Multi-population genome-wide association studies involving four distinct barley populations

The power of genome-wide association studies (GWAS) relies heavily on the
sample size. A strategy to increase sample size is to combine datasets
from different populations. However, this approach introduces challenges
due to heterogeneity between populations. With this data, we set up a
statistically sound model to account for such heterogeneity. Using this
model, we combined up to four distinct barley populations in GWAS to
detect genomic regions associated with heading date and stem lodging. Each
population represented an applied breeding program with unique
combinations of growth habit (winter versus spring) and row type (2-rowed
versus 6-rowed). By comparing single-population GWAS with
multi-population GWAS, we identified both quantitative trait loci (QTLs)
that were shared across populations and population-specific QTLs. We found
that multi-population GWAS provided greater statistical power than
single-population analyses, revealed QTLs that were undetectable in small
populations, and explained an overall larger proportion of the phenotypic
variance.  Our findings offer a promising approach to accelerate
genomics-based breeding in new breeding populations with limited data.
This methodology is applicable to a wide range of datasets where sample
sizes are limited for various reasons.