XSim version 2: simulation of modern breeding programs.

Simulation can be an efficient approach to design, evaluate, and optimize breeding programs. In the era of modern agriculture, breeding programs can benefit from a simulator that integrates various sources of big data and accommodates state-of-the-art statistical models. The initial release of XSim, in which stochastic descendants can be efficiently simulated with a drop-down strategy, has mainly been used to validate genomic selection results. In this article, we present XSim Version 2 that is an open-source tool and has been extensively redesigned with additional features to meet the needs in modern breeding programs. It seamlessly incorporates multiple statistical models for genetic evaluations, such as GBLUP, Bayesian alphabets, and neural networks, and it can effortlessly simulate successive generations of descendants based on complex mating schemes by the aid of its modular design. Case studies are presented to demonstrate the flexibility of XSim Version 2 in simulating crossbreeding in animal and plant populations. Modern biotechnology, including double haploids and embryo transfer, can all be simultaneously integrated into the mating plans that drive the simulation. From a computing perspective, XSim Version 2 is implemented in Julia, which is a computer language that retains the readability of scripting languages (e.g. R and Python) without sacrificing much computational speed compared to compiled languages (e.g. C). This makes XSim Version 2 a simulation tool that is relatively easy for both champions and community members to maintain, modify, or extend in order to improve their breeding programs. Functions and operators are overloaded for a better user interface so they may concatenate, subset, summarize, and organize simulated populations at each breeding step. With the strong and foreseeable demands in the community, XSim Version 2 will serve as a modern simulator bridging the gaps between theories and experiments with its flexibility, extensibility, and friendly interface.

The genetic basis for the selection of dairy goats with enhanced resistance to gastrointestinal nematodes.

Gastrointestinal nematodes (GIN) severely affect small ruminant production worldwide. Increasing problems of anthelmintic resistance have given strong impetus to the search for alternative strategies to control GIN. Selection of animals with an enhanced resistance to GIN has been shown to be successful in sheep. In goats, the corresponding information is comparatively poor. Therefore, the present study was designed to provide reliable data on heritabilities of and genetic correlations between phenotypic traits linked to GIN and milk yield in two major dairy goat breeds (Alpine and Saanen). In all, 20 herds totalling 1303 goats were enrolled in the study. All herds had (i) a history of gastrointestinal nematode infection, (ii) uniform GIN exposure on pasture and (iii) regular milk recordings. For all goats, individual recordings of faecal egg counts (FEC), FAMACHA© eye score, packed cell volume (PCV) and milk yield were performed twice a year with an anthelmintic treatment in between. The collected phenotypic data were multivariately modelled using animal as a random effect with its covariance structure inferred from the pedigree, enabling estimation of the heritabilities of the respective traits and the genetic correlation between them. The heritabilities of FEC, FAMACHA© and PCV were 0.07, 0.22 and 0.22, respectively. The genetic correlation between FEC and FAMACHA© was close to zero and -0.41 between FEC and PCV. The phenotypic correlation between FEC and milk yield was close to zero, whereas the genetic correlation was 0.49. Our data suggest low heritability of FEC in Saanen and Alpine goats and an unfavourable genetic correlation of FEC with milk yield.

Response and inbreeding from a genomic selection experiment in layer chickens.

BACKGROUND: Genomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken. METHODS: In the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production. RESULTS: Birds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line. CONCLUSIONS: The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.

Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model.

BACKGROUND: Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line. METHODS: The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV. RESULTS: Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.