PedigreeAnalysis {FamAgg} | R Documentation

## Pedigree analysis and familial aggregation methods

### Description

Various functions to perform pedigree analyses and to investigate familial clustering of e.g. cancer cases.

### Usage

    binomialTest(object, trait, traitName, global = FALSE, prob = NULL,
        alternative = c("greater", "less", "two.sided"))

    estimateTimeAtRisk(startDate=NULL, startDateFormat="%Y-%m-%d",
                       endDate=NULL, endDateFormat="%Y-%m-%d",
                       incidenceDate=NULL, incidenceDateFormat="%Y-%m-%d",
                       deathDate=NULL, deathDateFormat="%Y-%m-%d",
                       allowNegative=FALSE, affected=NULL,
                       incidenceSubtract=0.5)

    factor2matrix(x)

    ## S4 method for signature 'FAData'
    familialIncidenceRate(object, trait=NULL,
                                             timeAtRisk=NULL)

    ## S4 method for signature 'FAData'
    familialIncidenceRateTest(object, trait=NULL,
                                                nsim=50000, traitName=NULL,
                                                timeAtRisk=NULL,
                                                strata=NULL, ...)

    ## S4 method for signature 'FAData'
    fsir(object, trait=NULL, lambda=NULL, timeInStrata=NULL)

    ## S4 method for signature 'FAData'
    fsirTest(object, trait=NULL, nsim=50000, traitName=NULL,
                                lambda=NULL, timeInStrata=NULL,
                                strata=NULL, ...)

    ## S4 method for signature 'FAData'
    genealogicalIndexTest(object, trait, nsim=50000,
                                             traitName, perFamilyTest=FALSE,
                                             controlSetMethod="getAll",
                                             rm.singletons=TRUE, strata=NULL, ...)

    ## S4 method for signature 'FAData'
    kinshipGroupTest(object, trait, nsim=50000,
                                        traitName, strata=NULL, ...)

    ## S4 method for signature 'FAData'
    kinshipSumTest(object, trait, nsim=50000,
                                      traitName, strata=NULL, ...)

    ## S4 method for signature 'FAData'
    probabilityTest(object, trait, cliques,
                                       nsim=50000, traitName,
                                       ...)

    sliceAge(x, slices=c(0, 40, Inf))

### Arguments

(in alphabetic order)

`affected` | For `estimateTimeAtRisk`: optional parameter specifying which of the individuals are affected. This is useful if only `endDate` is specified, but not the `incidenceDate`. See method description for further details.
`allowNegative` | For `estimateTimeAtRisk`: if `FALSE` any negative time periods are set to `0`.
`alternative` | For `binomialTest`: the alternative hypothesis. See `binom.test` for more details. Defaults to `"greater"`, i.e. tests whether in a family a larger number of affected is present than expected by chance (given a global probability).
`cliques` | A named numeric or characted vector or factor with the names corresponding to ids of the individuals in the pedigree. The ids will be internally matched and sub-set to the ids available in the pedigree.
`controlSetMethod` | For `genealogicalIndexTest`: the method (i.e. name of the function) that should be used to define the set of (eventually matched) control individuals from which the random samples are taken. Supported functions are `getAll`, `getSexMatched` and `getExternalMatched`. For `perFamilyTest=TRUE` also `getGenerationMatched` and `getGenerationSexMatched` are supported. Note: for `getExternalMatched`, a numeric, character or factor vector to be used for the matching has to be submitted as additional argument `match.using`.
`deathDate` | For `estimateTimeAtRisk`: the date of death.
`deathDateFormat` | For `estimateTimeAtRisk`: the format in which the dates are submitted. See `as.Date` for more information.
`endDate` | For `estimateTimeAtRisk`: the end date, which can be the end date for the study or, if `deathDate` and `incidenceDate` are not specified, the earliest time point of: date of incidence, death or end of study.
`endDateFormat` | For `estimateTimeAtRisk`: the format in which the dates are submitted. See `as.Date` for more information.
`global` | For `binomialTest`: whether the binomial test should be applied to the whole pedigree, or family-wise (default). If `global = TRUE` the population probability has to be provided with parameter `prob`.
`incidenceDate` | For `estimateTimeAtRisk`: the date of the incidence for an individual, i.e. the date when the status was changed from un-affected to affected in the to be analyzed trait.
`incidenceDateFormat` | For `estimateTimeAtRisk`: the format in which the dates are submitted. See `as.Date` for more information.
`incidenceSubtract` | For `estimateTimeAtRisk`: the amount of time (of the time unit of the time at risk) that should be subtracted from the calculated time at risk for affected individuals. See method description below for more details.
`lambda` | Numeric vector with the incidence rates per stratum from the population. The length of this vector has to match the number of columns of argument `timeInStrata`.
`nsim` | The number of simulations.
`object` | The `FAData` object.
`perFamilyTest` | For `genealogicalIndexTest`: whether the test should be performed on the whole pedigree (default) or separately within each family. In the latter case the test evaluates the presence of clustered affected individuals within each family.
`prob` | For `binomialTest`: the hypothesized probability of success (being affected) from/for the whole population.
`rm.singletons` | For `genealogicalIndexTest`: whether unconnected individuals in the pedigree (singletons) should be removed from the pedigree prior to the analysis.
`slices` | For `sliceAge`: a numeric vector defining the age-slices. Similar to argument `vec` for `findInterval`. Defines the minimum and maximum age for the age slices, i.e. first number corresponds to the lower boundary of the first age slice, the second number to the upper boundary of the first and lower boundary of the second age slice and so on.
`startDate` | For `estimateTimeAtRisk`: the date of the start of the study. Can also be the birth date.
`startDateFormat` | For `estimateTimeAtRisk`: the format in which the dates are submitted. See `as.Date` for more information.
`strata` | For `genealogicalIndexTest`, `kinshipGroupTest` and `kinshipSumTest`: a numeric, character or factor characterizing each individual in the pedigree. The length of this vector and the ordering has to match the pedigree. This vector allows to perform stratified random sampling. See details for more information.
`timeAtRisk` | A numeric vector specifying the time at risk for each individual. The definition for this variable is taken from Kerber (1995). See description of the method below for more information. `timeAtRisk` has to have the same number of elements than there are individuals in the pedigree and it is assumed that the ordering of the vector matches the order of the individuals in the pedigree.
`timeInStrata` | For `fsir` and `fsirTest`: a numeric matrix specifying the time at risk for each individual in each strata. Columns represent the strata, rows the individuals, each cell the time at risk for the individual in the respective strata.
`trait` | A named numeric vector (values `0`, `1` and `NA`) or logical vector (values `FALSE`, `TRUE` and `NA`) with the names matching the ids of the individuals in the pedigree. The method internally matches and re-orders the trait vector to match the ordering of the ids in the pedigree. If trait is not specified, the trait information stored within the `FAData` object is used.
`traitName` | The name of the trait (optional).
`x` | For `sliceAge`: a numeric vector representing the age of individuals. For `factor2matrix`: a factor that should be converted into a matrix.
`...` | For `genealogicalIndexTest`: additional arguments passed to the choosen `controlSetMethod` function (e.g. `match.using` for `getExternalMatched`). For `familialIncidenceRateTest`: use `lowMem=TRUE` for very large pedigrees. This will use a faster and less memory demanding p-value estimation.

### Details

Stratified sampling: some of the familial aggregation methods allow to use stratified sampling for the Monte Carlo simulations. In stratified sampling, the same number of random samples will be selected within each class/stratum then there are among the affected. As example, if 5 female and 2 male individuals are affected in the analysed trait and sex stratified sampling is performed, in each permuatation the same number of random samples in each group (i.e. 5 females and 2 males) are selected.

A note on singletons: for all per-individual measures, unconnected individuals within the pedigree are automatically excluded from the calculations as no kinship based statistic can be estimated for them since they do, by definition, not share kinship with any other individual in the pedigree.

### Value

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

### Familial aggregation methods

binomialTest

  Evaluate whether the number of affected in a trait are higher than expected by chance using a simple binomial test. In contrast to most other methods presented here, this does not use the kinship between affected individuals, but simply performs a binomial test for each family considering the numbers of affected within the family, the size of the family and the global probability of being affected. The latter is by default calculated on the data set (ratio between the total number of affected in the pedigree and the total number of phenotyped individuals), can however also be specified with the `prob` argument.

  The test is performed using the `binom.test`.

  The function returns a `FABinTestResults` object.

familialIncidenceRate

  Calculate the familial incidence rate (FIR, or FR) as defined in [Kerber 1995], formula (3). The FIR is an estimate for the risk per gene-time for each individual for a certain disease (trait) given the disease experience in the cohort. The measure considers the kinship of each individual with any affected individual in the pedigree and the time at risk for each individual.

  Internally, the function first excludes individuals from the test which have a missing value (`NA`) either in the argument `trait` or in the argument `timeAtRisk`. Next, the thus reduced pedigree, is further cleaned by removing all resulting singletons (i.e. individuals that do not share kinship with any other individual in the above reduced data set).

  The method returns a vector with the FIR value for each individual. Individuals that were excluded from the test as described above habe an FIR of `NA`.

familialIncidenceRateTest

  Calculates the familial incidence rate for each individual and in addition assesses the significance of these based on Monte Carlo simulations. See `FAIncidenceRateResults` for more details.

  The method returns a `FAIncidenceRateResults` object.

fsir

  Calculate the familial standardized incidence rate (FSIR) as defined in [Kerber, 1995], formula (4). The FSIR weights the disease status of relatives based on their degree of relatedness with the proband [Kerber, 1995]. Formally, the FSIR is defined as the standardized incidence ratio (SIR) or standardized morality ratio in epidemiology, i.e. as the ratio between the observed and expected number of cases, only that both are in addition also weighted by the degree of relatedness (i.e. kinship value) between individuals in the pedigree.

  Similar to `familialIncidenceRate`, the function excludes individuals with missing values in any of the arguments `trait`, `timeInStrata` (and optionally `strata`) and all individuals that do not share any kinship with any other individual in the pedigree after removing the above individuals.

  The method returns a vector with the FSIR value for each individual. Individuals excluded as above describe have a FSIR value of `NA`.

fsirTest

  Calculates the familial standardized incidence rate (FSIR) for each individual and in addition assesses the significance of these based on Monte Carlo simulations. See `FAStdIncidenceRateResults` for more details.

  The method returns a `FAStdIncidenceRateResults` object.

genealogicalIndexTest

  Performs the genealogical index analysis from [Hill 1980] (also known as the genealogical index of familiality or `genetic index of familiality`) to identify familial clustering of traits (e.g. cancers etc).

  This test calculates the mean kinship among affected individuals in a pedigree along with mean kinships of equal sized random control sets drawn form the pedigree. The distribution of average kinship values among these random sets is used to estimate the probability that the observed mean kinship between the affected individuals is due to chance. The `controlSetMethod` argument allows to specify the method to define sets of matched control individuals in a pedigree or family.

  Note that by default singletons (i.e. unconnected individuals in the pedigree) are removed from the pedigree prior the analysis. Set `rm.singletons=FALSE` if you do not want them to be removed.

  The method can also be performed separately for each family within the larger pedigree (`perFamilyTest=TRUE` to evaluate the presence of clustered affected within each family). In this case it is also possible to use `controlSetMethod="getGenerationMatched"` or `controlSetMethod="getGenerationSexMatched"`, which allows to draw random control samples from the same generation(s) than the affected are.

  Stratified random sampling can be performed with the `strata` argument. See details for more information.

  The function returns a `FAGenIndexResults` object.

kinshipGroupTest

  Performs a familial aggregation test on a subset of a family. The idea behind this test is to narrow down the set of controls for each affected individual by considering only individuals that are as closely related as the most distant affected individual. This strategy incorporates more the family structure of the cases and is meant to be an alternative to the `kinshipSumTest` method.

  Initially, for an affected individual i a group C(i) is created that contains all individuals that share kinship as far as the most distantly related affected individual. This cluster can be interpreted as a circle that is centered at individual i with radius equal to the most distantly related case. Therefore, the cluster defines a narrowed, individual-specific set of individuals in which the phenotype is assumed to have been passed on. Groups consisting of the same set of affected individuals are reduced to a single group (i.e. the group with the smallest total number of individuals).

  Based on this definition of groups C(i), we compute two statistics by performing Monte Carlo simulations (which optionally allow to perform stratified random sampling). During each simulation step affected cases are randomly sampled from the population.

  1. The ratio test counts per group C(i) the number of times we observe a higher number of affected individuals in the simulation than in the observed case. Dividing this number by the number of simulation steps yields immediately the p-value that describes the event to observe by chance a higher number of affected individuals than in the given case.

  2. The kinship test addresses the degree of relatedness within the simulated set by a counting method where we count the number of times in a simulation step there is a pair of affected individuals that are more closely related than in the observed group C(i). In case the closest degree of relatedness is equal in both the simulation step and the observed case, we look at the number of pairs found in both and count it if this number is higher in the simulation step. Again, dividing this count by the number of simulation steps readily yields a p-value.

  See also the method `runSimulation` for `FAKinGroupResults`.

  The function returns a `FAKinGroupResults` object.

kinshipSumTest

  Performs a test for familial aggregation based on the sum of kinship values between affected cases. This test highlights individuals that exhibit a higher than chance relationship to other affected individuals, therefore highlighting individuals within families aggregating the phenotype. To achieve this, for each affected individual the sum of kinship values to all other affected cases is computed. In a Monte Carlo simulation this is repeated with the same number of cases (and optionally stratified with the `strata` argument), and the resulting background distribution is used to compute p-values for the kinship sums obtained from the observed cases. See also the method `runSimulation` for `FAKinSumResults`.

  The function returns a `FAKinSumResults` object.

probabilityTest

  DEPRECATED: this test will be removed in Bioconductor version 3.8 due to problems and incompatibilities of the `gap` package on MS Windows systems.

  This is only a convience method that calls the `gap` package's method `pfc.sim` to compute probabilities of familial clustering of phenotypes [Yu and Zelterman (2002)]. One drawback of that method is that it is limited to families with at most 22 individuals. Thus, pedigrees need to be split with specialized software such as Jenti [Falchi and Fuchsberger ea. (2008)], which within large families define cliques that can then be used as input to this algorithm.

  See also method `runSimulation` for `FAProbResults`.

  The function returns a `FAProbResults` object.

### Utility functions

factor2matrix

  Converts a factor into a matrix with columns corresponding to the levels and values (cell row i, column j) being either 0 or 1 depending on whether the ith factor was of the level j. See examples below for in or `FAStdIncidenceRateResults`.

estimateTimeAtRisk

  Function to calculate the time at risk based on the start date of the study or the birth date of an individual (`startDate`) and the study's end date (`endDate`), the date of an incidence (e.g. date of diagnosis of a cancer `incidenceDate`) or the death of the individual (`deathDate`). The time at risk for each individual is calculated as the minimal time period between `startDate` and any of `endDate`, `incidenceDate` or `deathDate`. Thus it is also possible to provide just the `endDate` along with the `startDate`, in which case the `endDate` should be the earliest time point of: end date of the study, incidence date or date of death.

  For affected individuals (those for which either an incidence date is provided or the value in the optional argument `affected` is `TRUE` or bigger than 0), by default half of the time unit is subtracted. For example, a individual that has an incidence after 2 days is 1.5 days at risk. The proportion of the time unit to subtract can be specified with the argument `incidenceSubtract`.

  The function returns a numeric vector with the time at risk in days.

sliceAge

  Generates a matrix with columns corresponding to age slices/strata defined by argument `slices` and rows to individuals. Each cell in a row represents the time spent by the individual in the age slice/strata. See example below.

### Author(s)

Johannes Rainer, Daniel Taliun, Christian Weichenberger.

### References

Rainer J, Talliun D, D'Elia Y, Domingues FS and Weichenberger CX (2016) FamAgg: an R package to evaluate familial aggregation of traits in large pedigrees. Bioinformatics.

Hill, J.R. (1980) A survey of cancer sites by kinship in the Utah Mormon population. In Cairns J, Lyon JL, Skolnick M (eds): Cancer Incidence in Defined Populations. Banbury Report 4. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, pp 299–318.

Kerber, R.A. (1995) Method for calculating risk associated with family history of a disease. Genet Epidemiol, pp 291–301.

Yu, C. and Zelterman, D. (2002) Statistical inference for familial disease clusters. Biometrics, pp 481–491

Falchi, M. and Fuchsberger, C. (2008) Jenti: an efficient tool for mining complex inbred genealogies. Bioinformatics, pp 724–726

### See Also

`pedigree`, `FAData`, `FAProbResults`, `FAKinGroupResults`, `FAKinSumResults`, `FAIncidenceRateResults`

### Examples

    ##########################
    ##
    ##  Defining a small pedigree
    ##
    ## load the Minnesota Breast Cancer record and subset to the
    ## first families.
    data(minnbreast)
    mbsub <- minnbreast[minnbreast$famid==4 | minnbreast$famid==5 |
                        minnbreast$famid==14 | minnbreast$famid==8, ]
    mbped <- mbsub[, c("famid", "id", "fatherid", "motherid", "sex")]
    ## renaming column names
    colnames(mbped) <- c("family", "id", "father", "mother", "sex")
    ## create the FAData object
    fad <- FAData(pedigree=mbped)

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

    ##########################
    ##
    ## Familial Incidence Rate
    ##
    ## Calculate the FR for each individual given the affected status of
    ## each individual in trait cancer and the time at risk for each
    ## participant. We use column "endage" in the minnbreast data.frame
    ## that specifies the age at the last follow-up or incident cancer as a
    ##rather impresice estimate for time at risk.
    fr <- familialIncidenceRate(fad, trait=tcancer, timeAtRisk=mbsub$endage)

    ## Plot the distribution of familial rates
    plot(density(fr, na.rm=TRUE))

    ## Perform in addition Monte Carlo simulations to assess the significance
    ## for the familial incidence rates.
    frRes <- familialIncidenceRateTest(fad, trait=tcancer,
                                       timeAtRisk=mbsub$endage,
                                       nsim=500)
    head(result(frRes))

    ##########################
    ##
    ## Familial Standardized Incidence Rate:
    ## Please see examples of FAStdIncidenceRateResults.

    ##########################
    ##
    ##  Perform familial aggregation analyses using the genealogical index
    ##
    gi <- genealogicalIndexTest(fad, trait=tcancer, traitName="cancer",
                                nsim=500)
    result(gi)
    ## A significant clustering of cancer cases was identified in the
    ## analyzed pedigree.

    ## Plotting the observed mean kinship and the distribution of mean kinship
    ## from the random sampling.
    plotRes(gi)

    ##########################
    ##
    ##  Perform familial aggregation analysis using the kinship sum test
    ##
    kcr <- kinshipSumTest(fad, trait=tcancer, traitName="cancer",
                          nsim=500)
    kcr
    head(result(kcr))

    ##########################
    ##
    ##  Perform familial aggregation analysis using the kinship group test,
    ##  stratifying by sex
    ##
    kr <- kinshipGroupTest(fad, trait=tcancer, traitName="cancer",
                           nsim=500, strata=fad$sex)
    kr
    head(result(kr))

    ##########################
    ##
    ##  Estimate the time at risk given
    ##
    ## Define some birth dates and incidence dates and end date of study
    bdates <- c("2012-04-17", "2014-05-29", "1999-12-31", "2002-10-10")
    idates <- c(NA, NA, "2007-07-13", "2013-12-23")
    edates <- rep("2015-09-15", 4)

    ## Estimate the time at risk. The time period is returned in days.
    riskDays <- estimateTimeAtRisk(startDate=bdates, incidenceDate=idates, endDate=edates)
    riskDays

    ##########################
    ##
    ##  Define the time spent in an age stratum given the indivduals'
    ##  age at incidence or end of study.
    head(mbsub$endage)
    ## We "slice" the age in specified intervals/slices
    stratAge <- sliceAge(mbsub$endage, slices=c(0, 40, 60, Inf))
    head(stratAge)

    ## The first column lists the number of years spent in the first age
    ## stratum (0 < age <= 40) and the second in the second stratum
    ## (40 < age <= Inf)

    ## We could also stratify the disk days from above in per year strata.
    sliceAge(riskDays/365, slices=c(0, 2.5, 5, 10, 20))

    ##########################
    ##
    ##  Simple example for factor2matrix: generate a matrix for factor $sex
    head(factor2matrix(fad$sex))

[Package FamAgg version 1.16.0 Index]