Search

Background In horned sheep breeds, breeding for polledness has been of interest for decades. The objective of this study was to improve prediction of the horned and polled phenotypes using horn scores classified as polled, scurs, knobs or horns. Derived phenotypes polled/non-polled (P/NP) and horned/non-horned (H/NH) were used to test four different strategies for prediction in 4001 purebred Merino sheep. These strategies include the use of single ‘single nucleotide polymorphism’ (SNP) genotypes, multiple-SNP haplotypes, genome-wide and chromosome-wide genomic best linear unbiased prediction and information from imputed sequence variants from the region including the RXFP2 gene. Low-density genotypes of these animals were imputed to the Illumina Ovine high-density (600k) chip and the 1.78-kb insertion polymorphism in RXFP2 was included in the imputation process to whole-genome sequence. We evaluated the mode of inheritance and validated models by a fivefold cross-validation and across- and between-family prediction. Results The most significant SNPs for prediction of P/NP and H/NH were OAR10_29546872.1 and OAR10_29458450, respectively, located on chromosome 10 close to the 1.78-kb insertion at 29.5 Mb. The mode of inheritance included an additive effect and a sex-dependent effect for dominance for P/NP and a sex-dependent additive and dominance effect for H/NH. Models with the highest prediction accuracies for H/NH used either single SNPs or 3-SNP haplotypes and included a polygenic effect estimated based on traditional pedigree relationships. Prediction accuracies for H/NH were 0.323 for females and 0.725 for males. For predicting P/NP, the best models were the same as for H/NH but included a genomic relationship matrix with accuracies of 0.713 for females and 0.620 for males. Conclusions Our results show that prediction accuracy is high using a single SNP, but does not reach 1 since the causative mutation is not genotyped. Incomplete penetrance or allelic heterogeneity, which can influence expression of the phenotype, may explain why prediction accuracy did not approach 1 with any of the genetic models tested here. Nevertheless, a breeding program to eradicate horns from Merino sheep can be effective by selecting genotypes GG of SNP OAR10_29458450 or TT of SNP OAR10_29546872.1 since all sheep with these genotypes will be non-horned. Electronic supplementary material The online version of this article (10.1186/s12711-018-0398-6) contains supplementary material, which is available to authorized users.

Recently, regression of phenotype on marker genotypes was described for quantitative trait loci (QTL) mapping in F populations and shown to be equivalent to regression interval mapping (RIM). In this study, regression on markers was extended to half-sib designs with uncertain marker allele transmission, and properties of QTL parameters were examined analytically. In this method, offspring phenotypes are first regressed on the probability of transmission of a given allele from the common parent at flanking marker loci. Resulting regression coefficients can then be interpreted based on an assumed genetic model. With presence of a single QTL in the marker interval, it was shown that expected values of regression coefficients for the flanking markers contained all information about position and effect of the QTL and were independent of the probability of marker allele transmission. Through simulation, it was shown that regression of phenotype on marker allele transmission probabilities is equivalent to RIM under the same assumed genetic model. Regression on marker genotypes is computationally less time consuming than QTL interval mapping, as it eliminates the need to search for the best QTL position across marker intervals. This can form the basis for more efficient methods of analysis with more complex models, including threshold or logistic models for the analysis of categorical traits. © Inra/Elsevier, Paris genetic marker / QTL mapping / half-sib design

The basic problems inherent in the estimation of maternal effects are considered. A maternal effect is defined as an effect contributed to the phenotypic value of an offspring by his dam. The two most important problems are the confounding of the maternal effect of the dam and her genetic contribution to the genotypic value of her offspring and the possibility of a negative genetic correlation between the direct and maternal effect. The literature on maternal effect theory is reviewed. A general genetic model for the study of maternal effects is developed. Then problems arising in the estimation of genetic variance and covariance using covariances between relatives are studied and the examination of problems arising in the estimation of maternal effects for genetic groups and their cross combinations are considered.