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.