Ewes heterozygous (I+) for the Inverdale prolificacy gene (FecXI) located on the X chromosome have ovulation rates about 1.0 units higher than noncarriers. The purpose of this study was to examine the reproductive performance of ewes that were either heterozygous or homozygous (II) carriers of the Inverdale gene. Carrier rams (I) were mated with heterozygous ewes (I+) to produce females, half of which were expected to be I+ and half II. The 59 female progeny were examined by laparoscopy at 8 mo or 1.5 yr of age; 48% were found to have nonfunctional "streak" ovaries, which were about one eighth the volume of normal ovaries and showed no sign of follicular activity. There were four examples of full sib pairs where within each pair one had normal ovaries and the other had streak ovaries. Since these streak ovaries have not been observed in ewes known to be I+ or noncarriers (++), it is concluded that this condition is associated with animals homozygous for the Inverdale gene.

A directed search for QTL affecting carcass traits was carried out in the region of growth differentiation factor 8 (GDF8, also known as myostatin) on ovine chromosome 2 in seven Texel-sired half-sib families totaling 927 progeny. Weights were recorded at birth, weaning, ultrasound scanning, and slaughter. Ultrasonic measures of LM cross-sectional dimensions and s.c. fat above the LM were made, with the same measurements made on the LM after slaughter. Following slaughter, linear measurements of carcass length and width were made on all carcasses, and legs and loins from 540 lambs were dissected. Genotyping was carried out using eight microsatellite markers from FCB128 to RM356 on OAR 2 and analyzed using Haley-Knott regression. There was no evidence for QTL for growth rates or linear carcass traits. There was some evidence for QTL affecting LM dimensions segregating in some sire families, although it was not consistent between ultrasound and carcass measures of the same traits. There was strong and consistent evidence for a QTL affecting muscle and fat traits in the leg that mapped between markers BM81124 and BULGE20 for the four sires that were heterozygous in this region, but not for the three sires that were homozygous. The size of the effect varied across the four sires, ranging from 0.5 to 0.9 of an adjusted SD for weight-adjusted leg muscle traits, and ranging from 0.6 to 1.2 of an adjusted SD for weight-adjusted leg fat traits. The clearest effect shown was for multivariate analysis combining all leg muscle and fat traits analyzed across sires, where the -log(10) probability was 14. Animals carrying the favorable haplotype had 3.3% more muscle and 9.9% less fat in the leg relative to animals carrying other haplotypes. There was evidence for a second peak in the region of marker TEXAN2 for one sire group. It seems that a QTL affecting muscle and fat traits exists within the New Zealand Texel population, and it maps to the region of GDF8 on OAR2.

A QTL affecting leg muscle and fat traits has been identified within the New Zealand Texel population. The QTL maps to a region on OAR 2 with a two-marker haplotype test established at markers BULGE20 and BM81124. These markers encompass the likely position of Growth Differentiation Factor 8 (GDF8). The pleiotropic effects of this QTL on meat quality traits are tested. Objective measures of meat quality including pH, color (L*, a*, and b*), and tenderness (as assessed by Warner-Bratzler shear force measurements) were assessed on longissimus and semi-membranosus muscles of 540 progeny from six Texel sires. Four of these sires were subsequently identified as segregating for leg muscle and fat traits. For these segregating sires, comparison of progeny that had inherited the favorable haplotype from their sire with those that had received the alternate haplotype revealed no significant differences in the meat quality traits assessed. This finding suggests that the muscling QTL does not have pleiotropic effects on meat quality. A general scan for meat quality QTL was carried out using genotype data for eight markers from FCB128 to RM356 flanking 122cM of OAR 2 using Haley-Knott regression. This analysis revealed two QTL for a single sire. A QTL detected in the region of Marker INRA40 for color L* mapped to a site close to the muscling QTL, but there was evidence to suggest it is at a distinct locus. The QTL in the region of Marker RM356 might map distal to Marker RM356, as no peak was observed. This QTL, which seems to affect pH, color a*, color b*, and Warner-Bratzler shear measurements, requires further characterization.

A selection experiment was established in Coopworth sheep in 1981 to breed for increased or reduced ultrasonic backfat depth (scan C). Foundation females came from four flocks recorded for scan C and live weight, with weight-adjusted scan C data within flock being used for initial screening and subsequent selection. Three groups of animals per source flock, comprising proportionally the fattest 0·12, a random sample, and the leanest 0·12, were used to establish the F, control and L lines, respectively. Ewe flock numbers from 1981 to 1992 averaged 51 per line. Foundation rams were selected in the same manner from four different farms (two sources each in 1981 and 1982) to provide F line (proportionally the fattest 0·04), control line and L line (leanest 0·04) rams for use in 1981 and 1982, with four mated per line per year. Thereafter homebred rams were selected, with 41 or 42 homebred rams being used per line until 1992. Average generation intervals were 2·13 years and annual inbreeding rates per line 0·004. Bivariate heritability estimates for log scan C and log live weight, and a univariate estimate for log scan C using restricted maximum likelihood with an animal model, were 0·28, 0·22 and 0·38, respectively (all with s.e. 0·03). There was a suggestion of lower heritabilities in the L line for log scan C after adjustment for live weight. Realized heritabilities in the F and L lines were 0·34 (s.e. 0·07) and 0·26 (s.e. 0·03), respectively. Deviations of back-transformed weight-adjusted scan C in the last 2 years ofF and L data analysed (1991 and 1992 birth years) from the control flocks were 2·08 and −0·85 mm, which corresponded to responses of +2·50 and −1·03 phenotypic standard deviations, respectively. In addition there were relatively large responses in live weight taken at scanning, with F and L lines averaging 34·0 and 40·2 kg, compared with 38·0 kg for controls in the 1991 and 1992 birth years. The genetic and phenotypic correlations between log scan C and log live weight at scanning were 0·16 (s.e. 0·07) and 0·46 (s.e. 0·01) respectively.

Genotypes are often used to assign parentage in agricultural and ecological settings. Sequencing can be used to obtain genotypes but does not provide unambiguous genotype calls, especially when sequencing depth is low in order to reduce costs. In that case, standard parentage analysis methods no longer apply. A strategy for using low-depth sequencing data for parentage assignment is developed here. It entails the use of relatedness estimates along with a metric termed excess mismatch rate which, for parent-offspring pairs or trios, is the difference between the observed mismatch rate and the rate expected under a model of inheritance and allele reads without error. When more than one putative parent has similar statistics, bootstrapping can provide a measure of the relatedness similarity. Putative parent-offspring trios can be further checked for consistency by comparing the offspring’s estimated inbreeding to half the parent relatedness. Suitable thresholds are required for each metric. These methods were applied to a deer breeding operation consisting of two herds of different breeds. Relatedness estimates were more in line with expectation when the herds were analyzed separately than when combined, although this did not alter which parents were the best matches with each offspring. Parentage results were largely consistent with those based on a microsatellite parentage panel with three discordant parent assignments out of 1561. Two models are investigated to allow the parentage metrics to be calculated with non-random selection of alleles. The tools and strategies given here allow parentage to be assigned from low-depth sequencing data.

Background Producing animal protein while reducing the animal’s impact on the environment, e.g., through improved feed efficiency and lowered methane emissions, has gained interest in recent years. Genetic selection is one possible path to reduce the environmental impact of livestock production, but these traits are difficult and expensive to measure on many animals. The rumen microbiome may serve as a proxy for these traits due to its role in feed digestion. Restriction enzyme-reduced representation sequencing (RE-RRS) is a high-throughput and cost-effective approach to rumen metagenome profiling, but the systematic (e.g., sequencing) and biological factors influencing the resulting reference based (RB) and reference free (RF) profiles need to be explored before widespread industry adoption is possible. Results Metagenome profiles were generated by RE-RRS of 4,479 rumen samples collected from 1,708 sheep, and assigned to eight groups based on diet, age, time off feed, and country (New Zealand or Australia) at the time of sample collection. Systematic effects were found to have minimal influence on metagenome profiles. Diet was a major driver of differences between samples, followed by time off feed, then age of the sheep. The RF approach resulted in more reads being assigned per sample and afforded greater resolution when distinguishing between groups than the RB approach. Normalizing relative abundances within the sampling Cohort abolished structures related to age, diet, and time off feed, allowing a clear signal based on methane emissions to be elucidated. Genus-level abundances of rumen microbes showed low-to-moderate heritability and repeatability and were consistent between diets. Conclusions Variation in rumen metagenomic profiles was influenced by diet, age, time off feed and genetics. Not accounting for environmental factors may limit the ability to associate the profile with traits of interest. However, these differences can be accounted for by adjusting for Cohort effects, revealing robust biological signals. The abundances of some genera were consistently heritable and repeatable across different environments, suggesting that metagenomic profiles could be used to predict an individual’s future performance, or performance of its offspring, in a range of environments. These results highlight the potential of using rumen metagenomic profiles for selection purposes in a practical, agricultural setting.

There is simultaneous interest in improving the feed efficiency of ruminant livestock and reducing methane (CH4) emissions. The relationship (genetic and phenotypic) between feed efficiency (characterized as residual feed intake: RFI) and greenhouse gases [methane (CH4) and carbon dioxide (CO2)] traits in New Zealand (NZ) maternal sheep has not previously been investigated, nor has their relationship with detailed estimates of body composition. To investigate these relationships in NZ maternal sheep, a feed intake facility was established at AgResearch Invermay, Mosgiel, NZ in 2015, comprising automated feeders that record individual feeding events. Individual measures of feed intake, feeding behavior (length and duration of eating events), and gas emissions (estimated using portable accumulation chambers) were generated on 986 growing maternal ewe lambs sourced from three pedigree recorded flocks registered in the Sheep Improvement Limited database (www.sil.co.nz). Additional data were generated from a subset of 591 animals for body composition (estimated using ultrasound and computed tomography scanning). The heritability estimates for RFI, CH4, and CH4/(CH4+CO2) were 0.42 ± 0.09, 0.32 ± 0.08, and 0.29 ± 0.06, respectively. The heritability estimates for the body composition traits were high for carcass lean and fat traits; for example, the heritability for visceral fat (adjusted for body weight) was 0.93 ± 0.19. The relationship between RFI and CH4 emissions was complex, and although less feed eaten will lead to a lowered absolute amount of CH4 emitted, there was a negative phenotypic and genetic correlation between RFI and CH4/(CH4+CO2) of −0.13 ± 0.03 and −0.41 ± 0.15, respectively. There were also genetic correlations, that were different from zero, between both RFI and CH4 traits with body composition including a negative correlation between the proportion of visceral fat in the body and RFI (−0.52 ± 0.16) and a positive correlation between the proportion of lean in the body and CH4 (0.54 ± 0.12). Together the results provide the first accurate estimates of the genetic correlations between RFI, CH4 emissions, and the body composition (lean and fat) in sheep. These correlations will need to be accounted for in genetic improvement programs.

The objective of this study was to determine whether differences in mRNA levels of key pituitary genes that regulate GH production, pituitary development, and growth were present and/or associated with divergent body composition phenotypes observed between sheep from genetically divergent lean and fat selection lines. Real-time PCR transcription profiles for pituitary specific transcription factor 1, prophet of pit1, GH, GH receptor, GH secretagogue receptor, GHRH receptor, leptin receptor, and somatostatin receptors 1 and 2 were determined in pituitary tissue. There was a difference in the amount of both GH (P < 0.001) and GH secretagogue receptor (P < 0.001) mRNA between the selection lines (5 females and 5 males per line; 20 wk of age); the lean line had greater abundance than the fat line, irrespective of which endogenous control gene was used. The results obtained for GHRH receptor were equivocal but suggestive; there were greater GHRH receptor mRNA levels (P < 0.001) in the lean line using beta-2-microglobulin as the endogenous control but not when hypoxanthine phosphoribosyltransferase and glyceraldehyde-3-phosphate dehydrogenase were used. No difference in pituitary specific transcription factor 1, prophet of pit1, GH receptor, leptin receptor, or somatostatin receptors 1 and 2 mRNA concentration was observed between the lines. The greater abundance of GH mRNA in the pituitary somatotropes from genetically lean animals appears to be associated with increased levels of GH secretagogue receptor mRNA and possibly GHRH receptor mRNA. This suggests that the difference in GH secretion between the lines may be due to differences in the afferent signals, such as ghrelin and/or GHRH, arising from the hypothalamus, or as a result of differential pituitary sensitivity to these hormones.