developmental profiles of five wheat genotypes, performance-related traits analyses, and new biological insights emerged from the application of the CropQuant platform.

plant biology