GenomeAdapt: Detecting signatures of local adaptation based on...

In GenomeAdapt: Detecting Signatures of Local Adaptation Based on Ancestry Trajectories

Description

This function implements genome scan to identify the signatures of local adaptation. See details.

Usage

GenomeAdapt(genfile, method = "EIGMIX", sample.id = NULL, snp.id =
                    NULL, autosome.only = TRUE, remove.monosnp = TRUE, maf
                    = NaN, missing.rate = NaN, num.thread = 1L, out.fn =
                    NULL, out.prec = c("double", "single"), out.compress =
                    "LZMA_RA", with.id = TRUE, verbose = TRUE,...)

## S3 method for class 'GenomeAdapt.bed'
GenomeAdapt.bed(genfile, method = "EIGMIX", sample.id = NULL, snp.id =
                    NULL, autosome.only = TRUE, remove.monosnp = TRUE, maf
                    = NaN, missing.rate = NaN, num.thread = 1L, out.fn =
                    NULL, out.prec = c("double", "single"), out.compress =
                    "LZMA_RA", with.id = TRUE, verbose = TRUE, ...)

## S3 method for class 'GenomeAdapt.vcf'
GenomeAdapt.vcf(genfile, method = "EIGMIX", sample.id = NULL, snp.id =
                    NULL, autosome.only = TRUE, remove.monosnp = TRUE, maf
                    = NaN, missing.rate = NaN, num.thread = 1L, out.fn =
                    NULL, out.prec = c("double", "single"), out.compress =
                    "LZMA_RA", with.id = TRUE, verbose = TRUE, ...)

## S3 method for class 'GenomeAdapt.gds'
GenomeAdapt.gds(genfile, method = "EIGMIX", sample.id = NULL, snp.id =
                    NULL, autosome.only = TRUE, remove.monosnp = TRUE, maf
                    = NaN, missing.rate = NaN, num.thread = 1L, out.fn =
                    NULL, out.prec = c("double", "single"), out.compress =
                    "LZMA_RA", with.id = TRUE, verbose = TRUE, ...)

Arguments

genfile	Genotype file containg sample ID and SNP ID. Genotype format can be plink, vcf, or GDS file.
method	The method used to measure IBD. Default is "EIGMIX" according to Zheng, X., & Weir, B. S. (2016). "GCTA" - genetic relationship matrix defined in CGTA; "Eigenstrat" - genetic covariance matrix in EIGENSTRAT; "EIGMIX" - two times coancestry matrix defined in Zheng & Weir (2015), "Weighted" - weighted GCTA, as the same as "EIGMIX", "Corr" - Scaled GCTA GRM (dividing each i,j element by the product of the square root of the i,i and j,j elements), "IndivBeta" - two times individual beta estimate relative to the minimum of beta.
sample.id	a vector of sample id specifying selected samples; if NULL, all samples are used
snp.id	a vector of snp id specifying selected SNPs; if NULL, all SNPs are used
autosome.only	use autosomal SNPs only; if it is a numeric or character value, keep SNPs according to the specified chromosome
remove.monosnp	remove monomorphic SNPs
maf	filter SNPs with ">= maf" only; if NaN, no MAF threshold
missing.rate	filter the SNPs with "<= missing.rate" only; if NaN, no missing threshold
num.thread	the number of (CPU) cores used; if NA, detect the number of cores automatically
out.fn	NULL for no GDS output, or a file name
out.prec	double or single precision for storage
out.compress	the compression method for storing the GRM matrix in the GDS file
with.id	if TRUE, the returned value with sample.id and sample.id
verbose	if TRUE, show information
...	passing to other SNP filtering parameters

Details

The method estimates the z-score of each locus/allele relating to the multidimensional ancestry spaces. If there are n samples, there will be n X n ancestry genetic trajectories, with an eigen decomposition, producting n dimensional spaces that represent the common ancestry maps. This method was conceived combining the idea of KLFDAPT (Qin, 2021) https://xinghuq.github.io/KLFDAPC/articles/Genome_scan_KLFDAPC.html and IBD-based genome scan (Albrechtsen et al., 2010). With an eigenvector decomposition of IBD (Zheng & Weir 2016), we can estimate the population ancestry propotion. It is competitive to pcadapt (Luu, 2016), as it considers n latent genetic spaces (which is different from pcadapt that chooses k components from p eigenverctors).

Value

A GenomeAdapt class, containing the loci z-scores and the eigen analysis results of IBD representing the genetic structure.

zscores	The locus Z-scores relating to n latent ancestry genetic spaces, n is equal to the number of individuals
eig	Eigen analysis of IBD represent ancestry or population genetic structure
chr	Chromosomes

Author(s)

[e-mail]

References

Qin, X., Chiang, C. W., & Gaggiotti, O. E. (2021). Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC) significantly improves the accuracy of predicting geographic origin of individuals.bioRxiv.

Zheng, X., & Weir, B. S. (2016). Eigenanalysis of SNP data with an identity by descent interpretation. Theoretical population biology, 107, 65-76.

Albrechtsen, A., Moltke, I., & Nielsen, R. (2010). Natural selection and the distribution of identity-by-descent in the human genome. Genetics, 186(1), 295-308.

Duforet-Frebourg, N., Luu, K., Laval, G., Bazin, E., & Blum, M. G. (2016). Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 genomes data. Molecular biology and evolution, 33(4), 1082-1093.

Examples

### using an example dataset (HapMap)to conduct the genome scan

HapmapScan=GenomeAdapt.gds(genfile = SNPRelate::snpgdsExampleFileName(),method="EIGMIX",
num.thread = 1L, autosome.only=TRUE, remove.monosnp=TRUE, maf=0.01, missing.rate=0.1)

GenomeAdapt documentation built on Nov. 12, 2021, 1:06 a.m.