P0369
Comparative Genomics in Pasture Species – a Powerful New Approach to Assist in Methane Mitigation Strategies for Australian Livestock Industries
Comparative Genomics in Pasture Species – a Powerful New Approach to Assist in Methane Mitigation Strategies for Australian Livestock Industries
Date: Monday, January 14, 2013
Room: Grand Exhibit Hall
Agriculture contributes around 17% of Australia’s greenhouse gas emissions, largely comprised of methane and nitrous oxide and 70% of methane emissions from agriculture come from livestock, through enteric fermentation and manure. Subterranean clover (Trifolium subterraneum L.) is the most important forage pasture species in Australia for ruminant production and is an ideal reference species for comparative mapping within the Leguminosae. It is a diploid (2n =16), predominantly inbreeding annual species that can be readily hybridised and these characters makes it much simpler to study than the tetraploid genomes of red and white clovers. It also has a wide range of diversity for both qualitative and quantitative agronomic and morphological characters. While it has good agronomic and nutritive properties, consumption of subterranean clover by ruminants is associated with methane production during microbial fermentation in the gut (‘methanogenic potential’). To date, 42 cultivars of subterranean clover have been released in Australia, but further genetic gains can be expected if plant breeders are able to exploit new traits, and one such trait is plant methanogenic potential. Two cultivars (Daliak and Woogenellup), that have significant difference in their methanogenic potential were crossed, and their F2 derived F4 population was examined for methanogenic potential in vitro. Following this, de novo genome sequencing of subterranean clover using a next generation sequencer (Illumina HiSeq 1000) was conducted to focus on polymorphic identification related to methanogenic potential. This work also generated a genome scaffold for the species which will facilitate eSNP mapping for high resolution QTL analyses of other traits in the future.
