hadasch.lettuce | R Documentation |
Lettuce resistance to downy mildew resistance (with marker data).
data("hadasch.lettuce")
A data frame with 703 observations on the following 4 variables.
loc
locations
gen
genotype
rep
replicate
dmr
downy mildew resistance
A biparental cross of 95 recombinant inbred lines of "Salinas 88" (susceptible) and "La Brillante" (highly resistant to downy mildew). The 89 RILs were evaluated in field experiments performed in 2010 and 2011 near Salinas, California. Each loc had a 2 or 3 rep RCB design. There were approximately 30 plants per plot. Plots were scored 0 (no disease) to 5 (severe disease).
The authors used the following model in a first-stage analysis to compute adjusted means for each genotype:
y = loc + gen + gen:loc + block:loc + error
where gen was fixed and all other terms random. The adjusted means were used as the response in a second stage:
mn = 1 + Zu + error
where Z is the design matrix of marker effects. The error term is fixed to have covariance matrix R be the same as from the first stage.
Genotyping was performed with 95 SNPs and 205 amplified fragment length polymporphism markers so that a marker matrix M (89×300) was provided. The biallelic marker M(iw) for the ith genotype and the wth marker with alleles A1 (i.e. the reference allele) and A2 was coded as 1 for A1,A1, -1 for A2,A2 and 0 for A1,A2 and A2,A2.
The electronic version of the lettuce data are licensed CC-BY 4 and were downloaded 20 Feb 2021. https://figshare.com/articles/dataset/Lettuce_trial_phenotypic_and_marker_data_/8299493
Hadasch, S., I. Simko, R. J. Hayes, J. O. Ogutu, and H.P. Piepho (2016). Comparing the predictive abilities of phenotypic and marker-assisted selection methods in a biparental lettuce population. Plant Genome 9. https://doi.org/10.3835/plantgenome2015.03.0014
Hayes, R. J., Galeano, C. H., Luo, Y., Antonise, R., & Simko, I. (2014). Inheritance of Decay of Fresh-cut Lettuce in a Recombinant Inbred Line Population from "Salinas 88" × "La Brillante". J. Amer. Soc. Hort. Sci., 139(4), 388-398. https://doi.org/10.21273/JASHS.139.4.388
## Not run:
library(agridat)
data(hadasch.lettuce)
data(hadasch.lettuce.markers)
dat <- hadasch.lettuce
datm <- hadasch.lettuce.markers
libs(agridat)
# loc 1 has 2 reps, loc 3 has higher dmr
dotplot(dmr ~ factor(gen)|factor(loc), dat,
group=rep, layout=c(1,3),
main="hadasch.lettuce")
# kinship matrix
# head( tcrossprod(as.matrix(datm[,-1])) )
if(require("asreml", quietly=TRUE)){
libs(asreml)
dat <- transform(dat, loc=factor(loc), gen=factor(gen), rep=factor(rep))
m1 <- asreml(dmr ~ 1 + gen, data=dat,
random = ~ loc + gen:loc + rep:loc)
p1 <- predict(m1, classify="gen")$pvals
}
libs(sommer)
m2 <- mmer(dmr ~ 0 + gen, data=dat,
random = ~ loc + gen:loc + rep:loc)
p2 <- coef(m2)
head(p1)
head(p2)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.