W326A Genome-Wide Association Study Including Common and Rare Genetic Variants Reveals Putative Regulators of Bioenergy Traits in Eastern Cottonwood
A Genome-Wide Association Study Including Common and Rare Genetic Variants Reveals Putative Regulators of Bioenergy Traits in Eastern Cottonwood
Date: Sunday, January 10, 2016
Time: 10:30 AM
Time: 10:30 AM
Room: Sunrise - Meeting House
The growing bioenergy industry requires an increase in availability of plant feedstocks with high biomass to biofuel conversion efficiency. Poplars are a suitable feedstock for bioenergy production, but cultivars specifically tailored for the biofuel industry are still under development. Understanding the genetic control of bioenergy-related traits can address this gap by accelerating poplar breeding. Genome-wide association studies (GWASs) have been successful in identifying common genetic variants that regulate commercially important traits in crop plants. Although these studies have revealed part of the genetic component that explains trait variance, much of the heritability remains missing. Rare variants may account for part of the missing heritability, especially in species with high genetic diversity such as forest trees. Here we report the first GWAS conducted in Populus deltoides, one of the main poplar species used for breeding, analyzing association of common and rare genetic variants with bioenergy traits. A population of 500 unrelated individuals was genotyped by resequencing 18,153 genes, followed by SNP identification. Single- and multiple-marker association analysis was performed, identifying significantly associated genes with eight biomass growth and wood composition traits phenotyped in the population. Including rare variants in this GWAS increased the number of associations identified by 62%. These results highlight the importance of including low frequency variants in GWAS of highly diverse tree species. This was made possible by utilizing flexible genotyping methods such the sequence-capture/next-generation sequencing approaches, and by applying statistical methods designed to detect trait associations with rare variants.
