Deliverables
Genetic maps of livestock based on molecular genetic markers (e.g. Maddox et al., 2001, for sheep), provide new tools for the detection and mapping of genes of economic importance in farm animals. To analyse the Sarda x Lacaune population a panel of 155 microsatellites made-up by INRA was tested. The markers were chosen in order to have an average distance of 20 cM in all chromosomes. The microsatellites were organised in multiplex to maximise the number of markers simultaneously tested. Genotypings were performed on DNA exctract from blood samples. The final panel of markers was made of 144 microsatellites plus Prp locus.
They are listed below (in bold type OAR number:
1) BMS2833; BMS0835; ILSTS044; BMS862; ILSTS029; MCM058; BL41; BMS0963; BMS0574; BMS2572; INRA49; LSCV06; MAF109; LSCV105; BMS1789; BMS2263 2) CSSM47; MCM064; BMS2072; ;BMS1341; TGLA010; BMS1591; OAECP79; ILSTS030; LSCV22; BMS0778; BM6444; BMS0356;LSCV38; OARFCB11; 3) ILSTS045; BMS0460; OARFCB129; INRA131; RM096; BMS2569 ; BMC1009; OARCP43; RM154; BMS0772; 4) BMS1788; MCM218; BMS1237; MAF50; OARHH35; OARHH64; 5) RM006 ; BMS2258; MCM527; 6) BM9058 ; INRA133; ILSTS090; OARAE101; BMS0360; OARJMP08; BL1038; 7) BMS0528 ; BMS0861; BMS0419; BMS2641; BP31; ILSTS005; 8) BM2504 ; BMS0434; BMS1290; BMS1724; EPCDV016; BMS1967; 9) CSSM66 ; ILSTS011; LSCV32; BM302; OARCP9; 10) BMS2252 ; BMS0712; ILSTS056; BMS1316; 11) HEL10; IDVGA46; BM17132; MAP2C MCM120; 12) HUJ614 ; BMS0109; BMS1185; IDVGA69; HUJ625; 13) BMC1222 ; ILSTS059; BMS1352; PRP; BMS1669;OARAE016; 14) TGLA357 ; BMS2213; LSCV29; BM7109; INRA210; 15) MAF65; BMS0812; 16) BM1225; MAF214; LSCV08; MCM150; 17) OARVH98 ; OARVH116; OARFCB48; TGLA322; 18) BM3413 ; BMS2815; BM7243; OARHH47; TGLA122; BMS1561; 19) BMS0517; OARAE119; BM3628; LSCV14; 20) INRA132 ; BM1258; OLADRBP; OARHH56; 21) ILSTS019 ; OARVH110; OCAM; MCM135; BMS1948; 22) BMS0651; BM4505; BMS882; MCM373; 23) BMS2526; BMS0066; MAF35; URB031; 24) ILSTS102; BM4005; OAREL001; 25) OARVH41; BMS1714; 26) BMS2104; LSCV41; CSSM43; OARJMP58; BM0203
Starting from corrected genotypes a specific map of the 26 ovine autosomes was then built, using the CRIMAP software (all chrompic, build and two point options). For each marker the information content was also calculated as the percentage of daughters of heterozygous sires for which it was possible to trace back the marker allele received from the father. The average information content ranged from 38 % (OAR7) to 81% (OAR14). We were able to locate 10 markers (out of the 145) not included in the current official map, in the following linkage groups : INRA49 on OAR1; ILSTS090 on OAR6; BMS2252 and BMS0712 on OAR10 ; MAP2C on OAR11; BMS0109 and IDVGA69 on OAR12 ; PrP on OAR13; INRA210 on OAR14 ; OCAM on OAR21. Furthermore two markers were assigned to different chromosomes LSCV38 from OAR12 to OAR2 and BMS0066 from OAR3 to OAR23.
This set of markers was used the backcross SardaxLacaune population. It is currently being used for further QTL detection studies on an experimental population derived from the previous one and will be available for future studies on the commercial population on wich we wish to confirm and transfer our results.
The following genetic parameters were estimated for the nematodirus and strgyles faecal egg counts (FEC):
- Trait h² s.e.
- Nematodirus FEC August 0.30 - 0.11
- Nematodirus FEC September0.21 - 0.09
- Nematodirus FEC October 0.19 - 0.09
- Nematodirus Average Animal Effect 0.24 - 0.09
- Strongyles a FEC August 0.50 - 0.12
- Strongyles FEC September 0.11 - 0.07
- Strongyles FEC October 0.21 - 0.09
- Strongyles Average Animal Effect 0.23 - 0.09
IgA Activity 0.18 - 0.09
- N.B. Strongyles refers to all species present other than Nematodirus
The average animal effect was calculated using a restricted maximum likelihood algorithm, ASREML (Gilmour et al., 1996), fitting a repeatability model to calculate the average weighted FEC across the three time points.
THese results are primarily of scientific importance, as they demonstrate that there is genetic variability in the measures. However, they can also be used in practical breeding programmes. To estimate breeding values, which farmers can then use for selection purposes, it is necessary to assume heritability values. These values can be inputted into current breeding programmes that are now underway in the UK. The results are public and have been published.
Nine traits were considered in the analysis: milk yield (MY), fat yield (FY), protein yield (PY), fat content (FC), protein content (PC), lactation SCS (LSCS), teat angle (TA), udder cleft (UC), and udder depth (UD).
Genetic parameters for milk production traits, LSCS and udder-type traits were estimated using a REML method applied to a sire model on 121,283 Lacaune first lactations and 86,975 udder appraisals recorded between 2001 and 2004 (table 6): all sampling sires born after 1997 and had at least 15 daughters, while proven sires were required to have at least 150 daughters and were considered as fixed effects.
The other fixed effects corresponded to those applied in the official BVE for each given trait.
Milk yield corresponded to milk at milking period only after a 25 days suckling period. Fat and protein contents, and LSCS came from the part-sampling design and data for these traits were from 2.9 test days on average per lactation at the morning milking versus 5.0 for milk yield.Individual test-day somatic cell count (SCC) were transformed to test-day somatic cell score (SCS) through the classical logarithmic transformation (SCS=log2(SCC/100,000)+3). Then Lactation somatic cell score(LSCS) was computed as the arithmetic mean of SCS adjusted for days in milk.
The three udder traits (teat angle, udder cleft, udder depth) were scored by 12 trained classifiers according to a linear scale from 1 to 9, and the results were presented such as 9 is the best appraisal whatever the trait to facilitate the interpretation of genetic correlations between production and functional traits.
Genetic parameters followed the well known patterns both for milk production traits and LSCS accounting for the part lactation sampling both for FC, PC and LSCS. It confirmed also that udder traits (TA, UC and UD) had moderate heritabilities (0.26 to 0.35).
The new results corresponded to the estimates of genetic correlations between all milk production traits (MY, FY, PY, FC and PC) and not only MY (as previously) and the functional traits (LSCS, udder traits) to be included at the moment in the global selection criteria of the dairy Lacaune breed. Except the correlations between FC or PC and UD, all the genetic correlations between milk traits and LSCS or udder traits were null to antagonistic with a range between 0.00 to -0.39: it was specially the case between the 2 main dairy traits (FY and PY) and LSCS (0.21 and 0.22 respectively, meaning genetic opposition a little higher than with MY 0.15); between FY or PY and TA (-0.11 and -0.07 respectively); between FY or PY and UD (-0.34 and -0.39 respectively).
Conversely all the genetic correlations between LSCS and udder traits were slightly favourable with an absolute range of 0.12 to 0.32. In other words selection for one of these functional trait will produce a genetic gain both for udder health and udder conformation related to milking ease as now expected by Lacaune breeders.
Finally, there is evidence that selection on milk traits only would lead in the long term to baggy udders more difficult to milk by machine and more susceptible to mastitis. Thus it is demonstrated that the inclusion of udder health and udder morphology is mandatory in an efficient breeding programme carried out on milk traits only for several decades.
DNA was extracted and sequenced in exon 2, which is the most important part of the DRB1 sequence. Only 14 alleles were found in studies involving over 2000 lambs from thirteen farms. These results demonstrate that most and possibly all alleles existing in the Scottish Blackface have been identified. The Scottish Blackface breed is now one of the best-defined breeds in the world at the important DRB1 locus. The extent of molecular variation is similar to that found in humans indicating that there are no serious concerns about selection reducing diversity. In all sheep breeds, nearly 100 alleles have been described at the DRB1 locus and they fall into a small number of allelic families; most but not all of these allele families are represented in the Scottish Blackface breed.
Due to the high levels of diversity, there are an enormous number of potential haplotypes within the mhc (>10,000) when six loci were considered. Not all haplotypes have been observed but this may reflect the need to study large numbers of animals to dtect rare haplotypes. There is strong linkage disequilibrium within the major histocompatibility complex and this appears to peak around the DRB1 locus.
For QTL detection, the purebred French families corresponded to a grand-daughter design (GDD), taking advantage of the extensive use of AI in the breeding programmes of the Lacaune and Manech breeds (table 2). At the end of the second year of the project, 3,836 blood samples (for DNA storage) of Lacaune, Basco-Bearnaise or Manech AI rams have been collected.
Twenty two GDD families were chosen, 18 in Lacaune, 1 in Basco-Bearnaise, 3 in Manech breeds, for QTL detection: these 22 GDD families corresponded to 783 AI rams (22 sires and 761 sons) progeny tested in the nucleus flocks, with on average 34.5 sons per family. The sons were born between 1992 and 2002 and had 89 daughters on average.
Nine traits were considered in the analysis: milk yield (MY), fat yield (FY), protein yield (PY), fat content (FC), protein content (PC), lactation SCS (LSCS), teat angle (TA), udder cleft (UC), and udder depth (UD). The phenotypic unit of measurement is daughter yield deviations (DYD), i.e. the average of the phenotypes of the daughters of each son adjusted for the environmental effects and additive genetic merit of the daughters dams, obtained from the French official dairy sheep breeding value estimates (BVE).
For milk traits considered on a lactation basis, a BLUP animal model with repeated records is used including the fixed effects of flock x year x parity, age and month at lambing within parity and year, for milk yield the lambing-first test-day interval within year and parity, for fat and protein yield, fat and protein contents the effect of qualitative recorded category defined with the number of test-days x average lactation stage at recording; and genetic groups of unknown parents.
Since 1999, the milk traits BVE account for heterogeneity of variance: genetic, residual, and permanent environment variances are fitted according to flocks, year and parity. The model for genetic evaluation of SCS was also defined as a repeatability model for the lactation average LSCS including fixed effects comparable to those defined for milk traits. Finally for udder-type traits, a multiple-trait animal model was applied including the fixed effects of flock x year x classifier, year x lactation stage x date of lambing, year x number of lambs.
When a QTL for milk traits is detected for the 22 families and also only for the 18 Lacaune families, it is tabulated 2 times in the results, but of course only one QTL is counted.
A total of 11 QTL were found at the genome wise significance threshold with a suggestive linkage (p=0.038): 1 for MY on OAR 14, 1 for FY on OAR 16, 2 for PY on OAR 16 and OAR 17, 4 for F% on OAR1, OAR 2, OAR 9 and OAR 10, and 3 for P % on OAR 2, OAR 5, OAR9. Each detected QTL segregated in 2 to 5 families. The estimated substitution effects ranged from 0.7 to 3.0 standard genetic deviation, illustrating as a trend the limited power of the design, and sometimes also a weak informativeness of the closest markers for the informative families. Furthermore 5 results were chromosome-wise (p < 0.05) or nearly chromosome-wise significant : 1 for FY on OAR 12, 2 for PY on OAR 3 and OAR 14, 1 for F % on OAR 4, and 1 for P % on OAR 6.
Main results in this point refers to multiplex combination used to genome scan for QTL detection in Churra sheep. For building multiplex reactions from 2 to 4 microsatellites were amplified simultaneously using 4-colours-one lane technologies using an ABI-Prism 377 automatic sequencer. Optimisation of multiplex-PCR reaction involved several different factors to control. We chose this technique with the aim of maximizing economical and time resources These results are interesting for scientific community and will be published in scientific journals for its diffusion.
The aim was to develop a panel of informative microsatellite markers that could subsequently be used in this or other similar projects for any study of this nature. For each chromosome we tested up a large number markers per sire (up to 30, depending upon the length of the chromosome) and chose a panel of markers for each sire for each chromosome that were heterozygous for that sire. In other words, each sire had panel of markers chosen per chromosome, but these marker sets differed between sires.
We then genotyped all progeny and all available grandparents for the markers we chose for that sire. The particular advantage of this approach is that we only genotype markers that are likely to be informative and we do not waste effort or resources genotyping markers that will not contribute information. This approach was successful in ensuring that our genotyping strategy was informative and cost-efficient.
The informativeness of our genotyped markers, i.e. the proportion of the time we can unambiguously trace chromosomal segments back to the parent of origin, was close to 0.7 across all regions genotyped.
In total we genotyped 139 markers, and just less than 50000 individual genotypes. The actual markers genotyped were:
- Chromosome 1: BMS835, ILSTS44, ILSTS29, MCM58, BMS963, RM65, BM6438, BMS2321, MAF64, ILSTS004, CSSM04, BMS4000, INRA11, BMS527, DB6, BMS4001, MCM137, BM7145, BM6506, BMS4008, SOX2, TGLA415, BM8246, RM509, MCM130, BMS4045, CSSM32, BM864, LSCV105, BMS1789, BM1824, BM3205, OarHH36, URB014
- Chromosome 2: CSSM47, FCB226, BM3412, BMS1341, BL1080, BMS678, TGLA10, BMS1591, BM81124, CP79, TEXAN2, FCB20, BMS1126, BMS2626, ARO28, BM6444, BMS356, FCB11
- Chromosome 3: BMS710, BMS2569, BM827, ILSTS42, AGLA293, FCB5, ILSTS22, BMC1009, KD0103, BL4, LYZ, IFNG, CP43, MAF23, CSRD111, BM8230, BMS1248, TEXAN15, BM6433, BMS772, BM2830
- Chromosome 5: TGLA176, RM006, TGLA48, TGLA303, BMS2258, BMS792, BM1853, SHP1, OarAE129, MCM527, CSRD2134, BMS1247
- Chromosome 14: TGLA357, TEXAN10, BMS2213, MT2, ILSTS10, BM8151, MCM133, BM7109, INRA63, ILSTS002, BMS833, LSCV30, MCMA19, BM6507
- Chromosome 18: MCM131, ILSTS52, VH54, BP33, HH47, BMC5221, TGLA337, OY15, TGLA122, ILSTS54, MCM38, OB2, MCMA26, CSSM018, OY5, DLK
- Chromosome 20: INRA132, DYA, MCMA36, CP73, BM1815, DRB1, OLADRB, OMHC1, BMS468, TGLA387, CSRD226, BM1818, BP34, HH56, MCMA23
- Chromosome 21: BMC2228, ILSTS19, INRA175, CP20, VH110, JP15, HH22, BMC1206, BMS1948
This set of markers was used in our population, and has formed the basis for subsequent studies in commercial populations of sheep in which we aim to independently verify our findings.
These markers are publicly available to anyone who wishes to use them. Applied within a sheep breeding context they can be used to enhance genetic progress ina variety of traits related to sheep health, product quality and performance.
For mastitis resistance somatic cell scores are measured routinarely in Churra selection flocks.
REgarding udder shape we have scored four basic udder traits and a fifth trait that globally defines udder morphology, on a nine-point linear scale. The five traits are associated with aptitude for machine milking.
- Udder depth is defined by the distance between rear attachment and the udder floor, using as a reference the hock. Udders with excessive depth (below the hock) usually reflect deficiencies in the suspensory ligament.
- Udder attachment is determined by the perimeter of the insertion to the abdominal wall of the ewe. The maximum insertion base (9 points) is considered optimum.
- Teat placement is defined by teat angle. The optimum is completely vertical teats (9 points), directed toward the ground, that coincide with minimum cistern height.
- Teat size is determined by length. Extreme sizes impede adaptation to standard teat cups, manual milking and suckling. In the Churra breed, the average teat length corresponding to 5 points on the scale.
- Udder shape measures the morphology of the udder relative to the optimum for machine milking (9 points), and corresponds to the udder machine.
- Udder shape is scored by its symmetry, depth, attachment, teat position and size. Although udder shape includes several udder traits, it is also considered in the method in order to have a general parameter of the udder, similar to the dairy form in cattle.
For mastitis resistance, a QTL on chromosome 20 was found for SCC (logarithm transformation of somatic cell counts), in the interval flanked by BM1905 and TFPA2 markers.
Regarding udder morphological traits we have evidenced four regions showing a significant effect (chromosome-wise P value <0.05) on Teat placement (chromosome OAR7), Udder attachment (chromosome OAR26) and Udder depth (chromosomes OAR14 and OAR20). Furthermore six genomic regions have evidenced an influence on udder traits with a suggestive signification level (P<0.1, chromosome-wise): on chromosome OAR4 for udder shape, on chromosome OAR22 for udder depth and on chromosomes OAR6, OAR8, OAR15 and OAR23 for udder depth.
These results are detected in our commercial population and should be verified in an independent experiment before a possible use in selection.
A genome scan on the 20 ovine autosomes was performed based on a panel of 163 microsatellite markers. The microsatellite markers were informative, since on average 77 % of the animals were heterozygous, with few differences between families (74 % for family 2 to 82 % for family 6). The average information content ranged from 34 % (chromosome 25) to 86 % (chromosomes 8 or 19).
The markers genotyped per chromosome were:
Chromosome 1 (13 markers): BMS2833 / BMS0835 / BL41 / BM6438 / BMS574 / DIK5019 / CSSM004 / BMS2572 / INRA49 / MAF109 / LSCV105 / BMS1789 / BMS2263 Chromosome 2 (14 markers) : CSSM47 / BMS1591 / BMS1864 / OARCP79 / DIK4469 / DIK1172 / TGLA377 / ILSTS030 / OARFCB20 / ILSTS082 / LSCV037 / LSCV22 / IDVGA72 / OARFCB11 Chromosome 3 (5 markers) : ILSTS045 / RM0096 / BMS2569 / AGLA293 / BMS0772 Chromosome 4 (3 markers): MAF50 / OARHH35 / OARHH64 Chromosome 5 (7 markers): RM006 / DIK2689 / DIK4386 / BMS2258 / OARAE129 / MCM527 / BMS1247 Chromosome 6 (15 markers): OARCP125 / BM9058 / INRA133 / MCM53 / LSCV043 / OARAE101 / DIK4053 / BM143 / OARJMP36 / DIK4656 / BMS483 / MCMA09 / DIK2320 / BM415 / OARJMP08 Chromosome 7 (2 markers): BM1853 / BMS2641 Chromosome 8 (6 markers): DIK4217 / INRA144 / BM4208 / URB024 / EPCDV016 / BMS1967 Chromosome 9 (13 markers): CSSM066 / DIK4230 / MCMA10 / ILSTS11 / EPCDV008 / DIK5287 / LSCV032 / MAF33 / DIK4730 / ILSTS008 / BM302 / MCM42 / OARCP9 Chromosome 10 (8 markers): BMS2252 / BMS712 / CSSM051 / DIK2750 / ILSTS056 / DIK5191 / DIK016 / INRA005 Chromosome 11 (3 markers): HEL10 / IDVGA46 / BM17132 Chromosome 12 (6 markers): HUJ614 / BMS109 / TGLA53 / BMS1185 / IDVGA69 / HUJ625 Chromosome 13 (3 markers): BMS1352 / ILSTS059 / OARAE016 Chromosome 14 (9 markers): TGLA357 / INRA038 / Texan10 / BMS2213 / DIK4059 / BM7109 / DIK4849 / LSCV030 / INRA210 Chromosome 15 (3 markers): MAF65 / BMS2684 / BMS820 Chromosome 16 (10 markers): BM1225 / NRDIKM033 / TGLA126 / BMS2461 / MAF214 / BMS2361 / BM4107 / LSCV008 / DIK059 / MCM150 Chromosome 17 (7 markers): OARVH98 / URB062 / AGLA299 / IL15 / BMS2780 / ILSTS023 / IDVGA40 Chromosome 18 (5 markers): BM3413 / ILSTS052 / BMS2815 / OARHH47 / TGLA122 Chromosome 19 (9 markers): BMS0517 / CSSM006 / INRA194 / DIK4888 / OARAE119 / BM3628 / DIK2716 / DIK612 / LSCV14 Chromosome 20 (3 markers): BM1258 / OLADRB / OARHH56 Chromosome 21 (6 markers): ILSTS019 / MNB034 / DIK2193 / MCM135 / DIK2078 / BMS1948 Chromosome 22 (2 markers): BM4505 / BMS882 Chromosome 23 (4 markers): BMS2526 / BMS0066 / MAF35 / URB31 Chromosome 24 (3 markers): ILSTS102 / INRA206 / BM4005 Chromosome 25 (2 markers): OARVH41 / BMS1714 Chromosome 26 (2 markers): BMS2104 / CSSM43 .
Genetic parameters for carcass composition and meat quality traits were estimated in Scottish Blackface sheep, previously divergently selected for carcass lean content (LEAN and FAT lines). Computerised X-ray tomography (CT) was used to obtain non-destructive in vivo estimates of the carcass composition of 700 lambs, at ca. 24 weeks of age, with tissue areas and image densities obtained for fat, muscle and bone components of the carcass. Comprehensive measures of meat quality and carcass fatness were made on 350 male lambs, at ca. 8 months of age, which had previously been CT scanned.
Meat quality traits included intramuscular fat content, initial and final pH of the meat, colour attributes, shear force, dry matter, moisture and nitrogen proportions, and taste panel assessments of the cooked meat. FAT line animals were significantly (p<0.05) fatter than the LEAN line animals in all measures of fatness (from CT and slaughter data), although the differences were modest and generally proportionately less than 0.1. Correspondingly, the LEAN line animals were superior to the FAT line animals in muscling measurements. Compared to the LEAN line, the FAT line had lower muscle density (as indicated by the relative darkness of the CT scan image), greater estimated subcutaneous fat (predicted from fat classification score) at slaughter, more intramuscular fat content, a more yellow as opposed to red muscle colour, and juicer meat (all p<0.05).
All CT tissue areas were moderately to highly heritable, with h2 values ranging from 0.23 to 0.76. Likewise, meat quality traits were also moderately heritable. Muscle density was the CT trait most consistently related to meat quality traits, and genetic correlations of muscle density with live weight, fat class, subcutaneous fat score, dry matter proportion, juiciness, flavour and overall liking were all moderately to strongly negative, and significantly different from zero.
In addition, intramuscular fat content was positively genetically correlated with juiciness and flavour, and negatively genetically correlated with shear force value. The results of this study demonstrate that altering carcass fatness will simultaneously change muscle density (indicative of changes in intramuscular fatness), and aspects of intramuscular fat content, muscle colour and juiciness. The heritabilities for the meat quality traits indicate ample opportunities for altering most meat quality traits. Moreover, it appears that colour, intramuscular fat content, juiciness, overall liking and flavour may be adequately predicted, both genetically and phenotypically, from measures of muscle density. Thus, genetic improvement of carcass composition and meat quality is feasible using in vivo measurements.
These results have now been published and are a unique contribution to literature on the genetic control of meat quality in sheep. Furthermore, they are now beign used in the design of potential breeding programmes for genetically improving meat quality. A specific novelty is that for the first time they indicate means by which meat quality may be improved using non-destructive in vivo measurements.
The QTL results for LSCS and udder-type traits were the following: a total of 3 QTL were found at the genome wise significance threshold with a suggestive linkage (p=0.038): 1 for LSCS on OAR 14, and 2 for Udder cleft on OAR 6 and OAR 17. Each detected QTL segregated in 2 to 7 families. The estimated substitution effects ranged from 0.7 to 2.7 standard deviation.
On the other hand, regarding the QTL for LSCS, the LRT profiles of the informative families may suggest the existence of 2 QTL, the first one for families 14 and 19 with a most likely location close to the marker BMS2213, the second one for families 5, 9 and 20 close to the marker BM7109. Furthermore 7 results were chromosome-wise (p < 0.05) or nearly chromosome-wise significant: 1 for LSCS on OAR 16, 1 for Teat angle on OAR 2, 3 for Udder cleft on OAR 9, OAR 18 and OAR 19, and 2 for Udder depth on OAR 1 and OAR 26.
For the backcross ewes, the basic measure of resistance to nematodes was faecal egg count (FEC) under natural conditions of infection. Out of the very dry season in July-August when grass was missing, all the ewes grazed every day some hours on irrigated annual pastures of ryegrass and berseem clover. Supplementary feed was freely offered in each of the 4 opened houses, composed of alfalfa hay, maize silage and concentrates.
In Sardinia, the most common internal parasites are gastrointestinal nematodes and Protostrongyles in flocks using communal pastures.
Individual samples were taken in appropriate times along the year, on 8 occasions (sampling 1 to 8). FEC were measured before first calving (sampling 1) and seven times during their first four lactations: from June 2000 to June 2003. At each sampling date all individuals were sampled in 4 consecutive days. Samples were processed at the Veterinary Faculty in Sassari the same day by floatation in saturated salt solution in a McMaster slide (70ml for 5g of faeces or the same proportion if less than 5 g were available) and the eggs counted, those of Nematodirus genus separately. Presence of the tapeworm Moniezia was noted. To identify the genus distribution, common faecal cultures were done, one per sampling day for each of the seven first sampling, with the remaining faeces having more than 100 eggs per gram to get higher number of L3 for genus identification.
Average FEC varied highly from one sampling to the other, with particularly low values (and high proportion of zero values) in seventh and eight sampling series, and highest values of FEC for September sampling (sampling series 3 and 5).As the proportion of ewes positive for Nematodirus or Moniezia were under 3%, only the total of the gastrointestinal nematode species were analysed. FEC were transformed to lbpFEC, using the Bosseray Plommet transformation: Ln[(FEC+7)/Ln(FEC+7)].The log transformation achieved better adjustment to Gaussian distribution than row data although distribution of the two last FEC samplings showed high percentage of zero values.
Nine FEC traits were defined: FEC1 to FEC8, corresponding to eight single traits for each sampling date, and repeated FEC measures from first to sixth sampling. The best model for each FEC trait was defined according to significance of environmental effects using general lineal models (proc GLM, SAS®) for FEC1 to FEC6 continuous measures. Given the high proportion of null values for FEC7 and FEC8, the latter variables were considered as binary and analysed with logistic regression linear models (proc logistic, SAS®). Repeated continuous FEC1 to FEC6 measures were analysed using mixed model (proc MIXED, SAS®).
Fixed effects included in models were age, physiological status, and weight within each sampling. Additionally, a group effect (4 to 5 levels) was defined to account for the different physical groups of ewes defined after each lambing for management purpose. All four environmental effects were categorised into 3 to 6 levels within sampling date.
At the genome wise significance threshold with a suggestive linkage (p=0.038), a total of 20 QTLs were found for the 9 FEC traits. Out of these results, five QTL with a high confidence level (several traits; genome wise significance threshold; position and LRTmax profiles similar across traits) were detected on OAR2 (closest markers LSCV022), OAR3 (closest markers BMS0772, MRM154 and INRA131), OAR6 (closest markers INRA133 and BMS0360), OAR12 (closest markers HUJ625), OAR13 (closest markers BMC1222 and ILST059). Effects of QTL varied from 0.10 to1.50 standard deviation.
Conjugated linoleic acid (CLA) is a collective term for positional and geometric isomers of linoleic acid (C18:2). The cis 9 trans 11 C18:2 (rumenic acid) is the most abundant CLA isomer in meat and milk of ruminants. A portion of CLA is formed in the rumen by biohydrogenation of linoleic acid, escapes further biohydrogenation and is absorbed in the digestive tract. The extent of this process is minimal, while the intermediate product of CLA biohydrogenation, (trans 11-C18:1; vaccenic acid), accumulates. The other important source of vaccenic acid (VA) in the rumen is the biohydrogenation of the linolenic acid (C18:3). This pathway does not have as intermediate CLA. VA produced in the rumen is desaturated to produce CLA in the mammary gland tissues by Stearoyl-CoA desaturase (Scd). Scd can use different fatty acids as substrate and influences the amount of several unsaturated fatty acids. On the whole, there is evidence that milk CLA basically comes from mammary synthesis by the action of Scd on VAAC provided by the rumen. Thus differences in ruminant species, cattle breeds and between individual cows can probably be explained by the different activities of Scd, even when they were fed with the same diet.
On the Sarda x Lacaune backcross ewes, individual samplings to determine FA content in milk were performed twice, in the middle of the 2nd and the 3rd lactations. Fatty acid content was determined as follows: fat separation by centrifuging at low temperature, storage of individual cream at 20°C, oil separation by thermal shock and centrifuging, acid trans-methylation. Fatty acid methyl esters (FAME) were determined by gas chromatography using a VARIAN GC 3600 equipped with FID and a fused silica capillary column (SP 2560 Supelco), 100 m 0.25 mm i.d., film thickness 0.20 µm. Helium was used as the carrier gas at a flow of 1 ml/min. The split ratio was 1:100. The oven temperature was programmed at 75°C and held for 1.50 min, then increased to 190°C at a rate of 8°C/min, held for 25 min, increased to 230°C at 15°C/min, held for 4.47 min. The temperatures of the injector and of the detector were set at 290°C. For QTL detection analyisis 2 traits were considered: the CLA content in the milk fat (mg/g of fat) and the ratio between CLA and vaccenic acid. The latter is the direct precursor of CLA in the mammary gland where it is desaturated by delta 9-desaturase to produce rumenic acid. A repeated measurement model including the fixed effect year x group of management and the random effects of individual and sire was applied prior to QTL analysis. An across family single trait QTL analysis was carried out, using the methodology proposed by Knott et al. (1996) and Elsen et al. (1999), by within-sire linear regression and implemented in the QTL map software.
The CLA content was consistent with previous results in Sarda sheep, although higher values of CLA (32.85 mg/g of fat) were found in Sarda sheep grazing during the spring. CLA content showed remarkable variability between families. Indeed sire variance was 8.4 % of the total phenotypic variance for the CLA content and 5.6 % for the ratio CLA/VACC. Total individual variance was 29.7 % for CLA content and 34.4 % for the ratio. Three QTL were detected for the CLA content in milk fat on OAR 4, 14, and 19. The effects ranged from 0.33 to 0.83, on average between 47% and 124% of the overall rsd. Only three families per QTL were significant. Two of the four QTL identified for CLA/VACC, on OAR 4 and 14, are in the same regions of those found for CLA. This suggests that these QTL affect CLA above all. By contrast, the QTL found on OAR 6 and 22 were specific to the ratio.
This indicates that CLA/VACC, which reflects the rate of desaturation of the vaccenic acid in rumenic acid, can be considered a specific trait. The number of significant families ranged from two to four with effects of around two thirds of the overall rsd. Of particular interest is the QTL detected on OAR 22 where the SCD gene encoding for delta 9-desaturase is located. This result can be considered as a first step, and suggests that SCD gene polymorphism is actually related to the delta 9-desaturase expression level and affects the quantity of CLA produced in the mammary tissues from vaccenic acid. The coincidence between the most probable location and the map position of the Scd gene supports future research aimed at finding causal mutations along that gene. Thus a fine mapping of OAR22 has been undertaken and the sequencing of the candidate gene has been planned for the next future.
One of the aim of the project was identifying quantitative trait loci affecting udder morphology and milk emission during machine milking in dairy ewes. Both these features greatly affect milkability of ewes and are thus of major interest for sheep farmers which wish to simplify milking activities and reduce milking time. To achieve our aim we first developed or improved two methods for appraising udder morphology. The first one was based on classical linear scoring of basic udder traits (teat placement, TP; udder attachment UA, udder depth UD, degree of separation of the halves DS). The second was based on the extraction of objective measurements of the udder from digital pictures. The reliability and feasibility of both techniques was assed by estimating repeatability and correlations of measures realised with the 2 methods and their correlations with measurements directly taken on the animals. Results indicated that both udder scoring and digital picture analysis are useful tools for appraising udder morphology in sheep.
The kinetics of milk emission during machine milking was recorded with an automatic device developed by INRA, which provides some measures of milk emission speed. Parameters provided at each individual milking are: average (AMF, ml/s) and maximum milk flow (MMF, ml/s), the moment of maximum milk flow occurrence (TMMF, s), and the time needed to collect the first 160 ml of milk in the jar (latency time, LT, s). Preliminary analysis showed that, among the traits recorded, MMF and TL are the most pertinent for describing milking individual speed, given that high MMF and low LT determine shorter milking time.
On the SardaXLacaune population udder scoring was performed once a month in 2000 and 3 times a year from 2001 to 2003; digital pictures of the udder were taken once a year in 2000 and 2001, milk emission data were collected twice a month for 4 years. Data were adjusted for the main environmental effects and QTL detection was performed on solutions of random individual effects for repeatability mixed models, using the QTLmap software. An original approach was used to increase the power of detection of non pleiotropic QTL when 2 correlated traits are investigated. It is based on the correction of the trait of interest for the genetic covariance with the correlated trait. In all, the analyses of different variables describing the udder morphology allowed for the detection of 53 locations significant at the 5% chromosome wise significance level.
Most of these locations concerned the same udder trait measured in different ways. Consistent results arise from data issued from different measurement approaches. QTL with a high confidence level were detected on OAR 3, 4, 9, 14, 16, 20, 22, 26. In particular, on OAR 3, 4, 14 and 16, QTL affecting measures of udder width were detected. The number of informative families ranged from 2 to 4. QTL effects varied from 0.42 to 1.90 within family s.d. unit. QTL affecting cistern height was mapped on OAR9, OAR14 and OAR26. These finding are of particular importance for sheep milkability, given that the height of the cistern greatly affects stripping. Finally significant QTL affecting udder height or udder attachment was detected on OAR22. The existence of QTL affecting udder UA and UD was suggested for OAR6, OAR20 and OAR 26. QTL which affected milk emission speed were detected on OAR9, 11, 17, and 20. In Particular, a highly significant QTL, which affected milk emission speed, but had no effect on milk production, as detected on OAR11. This finding lead to envisage direct selection on milking speed whenever genetic gain for this trait, due to indirect response on milk yield selection, was not considered sufficient.
At present, only few studies on QTL affecting udder traits and milkability have been published on dairy cattle and none on dairy ewes. Our results represent thus a 1st step towards the possibility of selecting dairy ewes for these functional traits on the basis molecular information. The 2nd step is to verify whether the QTL detected in the backcross population are also segregating in the parental breeds and to define more precisely their location. As far as the Sardinian breed is concerned, this step has already been undertaken. A resource population of approximately 900 7/8 or 15/16 Sardinian x Lacaune (Sardinian recurrent parent) has been procreated. It is organised in 24 half-sib sire families of around 40 daughters and will be measured for udder morphology and milk emission traits as well as for other traits of interest, for 4 years. Twenty-two sires came from the AI centre of the Sardinian breed and are thus representative of the selected population. Furthermore our Institute has the availability of a DNA bank which will allow us to validate QTL for traits routinely recorded in the herd-book farms, i.e., concerning milkability, for udder morphology.
Quantitative trait loci (QTL) were identified for traits related to carcass and meat quality in Scottish Blackface sheep. The population studied was a double backcross between lines of sheep divergently selected for carcass lean content (LEAN and FAT lines), comprising nine half-sib families. Carcass composition (600 lambs) was assessed non-destructively using computerised tomography (CT) scanning and meat quality measurements (initial and final pH of M.semimembranosus, colour, shear force value, carcass weight, lamb flavour, juiciness, tenderness and overall liking) were taken on 300 male lambs. Lambs and their sires were genotyped across candidate regions on chromosomes 1, 2, 3, 5, 14, 18, 20 and 21. QTL analyses were performed using regression interval mapping techniques. In total, nine genome-wise significant and 11 chromosome-wise and suggestive QTL were detected in seven out of eight chromosomes.
Genome-wise significant QTL were mapped for lamb flavour (OAR 1); for muscle densities (OAR 2 and OAR 3); for colour a (redness) (OAR 3); for bone density (OAR 1); for slaughter live weight (OAR 1 and OAR 2) and for the weights of cold and hot carcass (OAR 5). The QTL with the strongest statistical evidence affected the lamb flavour of meat and was on OAR 1, in a region homologous with a porcine SSC 13 QTL identified for pork flavour. This QTL segregated in 4 of the 9 families. This study provides new information on QTL affecting meat quality and carcass composition traits in sheep, which may lead to novel opportunities for genetically improving these traits.
The next step is the verification of these results in independent populations, hence the definition of results that can be used directly in breeding programmes. Because of the nature of the traits, this is more demanding of resources than verication or results for traits such as nematode resistance or growth performance, which can be readily measured on live animals. Meat quality traits generally require slaughter of the animal, hence a carefully designed measurement protocol is required to simultaneously allow verification of results and subequent selection.
If successful, we will be able to breed sheep whose meat better meets the requirements of EU consumers.
We have detected seven genomic regions showing a significant evidence of carrying a QTL for parasite resistance phenotypes (chromosome wise P-value<0.05). Two QTL affecting IgA level were detected in chromosomes OAR1 and OAR9. For faecal egg counts in day "0" FEC_0 we have evidence for genes located on OAR10 and OAR14.
Finally, three regions affect faecal egg counts after treatment FEC_1 phenotype and are located on OAR1, OAR6 and OAR20. Furthermore four regions display a suggestive evidence of QTL, two affecting serum pepsinogen (PEPSI) located on OAR1 and OAR24, one on chromosome 13 affecting plasma IgA level and one on OAR26 influencing FEC_1.
These results are detected in our commercial population and shoud be verifyed in an independent experiment before a possible use in selection.
We aimed to identify quantitative trait loci associated with endoparasitic infection in Scottish Blackface sheep. Data was collected from 789 animals over a 3-year period. All of the animals were continually exposed to a mixed nematode infection by grazing. Faecal samples were collected in August, September and October each year at ca. 16, 20 and 24 weeks of age; Nematodirus spp. eggs were counted separately from the other species of nematodes. Blood samples were collected in October from which IgA activity was measured and DNA was extracted for genotyping. 139 Microsatellite markers were genotyped across 8 chromosomal regions (chromosomes 1, 2, 3, 5, 14, 18, 20 and 21) in the sires and progeny were genotyped for the markers that were polymorphic in their sire. Evidence was found for QTL on chromosomes 2, 3, 14 and 20. QTL associated with specific IgA activity were identified in chromosomes 3 and 20, in regions close to IFNG (chromosome 3) and the MHC (chromosome 20). QTL associated with Nematodirus FEC were identified on chromosomes 2, 3 and14. Lastly, QTL associated with non-Nematodirus Strongyle FEC were identified on chromosomes 3 and 20. This study has shown that some aspects of host resistance to gastrointestinal parasites are under strong genetic control, therefore these QTL could be utilised in a marker assisted selection scheme to increase host resistance to gastrointestinal parasites.
Compared to previously publisged data, these are the strongest QTL yet dected for nematode resistance.
Exploitation of these results is desirable and conceptually straightforward - they need to be verified in independent populations (preferably commercial sheep), with animals containing haploytpes associated with increased resistance then preferentially used by the breeders. We are currently in this phase: we have collected blood and FEC samples from commercial sheep of known parentage, we are developing marker panels using previosuly described results from GeneSheepSafety, and we will genotype these phenotyped animals. Results will be fed back to farmers, who will then have the opportunity to utilise animals with the most favourable resistance genotypes.
Milk yield and milk composition were bimonthly recorded in the back-cross Sarda x Lacaune population in order to detected QTL wich affect this trait, which , at present, still represent the major selection criteria for dairy ewes. On a lactation basis, 5 traits were considered: milk yield (MY), fat and protein yields (FY, PY), fat and protein contents (FC, PC). Two kinds of traits were analyzed: either the first lactation (first parity) only, or the 4 first lactations considered as a repeated measurement trait for a given trait (for instance, milk yield). MY, FY and PY were pre-adjusted for length of milking period and parity using multiplicative factors. The fixed effects considered were year x group of management (during the milking period), year x age at lambing, and year x number of suckled lambs.
Phenotypes for QTL analysis were, in first lactation the residuals of the fixed model for single measurements of each trait (given the fixed effects described above), in all lactations the individual solutions of the linear mixed model for repeated measurements. At the genome wise significance threshold (p=0.0038), a total of 14 QTLs were found (table 32). This number reached 27 at the genome wise significance threshold with a suggestive linkage (p=0.038).
QTL for milk, fat or protein yields with the best confidence level were found on OAR 3, OAR 4 and OAR 20, while the level was only suggestive on OAR 16. For one of the given chromosomes OAR3, OAR 16, and OAR 20, the QTLs affect simultaneously milk, fat and protein yields, in first and all lactations, with estimates effects of the same sign, more or less for the same families and comparable LRT profiles: it suggests that it is the same QTL for milk, fat and protein yield. On the other hand, QTLs at the genome wise significance threshold (p=0.0038), were detected for protein content (PC) on OAR 1, for fat content (FC) on OAR 7 and OAR 20. Other consistent results at the suggestive level were obtained for FC on OAR 3 and OAR 16, and for PC on OAR 7.
At present those findings are being verified on the experimental population derived by crossing the the backcross ewes with 22 Sardinian rams. As far as milk yield is concerned, the availability of estimated breeding values for the commercial population and of DNA bank of all animals of the herd-book born in the last 4 years, will allows us to verify whether this QTL are also segregating the pure bred population and to better define their location, thus opening the possibility of the direct utilisation of our results for selection.
Milk somatic cell count (SCC) was considered as the trait pertaining to mastitis resistance. Individual milk samples were collected bimonthly at evening and morning milking in the first four lactations to determine SCC. Final data consisted of individual milk SCC of the 969 ewes, measured about 12 times per lactation at morning and evening milkings, approximately every 2 to 3 weeks from December to July, in 2000, 2001, 2003 and 2004 (parity 1 to 4, respectively). After data checking, 3,183 lactations and 32,929 elementary daily values of SCS from 917 ewes were used.
Elementary SCC were converted to SCS, after classic log-transformation. Morning and evening values were averaged to a single day measure. Analyses were done either on lactation means of daily SCS (LSCS), either directly in test day models.
Traits considered were therefore:
- LSCS in parity 1 to 4 considered as four different traits (LSCS1 to LSCS4),
- LSCS considered as a repeated trait across the four lactations (LSCS1234-rep),
- elementary SCS considered as a repeated trait (SCS 1234-TD) within lactation and across years.
For the 6 somatic cell count (SCC) traits (LSCS1, LSCS2, LSCS3, LSCS4, LSCS1234-rep, SCS 1234-TD), a total of 11 QTL were found at the genome wise significance threshold with a suggestive linkage (p=0.038). Out of these results, QTL with a high confidence level (several traits; genome wise significance threshold; position and LRTmax profiles similar across traits) were detected on two chromosomes: OAR6 (closest markers BMS0360) and OAR13 (closest markers INRA133 and BMC1222). Effect of the latter QTL varied from 0.27 to 1 standard deviation.
Regarding genomic regions underlying milk traits, a systematic recording of milk traits was undertaken in the Churra resource population. Using these records from the ANCHE database a whole genome scan from production traits in Churra population was carried out. Milk yield per lactation were estimated as the total sum of the milked milk and it was standardised to 30-120 days lactation using the Fleischman method.
Fat and protein percentages were estimated by periodic controls. The phenotypic unit of measurement was yield deviation (YD), which is the average of the phenotypes of the daughters adjusted for systematic environmental effects (ewes performances expressed as deviations from the population mean). Data for milk yield were analysed employing the following animal model using the PEST program (Groeneveld, 1998). We have found evidence in 3 genomic regions for QTL for protein percentage located in chromosomes OAR2, OAR3 and OAR 15. In fat percentage there are two putative QTL in chromosomes 20 and 24. Finally around 29 cM of OAR8 there is a QTL controlling milk yield in Churra population.
These results are detected in our commercial population and shoud be verifyed in an independent experiment before a possible use in selection.
Nematode resistance:
Based in the previous experience sampling routine was established in 8 farms, with two visits. In the first day we take faeces samples and after the sample collection an anthelmintic product is administrated in order to make sure that each egg count represents distinct infections.
In a second visit performed 60 days after treatment animals are sampled for blood and faeces. The experimental design includes two visits for each farm; in the first visit the experimental animals were selected and identified previously to faecal sampling.
After that all the animals were dewormed with an anthelmintic drug considered the most appropriate after a survey in the flocks. These animals were faecal and blood re-sampled 60 days later.
Parameters measured for resistancwe to parasites were: FEC in first visit to farm (FEC0), the same parameter 60 days after FEC FEC1, pepsinogen level in serum and IgA levels.
Heritability and repeatability values for nematode parasite resistance were: LFEC0 (0.12);
LFEC1 (0.09); PEPSI (0.21); IgA (0.19).
Results presented here are of scientific importance because is the first time that this phenotypes are studied in Churra population.
In a future with some confirmation they can also be used in practical breeding programmes. The results are public and will be published.
Larvae of Oestrus ovis (Insecta: Diptera: Oestridae) are common parasites of nasal and sinus cavities of sheep and goats causing losses of meat, wool and milk production. Nasal discharges and sneezing are commonly found in summer and constitute the major clinical signs of in O. ovis infected sheep.
During larval development, a specific immune reaction is initiated by the host with a humoral local and systemic response and the recruitment of eosinophils and mast cells in the upper airways mucosae. Moreover, an immunization of sheep with excretory-secretory products (ESP) of larvae provided an inhibitory effect on larval growth. Reducing Oestrus ovis mature larval weight could have a considerable effect on adult fly viability and consequently on adult populations.These results suggested that at least a partial immune regulation of O. ovis larvae populations could occur within the sheep.
As previously shown by Scala A. (unpublished data), the highest values of systemic IgG were reached in July-August in ewes and decreased in November-December. By considering the chronobiology of oestrosis in Sardinia and the seasonal pattern of the IgG response, the chosen period to investigate the oestrosis resistance in BC Sarda x Lacaune ewes was mid-July.
Blood samples were collected in July 2001 (n = 869), July 2002 (n = 789) and July 2003 (n = 699) on all BC SxL ewes and sera were analysed in the National Veterinary School of Toulouse. Additional sera were taken in April 2002 to monitor the IgG decrease during the winter 2001-2002. Serum specific IgG titers were evaluated by ELISA according to Jacquiet et al., 2005. Prevalences of Oestrus ovis infections in BC SxL were very high according to the ELISA data: from 91.5% in 2001 to 82.5% in 2003. High individual variability was shown in the intensities of IgG responses: from 0 to 276% in 2001, 0 to 213% in 2002 and 0 to 183% in 2003. Nevertheless, high phenotypic correlations were shown between the different years. This is of particular importance because IgG titers returned to very low levels during winter 2001-2002. Therefore, IgG titers monitored in July 2002 correspond to an immune reaction to newly acquired infections in spring and early summer 2002.
For the whole experimental period, a significant ram (or family) effect (P < 0.001) was shown in the intensity of specific IgG response. Daughters of three sires had high IgG levels during the three years of study whereas daughters from one other sire had low levels, the others being intermediate.ELISA data (2001, 2002, and 2003) were analyzed for QTL detection. A square root transformation of data was done in order to normalize the distribution.
Two QTL detections were done: one of the whole set of data and a second one on data over 20% of antibodies only (considered as high responders in ELISA). 2001, 2002 and 2003 data were considered as single traits and were corrected for age and group of management. Two July measurements (2001 AND 2002) and three July measurements (2001 AND 2002 AND 2003) were considered as repeated traits and corrected for year and group of management (mixed procedure SAS). Interval mapping by within-sire linear regression was used and rejection thresholds were evaluated by 10, 000 within family permutations. The QTLMAP software (INRA) was used.QTL have been found on chromosomes 1, 2, 15, 17 and 21. Two of them (QTL on chromosomes 17 and 21) are highly significant (P < 0.01). Others have a P value comprised between 0.01 and 0.038. Some QTL were detected on single traits (one year data only) (chromosomes 1, 15, 17, 21) whereas QTL on chromosomes 2 was established on repeated traits (two years data, 2001 and 2002). Averages substitution effects were high especially for QTL on chromosomes 1 and 17.
The prevalence of O. ovis infections in Sardinia was high and similar to those recorded in other Mediterranean areas as Sicily or Greece. Intensities of larval infections were not checked in this study but the presence of obvious clinical signs in July is in favour of the presence of large numbers of developing stages within the ewes.
A high individual variability of specific IgG titers, a family effect on O. ovis IgG values and the high repeatability of these values from one year to another suggest that the intensity of this antibody response is, at least partially, under genetic control. This is the first time that QTL associated with Oestrus ovis specific immune response were identified. Two QTL (chromosomes 17 and 21) were strongly associated with the intensity of IgG response, nevertheless, they were found with 2003 data only. Only one QTL (chromosome 2, position 245-250 cM) was found with two sets of data (2001 single trait and 2001 AND 2002, repeated trait). Three common informative families were identified.
The chromosomes exhibiting the QTL identified in this study do not have any candidate obvious to us and this requires further work to identify the genes underlying these regions.
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