A REVIEW OF QTL EXPERIMENTS IN SHEEP. A.M. Crawford

Transcription

1 A REVIEW OF QTL EXPERIMENTS IN SHEEP Proc. Assoc. Advmt. Anim. Breed. Genet. Vol 14 A.M. Crawford AgResearch Molecular Biology Unit, Department of Biochemistry, University of Otago, PO Box 56, Dunedin, New Zealand SUMMARY QTL experiments are expensive, long-term and often require expertise from a large number of contributors. To date few sheep QTL experiments are finished in the sense that the data analysis is complete and results published. QTL discoveries often have commercial value, but as IP is difficult to protect via patent until the mutation responsible for the QTL is identified, few results have been released. Fine mapping to identify the gene(s) responsible for the QTL is currently also a very expensive and laborious process. For these reasons, details of many QTL, particularly their chromosomal location, are likely to remain unpublished in the immediate future. This paper attempts to summarise all of the QTL experiments that have been undertaken around the world in sheep so that we may at least know what QTL are being sought and by whom. Finally a description of the next steps required if the genetic causes of QTL are to be found and utilised, is provided. Keywords: Sheep, Ovis aries, Quantitative Trait Loci. QTL. INTRODUCTION Mircrosatellite based linkage maps covering all chromosomes are now available for all the major domestic livestock species, including sheep. Most research today is not concerned with map generation per se but the use of on these maps to search for regions of the genome containing loci affecting production traits. (Crawford et al. 2000). This process has become known as quantitative trait loci (QTL) discovery or genome scanning. The search for and discovery of genes affecting multigenic traits in sheep is still in its infancy. Compared with cattle, chicken and pigs, sheep production is a relatively small animal industry world-wide and hence has not attracted the same level of research support for its QTL experiments. With the exception of three major genes, described later, few QTL discoveries have been reported in sheep. However based on better studied species it is likely that an increasing number of QTL experiments will be published in future years. To give this review some utility colleagues around the world were asked to provide a brief description of sheep QTL experiments that are underway in their laboratories. In this way readers will obtain some idea of what discoveries to expect from sheep studies in the near future. I have also supplied a contact addresses for each of the QTL studies so that further information can be obtained if a particular trait or QTL study is of interest. RESULTS AND DISCUSSION Only 3 gene mutations affecting production characteristics in sheep have been discovered to date. The Spider lamb syndrome, Inverdale and Booroola phenotypes have been identified as mutations of the FGFR3, BMP15 (Galloway et al. 2000) and BMP-1B receptor genes (Wilson et al. 2001) respectively. Another locus, Callipyge (Berghmans et al. 2001) which affects muscling has progressed to the point 33

2 where its location is restricted to a very small DNA region. Gene discovery via chromosome walking is a very laborious task and its difficulty is reflected in the small number of genes on the list. The list of all known major sheep QTL experiments is shown in table 1. A list of the contact person for these various experiments is provided in table 2. The size of this list is very encouraging. A very wide range of traits including wool characteristics and production, growth rate and carcass composition, milk production and resistance to a wide range of diseases are being examined. This will provide a wide range of QTL from which positional cloning can be undertaken. Table 1. QTL experiments currently underway in sheep QTL experiment(s) Strong wool Fine wool Merino Cross CSIRO Wool QTL and Genome Mapping Flock High Fleece weight Romney, Superfine Merino backcross to both breeds Texel Coopworth Backcross to Coopworth Facial Eczema disease in Romney outcross pedigrees Romney Parasite selection line outcross and backcross Merino Parasite + wool selection line outcross Main traits measured Fibre diameter, fleece weight, crimp definition, curvature Wool production and quality traits Fibre diameter Footrot resistance Wool colour, bulk, Fleece weight horns FEC (Trich) Vaccine response Fat composition Serum GGT and GDH responses to Sporidesmin dosing FEC (Trich) Dagginess Number of sheep 500 approx Genome scan status Initial scan complete with >150. Research Group(s) and Contact person Victorian Institute of Animal Science Nick Robinson 250 unknown CSIRO Ian Franklin 789 Completed and being prepared for publication Completed Vaccine response data to be published 600 Selective genotyping analysis 1500 Extreme phenotypes FEC (Trich) AgResearch, Lincoln University, Sydney University Ken Dodds AgResearch, Sydney University, Adelaide University John McEwan AgResearch, Landcorp Sin Phua AgResearch Allan Crawford CSIRO Ken Beh 34

3 Red Massai Dorper backcross FEC (Hemonchus) PCV Liveweight All phenotypes ILRI Kenya Leyden Baker Awassi, Merino cross backcrossed to Merino + F1 x backcross intercross Milk production, milk composition and feed conversion efficiency in milk production 600 ewes 4 Full genome scan ewes Sydney University Herman Raadsma carcass composition and quality, fat composition, growth and feed intake 600 wethers 4 Full genome scan wethers Wool production and quality, feed conversion efficiency vs wool production. Fleece rot 1200 ewes and wethers Indonesian Thin Tail, Merino cross backcrossed to Merino Parasite resistance- (FEC Hemonchus) Liver fluke resistance-putative major gene Low density genome scan 3 (n= 165) Sydney University Monash University, Indonesian research institutes Herman Raadsma Backcross between Sarda and Lacaune breeds Milk production CLA Mastitis resistance Udder score milkability seasonality, FEC Oestrus resistance 990 offspring genotyped amongst 130 SAGA Toulouse (INRA) Francis Barillet European project gene sheep safety, Genotyping mainly performed in Sardegna Churra Milk production Mastitis resistance Udder score Nematode parasite resistance 13 AI rams 1500 ewes 200,000 genotypes to be performed in 2003 Universidad de Leon J.J. Arranz European project: gene sheep safety, 35

4 INRA401 ARQ/VRQ Scrapie resistance 200 offspring INRA401 USDA reference Population Ramboulet or Suffolk crossed with Romanov backcrossed to Romanov Louisiana Native Suffolk Cross backcrossed Soay pedigrees from St Kilda Fat and Lean selection line backcross Texel, Suffolk and Charollais commercial sire reference animals Scottish Blackface lean and fat selection line backcross Disease resistance, carcass traits, Wool traits Fecundity Wool and hair colour Fertility FEC (Hemonchus) FEC (Trich) Fat deposition and composition Growth rate Growth, Fat and Meat Quality traits incl. carcass quality and taste panel data 40*30 offspring 247 progeny from 4 sires and 44 dams Families under devel. Approx lambs, 4 Up to 10 half-sib pedigrees Genome scan (130 ) beginning 2001 Genome scan of 60 terminated Approx 400 unknown Selected loci only 137 marker genome scan Selected genomic regions 600 Selected genomic regions INRA Jouy-en-Josas Daniel Vaiman And SAGA, Toulouse JM Elsen INRA, Toulouse JM Elsen INRA Jouy-en-Josas Daniel Vaiman INRA, Tours F.Lantier USDA Clay Centre Kreg Leymaster LSU Baton Rouge Jim Miller University of Edinburgh Josephine Pemberton AgResearch Tom Broad Roslin Institute Grant Walling Roslin Institute Steve Bishop Abbreviations: FEC = Fecal Egg Count; GGT = Gamma Glutamyl Transferase; GDH = Glutamate DeHydrogenase; PCV = Packed Cell Volume; CLA = Conjugated Linoleic Acid The challenge for those undertaking these QTL experiments will be trying to discover the genetic cause of the many QTL they will undoubtedly discover. The completion of the first draft of the sequence of the human genome has given the animal gene mapping and discovery community a new resource of immense value. Provided the human region that is equivalent to the animal genome region can be deduced from the comparative gene map, and this is possible for most regions, we now have a catalogue of all potential candidate genes responsible for each QTL. In many instances the QTL covers a broad region and the catalogue of genes may be quite large. Many of the genes simply qualify as genes on the 36

5 basis of a gene hunting algorithm, nevertheless the fact remains that for the first time in positional candidate gene discovery our list of candidates has become finite. Table 2. List of contact persons for information on Sheep QTL research Name Institute Address Nick Robinson Victorian Inst of Animal Science nick.robinson@nre.vic.gov.au Ian Franklin CSIRO ian@prospect.anprod.csiro.au Ken Dodds AgResearch Invermay ken.dodds@agresearch.co.nz John McEwan AgResearch Invermay john.mcewan@agresearch.co.nz Sin Phua AgResearch sin.phua@agresearch.co.nz Allan Crawford AgResearch. allan.crawford@agresearch.co.nz Dr Ken Beh CSIRO K.Beh@anprod.csiro.au Tom Broad AgResearch tom.broad@agresearch.co.nz Leyden Baker ILRI Kenya L.Baker@cgnet.com Herman Raadsma Sydney University raadsma@camden.usyd.edu.au Francis Barillet SAGA Toulouse barillet@toulouse.inra.fr J.J. Arranz Universidad de Leon dp1jas@unileon.es Daniel Vaiman INRA Jouy-en-Josas vaiman@biotec.jouy.inra.fr JM Elsen SAGA, Toulouse elsen@toulouse.inra.fr F.Lantier INRA, Tours lantier@tours.inra.fr Kreg Leymaster USDA Clay Centre leymast@marcvm.marc.usda.gov Jim Miller LSU Baton Rouge jmille1@unix1.sncc.lsu.edu Josephine Pemberton University of Edinburgh pembers@srv0.bio.ed.ac.uk Grant Walling Roslin Institute grant.walling@bbsrc.ac.uk Steve Bishop Roslin Institute stephen.bishop@bbsrc.ac.uk Now that we have a finite list of candidates, how rapidly we can use this information will define our success at gene discovery. Our current challenges are: 1. Complete with as much precision as possible the human / ruminant comparative map 2. Develop strategies for the rapid development of for those genes within the QTL regions using the human genome sequence 3. Develop robust statistical methods that can rapidly fine map QTL and so reduce the very broad region derived from QTL linkage studies. 4. Develop sampling, genotyping and analytical approaches that use commercial animal populations to validate the commercial use of gene tests or informative marker haplotypes by industry. 5. Develop gene introgression and marker assisted selection methods that enhance traditional breeding methods without compromising the progress they currently provide. Preferably, all these challenges need to be met and overcome within the next 5 years. By then we will also need to have some good examples where gene discovery from QTL studies has made a difference to 37

6 sheep breeding. If we don t our critics can rightly assert that we have been long on promise and short on delivery. REFERENCES Crawford, A.M., Dodds, K.G. and McEwan, J.C. (2000) In Breeding for Disease Resistance in Farm Animals, 2 nd Edition, p. 3. Eds R.F.E. Axford et al. Galloway, S.M., McNatty, K.P., Cambridge, et al. (2000) Nature Genetics 25: 279. Wilson, T., Wu, X., Juengel, J.L., Ross, I.K. et al. (2001) Biology of Reproduction 64: Berghmans, S., Segers, K., Shay, T. and Georges, M. (2001) Mammalian Genome 12: