Complex genetic architecture of the chicken Growth1 QTL region.

The genetic complexity of polygenic traits represents a captivating and intricate facet of biological inheritance. Unlike Mendelian traits controlled by a single gene, polygenic traits are influenced by multiple genetic loci, each exerting a modest effect on the trait. This cumulative impact of numerous genes, interactions among them, environmental factors, and epigenetic modifications results in a multifaceted architecture of genetic contributions to complex traits. Given the well-characterized genome, diverse traits, and range of genetic resources, chicken (Gallus gallus) was employed as a model organism to dissect the intricate genetic makeup of a previously identified major Quantitative Trait Loci (QTL) for body weight on chromosome 1. A multigenerational advanced intercross line (AIL) of 3215 chickens whose genomes had been sequenced to an average of 0.4x was analyzed using genome-wide association study (GWAS) and variance-heterogeneity GWAS (vGWAS) to identify markers associated with 8-week body weight. Additionally, epistatic interactions were studied using the natural and orthogonal interaction (NOIA) model. Six genetic modules, two from GWAS and four from vGWAS, were strongly associated with the studied trait. We found evidence of both additive- and non-additive interactions between these modules and constructed a putative local epistasis network for the region. Our screens for functional alleles revealed a missense variant in the gene ribonuclease H2 subunit B (RNASEH2B), which has previously been associated with growth-related traits in chickens and Darwin's finches. In addition, one of the most strongly associated SNPs identified is located in a non-coding region upstream of the long non-coding RNA, ENSGALG00000053256, previously suggested as a candidate gene for regulating chicken body weight. By studying large numbers of individuals from a family material using approaches to capture both additive and non-additive effects, this study advances our understanding of genetic complexities in a highly polygenic trait and has practical implications for poultry breeding and agriculture.

Sample size determination for training set optimization in genomic prediction.

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. 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.

Researching on the fine structure and admixture of the worldwide chicken population reveal connections between populations and important events in breeding history.

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.

Training set determination for genomic selection.

KEY MESSAGE: A new optimality criterion is proposed to determine a training set for genomic selection, which is derived from Pearson's correlation between GEBVs and phenotypic values of a test set. R functions are provided to generate the optimal training set. For a specified test set, we develop a highly efficient algorithm to determine an optimal subset from a large candidate set in which the individuals have been genotyped but not phenotyped yet. The chosen subset serves as a training set to be phenotyped, and then a genomic selection (GS) model is built based on its phenotype and genotype data. In this study, we consider the additive effects whole-genome regression model and adopt ridge regression estimation for marker effects in the GS model. The resulting GS model is then employed to predict genomic estimated breeding values (GEBVs) for the individuals of the test set, which have been genotyped only. We propose a new optimality criterion to determine the required training set, which is derived directly from Pearson's correlation between GEBVs and phenotypic values of the test set. Pearson's correlation is the standard measure for prediction accuracy of a GS model. Our proposed methods can be applied to data with the varying degree of population structure. All the R functions for implementing our training set determination algorithms are available from the R package TSDFGS. The algorithms are illustrated with two datasets which have strong (rice genome dataset) and mild (wheat genome dataset) population structures. Our methods are shown to be advantageous over existing ones, mainly because they fully use the genomic relationship between the test set and the training set by taking into account both the variance and bias for predicting GEBVs.