Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex

Phenotypic and genotypic data for a barley population of Steptoe x Morex. There were 150 doubled haploid crosses, evaluated at 223 markers. Phenotypic data wascollected on 8 traits at 16 environments.

data("steptoe.morex.pheno")

Format

steptoe.morex.pheno is a data.frame of phenotypic data with 2432 observations on 10 variables:

gen: genotype factor with parents Steptoe and Morex, and 150 crosses SM1, SM2, ..., SM200. Not all 200 numbers were used.
env: environment, 16 levels
amylase: alpha amylase (20 Deg Units)
diapow: diastatic power (degree units)
hddate: heading date (julian days)
lodging: lodging (percent)
malt: malt extract (percent)
height: plant height (centimeters)
protein: grain protein (percent)
yield: grain yield (Mt/Ha)

steptoe.morex.geno is a cross object from the qtl package with genotypic data of the 223 markers for the 150 crosses of Steptoe x Morex.

Details

As described by Hayes et al (1993), a population of 150 barley doubled haploid (DH) lines was developed by the Oregon State University Barley Breeding Program for the North American Barley Genome Mapping Project. The parentage of the population is Steptoe / Morex.

Steptoe is the dominant feed barley in the northwestern U.S.

Morex is the spring U.S. malting quality standard.

Seed from a single head of each parent was used to create the F1, from which a set of 150 lines was developed.

Phenotypic values for the parents Steptoe and Morex are here: https://wheat.pw.usda.gov/ggpages/SxM/parental_values.html

There are 16 locations, The average across locations is in column 17. Not all traits were collected at every location. At each location, all 150 lines were included in block 1, a random subset of 50 lines was used in block 2.

The traits are: Alpha Amylase (20 Deg Units), Diastatic Power (Deg Units), Heading Date (Julian Days), Lodging (percent), Malt Extract (percent), Grain Protein (percent), Grain Yield (Mt/Ha).

Phenotypic values of the 150 lines in the F1 population are here: https://wheat.pw.usda.gov/ggpages/SxM/phenotypes.html

Each trait is in a different file, in which each block of numbers represents one location.

The 223-markers Steptoe/Morex base map is here: https://wheat.pw.usda.gov/ggpages/SxM/smbasev2.map

The data for these markers on the 150 lines is https://wheat.pw.usda.gov/ggpages/SxM/smbasev2.mrk

These were hand-assembled (e.g. marker distances were cumulated to marker positions) into a .csv file which was then imported into R using qtl::read.cross. The class was manually changed from c('bc','cross') to c('dh','cross').

The marker data is coded as A = Steptoe, B = Morex, - = missing.

The pedigrees for the 150 lines are found here: https://wheat.pw.usda.gov/ggpages/SxM/pedigrees.html

Source

The Steptoe x Morex Barley Mapping Population. Map: Version 2, August 1, 1995 https://wheat.pw.usda.gov/ggpages/SxM. Accessed Jan 2015.

Data provided by the United States Department of Agriculture.

References

P.M. Hayes, B.H. Liu, S.J. Knapp, F. Chen, B. Jones, T. Blake, J. Franckowiak, D. Rasmusson, M. Sorrells, S.E. Ullrich, and others. 1993. Quantitative trait locus effects and environmental interaction in a sample of North American barley germplasm. Theoretical and Applied Genetics, 87, 392--401. https://doi.org/10.1007/BF01184929

Ignacio Romagosa, Steven E. Ullrich, Feng Han, Patrick M. Hayes. 1996. Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley. Theor Appl Genet, 93, 30-37. https://doi.org/10.1007/BF00225723

Piepho, Hans-Peter. 2000. A mixed-model approach to mapping quantitative trait loci in barley on the basis of multiple environment data. Genetics, 156, 2043-2050.

M. Malosetti, J. Voltas, I. Romagosa, S.E. Ullrich, F.A. van Eeuwijk. (2004). Mixed models including environmental covariables for studying QTL by environment interaction. Euphytica, 137, 139-145. https://doi.org/10.1023/B:EUPH.0000040511.4638

Examples

# \dontrun{
  
  library(agridat)
  data(steptoe.morex.pheno)
  dat <- steptoe.morex.pheno

  # Visualize GxE of traits
  libs(lattice)
  redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
  levelplot(amylase~env*gen, data=dat, col.regions=redblue,
            scales=list(x=list(rot=90)), main="amylase")
  ## levelplot(diapow~env*gen, data=dat, col.regions=redblue,
  ##           scales=list(x=list(rot=90)), main="diapow")
  ## levelplot(hddate~env*gen, data=dat, col.regions=redblue,
  ##           scales=list(x=list(rot=90)), main="hddate")
  ## levelplot(lodging~env*gen, data=dat, col.regions=redblue,
  ##           scales=list(x=list(rot=90)), main="lodging")
  ## levelplot(malt~env*gen, data=dat, col.regions=redblue,
  ##           scales=list(x=list(rot=90)), main="malt")
  ## levelplot(height~env*gen, data=dat, col.regions=redblue,
  ##           scales=list(x=list(rot=90)), main="height")
  ## levelplot(protein~env*gen, data=dat, col.regions=redblue,
  ##           scales=list(x=list(rot=90)), main="protein")
  ## levelplot(yield~env*gen, data=dat, col.regions=redblue,
  ##           scales=list(x=list(rot=90)), main="yield")


  # Calculate avg yield for each loc as in Romagosa 1996, table 3
  # t(t(round(tapply(dat$yield, dat$env, FUN=mean),2)))
  # SKo92,SKg92 means in table 3 are switched.  Who is right, him or me?

  # Draw marker map
  libs(qtl)
  data(steptoe.morex.geno)
  datg <- steptoe.morex.geno
  plot.map(datg, main="steptoe.morex.pheno")  # or just use plot()


  # This is a very rudimentary example.
    
  # Fit a simple multi-environment mixed model
  libs(asreml)
  m1 <- asreml(yield ~ env, data=dat, random=~gen)
#> Model fitted using the gamma parameterization.
#> ASReml 4.1.0 Fri Dec 11 17:50:12 2020
#>           LogLik        Sigma2     DF     wall    cpu
#>  1      -826.109      0.664231   2416 17:50:13    0.0
#>  2      -821.409      0.656597   2416 17:50:13    0.0
#>  3      -817.325      0.647509   2416 17:50:13    0.0
#>  4      -815.816      0.641196   2416 17:50:13    0.0
#>  5      -815.601      0.638204   2416 17:50:13    0.0
#>  6      -815.600      0.637998   2416 17:50:13    0.0
  
  libs(wgaim)
  qtl::plotMissing(datg)
  wgaim::linkMap(datg)
  # Create an interval object for wgaim
  dati <- wgaim::cross2int(datg, id="gen")
  
  # Whole genome qtl
  q1 <- wgaim::wgaim(m1, intervalObj=dati, merge.by="gen", na.action=na.method(x="include"))
#> Error in eval(baseModel$call$data): object 'dat' not found
  #wgaim::linkMap(q1, dati) # Visualize
  wgaim::outStat(q1, dati) # outlier statistic
#> Error in wgaim::outStat(q1, dati): object 'q1' not found
  summary(q1, dati)  # Table of important intervals
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'q1' not found
  # Chrom Left Marker dist(cM) Right Marker dist(cM)   Size Pvalue 
  #     3      ABG399     52.6       BCD828     56.1  0.254  0.000  45.0
  #     5      MWG912      148      ABG387A    151.2  0.092  0.001   5.9
  #     6     ABC169B     64.8       CDO497     67.5 -0.089  0.001   5.6
  
# }