Genetic Parameters for Visually Assessed Traits and Their Relationships to Wool Production and Liveweight in Australian Merino SheepHeritability was estimated for a range of visually assessed traits recorded on Merino sheep, together with the phenotypic and genetic correlations among the visually assessed traits and correlations of the visually assessed traits with measured wool production traits and liveweight. Data were derived from four research resource flocks, with a range of 12 958 to 57 128 records from animals with 478 to 1491 sires for the various traits. The estimates of heritability were high for the wool quality traits of handle, wool character and wool colour (0.33–0.34) and the conformation traits of face cover, neck wrinkle and body wrinkle (0.42–0.45), moderate for front leg structure (0.18) and low for back leg structure (0.13). Fleece rot score had low heritability (0.14), while classer grade was moderately heritable (0.20). Estimates of genetic correlations among the visually assessed wool quality traits were low to moderate in size and positive (0.17–0.47). Genetic correlation estimates among the assessed conformation traits were generally very low, except for the genetic correlations between scores for neck and body wrinkle (0.92 ± 0.01) and front and back leg structure (0.31 ± 0.09). Fleece rot score had low positive genetic correlations with neck and body wrinkle scores (0.18 ± 0.05 and 0.15 ± 0.05, respectively) and classer grade (0.26 ± 0.06). Classer grade was slightly positively correlated with the wool quality traits (0.17–0.45) and leg structure traits (0.21–0.25). The genetic correlations among the visually assessed traits were generally neutral to favourable. The visually assessed wool quality traits had low to moderate favourable genetic correlations with mean and coefficient of variation of fibre diameter (0.19 –0.47), but negative correlations with clean wool yield (–0.26 to –0.37). Face cover was unfavourably correlated with staple length (–0.27 ± 0.04) and liveweight (–0.23 ± 0.02). Neck and body wrinkle scores were genetically associated with higher greasy (0.33–0.39) and clean fleece weights (0.19–0.22), greater coefficient of variation of fibre diameter (0.24–0.26) and fibre curvature (0.27–0.28), but with reduced yield (–0.26 to –0.28) and staple length (–0.34 to –0.41). Fleece rot score was genetically correlated with clean fleece weight (0.26 ± 0.05) and coefficient of variation of fibre diameter (0.27 ± 0.04). Classer grade was favourably correlated with greasy and clean fleece weights (–0.41 to –0.43), staple length (–0.29 ± 0.04), liveweight (–0.36 ± 0.03) and coefficient of variation of fibre diameter (0.27 ± 0.03). Most genetic correlations between the visually assessed traits and the measured production traits and liveweight were close to zero and less than 0.2 in magnitude. This study provides accurate values for the parameter matrix required to incorporate visually assessed traits into breeding objectives and the genetic evaluation programs used in the Australian sheep industry, allowing the development of breeding objectives and indexes that optimally combine visually assessed performance and measured production in Merino sheep.
Genetic Parameter Estimation of 16-month Live Weight and Objectively Measured Wool Traits in the Tygerhoek Merino FlockGenetic evaluation systems require the accurate estimation of genetic parameters. The genetic, phenotypic and environmental parameters for live weight and objectively measured wool traits were estimated for a South African Merino flock. Records of the Tygerhoek Merino resource flock were used to estimate these parameters. The database consisted of records of 4 495 animals, the progeny of 449 sires and 1 831 dams born in the period 1989 to 2004. The pedigree records used have been collected between 1969 and 2004. Direct heritability estimates (h²a) for 16-month live weight (LW) and objectively measured wool traits ranged from 0.20 for staple strength (SS) to 0.68 for fibre diameter (FD). Maternal heritability estimates ranged from 0.05 for LW and FD, to 0.10 for clean fleece weight (CFW). The proportion of the total phenotypic variance due to the maternal permanent environment variance (c²pe) amounted to 5% for fleece weights. The genetic correlation between animal effects for LW, greasy fleece weight (GFW) and CFW were -0.28, -0.65 and -0.70 respectively. The genetic correlation between LW and CFW was positive, but low at 0.14. The other important genetic correlations among the wool traits ranged from low to high, and were variable in sign ((for GFW with CFW (0.87) and with staple length (SL – 0.18); CFW with clean yield (CY – 0.33) and with SL (0.29); FD with CY (-0.09), with SL (0.15), with SS (0.40) and with standard deviation of FD (SDFD – 0.38):CY with SL (0.33) and with SDFD (0.10); SS with coefficient of variation of FD (CVFD – -0.57) and with SDFD (-0.28); CVFD with SDFD (0.87)). These results suggested that worthwhile responses in the objectively measured traits can be achieved through direct and indirect selection.
- Felting of wool is a major problem in the manufacture of knitted and woven products, as it is related to yarn shrinkage, which is a critical problem of the finished product. Felting is a unique property of animal fibres and a desirable characteristic in the making of felted products. However, felting is a particular problem with fine wools. Non-shrink woollen products are currently produced using chemical treatments during processing. Chlorination is the first step and it degrades the fibre surface. Fibres are then coated with polymers to cover degraded scale structures and/or to bond fibres together to prevent felt shrinkage. This process minimises frictional effects on wool fibre surfaces, limits relative motion of fibres in all directions, and increases hydrophilic properties of the fibre surface (Chen et al., 2000). Although these processes have been highly successful in shrink-proofing wool, they are expensive and detrimental to the fibre. Furthermore, the chlorination process is environmentally unfriendly and there are difficulties with residue disposal. Greeff and Schlink (2001) have shown that felting is a heritable trait, which implies that altering the ability of wool to felt through breeding may make a considerable contribution to wool’s processing properties and will enhance wool’s clean and green image. However, felting is strongly influenced by fibre curvature, fibre diameter (Scheepers and Slinger, 1968 ; Hunter et al., 1982 ; Kenyon et al., 1999 ; Veldsman and Kritzinger, 1960) and clean yield (Schlink et al., 2000). Lipson and Rothery (1975) showed that Merino wool has a significantly higher felting ability than Polwarth wool in spite of the fact that there were no differences in fibre surface friction, scale frequency or elastic properties between the breeds.They did note significant differences between the breeds in “swellings and necks” at intervals along the fibres, but conclusions were not clear because these wools differed in micron and curvature was not recorded. The OFDA2000 (Brims, 1997) has algorithms to measure variability and unevenness traits along the fibre which may be used to identify samples that may cause spinning problems. The objective of this study was to identify whether these along fibre variability traits influence felting and whether they are heritable.
- Genome-wide association studies (GWAS) provide a powerful approach for identifying quantitative trait loci without prior knowledge of location or function. To identify loci associated with wool production traits, we performed a genome-wide association study on a total of 765 Chinese Merino sheep (JunKen type) genotyped with 50 K single nucleotide polymorphisms (SNPs). In the present study, five wool production traits were examined: fiber diameter, fiber diameter coefficient of variation, fineness dispersion, staple length and crimp. We detected 28 genome-wide significant SNPs for fiber diameter, fiber diameter coefficient of variation, fineness dispersion, and crimp trait in the Chinese Merino sheep. About 43% of the significant SNP markers were located within known or predicted genes, including YWHAZ, KRTCAP3, TSPEAR, PIK3R4, KIF16B, PTPN3, GPRC5A, DDX47, TCF9, TPTE2, EPHA5 and NBEA genes. Our results not only confirm the results of previous reports, but also provide a suite of novel SNP markers and candidate genes associated with wool traits. Our findings will be useful for exploring the genetic control of wool traits in sheep.
- Data from seven research resource flocks across Australia were combined to provide accurate estimates of genetic correlations among production traits in Merino sheep. The flocks represented contemporary Australian Merino fine, medium and broad wool strains over the past 30 years. Over 110,000 records were available for analysis for each of the major wool traits, and 50,000 records for reproduction and growth traits with over 2700 sires and 25,000 dams. Individual models developed from the single trait analyses were extended to the various combinations of two-trait models to obtain genetic correlations among six wool traits [clean fleece weight (CFW), greasy fleece weight, fibre diameter (FD), yield, coefficient of variation of fibre diameter and standard deviation of fibre diameter], four growth traits [birth weight, weaning weight, yearling weight (YWT), and hogget weight] and four reproduction traits [fertility, litter size, lambs born per ewe joined, lambs weaned per ewe joined (LW/EJ)]. This study has provided for the first time a comprehensive matrix of genetic correlations among these 14 wool, growth and reproduction traits. The large size of the data set has also provided estimates with very low standard errors. A moderate positive genetic correlation was observed between CFW and FD (0.29 +/- 0.02). YWT was positively correlated with CFW (0.23 +/- 0.04), FD (0.17 +/- 0.04) and LWEJ (0.58 +/- 0.06), while LW/EJ was negatively correlated with CFW (-0.26 +/- 0.05) and positively correlated with FD (0.06 +/- 0.04) and LS (0.68 +/- 0.04). These genetic correlations, together with the estimates of heritability and other parameters provide the basis for more accurate prediction of outcomes in complex sheep-breeding programmes designed to improve several traits.