Binomial test for familial aggregation

The FABinTestResults object contains the results from a simple binomial to test whether the number of affected in a trait are higher than expected by chance. For more details on the method please see binomialTest.

# S4 method for FABinTestResults
result(object, method="BH")

# S4 method for FABinTestResults
trait(object) <- value

Arguments

method	The multiple hypothesis testing method. All methods supported by `p.adjust` are allowed.
object	The `FABinTestResults` object.
value	For `trait<-`: can be a named numeric, character or factor vector. The names (at least some of them) have to match the ids in the pedigree of the object.

Objects from the Class

FABinTestResults objects are returned by the binomialTest function.

Extends

Class FAData directly.

Slots

result: The results data.frame.

Methods and Functions

result: Returns the result from the test as a data.frame with columns: "trait_name": the name of the trait. "total_phenotyped": total number of phenotyped individuals in the trait. "total_affected": total number of affected individuals in the trait. "family": the family id. If a global test is used (i.e. if the pedigree consists of a single family, or global = TRUE was provided, the column shows "full pedigree"). phenotyped: the number of phenotyped individuals in the family. affected: the number of affected individuals in the family. pvalue: the p-value from the binomial test (conducted using the binom.test function). prob: the probability of being affected. Either a local probability calculated based on all affected and phenotyped individuals in the whole pedigree, or a global (population) probability that has to be provided with argument prob. padj: the p-value adjusted for multiple hypothesis testing using the method defined with argument "method".
trait<-: Set the trait information. This method will reset all simulation results saved in the sim slot.

Details

A call to the setter methods trait<- resets any test results present in the result slot, thus, the object can be re-used to perform a simulation analysis using the new trait data.

Note

Subsetting (using the [ operator) is not supported.

Value

Refer to the method and function description above for detailed information on the returned result object.

Examples

##########################
##
##  Perform the analysis
##
## Load the test data.
data(minnbreast)

## Subset to some families and generate a pedigree data.frame.
mbsub <- minnbreast[minnbreast$famid == 4 | minnbreast$famid == 5 |
                    minnbreast$famid == 6 | minnbreast$famid == 7 |
                    minnbreast$famid == 8, ]
PedDf <- mbsub[, c("famid", "id", "fatherid", "motherid", "sex")]
colnames(PedDf) <- c("family", "id", "father", "mother", "sex")

## Generate the FAData.
fad <- FAData(pedigree=PedDf)
#> Generating the kinship matrix...
#> OK

## Specify the trait.
tcancer <- mbsub$cancer
names(tcancer) <- mbsub$id

## Perform the test:
bir <- binomialTest(fad, trait=tcancer, traitName="cancer")
#> 178 of in total 178 trait values can be matched to IDs in the pedigree.
result(bir)
#>   trait_name total_phenotyped total_affected family phenotyped affected
#> 8     cancer              142             12      8         37        6
#> 4     cancer              142             12      4         36        3
#> 7     cancer              142             12      7         12        1
#> 5     cancer              142             12      5         24        1
#> 6     cancer              142             12      6         33        1
#>       pvalue       prob      padj
#> 8 0.08808022 0.08450704 0.4404011
#> 4 0.59639694 0.08450704 0.9457231
#> 7 0.65337483 0.08450704 0.9457231
#> 5 0.87985099 0.08450704 0.9457231
#> 6 0.94572307 0.08450704 0.9457231

## Calculating the probability of being affected from the whole data set.
prob <- sum(minnbreast$cancer, na.rm = TRUE) / sum(!is.na(minnbreast$cancer))
bir <- binomialTest(fad, trait = tcancer, prob = prob)
#> 178 of in total 178 trait values can be matched to IDs in the pedigree.
result(bir)
#>   trait_name total_phenotyped total_affected family phenotyped affected
#> 8         NA              142             12      8         37        6
#> 4         NA              142             12      4         36        3
#> 7         NA              142             12      7         12        1
#> 5         NA              142             12      5         24        1
#> 6         NA              142             12      6         33        1
#>       pvalue       prob      padj
#> 8 0.03503093 0.06701734 0.1751546
#> 4 0.43728809 0.06701734 0.8986490
#> 7 0.56500639 0.06701734 0.8986490
#> 5 0.81078056 0.06701734 0.8986490
#> 6 0.89864903 0.06701734 0.8986490

## Plot the pedigree of the family with the smallest p-value.
plotPed(bir, family = "8")
#> Did not plot the following people: 160 164 167 168 169 170 171 172 173 174