Performing genetic association analysis between SAAPs and phenotypes

Description

This procedure quantifies the association between single amino acid polymorphisms (SAAPs) and phenotypes.

Usage

runGenphenSaap(genotype, phenotype, technique, fold.cv, boots)

Arguments

Details

This procedure takes two types of data as input: first a genotype data composed of a set of single amino acid polymorphisms (SAAPs), each of which is represented by a column of character amino acids; second a numerical phenotype vector, where the elements sorted to correspond to the rows of the genotype data.

Using these two data types, it quantifies the association between each SAAP and the phenotype. SAAPs are more complex than SNPs because they may be composed of more than two genetic states, i.e. more than two types of amino acid states, whereas SNPs are exclusively composed of only two genetics states, i.e. two nucleotide states. To compute the association between a SAAP and a phenotype, the SAAP is first deconstructed into its amino acid substitution pairs. Following the deconstruction of a SAAP, the procedure computes the association between each amino acid substitution pair and the phenotype with respect to the two metics “effect size” and “classification accuracy”.

The effect size of an amino acid substitution pair is estimated by computing the Cohen's d statistics (Cohen 1988). The 95% confidence intervals are computed as well. The effect size quantifies the phenotypic effect of substituting one amino acid state for the other at the specific SAAP site. Substitution pairs characterized with a substantial effect size and tight confidence intervals which do not include the null effect are to be prioritized.

Classification accuracy is the second metric which is computed using statistical learning techniques. This is the metric which is used to quantify the strength of the association between an amino acid substitution pair and a phenotype. The idea is to use either linear suppport vector machines or random forests to build a classification model between the phenotype vector and the substitution pair vector. The more accurate the model, the easier we can predict the two states of the substitution pair from the phenotype and hence the stronger is the mutual association between the two vectors. In order to obtain a robust classification accuracy measure, the classification analysis is done in a bootstrapping fashion. First a subset of the substitution- phenotype vectors is randomly selected to train a classifier, while the remaining data is used to test the classifier. This step is repeated multiple times after which the classification accuracies of all the classifiers are averaged into a single classification accuracy measure and the corresponding confidence intervals are computed.

In order to validate the classification accuracy, the tool also computes the Cohen's kappa statistics (Cohen 1960) which compares the observed classification accuracy with the expected classification accuracy. If the expected and observed classification accuracies are in concordance, the computed association can be taken seriously, otherwise it can be discarded as noise.

Value

Five classes of results are computed for each SAAP with respect to the phenotype, resulting in a 18 element vector which is stored as a row in the final data frame:

Author(s)

Simo Kitanovski <simo.kitanovski@uni-due.de>

References

Cohen, J. (1988) Statistical power analysis for the behavioral sciences (2nd ed.). New York:Academic Press.

Cohen, J. (1960) A coefficient of agreement for nominal scales.

See Also

runGenphenSnp, plotGenphenResults, plotSpecificGenotype, plotManhattan

Examples

data(genotype.saap)
#or data(genotype.saap.msa) in this case you cannot subset genotype.saap[, 1:3]
data(phenotype.saap)
genphen.results <- runGenphenSaap(genotype = genotype.saap[, 1:3],
phenotype = phenotype.saap, technique = "svm", fold.cv = 0.66, boots = 100)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(genphen)
Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
Loading required package: e1071
Loading required package: ggplot2

Attaching package: 'ggplot2'

The following object is masked from 'package:randomForest':

    margin

Loading required package: effsize
Loading required package: Biostrings
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following object is masked from 'package:randomForest':

    combine

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: XVector
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/genphen/genphenSAAP.Rd_%03d_medium.png", width=480, height=480)
> ### Name: runGenphenSaap
> ### Title: Performing genetic association analysis between SAAPs and
> ###   phenotypes
> ### Aliases: runGenphenSaap
> 
> ### ** Examples
> 
> data(genotype.saap)
> #or data(genotype.saap.msa) in this case you cannot subset genotype.saap[, 1:3]
> data(phenotype.saap)
> genphen.results <- runGenphenSaap(genotype = genotype.saap[, 1:3],
+ phenotype = phenotype.saap, technique = "svm", fold.cv = 0.66, boots = 100)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>