Modeling genetic differences of combined broiler chicken populations in single-step GBLUP

Definition of metafounders based on population structure analysis

BACKGROUND

Limitations of the concept of identity by descent in the presence of stratification within a breeding population may lead to an incomplete formulation of the conventional numerator relationship matrix ( A ). Combining A with the genomic relationship matrix ( G ) in a single-step approach for genetic evaluation may cause inconsistencies that can be a source of bias in the resulting predictions. The objective of this study was to identify stratification using genomic data and to transfer this information to matrix A , to improve the compatibility of A and G .

METHODS

Using software to detect population stratification (ADMIXTURE), we developed an iterative approach. First, we identified 2 to 40 strata ( k ) with ADMIXTURE, which we then introduced in a stepwise manner into matrix A , to generate matrixA Γ using the metafounder methodology. Improvements in consistency between matrix G andA Γ were evaluated by regression analysis and through the comparison of the overall mean and mean diagonal values of both matrices. The approach was tested on genotype and pedigree information of European and North American Brown Swiss animals (85,249). Analyses with ADMIXTURE were initially performed on the full set of genotypes (S1). In addition, we used an alternative dataset where we avoided sampling of closely related animals (S2).

RESULTS

Results of the regression analyses of standard A on G were - 0.489, 0.780 and 0.647 for intercept, slope and fit of the regression. When analysing S1 data results of the regression forA Γ on G corresponding values were - 0.028, 1.087 and 0.807 for k =7, while there was no clear optimum k . Analyses of S2 gave a clear optimal k =24, with - 0.020, 0.998 and 0.817 as results of the regression. For this k differences in mean and mean diagonal values between both matrices were negligible.

CONCLUSIONS

The derivation of hidden stratification information based on genotyped animals and its integration into A improved compatibility of the resultingA Γ and G considerably compared to the initial situation. In dairy breeding populations with large half-sib families as sub-structures it is necessary to balance the data when applying population structure analysis to obtain meaningful results.

Combining large broiler populations into a single genomic evaluation: dealing with genetic divergence

Combining breeding populations that have diverged at some point is a conventional practice, particularly in the poultry industry, where generation intervals are short and genetic evaluations should be frequently available. This study aimed to assess the feasibility of combining large, distantly genetically connected broiler populations into a single genomic evaluation within the single-step GBLUP framework. The pedigree data for broiler lines 1 and 2 consisted of 428,790 and 477,488 animals, being 156,088 and 186,387 genotyped, respectively. Phenotypic data for body weight (kg), carcass yield (%), mortality (1 to 2), and feet health (1 to 7) were collected for 397,974 animals in line 1 and 458,881 in line 2. A 4-trait model was employed for the analyses, and genetic differences between the populations were addressed through different approaches: introducing an additional fixed effect accounting for the line of origin (M2) or making each fixed effect origin-specific (M3). Those models were compared against a conventional model (M1) that did not account for animal origin in the evaluation. Unknown parent groups (UPG) and Metafounders (MF) were fit to account for the genetic differences in M1, M2, and M3; they were set based on the animal's line of origin and sex. Accuracy, bias, and dispersion were used to assess the performances of the models using the Linear Regression method. Validations were performed separately within individual lines and collectively after combining the 2 lines to better assess the advantages of combining the 2 populations. Overall, the accuracy increased when the 2 populations were combined compared to the accuracies obtained from evaluating each line individually. Notably, there were no apparent differences among the models regarding accuracy and dispersion. Regarding bias, using models M2 or M3 with UPG yields the least biased estimates in the combined evaluation. Thus, when combining different populations into a single genomic evaluation, accounting for the genetic and non-genetic differences among the lines ensures accurate and less biased predictions.

A guidance of model selection for genomic prediction based on linear mixed models for complex traits

Brain imaging outcomes are important for Alzheimer's disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to make the best use of them for prediction analyses remains unknown. In this research, we first explored the relationship between genetic architecture and prediction accuracy of linear mixed models via visualizing the Manhattan plots generated based on the data obtained from the Wellcome Trust Case Control Consortium, and then constructed prediction models for eleven AD-related brain imaging outcomes using data from United Kingdom Biobank and Alzheimer's Disease Neuroimaging Initiative studies. We found that the simple Manhattan plots can be informative for the selection of prediction models. For traits that do not exhibit any significant signals from the Manhattan plots, the simple genomic best linear unbiased prediction (gBLUP) model is recommended due to its robust and accurate prediction performance as well as its computational efficiency. For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. For the prediction of AD-related traits, the Manhattan plots suggest their polygenic nature, and gBLUP has achieved robust performance for all these traits. We found that for these AD-related traits, genetic factors themselves only explain a very small proportion of the heritability, and the well-known AD risk factors can substantially improve the prediction model.