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Background Structural variants (SV) are causative for some prominent phenotypic traits of livestock as different comb types in chickens or color patterns in pigs. Their effects on production traits are also increasingly studied. Nevertheless, accurately calling SV remains challenging. It is therefore of interest, whether close-by single nucleotide polymorphisms (SNPs) are in strong linkage disequilibrium (LD) with SVs and can serve as markers. Literature comes to different conclusions on whether SVs are in LD to SNPs on the same level as SNPs to other SNPs. The present study aimed to generate a precise SV callset from whole-genome short-read sequencing (WGS) data for three commercial chicken populations and to evaluate LD patterns between the called SVs and surrounding SNPs. It is thereby the first study that assessed LD between SVs and SNPs in chickens. Results The final callset consisted of 12,294,329 bivariate SNPs, 4,301 deletions (DEL), 224 duplications (DUP), 218 inversions (INV) and 117 translocation breakpoints (BND). While average LD between DELs and SNPs was at the same level as between SNPs and SNPs, LD between other SVs and SNPs was strongly reduced (DUP: 40%, INV: 27%, BND: 19% of between-SNP LD). A main factor for the reduced LD was the presence of local minor allele frequency differences, which accounted for 50% of the difference between SNP – SNP and DUP – SNP LD. This was potentially accompanied by lower genotyping accuracies for DUP, INV and BND compared with SNPs and DELs. An evaluation of the presence of tag SNPs (SNP in highest LD to the variant of interest) further revealed DELs to be slightly less tagged by WGS SNPs than WGS SNPs by other SNPs. This difference, however, was no longer present when reducing the pool of potential tag SNPs to SNPs located on four different chicken genotyping arrays. Conclusions The results implied that genomic variance due to DELs in the chicken populations studied can be captured by different SNP marker sets as good as variance from WGS SNPs, whereas separate SV calling might be advisable for DUP, INV, and BND effects.

Key Message The accuracy of genomic prediction of phenotypes can be increased by including the top-ranked pairwise SNP interactions into the prediction model. Abstract We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from two European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested at six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings for predictions within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Across all studied traits, locations and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from −0.3 to + 27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP, with an average increase of 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for “sparse testing” approaches in which only a subset of the lines/hybrids of interest is observed at each location.

Models based on additive marker effects and on epistatic interactions can be translated into genomic relationship models. This equivalence allows to perform predictions based on complex gene interaction models and reduces computational effort significantly. In the theory of genome-assisted prediction, the equivalence of a linear model based on independent and identically normally distributed marker effects and a model based on multivariate Gaussian distributed breeding values with genomic relationship as covariance matrix is well known. In this work, we demonstrate equivalences of marker effect models incorporating epistatic interactions and corresponding mixed models based on relationship matrices and show how to exploit these equivalences computationally for genome-assisted prediction. In particular, we show how models with epistatic interactions of higher order (e.g., three-factor interactions) translate into linear models with certain covariance matrices and demonstrate how to construct epistatic relationship matrices for the linear mixed model, if we restrict the model to interactions defined a priori. We illustrate the practical relevance of our results with a publicly available data set on grain yield of wheat lines growing in four different environments. For this purpose, we select important interactions in one environment and use this knowledge on the network of interactions to increase predictive ability of grain yield under other environmental conditions. Our results provide a guide for building relationship matrices based on knowledge on the structure of trait-related gene networks.

A whole-genome scan for identifying selection acting on pairs of linked loci is proposed and implemented. The scan is based on , one of Ohta's 1982 measures of between-population linkage disequilibrium (LD). An approximate empirical null distribution for the statistic is suggested. Although the partitioning of LD into between-population components was originally used to investigate epistatic selection, we demonstrate that values of may also be influenced by single-locus selective sweeps with linkage but no epistasis. The proposed scan is implemented in a diverse panel of chickens including 72 distinct breeds genotyped at 538 298 single-nucleotide polymorphisms. In all, 1723 locus pairs are identified as putatively corresponding to a selective sweep or epistatic selection. These pairs of loci generally cluster to form overlapping or neighboring signals of selection. Known variants that were expected to have been under selection in the panel are identified, as well as an assortment of novel regions that have putatively been under selection in chickens. Notably, a promising pair of genes located 8 MB apart on chromosome 9 are identified based on as demonstrating strong evidence of dispersive epistatic selection between populations.

Designing strategies for conservation and improvement livestock should be based on assessment of genetic characteristics of populations under consideration. In Oman, conservation programs for local livestock breeds have been started. The current study assessed the genetic diversity and conservation potential of local chickens from Oman. Twenty-nine microsatellite markers were analyzed in 158 birds from six agro-ecological zones: Batinah, Dhofar, North Hajar, East Hajar, Musandam, and East Coast. Overall, a total of 217 alleles were observed. Across populations, the average number of alleles per locus was 7.48 and ranged from 2 (MCW98 and MCW103) to 20 (LEI094). The mean expected heterozygosity (H E) was 0.62. Average fixation index among populations (F ST) was 0.034, indicating low population differentiation, while the mean global deficit of heterozygotes across populations (F IT) was 0.159. Based on Nei's genetic distance, a neighbor-joining tree was constructed for the populations, which clearly identified the Dhofar population as the most distant one of the Omani chicken populations. The analysis of conservation priorities identified Dhofar and Musandam populations as the ones that largely contribute to the maximal genetic diversity of the Omani chicken gene pool.

Free ranging chickens reared by smallholder farmers represent genetic diversity suited for particular environments and shaped by the socio-economic and cultural values of the farming systems. This study sought to investigate the existence of chicken strains and evaluate the breeding goals and strategies used by village chicken farmers in Zimbabwe. A semi-structured questionnaire was administered to 97, 56, 70, 104 and 37 households randomly selected from five agro-ecological-zones I-V, respectively. Fifteen chicken strains mostly defined by morphological traits were reported in the five eco-zones. Production criteria such as body size, and fertility were highly ranked (ranging from 1.3 – 2.6) by farmers across all the eco-zones, while cultural traits were the least preferred production traits. As a common breeding practice, farmers chose the type of hens and cocks to retain for breeding purposes and these randomly mixed and mated with others from community flocks. Chicken body size was ranked the major determinant in choosing breeding animals followed by mothering ability, and fertility. More households culled chickens associated with poor reproductive performance, poor growth rates and those intolerant to disease pathogens. The focus on many negatively correlated production traits and the absence of farmer records compromises breeding strategies in these production systems.

The coefficient of relationship is defined as the correlation between the additive genetic values of two individuals. This coefficient can be defined specifically for a single quantitative trait locus (QTL) and may deviate considerably from the overall expectation if it is taken conditional on information from linked marker loci. Conditional halfsib correlations are derived under a simple genetic model with a biallelic QTL linked to a biallelic marker locus. The conditional relationship coefficients are shown to depend on the recombination rate between the marker and the QTL and the population frequency of the marker alleles, but not on parameters of the QTL, i.e. number and frequency of QTL alleles, degree of dominance etc., nor on the (usually unknown) QTL genotype of the sire. Extensions to less simplified cases (multiple alleles at the marker locus and the QTL, two marker loci flanking the QTL) are given. For arbitrary pedigrees, conditional relationship coefficients can also be derived from the conditional gametic covariance matrix suggested by Fernando and Grossman (1989). The connection of these two approaches is discussed. The conditional relationship coefficient can be used for marker-assisted genetic evaluation as well as for the detection of QTL and the estimation of their effects.

To overcome limitations of diversity measures applied to livestock breeds marker based estimations of kinship within and between populations were proposed. This concept was extended from the single locus consideration to chromosomal segments of a given length in Morgan. Algorithms for the derivation of haplotype kinship were suggested and the behaviour of marker based haplotype kinship was investigated theoretically. In the present study the results of the first practical application of this concept are presented. Full sib pairs of three sub-populations of the Goettingen minipig were genotyped for six chromosome segments. After haplotype reconstruction the haplotypes were compared and mean haplotype kinships were estimated within and between populations. Based on haplotype kinships a distance measure is proposed which is approximatively linear with the number of generations since fission. The haplotype kinship distances, the respective standard errors and the pedigree-based expected values are presented and are shown to reflect the true population history better than distances based on single-locus kinships. However the marker estimated haplotype kinship reveals variable among segments. This leads to high standard errors of the respective distances. Possible reasons for this phenomenon are discussed and a pedigree-based approach to correct for identical haplotypes which are not identical by descent is proposed.