Alison Van Eenennaam, Ph.D. Cooperative Extension Specialist Animal Biotechnology and Genomics Department of Animal Science University of California, Davis, USA
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Uso Sinérgico de Biotecnologías, Selección Genómica y Tecnologías de Reproducción Asistida en Programas de Cría de Animales de Producción Alison Van Eenennaam, Ph.D. Cooperative Extension Specialist Animal Biotechnology and Genomics Department of Animal Science University of California, Davis, USA Email: [email protected] Twitter: @BioBeef Blog: http://biobeef.faculty.ucdavis.edu Website:http://animalscience.ucdavis.edu/animalbiotech Uruguay 10/10/2016 Animal Genomics and Biotechnology Education Criadores de animales han hecho importantes avances genéticos basados únicamente en selección fenotípica Uruguay 10/10/2016 Animal Genomics and Biotechnology Education El peso de pollos de carne de 8 semanas ha aumentado de 0.81 kg a 3.14 kg entre 1957 y el 2001 . Aproximadamente 80% de este incremento es el resultado de selección genética Havenstein, G., et al. (2003). Growth, livability, and feed conversion of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poultry Science 82, 1500-1508. Uruguay 10/10/2016 Animal Genomics and Biotechnology Education Y si no # de animales hubiéramos usados para consumo en el mejorado 2009 genéticamente nuestros animales de producción? 1.3 billones de cerdos 2.6 billones de patos 52 billones de pollos • 59 millones de toneladas de huevos. • 90 millones de toneladas de carne Uruguay 10/10/2016 Animal Genomics and Biotechnology Education Producción total 2014 Cantidad Otras en USA en 2014 necesitada para necesidades 1950 Soybeans 3,927,090,000 BU 82,591,000 180,971,889 ~ 98 million Acres Acres Acres (235,562,540,000 lb) (106,849,370,802 kg) (33,423,392 ha) (73,236,725 ha) (~40 million ha) Corn 14,215,532,000 BU 83,136,000 372,134,346 ~ 289 million Acres Acres Acres (796,069,979,000 lb) (361,091,268,460 kg) (33,643,946 ha) (150,597,427 ha) (~120 million ha) Dairy cattle 206,046,000,000 lbs 9,257,166 head 38,774,181 head ~ 30 million head milk (93,460,893,469 kg) Broilers 51,373,100,000 lbs 8,544,100,000 16,679,545,455 ~ 8 billion head meat head head + an additional 81.5 billion lbs (23,302,446,000 kg) feed due to less efficient FCR Uruguay 10/10/2016 Animal Genomics and Biotechnology Education La tasa de ganancia genetica depende de los 4 componentes de la ecuación del criador ΔG = Intensidad de selección X Exactitud de selección X (√Variación genética en la población/ intervalo generacional ) Uruguay 10/10/2016 Animal Biotechnology and Genomics Education Convenio en Biodiversidad Genética : “Biotecnologías y cualquier aplicación de tecnologías que use sistemas biológicos, organismos vivos, o derivados para hacer o modificar productos o procesos para usos específicos.” Genetics/breeding Nutrition Health Artificial insemination Feed additives: Amino acids, Molecular diagnostics enzymes & probiotics Progesterone monitoring Prebiotics Recombinant vaccines Estrus synchronization Silage additives (enzymes and Conventional vaccines microbial inoculants) Invito fertilization and Ionophores Sterile insect technique embryo transfer (SIT) Cryopreservation Molecular Single cell proteins Bioinformatics markers; genomic selection Semen and embryo sexing Recombinant somatotropins Molecular markers; genomic Solid state fermentation of selection lignocellulosics GREEN = Potential for generating impact Cloning Molecular gut microbiology (time frame <10 years) Genetic Engineering/ Genome editing Uruguay 10/10/2016 Animal Genomics and Biotechnology Education 1944: 25.6 million animals; total annual milk production of 53.1 billion kg. 1997: 9.2 million animals; total annual milk production of 84.2 billion kg. Cerca de la mitad de este 369% aumento en eficiencia de producción es atribuible a mejoramiento genético gracias a la inseminación artificial A I VandeHaar, M.J. and St-Pierre, N. (2006). Major Advances in Nutrition: Relevance to the Sustainability of the Dairy Industry. Journal of Dairy Science 89, 1280-1291. Uruguay 10/10/2016 Animal Genomics and Biotechnology Education La inseminación artificial fue inicialmente un tema controversial “In the initial stages of attempting to develop AI there were several obstacles. The general public was against research that had anything to do with sex. Associated with this was the fear that AI would lead to abnormalities. Finally, it was difficult to secure funds to support research because influential cattle breeders opposed AI, believing that this would destroy their bull market.” Foote, R.H. 2002. The history of artificial insemination: Selected notes and notables. J. Anim. Sci., 80 (E. Suppl.) (2002), pp. E22–E32 Uruguay 10/10/2016 Animal Genomics and Biotechnology Education Uso de recursos y desechos en la industria lechera en USA en el 2007 comparada con la de 1944 GHG = Greenhouse gas 1/3 Capper, JL and DE Bauman, 2013. The Role of Productivity in Improving the Environmental Sustainability of Ruminant Production Systems. Annual Review of Animal Biosciences. 1 pp. 9.1–9.21 Uruguay 10/10/2016 Animal Genomics and Biotechnology Education Que es un marcador genetico? A DNA sequence variation that has been associated with a given trait in one or more populations Uruguay 10/10/2016 Animal Genomics and Biotechnology Education Queremos usar marcadores genéticos (SNPs), pedigríes e información de producción para seleccionar los mejores animales Animal Biotechnology and Genomics Uruguay 10/10/2016 Education Tecnologías genotípicas de alto rendimiento permitieron el desarrollo de “SNP chips” (marcadores) de alta densidad The sequencing of the bovine genome allowed for the development of a 50,000 SNP chip, then the 800,000 SNP chip; and now whole genome sequence (3 billion)! Uruguay 10/10/2016 Podemos usar estos SNP CHIPS para selección “genética”? TRAINING POPULATION 1,000s animals Training = estimate the value of every chromosome – Phenotypes fragment contributing – Genotypes variation in a population with phenotypic observations Prediction = the results of training can then be used to develop prediction equations to predict the merit of new animals (e.g. young bulls) Uruguay 10/10/2016 Animal Biotechnology and Genomics Education Registros en bases de datos de lecherias en USA Pedigree records 71,974,045 Animal genotypes 1,035,590 Lactation records (since 1960) 132,629,200 Daily yield records (since 1990) 641,864,015 Reproduction event records 179,559,035 Calving difficulty scores 29,528,607 Stillbirth scores 19,567,198 Data from George Wiggins, USDA ARS (7/2015) Animal Genomics and Biotechnology Education Slide courtesy Curt Van Tassell Van Curt courtesy Slide Genotipos 1000000 1200000 200000 400000 600000 800000 0 Jun-09 Oct-09 M-50-Old F-50-Old 50-Young Low-Old Low-Young Feb-10 Jun-10 Oct-10 Feb-11 Jun-11 New Genotypes per Month 30000 35000 40000 45000 10000 15000 20000 25000 5000 Oct-11 0 Feb-12 May-10 Oct-10 Jun-12 Mar-11 Aug-11 Oct-12 Jan-12 Feb-13 Jun-12 Nov-12 Jun-13 Apr-13 Sep-13 Oct-13 Feb-14 Jul-14 Feb-14 Dec-14 Jun-14 May-15 Oct-15 Oct-14 Mar-16 Feb-15 May 2016 24, May Jun-15 Oct-15 Feb-16 Prediccion del valor de cruce en toros lecheros Graphic kindly provided by Gonzalo Rincon Young sire Young sire Young sire Parent Average Progeny Test Genomic Selection x x x Birth 5 years; $50,000 cost Birth; << $50,000 cost AS AD AS AD AS AD Mendelian Sampling ? Mendelian Sampling Mendelian Sampling Accuracy 0.20 Accuracy 0.90 Accuracy 0.80 Uruguay 10/10/2016 Animal Biotechnology and Genomics Education Seleccion genetica puede doblar la tasa de ganancia genetica Rate of genetic gain ΔG ΔG = (im rm +if rf)/ (Lm + Lf) genetic standard deviation/year = (2*0.9 + 0)/ (6+2) = 0.225 s.d./year (progeny test) = (2*0.8 + 0.8*0.8)/ (2+2) = 0.56 (genomic selection) i = intensity of selection r = accuracy of selection L = generation interval Uruguay 10/10/2016 Animal Biotechnology and Genomics Education La tasa de ganancia genética en toros comerciales Holando se ha duplicado 600 Average gain: 500 $87.49/year 400 ($) 300 Average gain: 200 $47.95/year 100 Average gain: net merit merit net 0 -100 $19.42/year -200 -300 Average 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 Year entered AI Data from George Wiggins, USDA ARS (7/2015) La selección genómica puede ayudar criadores a identificar animales con valores de producción superiores desde que son jóvenes ΔG = intensity of selection X accuracy of selection X (√genetic variance in population / generation interval) Uruguay 10/10/2016 Animal Genomics and Biotechnology Education Secuenciación masiva ha sido integrada a la industria lechera • High use of AI • Only one breed • Clear selection goal (total net merit) • Large number of high accuracy A.I. sires for training • Extensive, uniform collection of data on traits • Central evaluation (AIPL) receiving genotypes • Obvious way to increase rate of genetic gain • AI companies funding the genotyping because they get a clear cost savings in terms of young sire program Uruguay 10/10/2016 Animal Biotechnology and Genomics Education Kasinathan, P. et al. 2015. Acceleration of genetic gain in cattle by reduction of generation interval. Sci. Rep. 5, 8674; DOI:10.1038/srep08674 Uruguay 10/10/2016 Animal Genomics and Biotechnology Education Animal Biotechnology and Genomics Education Approximate genetic AN: Angus GV: Gelbvieh BM: Beefmaster LM: Limousin distance between BN: Brangus MA: Maine Anjou breeds using data from BR: Brahman RA: Red Angus the 2,000 Bull Project. BU: Braunvieh SA: Salers Larry Keuhn, USDA MARC CA: Chiangus SG: Santa Gertrudis http://www.nbcec.org/topics/ CH: Charolais SH: Shorthorn BeefBreeds.pdf HH: Hereford SM: Simmental HL: Line 1 HH Uruguay 10/10/2016 Animal Biotechnology and Genomics Education La tasa de ganancia genética (ΔG) depende de los 4 componentes de la ecuación del criador ΔG = intensity of selection X accuracy of selection X (√genetic variance in population / generation interval) Uruguay 10/10/2016 Animal Biotechnology and Genomics Education Aplicaciones de la ingeniería genética en agricultura engineering Van Eenennaam, A.L.