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Here, we have evaluated the general genomic structure and diversity and studied the divergence resulting from selection and historical admixture events for a collection of worldwide chicken breeds. In total, 636 genomes (43 populations) were sequenced from chickens of American, Chinese, Indonesian, and European origin. Evaluated populations included wild junglefowl, rural indigenous chickens, breeds that have been widely used to improve modern western poultry populations and current commercial stocks bred for efficient meat and egg production. In‐depth characterizations of the genome structure and genomic relationships among these populations were performed, and population admixture events were investigated. In addition, the genomic architectures of several domestication traits and central documented events in the recent breeding history were explored. Our results provide detailed insights into the contributions from population admixture events described in the historical literature to the genomic variation in the domestic chicken. In particular, we find that the genomes of modern chicken stocks used for meat production both in eastern (Asia) and western (Europe/US) agriculture are dominated by contributions from heavy Asian breeds. Further, by exploring the link between genomic selective divergence and pigmentation, connections to functional genes feather coloring were confirmed.

Seedling vigour is an important characteristic in relation to crop growth and yield. Traits such as photosynthetic capacity and chlorophyll content contribute significantly to seedling establishment at the early growth stage in various crop species, including rice. A diverse panel of 227 rice varieties from several countries was evaluated to determine chlorophyll contents at multiple time points during the seedling stage using a soil-plant analysis development (SPAD) meter, a non-destructive portable device. Using new statistical approaches, several chromosomal regions associated with variations in chlorophyll content in the third leaf at 13, 16 and 19 days after imbibition were detected. A single nucleotide polymorphism (SNP) cluster on the end of chromosome 11 was significantly associated with the onset of leaf senescence. This region was enriched with genes related to cell death and the stress response. We have identified rice germplasm showing delayed-senescence phenotypes, these could be suitable donors and genetic resources for breeding, and the use of significant SNP markers associated with these traits could enhance the efficiency of their selection in breeding programmes. K E Y W O R D S chlorophyll content, genomewide association study, onset of leaf senescence, quantitative trait loci, seedling vigour, stay-green 1 | BACKGROUND Photosynthesis converts CO 2 to energy and provides the carbon source to support plant growth and development. Changes in the photosynthetic capacity and efficiency of plants can reflect their internal responses to stresses and the natural ageing process. Many biological factors contribute to photosynthetic performance. One of those factors, chlorophyll content, is directly involved in plant growth and yield production, and is controlled by quantitative trait loci

Key message A practical approach is developed to determine a cost-effective optimal training set for selective phenotyping in a genomic prediction study. An R function is provided to facilitate the application of the approach. Abstract Genomic prediction (GP) is a statistical method used to select quantitative traits in animal or plant breeding. For this purpose, a statistical prediction model is first built that uses phenotypic and genotypic data in a training set. The trained model is then used to predict genomic estimated breeding values (GEBVs) for individuals within a breeding population. Setting the sample size of the training set usually takes into account time and space constraints that are inevitable in an agricultural experiment. However, the determination of the sample size remains an unresolved issue for a GP study. By applying the logistic growth curve to identify prediction accuracy for the GEBVs and the training set size, a practical approach was developed to determine a cost-effective optimal training set for a given genome dataset with known genotypic data. Three real genome datasets were used to illustrate the proposed approach. An R function is provided to facilitate widespread application of this approach to sample size determination, which can help breeders to identify a set of genotypes with an economical sample size for selective phenotyping.