summary.wgaim: Summary and print methods for the class '"wgaim"'

In wgaim: Whole Genome Average Interval Mapping for QTL Detection and Estimation using ASReml-R

Description

Prints a QTL summary form the "wgaim" object in a presentable format

Usage

## S3 method for class 'wgaim'
summary(object, intervalObj, LOD = TRUE, ...)
## S3 method for class 'wgaim'
print(x, intervalObj, ...)

Arguments

object	an object of class "wgaim" (see Details)
x	an object of class "wgaim"
intervalObj	a data structure of class "cross" or "interval" containing the genotypic data
LOD	logical value. If TRUE LOD scores for QTL are calculated, defaults to TRUE
...	further arguments passed to or from other methods

Details

It is important that the intervalObj is not missing in summary.wgaim() or print.wgaim() as it contains vital summary information about each of the QTL detected.

The summary of the QTL differs depending on the method chosen in the wgaim.asreml call. If method = "random" then the significance of the QTL are summarized using a probablistic argument based on the conditional distribution of the QTL sizes given the data (see Verbyla et. al, 2012 in References) Thus, for each QTL, a value is calculated that represents the probability that the QTL size is greater than zero (or less than zero if the effect is negative). If method = "fixed" then the significance of the QTL is summarized using a one degree of freedom Wald statistic.

Value

A summary of the QTL component of the "wgaim" object is printed to the screen. For each QTL detected, if an "interval" analysis was performed then summary.wgaim() prints which chromosome, name and distance of each flanking marker, size, probability/p-value, contribution of genetic variance and LOD score if desired. If a "marker" analysis was performed then the chromosome, name and distance of the associated marker, size, probability/p-value, contribution of genetic variance and LOD score are printed. print.wgaim() provides a narrative brief of the QTL detected.

Author(s)

Julian Taylor and Ari Verbyla

References

Verbyla, A. P & Taylor, J. D, Verbyla, K. L (2012). RWGAIM: An efficient high dimensional random whole genome average (QTL) interval mapping approach. Genetics Research. 94, 291-306.

Julian Taylor, Arunas Vebyla (2011). R Package wgaim: QTL Analysis in Bi-Parental Populations Using Linear Mixed Models. Journal of Statistical Software, 40(7), 1-18. URL http://www.jstatsoft.org/v40/i07/.

Verbyla, A. P., Cullis, B. R., Thompson, R (2007) The analysis of QTL by simultaneous use of the full linkage map. Theoretical and Applied Genetics, 116, 195-211.

See Also

wgaim.asreml

Examples

## Not run: 
# read in data

data(phenoRxK, package = "wgaim")
data(genoRxK, package = "wgaim")

# subset linkage map and convert to "interval" object

genoRxK <- subset(genoRxK, chr = c("1A", "2D1", "2D2", "3B"))
genoRxK <- cross2int(genoRxK, impute = "Martinez", id = "Genotype")

# base model

rkyld.asf <- asreml(yld ~ lrow, random = ~ Genotype + Range,
                   residual = ~ ar1(Range):ar1(Row), data = phenoRxK)

# find QTL

rkyld.qtl <- wgaim(rkyld.asf, intervalObj = genoRxK, merge.by = "Genotype",
                   trace = "trace.txt", na.action = na.method(x = "include"))

# summarise

print(rkyld.qtl, genoRxK)
summary(rkyld.qtl, genoRxK)


## End(Not run)

wgaim documentation built on Oct. 3, 2019, 9:03 a.m.