A finite mixture distribution to model genetic architecture of image‐based oat grain morphology

Abstract The multi‐floral oat ( Avena sativa L.) inflorescence influences grain size and shape distributions, affecting the physical attributes of grain quality such as plumpness, size, and uniformity. While the grain size and shape distribution has been characterized as multi‐modal, very little is known about the genetic determinants of those distributions and their properties. The goal of this study was to model grain size and shape distribution using a finite mixture distribution approach and propose new distributional traits (i.e., emerging distributional traits) to characterize genotypes. We evaluated 47 oat genotypes in four highly replicated field experiments. Grains of three panicles per plot were individually threshed and scanned. Grain area, length, width, and roundness were obtained from each grain‐based image, while emerging distributional traits were evaluated using a finite mixture distribution approach. Finally, grain size distributions from hand‐threshed panicles (representing the full biological distribution) were compared with the grain size distributions of grains harvested with a combine harvester representing commercial harvest where small grains may be blown out. The heritability of all grain traits was high (0.89–0.94), and trait distributions differed among genotypes. Grain area and length show bi‐ and trimodal distributions, while grain width and roundness are uni‐ and bimodal. Although the full biological distribution of grains differed from the combine‐harvested grains, their genetic correlations were high, suggesting the combine‐harvested distributions can be used as a proxy for full biological distributions. This study proposes a straightforward methodological approach to model grain attributes that can aid in quality evaluations for genetic studies, breeding decisions, and industry characterization.