Complex traits in complex environments: Progress with root architecture, insect resistance, and genotype x environment interaction

An IRRI Seminar

By Storrs Thomas Mitchell-Olds
Professor
Institute for Genome Sciences and Policy
Department of Biology
Duke University
Durham, NC 27708

1315-1415 H, Thursday, 25 April 2013
Havener Auditorium, IRRI

Abstract:

In several interdisciplinary collaborations, we are studying genetic influences on agriculturally important traits that impact rice yields across a range of environments.

First, we used high-throughput laboratory methods to quantify root system architecture (RSA) in a clear gel system. We used a semi-automated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in the Azucena x Bala RILs, using more than 57,000 2D images for a suite of 25 traits that quantified the distribution and shape of root networks. These RSA QTLs co-localize with previous analyses of root development in soil, suggesting that high-throughput laboratory studies may identify genes that predict root development under field conditions.

Next, with colleagues at AfricaRice, we mapped QTLs for resistance to AfricaRice Gall Midge (AfRGM), a major insect pest across broad areas of Sub-Saharan Africa. On Chromosome 4, the largest QTL (LOD > 33.1) is our prime target for fine mapping and positional cloning. More than 200 SSR markers near the major resistance QTL have been identified, and we are fine-mapping this resistance gene to facilitate marker-assisted introgression of AfRGM resistance into high-yielding cultivars. In addition, analysis of the resistance mechanism may enable discovery of additional genetic sources of resistance.

Finally, we developed computational methods for genome-wide quantification of allelic effects on yield across multiple environments and years. We found highly significant correlations across the genome for performance in different environments and stages of the life cycle. To date, this approach has revealed consistent and interpretable genome-wide patterns for multiple traits and environments.