Tomato: Tomato data to find genes for growth

In byandell/pda: Practial Data Analysis for Designed Experiments

Description

A plant scientist is interested in finding out the location of genes which control various plant growth attributes. He is using molecular markers to do this. He thinks he has located a major gene for fruit weight (fw) close to marker tg430.

The original experiment covered two years (yr), with 93 unique plant entries (entry). There were several replications each year. Unfortunately, the data now available to the scientist consists of the mean fruit weight (mfw), averaged across the replicates for each year. [The raw data are in notebooks halfway around the world!] The scientist believes a log10 transformation (mfwlog) is reasonable, and has presented that data in that way.

Nevertheless there are still 2 years of data for most entries. The marker tg430 can be used to classify entries into one of three categories, 1 = parent A, 3 = parent B, 2 = Hybrid of A and B (and . = missing marker value). The scientist is particularly interested in the ‘additive effect’ (parent A – parent B) and the ‘dominance effect’ (Hybrid – mean of parents). Note that if the dominance is zero, then the hybrid would be halfway between the two parents.

Usage

data(Tomato)

Format

A data frame with 194 observations on 4 variables.

[,1]	entry	factor	identifier for line entry
[,2]	yr	factor	year of measurement
[,3]	mfwlog	numeric	log( mean flowering time )
[,4]	tg430	ordered	A<H<B genotype at marker TG430

Source

Professor Irwin Goldman (mailto:[e-mail]), Horticulture Department, UW-Madison

References

Goldman IL, Paran I, Zamir D (1995) 'Quantitative trait locus anlaysis of a recombinant inbred line population derived from a Lycopersicon esculentum x Lycopersicon cheesmanii cross', Theoretical & Applied Genetics 90, 925-932.

Examples

data( Tomato )

# make sure entry, tg430 and yr are all factors
Tomato$entry <- factor( Tomato$entry )
Tomato$tg430 <- ordered( Tomato$tg430, c("A","H","B") )
Tomato$yr <- ordered( Tomato$yr )
# reduce to complete data
Tomato1 <- Tomato[ !( is.na( Tomato$tg430 )
   | is.na( Tomato$mfwlog ))
   & Tomato$yr == 1, ]

# Figure 4.1: Histograms
histogram( ~ mfwlog | tg430, Tomato1, nint = 30, layout = c(1,3),
  main = "Figure B:4.1. Tomato Histograms by Group" )

# Figure 4.2: Box-Plots
bwplot( mfwlog ~ tg430, Tomato1,
  xlab = "Tomato Allele Type", ylab= "Log Flower Time",
  main = "Figure B:4.2. Tomato Box-Plots by Group" )

# Figure 5.1: Confidence Intervals

# fit one-factor anova
Tomato.aov <- aov( mfwlog ~ tg430, Tomato1 )
Tomato.aov
summary( Tomato.aov )
# least squares means ( uses library( pda ) )
lsmean( Tomato.aov )

# 95% confidence intervals by genotype ( uses library( pda ) )
ci.plot( Tomato.aov, level = 0.05,
   crit=qt( 1 - 0.05 / 2, df.resid( Tomato.aov )) / sqrt(2),
   xlab=paste("(a) ",100 * ( 1 - 0.05 ),
      "% CI / sqrt(2)", sep = "" ),
   ylab = "log flower time",
   main = "Figure 5.1(a) Confidence Intervals",
  split = c(1,1,1,1) )

# notched box-plots to compare with CIs (NA in bwplot currently)
attach( Tomato1 )
boxplot( split( mfwlog, tg430 ), notch = TRUE,
   xlab = "(b) notched box-plots",
   ylab = "log flower time",
   main = "Figure 5.1(b) Tomato Notched Box-Plots" )
detach()

byandell/pda documentation built on May 13, 2019, 9:27 a.m.