pcair: PC-AiR: Principal Components Analysis in Related Samples

Description

pcair is used to perform a Principal Components Analysis using genome-wide SNP data for the detection of population structure in a sample. Unlike a standard PCA, PC-AiR accounts for sample relatedness (known or cryptic) to provide accurate ancestry inference that is not confounded by family structure.

Usage

pcair(genoData, v = 20, kinMat = NULL, kin.thresh = 2^(-11/2),  divMat = NULL, div.thresh = -2^(-11/2), unrel.set = NULL,  scan.include = NULL, snp.include = NULL, chromosome = NULL, snp.block.size = 10000, MAF = 0.01, verbose = TRUE)
"print"(x, ...)
"summary"(object, ...)
"print"(x, ...)

Arguments

genoData

An object of class GenotypeData from the package GWASTools containing the genotype data for SNPs and samples to be used for the analysis. This object can easily be created from a matrix of SNP genotype data, PLINK files, or GDS files.

The number of principal components to be returned; the default is 20. If v = NULL, then all the principal components are returned.

kinMat

An optional symmetric matrix of pairwise kinship coefficients for every pair of individuals in the sample (the values on the diagonal do not matter, but the upper and lower triangles must both be filled) used for partitioning the sample into the 'unrelated' and 'related' subsets. See 'Details' for how this interacts with kin.thresh and unrel.set. IDs for each individual must be set as the row and column names of the matrix.

kin.thresh

Threshold value on kinMat used for declaring each pair of individuals as related or unrelated. The default value is 2^(-11/2) ~ 0.022. See 'Details' for how this interacts with kinMat.

divMat

An optional symmetric matrix of pairwise divergence measures for every pair of individuals in the sample (the values on the diagonal do not matter, but the upper and lower triangles must both be filled) used for partitioning the sample into the 'unrelated' and 'related' subsets. See 'Details' for how this interacts with div.thresh. IDs for each individual must be set as the row and column names of the matrix.

div.thresh

Threshold value on divMat used for deciding if each pair of individuals is ancestrally divergent. The default value is -2^(-11/2) ~ -0.022. See 'Details' for how this interacts with divMat.

unrel.set

An optional vector of IDs for identifying individuals that are forced into the unrelated subset. See 'Details' for how this interacts with kinMat.

scan.include

A vector of IDs for samples to include in the analysis. If NULL, all samples are included.

snp.include

A vector of SNP IDs to include in the analysis. If NULL, see chromosome for further details.

chromosome

A vector of integers specifying which chromosomes to analyze. This parameter is only considred when snp.include is NULL; if chromosome is also NULL, then all SNPs are included.

snp.block.size

The number of SNPs to read-in/analyze at once. The default value is 10000.

MAF

Minor allele frequency filter; any SNPs with MAF less than this value will be excluded from the analysis; the default value is 0.01.

verbose

Logical indicator of whether updates from the function should be printed to the console; the default is TRUE.

object

An object of class 'pcair', i.e. output from the pcair function.

...

Further arguments passed to or from other methods.

Value

vectors: A matrix of the top v principal components; each column is a principal component. Sample IDs are provided as rownames.
values: A vector of eigenvalues matching the top v principal components. These values are determined from the standard PCA run on the 'unrelated subset'.
sum.values: The sum of all the eigenvalues from the standard PCA run on the 'unrelated subset' (regardless of how many were returned).
rels: A vector of IDs for individuals in the 'related subset'.
unrels: A vector of IDs for individuals in the 'unrelated subset'.
kin.thresh: The threshold value used for declaring each pair of individuals as related or unrelated.
div.thresh: The threshold value used for determining if each pair of individuals is ancestrally divergent.
nsamp: The total number of samples in the analysis.
nsnps: The total number of SNPs used in the analysis, after filtering on MAF.
MAF: The minor allele frequency (MAF) filter used on SNPs.
call: The function call passed to pcair.
method: A character string. Either "PC-AiR" or "Standard PCA" identifying which method was used for computing principal components.

Details

The basic premise of PC-AiR is to partition the entire sample of individuals into an ancestry representative 'unrelated subset' and a 'related set', perform standard PCA on the 'unrelated subset', and predict PC values for the 'related subset'. We recommend using software that accounts for population structure to estimate pairwise kinship coefficients to be used in kinMat. Any pair of individuals with a pairwise kinship greater than kin.thresh will be declared 'related.' Kinship coefficient estimates from the KING-robust software are used as measures of ancestry divergence in divMat. Any pair of individuals with a pairwise divergence measure less than div.thresh will be declared ancestrally 'divergent'. Typically, kin.thresh and div.thresh are set to be the amount of error around 0 expected in the estimate for a pair of truly unrelated individuals. If divMat = NULL and kinMat is specified, the kinship coefficient estimates in kinMat will also be used as divergence measures in place of divMat. It is important that the order of individuals in the matrices kinMat and divMat match the order of individuals in the genoData. There are multiple ways to partition the sample into an ancestry representative 'unrelated subset' and a 'related subset'. If kinMat is specified and unrel.set = NULL, then the PC-AiR algorithm is used to find an 'optimal' partition (see 'References' for a paper describing the algorithm). If kinMat = NULL and unrel.set is specified, then the individuals with IDs in unrel.set are used as the 'unrelated subset'. If both kinMat and unrel.set are specified, then all individuals with IDs in unrel.set are forced in the 'unrelated subset' and the PC-AiR algorithm is used to partition the rest of the sample; this is especially useful for including reference samples of known ancestry in the 'unrelated subset'. If kinMat = NULL and unrel.set = NULL, then a standard principal components analysis that does not account for relatedness is performed.

References

Conomos M.P., Miller M., & Thornton T. (2015). Robust Inference of Population Structure for Ancestry Prediction and Correction of Stratification in the Presence of Relatedness. Genetic Epidemiology, 39(4), 276-293. Gogarten, S.M., Bhangale, T., Conomos, M.P., Laurie, C.A., McHugh, C.P., Painter, I., ... & Laurie, C.C. (2012). GWASTools: an R/Bioconductor package for quality control and analysis of Genome-Wide Association Studies. Bioinformatics, 28(24), 3329-3331. Manichaikul, A., Mychaleckyj, J.C., Rich, S.S., Daly, K., Sale, M., & Chen, W.M. (2010). Robust relationship inference in genome-wide association studies. Bioinformatics, 26(22), 2867-2873.

Examples

Run this code

# file path to GDS file
gdsfile <- system.file("extdata", "HapMap_ASW_MXL_geno.gds", package="GENESIS")
# read in GDS data
HapMap_geno <- GdsGenotypeReader(filename = gdsfile)
# create a GenotypeData class object
HapMap_genoData <- GenotypeData(HapMap_geno)
# load saved matrix of KING-robust estimates
data("HapMap_ASW_MXL_KINGmat")
# run PC-AiR
mypcair <- pcair(genoData = HapMap_genoData, kinMat = HapMap_ASW_MXL_KINGmat, 
                divMat = HapMap_ASW_MXL_KINGmat)
close(HapMap_genoData)

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