PPT-Analyzing Genetic Diversity and Applying Genomic Selection Methods for Plant Breeding

Luciano Silva PhD Lucianosilvajmpcom SAS Institute Inc Introduction A case study in Rice Exploring genetic diversity G enomic s election Live demonstration Oultine Marker Assisted Selection MAS. livestock. Dr Indrasen Chauhan. Scientist, CSWRI, Avikanagar. Tonk-304501. – . B. ased . on individual records, pedigree or progeny performance or family performance. . SI=WT9 +1DMY90. Conventional selection . Module 11: Multiple Traits. Genetic Correlations. . Index Selection. Genetic correlations. Correlations in phenotype may be due to genetic or environmental causes. May be positive or negative. Genetic causes may be due to. National Council of Farmer Cooperatives. . Government Affairs Committee. November. . 12. , . 2015. Jane DeMarchi. The Goals of Plant Breeders Remain the Same. Source: BDP. Genetic Variability: Fundamental to Plant Breeding. Classic Breeding. Main Street. Molecular . breeding. Abiotic and biotic resistance breeding . (. disease/pest resistance, . drought and salt tolerance). Parent selection and progeny testing. Marker-assisted selection (MAS). Youngmo. Ph.D.. Advisor of KOICA. Principle of Plant Genetics and Breeding. GENERAL CONCEPTS OF . TREE IMPROVEMENT. . ◆. . Forest Genetic. . ◆. . Forest . First lecture: Introduction to Evolutionary Computation. Second lecture: Genetic Programming. Inverted CERN School of Computing 2017. Daniel Lanza - CERN. Agenda. Genetic Programming. Introduction . to . -----. Module . 5 – Introduction to Tree Breeding and Provenance Trials. Nicholas Wheeler & David Harry – Oregon State University. Basic principles of forest tree breeding. Three primary goals. Introduction. How genetic selection works. Gains due to genetic selection. Use of genomic technology. Future research opportunities. How does genetic selection work?. ΔG = . genetic gain each year. reliability =. A “big data” project of the . Animal Genomics and Improvement Laboratory (AGIL). AGIL mission. Discover and develop improved methods for the genetic and genomic evaluation of economically important traits of dairy animals and small ruminants. A “big data” project of the . Animal Genomics and Improvement Laboratory (AGIL). AGIL mission. Discover and develop improved methods for the genetic and genomic evaluation of economically important traits of dairy animals and small ruminants. livestock. Dr Indrasen Chauhan. Scientist, CSWRI, Avikanagar. Tonk-304501. – . B. ased . on individual records, pedigree or progeny performance or family performance. . SI=WT9 +1DMY90. Conventional selection . Allele Frequencies. The total number of copies of an allele divided by (for mammals) 2N. . Aa. AA. aa. Aa. AA. aa. Aa. Aa. Aa. Aa. AA. aa. AA. p. = 14/26 = 0.53; . q. = 12/26 = 0.47 . Aa. BB. cc. Dd. 1). , . J.R. . Thomasen. 3). 1) Aarhus . University, Center for Quantitative Genetics and . Genomics,. 2) . SEGES, . Cattle, 3. ) . VikingGenetics. . Possibilities and requirements for organic dairy breeding lines. of Nile Tilapia for African Aquaculture . Agustin . Barria. , Carolina . Penaloza. , Diego Robledo, . Pam Wiener, Andrea . Doeschl. -Wilson, Trong . Quoc. Trinh, . Mahirah. . Mahmuddin. , . Mohan .