R: Fast score test for association

qtscore {GenABEL} | R Documentation

## Fast score test for association

### Description

Fast score test for association between a trait and genetic polymorphism

### Usage

      qtscore(formula, data, snpsubset, idsubset, strata,
        trait.type = "gaussian", times = 1, quiet = FALSE,
        bcast = 10, clambda = TRUE, propPs = 1, details = TRUE)

### Arguments

`formula` | Formula describing fixed effects to be used in analysis, e.g. y ~ a + b means that outcome (y) depends on two covariates, a and b. If no covariates used in analysis, skip the right-hand side of the equation.
`data` | An object of `gwaa.data-class`
`snpsubset` | ndex, character or logical vector with subset of SNPs to run analysis on. If missing, all SNPs from `data` are used for analysis.
`idsubset` | ndex, character or logical vector with subset of IDs to run analysis on. If missing, all people from `data/cc` are used for analysis.
`strata` | Stratification variable. If provieded, scores are computed within strata and then added up.
`trait.type` | "gaussian" or "binomial" or "guess" (later option guesses trait type)
`times` | If more than one, the number of replicas to be used in derivation of empirical genome-wide significance. See `emp.qtscore`, which calls qtscore with times>1 for details
`quiet` | do not print warning messages
`bcast` | If the argument times > 1, progress is reported once in bcast replicas
`clambda` | If inflation facot Lambda is estimated as lower then one, this parameter controls if the original P1df (clambda=TRUE) to be reported in Pc1df, or the original 1df statistics is to be multiplied onto this "deflation" factor (clambda=FALSE). If a numeric value is provided, it is used as a correction factor.
`propPs` | proportion of non-corrected P-values used to estimate the inflation factor Lambda, passed directly to the `estlambda`
`details` | when FALSE, SNP and ID names are not reported in the returned object (saves some memory). This is experimental and will be not mantained anymore as soon as we achieve better memory efficiency for storage of SNP and ID names (currently default R character data type used)

### Details

When formula contains covariates, the traits is analysed using GLM and later residuals used when score test is computed for each of the SNPs in analysis. Coefficients of regression are reported for the quantitative trait.

For binary traits, odds ratios (ORs) are reportted. When adjustemnt is performed, first, "response" residuals are estimated after adjustment for covariates and scaled to [0,1]. Reported effects are approximately equal to ORs expected in logistic regression model.

With no adjustment for binary traits, 1 d.f., the test is equivalent to the Armitage test.

This is a valid function to analyse GWA data, including X chromosome. For X chromosome, stratified analysis is performed (strata=sex).

### Value

Object of class `scan.gwaa-class`

### Author(s)

Yurii Aulchenko

### References

Aulchenko YS, de Koning DJ, Haley C. Genomewide rapid association using mixed model and regression: a fast and simple method for genome-wide pedigree-based quantitative trait loci association analysis. Genetics. 2007 177(1):577-85.

Amin N, van Duijn CM, Aulchenko YS. A genomic background based method for association analysis in related individuals. PLoS ONE. 2007 Dec 5;2(12):e1274.

### See Also

`mlreg`, `mmscore`, `egscore`, `emp.qtscore`, `plot.scan.gwaa`, `scan.gwaa-class`

### Examples

    require(GenABEL.data)
    data(srdta)
    #qtscore with stratification
    a <- qtscore(qt3~sex,data=srdta)
    plot(a)
    b <- qtscore(qt3,strata=phdata(srdta)$sex,data=srdta)
    add.plot(b,col="green",cex=2)
    # qtscore with extra adjustment
    a <- qtscore(qt3~sex+age,data=srdta)
    a
    plot(a)
    # compare results of score and chi-square test for binary trait
    a1 <- ccfast("bt",data=srdta,snps=c(1:100))
    a2 <- qtscore(bt,data=srdta,snps=c(1:100),trait.type="binomial")
    plot(a1,ylim=c(0,2))
    add.plot(a2,col="red",cex=1.5)
    # the good thing about score test is that we can do adjustment...
    a2 <- qtscore(bt~age+sex,data=srdta,snps=c(1:100),trait.type="binomial")
    points(a2[,"Position"],-log10(a2[,"P1df"]),col="green")

[Package GenABEL version 1.8-0 Index]