Genomic Prediction and Selection for Multi-Environments
|
|
- Dora Ford
- 6 years ago
- Views:
Transcription
1 Genomic Prediction and Selection for Multi-Environments J. Crossa 1 j.crossa@cgiar.org P. Pérez 2 perpdgo@gmail.com G. de los Campos 3 gcampos@gmail.com 1 CIMMyT-México 2 ColPos-México 3 Michigan-USA. June, CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 1/24
2 Contents 1 The problem 2 Models 3 Model fitting 4 Cross validation 5 Application examples (Part 1) 6 Model extensions with environmental covariates CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 2/24
3 The problem The problem In most agronomic traits, the effects of genes are modulated by environmental conditions, generating G E. Researchers working in plant breeding have developed multiple methods for accounting for, and exploiting G E in multi-environment trials. Genomic selection is gaining ground in plant breeding. Most applications so far are based on single-environment/single-trait models. Preliminary evidence (e.g., Burgueño et al., 2012) suggests that there is great scope for improving prediction accuracy using multi-environment models. The ideas can be taken one step further by incorporating information on environmental covariates. CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 3/24
4 Continue... The problem CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 4/24
5 Continue... The problem CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 5/24
6 Models Models Model 1 (EL, Environment + Line, no pedigree) y ij = µ + E i + L i + e ij Model 2 (EA, Environment + Line, with markers) y ij = µ + E i + g j + e ij Model 3 (Environments, Line and interactions markes and environment) y ij = µ + E i + g j + Eg ij + e ij CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 6/24
7 Assumptions Models It is assumed that E i N(0, σ 2 E ), g N(0, σ2 gg) with G being the genomic relationship matrix and Eg ij the interaction term between genotypes and environment. Eg N(0, (Z g GZ T g ) Z E Z T E), Z g connects genotypes with phenotypes, Z E connects phenotypes with environments, and stands for Hadamart product between two matrices. CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 7/24
8 Model fitting Description of Data Objects - Y, data frame containing the elements described below; - Y$yield: (nx1), a numeric vector with centered and standardized yield; - Y$VAR (nx1), a factor giving the IDs for the varieties; - Y$ENV (nx1), a factor giving the IDs for the environments; - A, a symmetric positive semi-definite matrix containing the pedigree or marker-based relationships (dimensions equal to number of lines by number of lines). We assume that the rownames(a)=colnames(a) gives the IDs of the lines; CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 8/24
9 Model fitting Model fitting Model 1 (EL, Environment + Line, no pedigree) library(bglr) # incidence matrix for main eff. of environments. ZE<-model.matrix(~factor(Y$ENV)-1) # incidence matrix for main eff. of lines. Y$VAR<-factor(x=Y$VAR,levels=rownames(A),ordered=TRUE) ZVAR<-model.matrix(~Y$VAR-1) # Model Fitting ETA<-list( ENV=list(X=ZE,model="BRR"), VAR=list(X=ZVAR,model="BRR")) fm1<-bglr(y=y$yield,eta=eta,saveat="m1_",niter=6000,burnin=1000) CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 9/24
10 Model fitting Model fitting Model 2 (EA, Environment + Line, with markers) X<-scale(X,center=TRUE,scale=TRUE) G<-tcrossprod(X)/ncol(X) G<-G/mean(diag(G)) L<-t(chol(G)) ZL<-ZVAR%*%L ETA<-list( ENV=list(X=ZE,model="BRR"), Grm=list(X=ZL,model="BRR") ) fm2<-bglr(y=y$yield,eta=eta,saveat="m2_",niter=6000,burnin=1000) CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 10/24
11 Model fitting Model 3 (Environments, Line and interactions markers and environment) ZGZ<-tcrossprod(ZL) ZEZE<-tcrossprod(ZE) K<-ZGZ*ZEZE diag(k)<-diag(k)+1/200 K<-K/mean(diag(K)) ETA<-list( ENV=list(X=ZE,model="BRR"), Grm=list(X=ZL,model="BRR"), EGrm=list(K=K,model="RKHS") ) fm3<-bglr(y=y$yield,eta=eta, saveat= M3_,nIter=6000,burnIn=1000) CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 11/24
12 Cross validation Cross validation 1 CV1: Prediction of performance of newly developed lines (i.e., lines that have not been evaluated in any field trials). 2 CV2: Prediction in incomplete field trials; here the aim was to predict performance of lines that have been evaluated in some environments but not in others. See Figure in next slide. CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 12/24
13 Continue... Cross validation Figure 1: Two hypothetical cross-validation schemes (CV1 and CV2) for five lines (Lines 1-5) and five environments (E1-E5), source: Jarquín et al. (2014). CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 13/24
14 Application examples (Part 1) Example Wheat dataset (CIMMyT) Data for n = 599 wheat lines evaluated in 4 environments, wheat improvement program, CIMMyT. The dataset includes p = 1279 molecular markers (x ij, i = 1,..., n, j = 1,..., p) (coded as 0,1). The pedigree information is also available. Histogram of Y$yield Yield Frequency Environment Y$yield Figure 2: Grain yield by environment. CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 14/24
15 Application examples (Part 1) Data preparation... #Load genotypic data load("pedigree_markers.rdata") #Load phenotypic data pheno=read.table(file="599_yield_raw-1.prn",header=true) pheno=pheno[,c(2,5,6)] yavg=tapply(pheno$gy,index,"mean") tmp=names(yavg) gen=character() env=character() for(i in 1:length(tmp2)) { env[i]=tmp2[[i]][1] gen[i]=tmp2[[i]][2] } Y=data.frame(yield=yavg,VAR=gen,ENV=env) index=order(as.character(y$env),as.character(y$var)) Y=Y[index,] CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 15/24
16 Continue... Application examples (Part 1) index=order(colnames(a)) A=A[index,index] X=X[index,] save(y,a,x,file="standarized_data.rdata") CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 16/24
17 Application examples (Part 1) Code for cross validation schemas... #CV=1: assigns lines to folds #CV=2: assigns entries of a line to folds CV<-2 nfolds<-5 sets<-rep(na,nrow(y)) set.seed(123) IDs<-as.character(unique(Y$VAR)) if(cv==1) { folds<-sample(1:nfolds,size=length(ids),replace=true) for(i in 1:nrow(Y)){ sets[i]<-folds[which(ids==y$var[i])] } } if(cv==2) { IDy<-as.character(Y$VAR) for(i in IDs){ tmp=which(idy==i) ni=length(tmp) tmpfold<-sample(1:nfolds,size=ni,replace=ni>nfolds) sets[tmp]<-tmpfold } } CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 17/24
18 Application examples (Part 1) Fitting model and extracting results... ################################################### #Model 1 ################################################### # incidence matrix for main eff. of environments. ZE<-model.matrix(~factor(Y$ENV)-1) # incidence matrix for main eff. of lines. Y$VAR<-as.factor(Y$VAR) ZVAR<-model.matrix(~Y$VAR-1) # Model Fitting ETA<-list( ENV=list(X=ZE,model="BRR"), VAR=list(X=ZVAR,model="BRR")) y=y$yield testing=(sets==1) y[testing]=na fm1<-bglr(y=y,eta=eta,saveat="m1_",niter=6000,burnin=1000) unlink("*.dat") #Extract the predictions predictions=data.frame(env=y$env[testing], Individual=Y$VAR[testing], y=y$yield[testing], yhat=fm1$yhat[testing]) CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 18/24
19 Continue... Application examples (Part 1) #write.table(predictions,file=paste("predictions.csv",sep=""), # row.names=false,sep=",") #doby version predictions=orderby(~env,data=predictions) lapplyby(~env,data=predictions,function(x){cor(x$yhat,x$y)}) > lapplyby(~env,data=predictions,function(x){cor(x$yhat,x$y)}) $ 1 [1] $ 2 [1] $ 4 [1] $ 5 [1] CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 19/24
20 Application examples (Part 1) Results for one fold... Correlation M1 M2 M3 Figure 3: Results from CV1 CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 20/24
21 Continue... Application examples (Part 1) Correlation M1 M2 M3 Figure 4: Results from CV2 CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 21/24
22 Model extensions with environmental covariates Model extensions with environmental covariates This model is obtained by extending model EA by incorporating the environmental covariates. Model 4 (EAW) y ij = µ + E i + a j + t ij + e ij, where t ij = Q q=1 W ijqγ q represent a regression on ECs and W ijq is the evaluation of the q-th EC at the ij-th environmental-line combination and γ q represents the effect of the q-th EC. Assumptions: γ q N(0, σ 2 γ), t = W γ N(0, σ 2 t W W T ). CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 22/24
23 Model extensions with environmental covariates Continue... Model 5 (EAW-A W) y ij = µ + E i + a j + t ij + at ij + e ij Assumptions: at N(0, (Z p GZ T p ) WW T σ 2 at ) CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 23/24
24 Model extensions with environmental covariates References Burgueño, J., G. de-los-campos, K. Weigel, and J. Crossa. (2012). Genomic prediction of breeding values when modeling genotype environment interaction using pedigree and dense molecular markers. Crop Science, 43: Jarquín, D., J. Crossa, X. Lacaze, P. Cheyron, J. Daucourt, J. Lorgeou, F. Piraux, et al. (2014). A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theoretical and Applied Genetics, 127 (3): CIMMYT, México-SAGPDB Genomic Prediction and Selection for Multi-Environments 24/24
Computations with Markers
Computations with Markers Paulino Pérez 1 José Crossa 1 1 ColPos-México 2 CIMMyT-México September, 2014. SLU, Sweden Computations with Markers 1/20 Contents 1 Genomic relationship matrix 2 Examples 3 Big
More informationGenomic Selection in R
Genomic Selection in R Giovanny Covarrubias-Pazaran Department of Horticulture, University of Wisconsin, Madison, Wisconsin, Unites States of America E-mail: covarrubiasp@wisc.edu. Most traits of agronomic
More informationConifer Translational Genomics Network Coordinated Agricultural Project
Conifer Translational Genomics Network Coordinated Agricultural Project Genomics in Tree Breeding and Forest Ecosystem Management ----- Module 4 Quantitative Genetics Nicholas Wheeler & David Harry Oregon
More informationOPTIMIZATION OF BREEDING SCHEMES USING GENOMIC PREDICTIONS AND SIMULATIONS
OPTIMIZATION OF BREEDING SCHEMES USING GENOMIC PREDICTIONS AND SIMULATIONS Sophie Bouchet, Stéphane Lemarie, Aline Fugeray-Scarbel, Jérôme Auzanneau, Gilles Charmet INRA GDEC unit : Genetic, Diversity
More informationGenome-wide prediction of maize single-cross performance, considering non-additive genetic effects
Genome-wide prediction of maize single-cross performance, considering non-additive genetic effects J.P.R. Santos 1, H.D. Pereira 2, R.G. Von Pinho 2, L.P.M. Pires 2, R.B. Camargos 2 and M. Balestre 3 1
More informationAssociation Mapping in Plants PLSC 731 Plant Molecular Genetics Phil McClean April, 2010
Association Mapping in Plants PLSC 731 Plant Molecular Genetics Phil McClean April, 2010 Traditional QTL approach Uses standard bi-parental mapping populations o F2 or RI These have a limited number of
More informationEvaluation of random forest regression for prediction of breeding value from genomewide SNPs
c Indian Academy of Sciences RESEARCH ARTICLE Evaluation of random forest regression for prediction of breeding value from genomewide SNPs RUPAM KUMAR SARKAR 1,A.R.RAO 1, PRABINA KUMAR MEHER 1, T. NEPOLEAN
More informationSupplementary material
Supplementary material Journal Name: Theoretical and Applied Genetics Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize Zhigang Guo* 1, Michael M. Magwire*,
More informationAccuracy of whole genome prediction using a genetic architecture enhanced variance-covariance matrix
G3: Genes Genomes Genetics Early Online, published on February 9, 2015 as doi:10.1534/g3.114.016261 1 2 Accuracy of whole genome prediction using a genetic architecture enhanced variance-covariance matrix
More informationGeneral aspects of genome-wide association studies
General aspects of genome-wide association studies Abstract number 20201 Session 04 Correctly reporting statistical genetics results in the genomic era Pekka Uimari University of Helsinki Dept. of Agricultural
More informationPackage FSTpackage. June 27, 2017
Type Package Package FSTpackage June 27, 2017 Title Unified Sequence-Based Association Tests Allowing for Multiple Functional Annotation Scores Version 0.1 Date 2016-12-14 Author Zihuai He Maintainer Zihuai
More informationAssociation Mapping in Wheat: Issues and Trends
Association Mapping in Wheat: Issues and Trends Dr. Pawan L. Kulwal Mahatma Phule Agricultural University, Rahuri-413 722 (MS), India Contents Status of AM studies in wheat Comparison with other important
More informationPrediction of clinical mastitis outcomes within and between environments using whole-genome markers
J. Dairy Sci. 96 :3986 3993 http://dx.doi.org/ 10.3168/jds.2012-6133 American Dairy Science Association, 2013. Prediction of clinical mastitis outcomes within and between environments using whole-genome
More informationMAS refers to the use of DNA markers that are tightly-linked to target loci as a substitute for or to assist phenotypic screening.
Marker assisted selection in rice Introduction The development of DNA (or molecular) markers has irreversibly changed the disciplines of plant genetics and plant breeding. While there are several applications
More informationPlant Science 446/546. Final Examination May 16, 2002
Plant Science 446/546 Final Examination May 16, 2002 Ag.Sci. Room 339 10:00am to 12:00 noon Name : Answer all 16 questions A total of 200 points are available A bonus question is available for an extra
More informationSeed Projects in Peru. Strengthening the seed sector improving food security
Seed Projects in Peru Strengthening the seed sector improving food security KWS Seed Projects in Peru Peru is a diverse country. A tropical climate prevails in the Eastern rain forests ( Selva ) while
More informationDeveloping New GM Products and Detection Methods
Developing New GM Products and Detection Methods Dave Grothaus Monsanto Company Slides Thanks to: International Life Sciences Institute Crop Life International Indus try Colleagues Hope Hart - Syngenta
More informationEdinburgh Research Explorer
Edinburgh Research Explorer Genetic prediction of complex traits: integrating infinitesimal and marked genetic effects Citation for published version: Carré, C, Gamboa, F, Cros, D, Hickey, JM, Gorjanc,
More informationOBJECTIVES-ACTIVITIES 2-4
OBJECTIVES-ACTIVITIES 2-4 Germplasm Phenotyping Genomics PBA BIMS MAB Pipeline Implementation GOALS, ACTIVITIES, & DELIVERABLES Cameron Peace, project co-director & MAB Pipeline Team leader Outline of
More informationTraining population selection for (breeding value) prediction
Akdemir RESEARCH arxiv:1401.7953v2 [stat.me] 21 May 2014 Training population selection for (breeding value) prediction Deniz Akdemir Correspondence: da346@cornell.edu Department of Plant Breeding & Genetics,
More informationGenomic Selection: A Step Change in Plant Breeding. Mark E. Sorrells
Genomic Selection: A Step Change in Plant Breeding Mark E. Sorrells People who contributed to research in this presentation Jean-Luc Jannink USDA/ARS, Cornell University Elliot Heffner Pioneer Hi-Bred
More informationMMAP Genomic Matrix Calculations
Last Update: 9/28/2014 MMAP Genomic Matrix Calculations MMAP has options to compute relationship matrices using genetic markers. The markers may be genotypes or dosages. Additive and dominant covariance
More informationBy the end of this lecture you should be able to explain: Some of the principles underlying the statistical analysis of QTLs
(3) QTL and GWAS methods By the end of this lecture you should be able to explain: Some of the principles underlying the statistical analysis of QTLs Under what conditions particular methods are suitable
More informationPractical integration of genomic selection in dairy cattle breeding schemes
64 th EAAP Meeting Nantes, 2013 Practical integration of genomic selection in dairy cattle breeding schemes A. BOUQUET & J. JUGA 1 Introduction Genomic selection : a revolution for animal breeders Big
More informationTECHNICAL BULLETIN GENEMAX FOCUS - EVALUATION OF GROWTH & GRADE FOR COMMERCIAL USERS OF ANGUS GENETICS. November 2016
TECHNICAL BULLETIN November 2016 GENEMAX FOCUS - EVALUATION OF GROWTH & GRADE FOR COMMERCIAL USERS OF ANGUS GENETICS Zoetis Genetics 333 Portage Street Kalamazoo, MI 49007-4931 KEY POINTS GeneMax Focus
More informationExploring Similarities of Conserved Domains/Motifs
Exploring Similarities of Conserved Domains/Motifs Sotiria Palioura Abstract Traditionally, proteins are represented as amino acid sequences. There are, though, other (potentially more exciting) representations;
More informationBiology Genetics Practice Quiz
Biology Genetics Practice Quiz Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The table above shows information related to blood types. What genotype(s)
More informationPOPULATION GENETICS Winter 2005 Lecture 18 Quantitative genetics and QTL mapping
POPULATION GENETICS Winter 2005 Lecture 18 Quantitative genetics and QTL mapping - from Darwin's time onward, it has been widely recognized that natural populations harbor a considerably degree of genetic
More informationMapping and Mapping Populations
Mapping and Mapping Populations Types of mapping populations F 2 o Two F 1 individuals are intermated Backcross o Cross of a recurrent parent to a F 1 Recombinant Inbred Lines (RILs; F 2 -derived lines)
More information2016 Management Yield Potential
2016 Management Yield Potential Adriano T. Mastrodomenico and Fred E. Below Crop Physiology Laboratory Department of Crop Sciences Univeristy of Illinois at Urbana-Champaign Table of contents Introduction...
More informationA. COVER PAGE. Oswaldo Chicaiza, Alicia del Blanco (50%), Xiaoqin Zhang (70%), and Marcelo Soria (20%).
A. COVER PAGE PROJECT TITLE Development of wheat varieties for California 2017-2018 PRINCIPAL INVESTIGATOR Jorge Dubcovsky OTHER INVESTIGATORS Oswaldo Chicaiza, Alicia del Blanco (50%), Xiaoqin Zhang (70%),
More informationGenomic evaluation by including dominance effects and inbreeding depression for purebred and crossbred performance with an application in pigs
DOI 10.1186/s1711-016-071-4 Genetics Selection Evolution RESEARCH ARTICE Open Access Genomic evaluation by including dominance effects and inbreeding depression for purebred and crossbred performance with
More informationGenome-Wide Association Studies (GWAS): Computational Them
Genome-Wide Association Studies (GWAS): Computational Themes and Caveats October 14, 2014 Many issues in Genomewide Association Studies We show that even for the simplest analysis, there is little consensus
More informationSTATISTICAL APPLICATIONS IN PLANT BREEDING AND GENETICS CARL ALAN WALKER
STATISTICAL APPLICATIONS IN PLANT BREEDING AND GENETICS By CARL ALAN WALKER A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY IN CROP SCIENCE WASHINGTON
More informationStatistical Methods in Bioinformatics
Statistical Methods in Bioinformatics CS 594/680 Arnold M. Saxton Department of Animal Science UT Institute of Agriculture Bioinformatics: Interaction of Biology/Genetics/Evolution/Genomics Computer Science/Algorithms/Database
More informationThe 150+ Tomato Genome (re-)sequence Project; Lessons Learned and Potential
The 150+ Tomato Genome (re-)sequence Project; Lessons Learned and Potential Applications Richard Finkers Researcher Plant Breeding, Wageningen UR Plant Breeding, P.O. Box 16, 6700 AA, Wageningen, The Netherlands,
More informationGenomic selection in American chestnut backcross populations
Genomic selection in American chestnut backcross populations Jared Westbrook The American Chestnut Foundation TACF Annual Meeting Fall 2017 Portland, ME Selection against blight susceptibility in seed
More informationTSB Collaborative Research: Utilising i sequence data and genomics to improve novel carcass traits in beef cattle
TSB Collaborative Research: Utilising i sequence data and genomics to improve novel carcass traits in beef cattle Dr Mike Coffey SAC Animal Breeding Team 1 Why are we doing this project? 1 BRITISH LIMOUSIN
More informationAdvanced breeding of solanaceous crops using BreeDB
Part 6 3 rd transplant Training Workshop - October 2014 Exploiting and understanding Solanaceous genomes Advanced breeding of solanaceous crops using BreeDB Richard Finkers Plant Breeding, Wageningen UR
More informationMaja Boczkowska. Plant Breeding and Acclimatization Institute (IHAR) - NRI
Genotypic, Phenotypic and FTIR-based Metabolic Fingerprint Diversity in Oat Landraces in Relation to the Environment at the Place of Origin Maja Boczkowska Plant Breeding and Acclimatization Institute
More informationApplication GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype environment interaction and seasonal adaptation
Indian Journal of Agricultural Sciences 81 (5): 438 44, May 2011 Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype environment interaction and seasonal
More informationQuantitative Genetics
Quantitative Genetics Polygenic traits Quantitative Genetics 1. Controlled by several to many genes 2. Continuous variation more variation not as easily characterized into classes; individuals fall into
More informationAnimal breeding for the future
Animal breeding for the future Phenotype or genotype An important issue that farmers and breeders have to solve is: Can I breed / select my animals on visual appraisal alone, EBV s s alone, a balanced
More informationGenetic evaluation using single-step genomic best linear unbiased predictor in American Angus 1
Published June 25, 2015 Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus 1 D. A. L. Lourenco,* 2 S. Tsuruta,* B. O. Fragomeni,* Y. Masuda,* I. Aguilar, A. Legarra,
More informationExperimental Design and Sample Size Requirement for QTL Mapping
Experimental Design and Sample Size Requirement for QTL Mapping Zhao-Bang Zeng Bioinformatics Research Center Departments of Statistics and Genetics North Carolina State University zeng@stat.ncsu.edu 1
More informationFundamentals of Genetics. 4. Name the 7 characteristics, giving both dominant and recessive forms of the pea plants, in Mendel s experiments.
Fundamentals of Genetics 1. What scientist is responsible for our study of heredity? 2. Define heredity. 3. What plant did Mendel use for his hereditary experiments? 4. Name the 7 characteristics, giving
More informationPROPOSAL AND APPLICATION GUIDELINES. International Wheat Yield Partnership 1st Competitive Call
PROPOSAL AND APPLICATION GUIDELINES International Wheat Yield Partnership 1st Competitive Call Launch: 15 January 2015 Closing Date for Pre-proposals: 15 March 2015 24:00 GMT Contents Summary.... Page
More informationComparison of bread wheat lines selected by doubled haploid, single-seed descent and pedigree selection methods
Theor Appl Genet (1998) 97: 550 556 Springer-Verlag 1998 M. N. Inagaki G. Varughese S. Rajaram M. van Ginkel A. Mujeeb-Kazi Comparison of bread wheat lines selected by doubled haploid, single-seed descent
More informationIntrogression of genetic material from primary synthetic hexaploids into an Australian bread wheat (Triticum aestivum L.)
Introgression of genetic material from primary synthetic hexaploids into an Australian bread wheat (Triticum aestivum L.) A thesis submitted in fulfilment of the requirements for the degree of Master of
More informationEstimating Cell Cycle Phase Distribution of Yeast from Time Series Gene Expression Data
2011 International Conference on Information and Electronics Engineering IPCSIT vol.6 (2011) (2011) IACSIT Press, Singapore Estimating Cell Cycle Phase Distribution of Yeast from Time Series Gene Expression
More informationNear-Balanced Incomplete Block Designs with An Application to Poster Competitions
Near-Balanced Incomplete Block Designs with An Application to Poster Competitions arxiv:1806.00034v1 [stat.ap] 31 May 2018 Xiaoyue Niu and James L. Rosenberger Department of Statistics, The Pennsylvania
More informationIHIC 2011 Orlando, FL
CDA Implementation Guide for Genetic Testing Report (GTR): Towards a Clinical Genomic Statement IHIC 2011 Orlando, FL Amnon Shabo (Shvo), PhD shabo@il.ibm.com HL7 Clinical Genomics WG Co-chair and Modeling
More informationIntroduction to quantitative genetics
8 Introduction to quantitative genetics Purpose and expected outcomes Most of the traits that plant breeders are interested in are quantitatively inherited. It is important to understand the genetics that
More informationRethinking Realizing Value from Genetic Resources
Rethinking Realizing Value from Genetic Resources Philip G. Pardey University of Minnesota Funding the CGIAR Genebanks Side Meeting to the CGIAR System Council Meeting, 9 May 2017 Royal Tropical Institute,
More informationFunding breeding research in Canadian Pulses Carl Potts Executive Director April 5, /8/2013 1
Funding breeding research in Canadian Pulses Carl Potts Executive Director April 5, 2013 4/8/2013 1 Global Production various crops Crop Global Production (million tonnes) Corn 850 Rice 700 Wheat 650 Soybean
More informationA Fresh Look at Field Pea Breeding. Dr Garry Rosewarne Senior Research Scientist
A Fresh Look at Field Pea Breeding Dr Garry Rosewarne Senior Research Scientist Outline Field Pea Breeding in Australia Breeding approach Yield progression Modern statistical analysis Alternative Breeding
More informationGenomic models in bayz
Genomic models in bayz Luc Janss, Dec 2010 In the new bayz version the genotype data is now restricted to be 2-allelic markers (SNPs), while the modeling option have been made more general. This implements
More informationThe HL7 Clinical Genomics Work Group
HL7 Clinical Genomics Domain Information Model The HL7 Clinical Genomics Work Group Prepared by Amnon Shabo (Shvo), PhD HL7 Clinical Genomics WG Co-chair and Modeling Facilitator Pedigree (family health
More informationPlant Science 546. Final Examination May 12, Ag.Sci. Room :00am to 12:00 noon
Plant Science 546 Final Examination May 12, 2004 Ag.Sci. Room 141 10:00am to 12:00 noon Name : Answer all 16 questions A total of 200 points are available A bonus question is available for an extra 10
More informationReport to California Wheat Commission: GH Experiments
Report to California Wheat Commission: GH 2011-2012 Experiments J. G. Waines, UC Riverside. Title: Determination of optimum root and shoot size in bread wheat for increased water and nutrient-use efficiency
More informationThe Challenges of [high-throughput] Phenotyping
The Challenges of [high-throughput] Phenotyping Mount Hood - sept 2008 Topics Introducing BASF Plant Science Phenotyping, for what purposes? What are the challenges? High-throughput phenotyping The TraitMill
More informationThe new infrastructure for cattle and sheep breeding in Ireland.
IRISH CATTLE BREEDING FEDERATION & SHEEP IRELAND The new infrastructure for cattle and sheep breeding in Ireland. Brian Wickham Chief Executive ICBF & Sheep Ireland Irish Cattle Breeding Federation Soc.
More informationBull Selection Strategies Using Genomic Estimated Breeding Values
R R Bull Selection Strategies Using Genomic Estimated Breeding Values Larry Schaeffer CGIL, University of Guelph ICAR-Interbull Meeting Niagara Falls, NY June 8, 2008 Understanding Cancer and Related Topics
More informationAn economic assessment of the value of molecular markers in plant breeding programs
An economic assessment of the value of molecular markers in plant breeding programs John P. Brennan 1, Ata Rehman 1, Harsh Raman 1, Andrew W. Milgate 1, Denise Pleming 1 and Peter J. Martin 1 1 NSW Department
More informationPoultryTechnical NEWS. GenomChicks Advanced layer genetics using genomic breeding values. For further information, please contact us:
PoultryTechnical LOHMANN TIERZUCHT LOHMANN TIERZUCHT GmbH will continue to conduct comprehensive performance testing and is continuously investing in extending the testing capacities as well as looking
More informationTTT: 7 WT: Text book by N.C.E.R.T. 2. Reference book by Dinesh Publications.
BLOOM PUBLIC SCHOOL Vasant Kunj, New Delhi Lesson Plan Class : XII Subject: Biology Month : May Chapter : 5 Principles of Inheritance and Variation No. of Periods:15 TTT: 7 WT: 8 Chapter : 5 Chapter :
More informationMelding genomics and quantitative genetics in sheep breeding programs: opportunities and limits
Melding genomics and quantitative genetics in sheep breeding programs: opportunities and limits Ron Lewis Genetics Stakeholders Committee ASI Convention, Scottsdale, AZ Jan. 28, 2016 My talk Genomics road
More informationCHARACTERIZATION, CHALLENGES, AND USES OF SORGHUM DIVERSITY TO IMPROVE SORGHUM THROUGH PLANT BREEDING
1 ST EUROPEAN SORGHUM CONGRESS WORKSHOP INNOVATIVE RESEARCH TOWARDS GENETIC PROGRESS CHARACTERIZATION, CHALLENGES, AND USES OF SORGHUM DIVERSITY TO IMPROVE SORGHUM THROUGH PLANT BREEDING MAXIMISING RESULTS
More informationGenome-wide association studies (GWAS) Part 1
Genome-wide association studies (GWAS) Part 1 Matti Pirinen FIMM, University of Helsinki 03.12.2013, Kumpula Campus FIMM - Institiute for Molecular Medicine Finland www.fimm.fi Published Genome-Wide Associations
More informationhttp://genemapping.org/ Epistasis in Association Studies David Evans Law of Independent Assortment Biological Epistasis Bateson (99) a masking effect whereby a variant or allele at one locus prevents
More informationA GENOTYPE CALLING ALGORITHM FOR AFFYMETRIX SNP ARRAYS
Bioinformatics Advance Access published November 2, 2005 The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
More informationA strategy for multiple linkage disequilibrium mapping methods to validate additive QTL. Abstracts
Proceedings 59th ISI World Statistics Congress, 5-30 August 013, Hong Kong (Session CPS03) p.3947 A strategy for multiple linkage disequilibrium mapping methods to validate additive QTL Li Yi 1,4, Jong-Joo
More informationWorkshop Wheat Production Technologies for farmers to face Climate Change challenges
Workshop Wheat Production Technologies for farmers to face Climate Change challenges General Programme 25 April 2010 Arrival of participants Transfer to hotel El MOURADI (Gammarth) 26 April 2010 Field
More informationH3A - Genome-Wide Association testing SOP
H3A - Genome-Wide Association testing SOP Introduction File format Strand errors Sample quality control Marker quality control Batch effects Population stratification Association testing Replication Meta
More informationImplementation of Genomic Selection in Pig Breeding Programs
Implementation of Genomic Selection in Pig Breeding Programs Jack Dekkers Animal Breeding & Genetics Department of Animal Science Iowa State University Dekkers, J.C.M. 2010. Use of high-density marker
More informationSupplementary Note: Detecting population structure in rare variant data
Supplementary Note: Detecting population structure in rare variant data Inferring ancestry from genetic data is a common problem in both population and medical genetic studies, and many methods exist to
More informationRuns of Homozygosity Analysis Tutorial
Runs of Homozygosity Analysis Tutorial Release 8.7.0 Golden Helix, Inc. March 22, 2017 Contents 1. Overview of the Project 2 2. Identify Runs of Homozygosity 6 Illustrative Example...............................................
More informationMolecular markers in plant breeding
Molecular markers in plant breeding Jumbo MacDonald et al., MAIZE BREEDERS COURSE Palace Hotel Arusha, Tanzania 4 Sep to 16 Sep 2016 Molecular Markers QTL Mapping Association mapping GWAS Genomic Selection
More informationGenome-wide association mapping using single-step GBLUP!!!!
Genome-wide association mapping using single-step GBLUP!!!! Ignacy'Misztal,'Joy'Wang'University!of!Georgia Ignacio'Aguilar,'INIA,!Uruguay! Andres'Legarra,!INRA,!France! Bill'Muir,!Purdue!University! Rohan'Fernando,!Iowa!State!!!'
More informationGenomic Estimated Breeding Values Using Genomic Relationship Matrices in a Cloned. Population of Loblolly Pine. Fikret Isik*
G3: Genes Genomes Genetics Early Online, published on April 5, 2013 as doi:10.1534/g3.113.005975 Genomic Estimated Breeding Values Using Genomic Relationship Matrices in a Cloned Population of Loblolly
More informationLate blight resistance of potato hybrids with diverse genetic background
Late blight resistance of potato hybrids with diverse genetic background E. Rogozina, M. Kuznetsova, O. Fadina, M. Beketova, E. Sokolova and E. Khavkin EuroBlight workshop 14-17 May 2017, Aarhus, Denmark
More informationAccuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats
Agronomy Publications Agronomy 7-2011 Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats Franco G. Asoro Iowa State University Mark A. Newell
More informationCrop Science Society of America
Crop Science Society of America Grand Challenge Statements Crop science is a highly integrative science employing the disciplines of conventional plant breeding, transgenic crop improvement, plant physiology,
More informationDO NOT OPEN UNTIL TOLD TO START
DO NOT OPEN UNTIL TOLD TO START BIO 312, Section 1, Spring 2011 February 21, 2011 Exam 1 Name (print neatly) Instructor 7 digit student ID INSTRUCTIONS: 1. There are 11 pages to the exam. Make sure you
More informationBuilding Better Algae
Building Better Algae Craig Marcus, Ph.D. Dept. of Environmental & Molecular Toxicology Domestication of Algae as a New Crop Must develop a rapid process (corn first domesticated ~4000 B.C.) Requires a
More informationOrchardgrass Breeding and Genetics. Joseph G. Robins B. Shaun Bushman Kevin B. Jensen. Forage and Range Research Laboratory
Orchardgrass Breeding and Genetics Forage and Range Research Laboratory Joseph G. Robins B. Shaun Bushman Kevin B. Jensen Orchardgrass Grazing Mechanical harvest Seed production FRRL orchardgrass improvement
More informationPathway approach for candidate gene identification and introduction to metabolic pathway databases.
Marker Assisted Selection in Tomato Pathway approach for candidate gene identification and introduction to metabolic pathway databases. Identification of polymorphisms in data-based sequences MAS forward
More informationMeasurement error variance of testday observations from automatic milking systems
Measurement error variance of testday observations from automatic milking systems Pitkänen, T., Mäntysaari, E. A., Nielsen, U. S., Aamand, G. P., Madsen, P. and Lidauer, M. H. Nordisk Avlsværdivurdering
More informationI See Dead People: Gene Mapping Via Ancestral Inference
I See Dead People: Gene Mapping Via Ancestral Inference Paul Marjoram, 1 Lada Markovtsova 2 and Simon Tavaré 1,2,3 1 Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street,
More informationCowpea Breeding. Ainong Shi. University of Arkansas
Cowpea Breeding Ainong Shi University of Arkansas UAF Vegetable Breeding UAF AR USA International Collaboration Classic Breeding Molecular Breeding Student Training Classic breeding such as crossing, generation
More informationGenetics of dairy production
Genetics of dairy production E-learning course from ESA Charlotte DEZETTER ZBO101R11550 Table of contents I - Genetics of dairy production 3 1. Learning objectives... 3 2. Review of Mendelian genetics...
More informationStrategy for applying genome-wide selection in dairy cattle
J. Anim. Breed. Genet. ISSN 0931-2668 ORIGINAL ARTICLE Strategy for applying genome-wide selection in dairy cattle L.R. Schaeffer Department of Animal and Poultry Science, Centre for Genetic Improvement
More information1 why study multiple traits together?
Multiple Traits & Microarrays why map multiple traits together? central dogma via microarrays diabetes case study why are traits correlated? close linkage or pleiotropy? how to handle high throughput?
More informationRECOLAD. Introduction to the «atelier 1» Genetic approaches to improve adaptation to climate change in livestock
RECOLAD. Introduction to the «atelier 1» Genetic approaches to improve adaptation to climate change in livestock Ahmed El Beltagy (ahmed_elbeltagi@yahoo.com and D. Laloë (denis.laloe@jouy.inra.fr) Introduction
More informationA journey: opportunities & challenges of melding genomics into U.S. sheep breeding programs
A journey: opportunities & challenges of melding genomics into U.S. sheep breeding programs Presenter: Dr. Ron Lewis, Department of Animal Science, University of Nebraska-Lincoln Host/Moderator: Dr. Jay
More informationEvaluation of experimental designs in durum wheat trials
DOI: 10.1515/bile-2015-0010 Biometrical Letters Vol. 52 (2015), No. 2, 105-114 Evaluation of experimental designs in durum wheat trials Anastasios Katsileros 1 and Christos Koukouvinos 2 1 Faculty of Crop
More informationARTICLE ROADTRIPS: Case-Control Association Testing with Partially or Completely Unknown Population and Pedigree Structure
ARTICLE ROADTRIPS: Case-Control Association Testing with Partially or Completely Unknown Population and Pedigree Structure Timothy Thornton 1 and Mary Sara McPeek 2,3, * Genome-wide association studies
More informationBS 50 Genetics and Genomics Week of Nov 29
BS 50 Genetics and Genomics Week of Nov 29 Additional Practice Problems for Section Problem 1. A linear piece of DNA is digested with restriction enzymes EcoRI and HinDIII, and the products are separated
More informationUncertainties and certainties in GMO analytics using qpcr
Uncertainties and certainties in GMO analytics using qpcr PD Dr. Philipp Hübner Kantonales Labor Basel-Stadt Abteilung Lebensmittel Postfach CH-412 Basel using qpcr historical review Short crash course
More informationModeling and simulation of plant breeding with applications in wheat and maize
The China - EU Workshop on Phenotypic Profiling and Technology Transfer on Crop Breeding, Barcelona, Spain, 17-21 September 2012 Modeling and simulation of plant breeding with applications in wheat and
More information