The Booroola fecundity gene (FecB) increases ovulation rate and litter size in sheep and is inherited as a single autosomal locus. The effect of FecB

Geneticists have been interested in inbreeding and inbreeding depression since the time of Darwin. Two alternative approaches that can be used to measure how inbred an individual is involve the use of pedigree records to estimate inbreeding coefficients or molecular markers to measure multilocus heterozygosity. However, the relationship between inbreeding coefficient and heterozygosity has only rarely been investigated. In this paper, a framework to predict the relationship between the two variables is presented. In addition, microsatellite genotypes at 138 loci spanning all 26 autosomes of the sheep genome were used to investigate the relationship between inbreeding coefficient and multilocus heterozygosity. Multilocus heterozygosity was only weakly correlated with inbreeding coefficient, and heterozygosity was not positively correlated between markers more often than expected by chance. Inbreeding coefficient, but not multilocus heterozygosity, detected evidence of inbreeding depression for morphological traits. The relevance of these findings to the causes of heterozygosity–fitness correlations is discussed and predictions for other wild and captive populations are presented.

Identification of genomic regions that have been targets of selection for phenotypic traits is one of the most important and challenging areas of research in animal genetics. However, currently there are relatively few genomic regions identified that have been subject to positive selection. In this study, a genome-wide scan using ~50,000 Single Nucleotide Polymorphisms (SNPs) was performed in an attempt to identify genomic regions associated with fat deposition in fat-tail breeds. This trait and its modification are very important in those countries grazing these breeds.

The objective of this study was to determine the genetic parameters of methane (CH4) emissions and their genetic correlations with key production traits. The trial measured the CH4 emissions, at 5-min intervals, from 1225 sheep placed in respiration chambers for 2 days, with repeat measurements 2 weeks later for another 2 days. They were fed in the chambers, based on live weight, a pelleted lucerne ration at 2.0 times estimated maintenance requirements. Methane outputs were calculated for g CH4/day and g CH4/kg dry matter intake (DMI) for each of the 4 days. Single trait models were used to obtain estimates of heritability and repeatability. Heritability of g CH4/day was 0.29 ± 0.05, and for g CH4/kg DMI 0.13 ± 0.03. Repeatability between measurements 14 days apart were 0.55 ± 0.02 and 0.26 ± 0.02, for the two traits. The genetic and phenotypic correlations of CH4 outputs with various production traits (weaning weight, live weight at 8 months of age, dag score, muscle depth and fleece weight at 12 months of age) measured in the first year of life, were estimated using bivariate models. With the exception of fleece weight, correlations were weak and not significantly different from zero for the g CH4/kg DMI trait. For fleece weight the phenotypic and genetic correlation estimates were −0.08 ± 0.03 and −0.32 ± 0.11 suggesting a low economically favourable relationship. These results indicate that there is genetic variation between animals for CH4 emission traits even after adjustment for feed intake and that these traits are repeatable. Current work includes the establishment of selection lines from these animals to investigate the physiological, microbial and anatomical changes, coupled with investigations into shorter and alternative CH4 emission measurement and breeding value estimation techniques; including genomic selection.

Genotyping-by-sequencing (GBS) is becoming an attractive alternative to array-based methods for genotyping individuals for a large number of single nucleotide polymorphisms (SNPs). Costs can be lowered by reducing the mean sequencing depth, but this results in genotype calls of lower quality. A common analysis strategy is to filter SNPs to just those with sufficient depth, thereby greatly reducing the number of SNPs available. We investigate methods for estimating relatedness using GBS data, including results of low depth, using theoretical calculation, simulation and application to a real data set. We show that unbiased estimates of relatedness can be obtained by using only those SNPs with genotype calls in both individuals. The expected value of this estimator is independent of the SNP depth in each individual, under a model of genotype calling that includes the special case of the two alleles being read at random. In contrast, the estimator of self-relatedness does depend on the SNP depth, and we provide a modification to provide unbiased estimates of self-relatedness. We refer to these methods of estimation as kinship using GBS with depth adjustment (KGD). The estimators can be calculated using matrix methods, which allow efficient computation. Simulation results were consistent with the methods being unbiased, and suggest that the optimal sequencing depth is around 2–4 for relatedness between individuals and 5–10 for self-relatedness. Application to a real data set revealed that some SNP filtering may still be necessary, for the exclusion of SNPs which did not behave in a Mendelian fashion. A simple graphical method (a ‘fin plot’) is given to illustrate this issue and to guide filtering parameters. We provide a method which gives unbiased estimates of relatedness, based on SNPs assayed by GBS, which accounts for the depth (including zero depth) of the genotype calls. This allows GBS to be applied at read depths which can be chosen to optimise the information obtained. SNPs with excess heterozygosity, often due to (partial) polyploidy or other duplications can be filtered based on a simple graphical method.