gpmap (version 0.1.2)

decompose_monotone: Decompose genotype-phenotype map(s) using monotone regression

Description

Functions for decomposing genotype-phenotype (GP) maps with \(N\) biallelic loci using monotone regression from the isotone package.

Usage

decompose_monotone(gpmap)
decompose_monotone_single(gpmap)

Arguments

gpmap

A gpmap object

Value

The input gpmap is returned with two added elements

monoR2

The coefficient of determination of the monotone regression

values.mono

A matrix of genotypic values for the monotone component of genotype-phenotype map(s)

Details

decompose_monotone works for any gpmap object (values is \((3^N)xK\) matrix of genotypic values) and calls the internal function decompose_monotone_single for each column. decompose_monotone_single takes a gpmap object with a single set of genotypic values (\(K=1\)), loops through all \(2^N\) possible combinations of plusalleles, calls monotone_regression and identifies the best fit. The code uses the foreach package and will run in parallel if a parallel backend is registered (see foreach documentation).

References

Leeuw J, Hornik K and Mair P (2009) Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods. Journal of Statistical Software 32(5) [link]

Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Front. Genet. 4:216. doi: 10.3389/fgene.2013.00216 [link]

Examples

# NOT RUN {
data(GPmaps)

#Additive GP map is monotone so monoR2=1 and values.mono=values
decompose_monotone(A)

#Pure AxA epistasis map
decompose_monotone(AA)

#two-locus example in Cheverud & Routman (1995)
decompose_monotone(mouseweight)

#decompose four random 3-locus GP maps
set.seed(0)
randomGP <- rnorm(3^2*4)
dim(randomGP) <- c(9,4)
decompose_monotone(generate_gpmap(randomGP))

# }