read_vcf: Data Input VCF

In mappoly: Genetic Linkage Maps in Autopolyploids

Data Input VCF

Description

Reads an external VCF file and creates an object of class mappoly.data

Usage

read_vcf(
  file.in,
  parent.1,
  parent.2,
  ploidy = NA,
  filter.non.conforming = TRUE,
  thresh.line = 0.05,
  min.gt.depth = 0,
  min.av.depth = 0,
  max.missing = 1,
  elim.redundant = TRUE,
  verbose = TRUE,
  read.geno.prob = FALSE,
  prob.thres = 0.95
)

Arguments

file.in	a character string with the name of (or full path to) the input file which contains the data (VCF format)
parent.1	a character string containing the name of parent 1
parent.2	a character string containing the name of parent 2
ploidy	the species ploidy (optional, it will be automatically detected)
filter.non.conforming	if TRUE (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function segreg_poly for information on expected classes and their respective frequencies.
thresh.line	threshold used for p-values on segregation test (default = 0.05)
min.gt.depth	minimum genotype depth to keep information. If the genotype depth is below min.gt.depth, it will be replaced with NA (default = 0)
min.av.depth	minimum average depth to keep markers (default = 0)
max.missing	maximum proportion of missing data to keep markers (range = 0-1; default = 1)
elim.redundant	logical. If TRUE (default), removes redundant markers during map construction, keeping them annotated to export to the final map.
verbose	if TRUE (default), the current progress is shown; if FALSE, no output is produced
read.geno.prob	If genotypic probabilities are available (PL field), generates a probability-based dataframe (default = FALSE).
prob.thres	probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than prob.thres are considered as missing data for the dosage calling purposes (default = 0.95)

Details

This function can handle .vcf files versions 4.0 or higher. The ploidy can be automatically detected, but it is highly recommended that you inform it to check for mismatches. All individual and marker names will be kept as they are in the .vcf file.

Value

An object of class mappoly.data which contains a list with the following components:

ploidy	ploidy level
n.ind	number individuals
n.mrk	total number of markers
ind.names	the names of the individuals
mrk.names	the names of the markers
dosage.p1	a vector containing the dosage in parent P for all n.mrk markers
dosage.p2	a vector containing the dosage in parent Q for all n.mrk markers
chrom	a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
genome.pos	Physical position of the markers into the sequence
seq.ref	Reference base used for each marker (i.e. A, T, C, G)
seq.alt	Alternative base used for each marker (i.e. A, T, C, G)
prob.thres	(unused field)
geno.dose	a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1
geno	a dataframe containing all genotypic probabilities columns for each marker and individual combination (rows). Missing data are represented by ploidy_level + 1
nphen	(unused field)
phen	(unused field)
all.mrk.depth	DP information for all markers on VCF file
chisq.pval	a vector containing p-values related to the chi-squared test of Mendelian segregation performed for all markers
kept	if elim.redundant = TRUE, holds all non-redundant markers
elim.correspondence	if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Gabriel Gesteira, [e-mail]

References

Mollinari M., Olukolu B. A., Pereira G. da S., Khan A., Gemenet D., Yencho G. C., Zeng Z-B. (2020), Unraveling the Hexaploid Sweetpotato Inheritance Using Ultra-Dense Multilocus Mapping, _G3: Genes, Genomes, Genetics_. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1534/g3.119.400620")}

Mollinari, M., and Garcia, A. A. F. (2019) Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models, _G3: Genes, Genomes, Genetics_. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1534/g3.119.400378")}

Examples

## Hexaploid sweetpotato: Subset of chromosome 3
fl = "https://github.com/mmollina/MAPpoly_vignettes/raw/master/data/sweet_sample_ch3.vcf.gz"
tempfl <- tempfile(pattern = 'chr3_', fileext = '.vcf.gz')
download.file(fl, destfile = tempfl)
dat.dose.vcf = read_vcf(file = tempfl, parent.1 = "PARENT1", parent.2 = "PARENT2")
print(dat.dose.vcf)
plot(dat.dose.vcf)

mappoly documentation built on May 29, 2024, 6:05 a.m.