Genomic Selection in Breeding Programs BIOL 509 November 26, 2013 Omnia Ibrahim omniyagamal@yahoo.com 1
Outline 1- Definitions 2- Traditional breeding 3- Genomic selection (as tool of molecular breeding) Basic concept of GS Statistical analysis of GS Experimental Results of GS in Forest Trees 2
Definitions Breeding Program The planned breeding of a selected group of animals or plants and extending over several generations. - To improve certain characteristic trait (produce genetic gain). - By manipulating gene frequencies for chosen traits over generations. Progeny Testing The test of the selected individual's genotype by looking at their progeny performance produced by different matings. Breeding Value The genetic value of an individual determined by the mean value of its progeny. http://en.wikipedia.org 3
Linkage Disequilibrium (LD) The non-random association of alleles at two or more loci, that may or not be on the same chromosome Quantitative Trait Loci (QTLs) Stretches of DNA containing or linked to the genes that underlie a quantitative trait Genetic marker A gene or DNA sequence having a known location in the genome and associated with the gene or trait of interest, it is used for indirect selection of these traits. Single-Nucleotide Polymorphism (SNP) DNA sequence variation occurring when a single nucleotide in the genome differs between individuals of the same species AAGCCTA AAGCTTA http://en.wikipedia.org 4
Traditional breeding (Phenotype-based selection) Natural Populations Selected individuals Phenotypic selection Progeny Testing Time and cost consuming Elite individuals highest BV EBV Breeding population Breeding Testing Selection Repeated over generation Infusion Production of Improved individuals EBV: Experimentally Estimated Breeding Value 5
Molecular breeding (genotype - based selection) Association between molecular marker and causative gene LD Causative gene SNP inside the gene SNPs are in Linkage Disequilibrium with the gene SNP in LD with the gene Hirschhorn & Daly, 2005 6
Marker assisted selection (MAS) Prior knowledge of GENES or MARKERS associated with the trait and their EFFECTS Simple quantitative traits are controlled by FEW genes each with LARGE effect Bernardo and Charcosset, 2006 7
Genomic selection (GS) Genotype data Complex quantitative traits are controlled by MANY genes, each with SMALL effect ALL marker data SNPs Predict Phenotype breeding values GEBV Meuwissen et al. 2001 GEBV: Genomic Estimated Breeding Value 8
Statistical Analysis Multiple Regression Analysis Y = a + b 1 X 1 + b 2 X 2 +. + b n X n Training Population Y MLR dependent variables GS Model Breeding value Phenotyping EBV Genotyping For SNPs bs estimated coefficients SNPs effects on BV Xs independent variables SNPs genotype (A, T, G or C) http://en.wikipedia.org 9
Y GS Model breeding value bs SNPs effects on BV Xs SNPs genotype (A, T, G or C) Breeding Population Genotyping For SNPs (SNPs genotype, Xs) Y= a + b 1 X 1 + b 2 X 2 +. + b n X n GEBV Selection 10
Genomic data layout Training Population n : Training population size m : # of SNPs SNP ID Tree ID Genotype of SNP 1 1 G - 2 G - 3 A - - - SNP m Thousands m >>>> n Tree n http://www.treeimprovement.org/public
Simple diagram for GS SNPs markers EBV GEBV: Genomic Estimated Breeding Value EBV: Experimentally Estimated Breeding Value 12
Prediction Accuracy of GS Correlation between GEBV EBV Affected by: 1- LD between markers and QTLs ( LD ) 2- Training population size ( n) 3- Heritability of the trait in question ( h 2 ) 4- Genetic structure of the trait ( # QTLs) Accuracy of GS Hayes et al., 2009 13
GS against traditional breeding Goal of Breeding Program improved individuals Time to identify superior individuals Cost Traditional Breeding Individuals must mature to estimate BV Spatial requirements of progeny trials and phenotype measurements are very costly GS BV can be estimated earlier traits expressed LATELY in the life cycle Ex: Wood density, 40 years Continuing decline in the cost of marker technologies Genotyping GS Increase gain / unit time Ex : GS can reduce four YEARS breeding cycle into four MONTHS Lorenz et al., 2011 14
Experimental Results In Forest Trees Species -Eucalyptus (one study) - Loblolly Pine (two studies) Training population Size (n) # of marker used Traits 150-950 3,000-5,000 - Growth Height and Diameter - Health Disease resistance - Quality Wood density Accuracy of GEBV 0.2-0.9 Resende et al., 2012 Zapata-Valenzuela et al., 2012 15
Summary - Traditional methods of breeding Phenotype - based selection result in high genetic gain but it is time and cost consuming. - Molecular breeding genotype - based selection depend on the concept of Linkage between molecular marker and trait or gene of interest. - GS use ALL the marker data GEBV - Genomic Selection Increased gain / unit time (early selection of superior individuals) - GS Complex Quantitative Traits (Many genes, each with small effect) Lately expressed traits - GS proofed success in animal breeding and agriculture, but it is still in infancy in forest tree breeding programs. More studies are needed. Thank You, omniyagamal@yahoo.com 16
References: 1- Lorenz AJ, Chao SM, Asoro FG, Heffner EL et al (2011) Genomic Selection in Plant Breeding:Knowledge and Prospects. Adv Agron 110(110):77 123 2-Bernardo, R., and Charcosset, A. (2006). Usefulness of gene information in markerassisted recurrent selection: A simulation appraisal. Crop Sci. 46, 614 621. 3-MeuwissenTH,Hayes BJ,GoddardME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819 1829 4-ResendeMDV, ResendeMFR, Sansaloni CP, Petroli CD et al (2012a) Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol 194:116 128 5-Resende MFR, Munoz P, Acosta JJ, Peter GF et al (2012b) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol 193:617 624 6-Resende MFR, Munoz P, Resende MDV, Garrick DJ et al (2012c) Accuracy of genomic selection methods in a standard data set of Loblolly Pine (Pinus taeda L.). Genetics 190:1503 1510 7- Zapata-Valenzuela J, Isik F,Maltecca C,Wegrzyn J et al (2012) SNPmarkers trace familial linkages in a cloned population of Pinus taeda prospects for genomic selection. Tree Genet Genomes 8:1307 1318 17