rehh: Searching for Footprints of Selection using 'Extended Haplotype Homozygosity' Based Tests

Description

Population genetic data such as 'Single Nucleotide Polymorphisms' (SNPs) is often used to identify genomic regions that have been under recent natural or artificial selection and might provide clues about the molecular mechanisms of adaptation. One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), introduced by (Sabeti 2002) <doi:10.1038/nature01140>, has given rise to several statistics designed for whole genome scans. The package provides functions to compute three of these, namely: 'iHS' (Voight 2006) <doi:10.1371/journal.pbio.0040072> for detecting positive or 'Darwinian' selection within a single population as well as 'Rsb' (Tang 2007) <doi:10.1371/journal.pbio.0050171> and 'XP-EHH' (Sabeti 2007) <doi:10.1038/nature06250>, targeted at differential selection between two populations. Various plotting functions are included to facilitate visualization and interpretation of these statistics.

Details

See vignette("rehh", package = "rehh") for an overview of the package and vignette("examples", package = "rehh") for a more detailed discussion of two small example data sets.

Author(s)

Maintainer: Alexander Klassmann rehh@oneoverx.eu

Authors:

• Mathieu Gautier mathieu.gautier@inrae.fr

• Renaud Vitalis

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128-3143.

Gautier M. and Vitalis R. (2012). rehh: An R package to detect footprints of selection in genome-wide SNP data from haplotype structure. Bioinformatics, 28(8), 1176-1177.

Gautier M., Klassmann A., and Vitalis R. (2017). rehh 2.0: a reimplementation of the R package rehh to detect positive selection from haplotype structure. Molecular Ecology Resources, 17, 78-90.

Klassmann, A. and Gautier, M. (2020). Detecting selection using Extended Haplotype Homozygosity-based statistics on unphased or unpolarized data (preprint). https://doi.org/10.22541/au.160405572.29972398/v1

Sabeti, P.C. et al. (2002). Detecting recent positive selection in the human genome from haplotype structure. Nature, 419, 832-837.

Sabeti, P.C. et al. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449, 913-918.

Tang, K. and Thornton, K.R. and Stoneking, M. (2007). A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome. Plos Biology, 7, e171.

Voight, B.F. and Kudaravalli, S. and Wen, X. and Pritchard, J.K. (2006). A map of recent positive selection in the human genome. Plos Biology, 4, e72.

See Also

Useful links:

• https://CRAN.R-project.org/package=rehh

• https://gitlab.com/oneoverx/rehh

• Report bugs at https://gitlab.com/oneoverx/rehh/-/issues

An S4 class containing furcation trees for one allele of a focal marker

Description

An S4 class containing the furcation trees for both sides of a focal marker for one allele.

Slots

allele

the allele of the focal marker.

description

"ancestral", "derived", "major", "minor", etc.

count

the number of chromosomes with that allele.

left

furcation tree to the left of the marker.

right

furcation tree to the right of the marker.

See Also

ftree, furcation

Convert a furcation tree into Newick format

Description

Convert a furcation tree into Newick format.

Usage

as.newick(furcation, allele = 0, side, hap.names = seq_len(furcation@nhap))

Arguments

See Also

ftree-class, calc_furcation, plot.furcation

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#calculate furcation for the marker "F1205400"
#which displays a strong signal of selection
f <- calc_furcation(haplohh_cgu_bta12, mrk = "F1205400")
#get left tree of ancestral allele (coded as '0')
as.newick(f, 0, "left")

Determine candidate regions of selection

Description

Determine candidate regions of selection.

Usage

calc_candidate_regions(
  scan,
  threshold = NA,
  pval = FALSE,
  ignore_sign = FALSE,
  window_size = 1e+06,
  overlap = 0,
  right = TRUE,
  min_n_mrk = 1,
  min_n_extr_mrk = 1,
  min_perc_extr_mrk = 0,
  join_neighbors = TRUE
)

Arguments

Details

There is no generally agreed method how to determine genomic regions which might have been under recent selection. Since selection tends to yield clusters of markers with outlier values, a common approach is to search for regions with an elevated number or fraction of outlier or extremal markers. This function allows to set three conditions a window must fulfill in order to classify as candidate region:

• min_n_mrk a minimum number of (any) markers.

• min_n_extr_mrk a minimum number of markers with outlier / extreme value.

• min_perc_extr_mrk a minimum percentage of extremal markers among all markers.

"Extreme" markers are defined by having a score above the specified threshold.

Value

A data frame with chromosomal regions, i.e. windows that fulfill the necessary conditions to qualify as candidate regions under selection. For each region the overall number of markers, their mean and maximum, the number of markers with extremal values, their percentage of all markers and their average are reported.

See Also

calc_region_stats

EHH and iHH computation for a given focal marker

Description

Compute Extended Haplotype Homozygosity (EHH) and integrated EHH (iHH) for a given focal marker.

Usage

calc_ehh(
  haplohh,
  mrk,
  limhaplo = 2,
  limhomohaplo = 2,
  limehh = 0.05,
  include_zero_values = FALSE,
  include_nhaplo = FALSE,
  phased = TRUE,
  polarized = TRUE,
  scalegap = NA,
  maxgap = NA,
  discard_integration_at_border = TRUE,
  lower_y_bound = limehh,
  interpolate = TRUE
)

Arguments

Details

Values for allele-specific Extended Haplotype Homozygosity (EHH) are computed upstream and downstream of the focal marker for each of its alleles. These values are integrated with respect to their genomic positions to yield an 'integrated EHH' (iHH) value for each allele.

Value

The returned value is a list containing the following elements:

mrk.name

The name/identifier of the focal marker.

freq

A vector with the frequencies of the alleles of the focal marker.

ehh

A data frame with EHH values for each allele of the focal marker.

ihh

A vector with iHH (integrated EHH) values for each allele of the focal marker.

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128-3143.

Klassmann, A. and Gautier, M. (2020). Detecting selection using Extended Haplotype Homozygosity-based statistics on unphased or unpolarized data (preprint). https://doi.org/10.22541/au.160405572.29972398/v1

Sabeti, P.C. et al. (2002). Detecting recent positive selection in the human genome from haplotype structure. Nature, 419, 832-837.

Sabeti, P.C. et al. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449, 913-918.

Tang, K. and Thornton, K.R. and Stoneking, M. (2007). A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome. Plos Biology, 7, e171.

Voight, B.F. and Kudaravalli, S. and Wen, X. and Pritchard, J.K. (2006). A map of recent positive selection in the human genome. Plos Biology, 4, e72.

See Also

data2haplohh, plot.ehh, calc_ehhs, scan_hh.

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#computing EHH statistics for the marker "F1205400"
#which displays a strong signal of selection
ehh <- calc_ehh(haplohh_cgu_bta12, mrk = "F1205400")

EHHS and iES computation for a given focal marker

Description

Compute site-specific Extended Haplotype Homozygosity (EHHS) and integrated EHHS (iES) for a given focal marker.

Usage

calc_ehhs(
  haplohh,
  mrk,
  limhaplo = 2,
  limhomohaplo = 2,
  limehhs = 0.05,
  include_zero_values = FALSE,
  include_nhaplo = FALSE,
  phased = TRUE,
  scalegap = NA,
  maxgap = NA,
  discard_integration_at_border = TRUE,
  lower_y_bound = limehhs,
  interpolate = TRUE
)

Arguments

Details

Values for site-specific Extended Haplotype Homozygosity (EHHS) are computed at each position upstream and downstream of the focal marker. These values are integrated with respect to their genomic position to yield an 'integrated EHHS' (iES) value.

Value

The returned value is a list containing the following elements:

mrk.name

The name/identifier of the focal marker.

ehhs

A table containing EHHS values as used by Sabeti et al. (2007), resp. the same values normalized to 1 at the focal marker (nEHHS) as used by Tang et al. (2007).

IES

Integrated EHHS.

INES

Integrated normalized EHHS.

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128-3143.

Klassmann, A. and Gautier, M. (2020). Detecting selection using Extended Haplotype Homozygosity-based statistics on unphased or unpolarized data (preprint). https://doi.org/10.22541/au.160405572.29972398/v1

Sabeti, P.C. et al. (2002). Detecting recent positive selection in the human genome from haplotype structure. Nature, 419, 832-837.

Sabeti, P.C. et al. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449, 913-918.

Tang, K. and Thornton, K.R. and Stoneking, M. (2007). A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome. Plos Biology, 7, e171.

Voight, B.F. and Kudaravalli, S. and Wen, X. and Pritchard, J.K. (2006). A map of recent positive selection in the human genome. Plos Biology, 4, e72.

See Also

data2haplohh, plot.ehhs, calc_ehh, scan_hh.

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#computing EHHS statistics for the marker "F1205400"
#which displays a strong signal of selection
ehhs <- calc_ehhs(haplohh_cgu_bta12, mrk = "F1205400")

calculate furcation trees around a focal marker

Description

Calculate furcation trees around a focal marker. A furcation tree captures in greater detail than EHH values the decrease of extended haplotype homozygosity at increasing distances from the selected focal marker.

Usage

calc_furcation(
  haplohh,
  mrk,
  allele = NA,
  limhaplo = 2,
  phased = TRUE,
  polarized = TRUE
)

Arguments

Details

A haplotype furcation tree visualizes the breakdown of LD at increasing distances from the focal marker. The root of each tree is an allele of the focal marker, which in turn is identified by a vertical dashed line. Moving either to the "left" or to the "right" of the focal marker, each further marker is an opportunity for a node; the tree either divides or does not, based on whether alleles at that marker distinguish between hitherto identical extended haplotypes. The thickness of the lines corresponds to the number of chromosomes sharing an extended haplotype.

Value

An object of class furcation, containing the furcation structure of the specified alleles at the focal marker.

References

Sabeti, P.C. and Reich, D.E. and Higgins, J.M. and Levine, H.Z.P and Richter, D.J. and Schaffner, S.F. and Gabriel, S.B. and Platko, J.V. and Patterson, N.J. and McDonald, G.J. and Ackerman, H.C. and Campbell, S.J. and Altshuler, D. and Cooper, R. and Kwiatkowski, D. and Ward, R. and Lander, E.S. (2002). Detecting recent positive selection in the human genome from haplotype structure. Nature, 419, 832-837.

See Also

plot.furcation, calc_haplen.

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#plotting a furcation diagram for both ancestral and derived allele
#from the marker "F1205400"
#which display a strong signal of selection
f <- calc_furcation(haplohh_cgu_bta12, mrk = "F1205400")
plot(f)

Calculate length of longest shared haplotypes around a focal marker

Description

Calculate for each chromosome the maximum length of its extended haplotype homozygosity.

Usage

calc_haplen(furcation)

Arguments

Details

Extended haplotype homozygosity is defined as the region around a focal marker in which a particular chromosome shares a haplotype with (its sequence is identical to) another chromosome. The function calculates for each chromosome the boundaries of its longest shared haplotype. These correspond to the last furcations of a chromsome in a furcation diagram. Note that the calculation is performed independently upstream and downstream of the focal marker and hence upper and lower boundaries do not necessarily arise from the same chromosomal pair.

Value

The functions returns a list containing four elements:

mrk.name

name/identifier of the focal marker.

position

position of the focal marker.

xlim

positions of left- and rightmost markers covered by extended haplotypes.

haplen

a data frame with the coordinates of extended haplotypes around the focal marker.

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#plotting haplotype lengths for both ancestral and derived allele
#of the marker "F1205400"
#which displays a strong signal of selection
f <- calc_furcation(haplohh_cgu_bta12, mrk = "F1205400")
h <- calc_haplen(f)
plot(h)

Calculate pairwise shared haplotype length between all chromosomes

Description

Calculate pairwise shared haplotype length between all chromosomes at a focal marker.

Usage

calc_pairwise_haplen(
  haplohh,
  mrk,
  phased = TRUE,
  maxgap = NA,
  max_extend = NA,
  side = "both"
)

Arguments

Details

The function computes the length of shared haplotypes (stretches of identical sequence) around the focal marker.

Note that the function calc_haplen calculates for each chromosome the boundaries of its longest shared haplotype; separately upstream and downstream of the focal marker.

Value

The returned value is a matrix with pairwise shared haplotype lengths.

See Also

data2haplohh, scan_hh_full.

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#computing shared haplotype lengths around the marker "F1205400"
#which displays a strong signal of selection
m <- calc_pairwise_haplen(haplohh_cgu_bta12, mrk = "F1205400")

Calculate score statistics for given regions

Description

Calculate score statistics (extremal values) for given regions. This function is intended for the comparison of different scores for the same chromosomal regions.

Usage

calc_region_stats(
  scan,
  regions,
  threshold = NA,
  pval = FALSE,
  ignore_sign = FALSE,
  right = TRUE
)

Arguments

Value

A data frame with chromosomal regions. For each region the overall number of markers, their mean and maximum, the number of markers with extremal values, their percentage of all markers and their average are reported.

See Also

calc_candidate_regions

Calculate site frequency spectrum test statistics

Description

Calculate site frequency spectrum (SFS) tests Tajima's D, Fay & Wu's H and Zeng's E.

Usage

calc_sfs_tests(
  haplohh,
  polarized = TRUE,
  window_size = NA,
  overlap = 0,
  right = TRUE,
  min_n_mrk = 1,
  verbose = TRUE
)

Arguments

Details

Neutrality tests based on the site frequency spectrum (SFS) are largely unrelated to EHH-based methods. The tests provided here are implemented elsewhere, too (e.g. in package PopGenome).

Each test compares two estimations of the scaled mutation rate theta, which all have the same expected value under neutrality. Deviations from zero indicate violations of the neutral null model, typically population size changes, population subdivision or selection. Tajima's D and Fay & Wu's H become negative in presence of an almost completed sweep, Zeng's E becomes positive for some time after it. Significance can typically be assigned only by simulations.

The standard definition of the tests cannot cope with missing values and typically markers with missing genotypes must be discarded. Ferretti (2012) provides an extension that can handle missing values (without discarding any non-missing values). In this package, only the first moments (the theta-estimators themselves) are adapted accordingly, but not the second moments (their variances), because the latter is computationally demanding and the resulting bias relatively small. It is recommended, though, to discard markers or haplotypes with more than 20% missing values.

Multi-allelic markers are always removed since the tests rely on the "infinite sites model" which implies that all polymorphic markers are bi-allelic. Monomorphic markers can be present, but are irrelevant for the tests.

Value

A data frame with window coordinates, the number of contained (polymorphic) markers, Watterson's, Tajima's and Zeng's estimators of theta and the test statistics of Tajima's D, Fay & Wu's H and Zeng's E.

References

Watterson, G.A. (1975). On the number of segregating sites in genetical models without recombination. Theoretical Population Biology 7(2) 256-276.

Tajima, F. (1983). Evolutionary relationship of DNA sequences in finite populations. Genetics 105(2) 437-60.

Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123(3) 585-95.

Fay, J. and Wu, C. (2000). Hitchhiking under positive Darwinian selection. Genetics 155(3) 1405-13.

Zeng, E. et al. (2006). Statistical tests for detecting positive selection by utilizing high-frequency variants. Genetics 174(3) 1431-9.

Ferretti, L. and Raineri, E. and Ramos-Onsins, S. (2012). Neutrality tests for sequences with missing data. Genetics 191(4) 1397-401.

Examples

make.example.files()
# neutral evolution
hh <- data2haplohh("example_neutral.vcf", verbose = FALSE)
calc_sfs_tests(hh)
# strong selective sweep
hh <- data2haplohh("example_sweep.vcf", verbose = FALSE)
calc_sfs_tests(hh)
remove.example.files()

Convert data from input file to an object of class haplohh

Description

Convert input data files to an object of haplohh-class.

Usage

data2haplohh(
  hap_file,
  map_file = NA,
  min_perc_geno.hap = NA,
  min_perc_geno.mrk = 100,
  min_maf = NA,
  chr.name = NA,
  popsel = NA,
  recode.allele = FALSE,
  allele_coding = "12",
  haplotype.in.columns = FALSE,
  remove_multiple_markers = FALSE,
  polarize_vcf = TRUE,
  capitalize_AA = TRUE,
  vcf_reader = "data.table",
  position_scaling_factor = NA,
  verbose = TRUE
)

Arguments

Details

Five haplotype input formats are supported:

• a "standard format" with haplotypes in rows and markers in columns (with no header, but a haplotype ID/name in the first column).

• a "transposed format" similar to the one produced by the phasing program SHAPEIT2 (O'Connell et al., 2014) in which haplotypes are in columns and markers in rows (with neither header nor marker IDs nor haplotype IDs).

• output files from the fastPHASE program (Sheet and Stephens, 2006). If haplotypes from several different population were phased simultaneously (-u fastPHASE option was used), it is necessary to specify the population of interest by parameter popsel (if this parameter is not or wrongly set, the error message will provide a list of the population numbers contained in the file).

• files in variant call format (vcf). No mapfile is needed is this case. If the file contains several chromosomes, it is necessary to choose one by parameter chr.name.

• output of the simulation program 'ms'. No mapfile is needed in this case. If the file contains several 'runs', a specific number has to be specified by the parameter chr.name.

The "transposed format" has to be explicitly set while the other formats are recognized automatically.

The map file contains marker information in three, or, if it is used for polarization (see below), five columns:

• marker name/id

• chromosome

• position (physical or genetic)

• ancestral allele encoding

• derived allele encoding

The markers must be in the same order as in the haplotype file. If several chromosomes are represented in the map file, it is necessary to choose that which corresponds to the haplotype file by parameter chr.name.

Haplotypes can be given either with alleles already coded as numbers (in two possible ways) or with the actual alleles (e.g. nucleotides) which can be translated into numbers either using the fourth and fifth column of the map file or by their alpha-numeric order. Correspondingly, the parameter allele_coding has to be set to either "12", "01", "map" or "none":

• "12": 0 represents missing values, 1 the ancestral allele and 2 (or higher integers) derived allele(s).

• "01": NA or '.' (a point) represent missing values, 0 the ancestral and 1 (or higher integers) derived allele(s).

• "map": for each marker, the fourth column of the map file defines the ancestral allele and the fifth column derived alleles. In case of multiple derived alleles, they must be separated by commas without space. Alleles in the haplotype file which do not appear in neither of the two columns of the map file are regarded as missing values (NA).

• "none": NA or '.' (a point) represent missing values, otherwise for each marker the allele that comes first in alpha-numeric order is coded by 0, the next by 1, etc. Evidently, this coding does not convey any information about allele status as ancestral or derived, hence the alleles cannot be regarded as polarized.

The information of allelic ancestry is exploited only in the frequency-bin-wise standardization of iHS (see ihh2ihs). However, although ancestry status does not figure in the formulas of the cross populations statistics Rsb and XP-EHH, their values do depend on the assigned status.

The arguments min_perc_geno.hap, min_perc_geno.mrk and min_maf are evaluated in this order.

Value

The returned value is an object of haplohh-class.

References

Scheet P, Stephens M (2006) A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet, 78, 629-644.

O'Connell J, Gurdasani D, Delaneau O, et al (2014) A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet, 10, e1004234.

Examples

#copy example files into the current working directory.
make.example.files()
#create object using a haplotype file in "standard format"
hap <- data2haplohh(hap_file = "bta12_cgu.hap",
                   map_file = "map.inp",
                   chr.name = 12,
                   allele_coding = "map")
#create object using fastPHASE output
hap <- data2haplohh(hap_file = "bta12_hapguess_switch.out",
                   map_file = "map.inp",
                   chr.name = 12,
                   popsel = 7,
                   allele_coding = "map")
#clean up demo files
remove.example.files()

Plot distribution of standardized iHS, Rsb or XP-EHH values

Description

Plot the observed distribution of standardized iHS, Rsb or XP-EHH values together with the standard Gaussian distribution.

Usage

distribplot(
  data,
  lty = 1,
  lwd = 1.5,
  col = c("blue", "red"),
  qqplot = FALSE,
  resolution = 0.01,
  ...
)

Arguments

Value

The function returns a plot.

See Also

ihh2ihs, ines2rsb, ies2xpehh, manhattanplot.

Examples

library(rehh.data)
#results from a genome scan (44,057 SNPs) see ?wgscan.cgu for details
data(wgscan.cgu)
#extract vector with iHS values from data frame
IHS <- ihh2ihs(wgscan.cgu)$ihs[["IHS"]]
distribplot(IHS, main = "iHS (CGU population)")
distribplot(IHS, main = "iHS (CGU population)", qqplot = TRUE)

Extract regions from a scan

Description

Extract regions from a scan data frame.

Usage

extract_regions(scan, regions, right = TRUE)

Arguments

Value

A subset of data frame scan, retaining only positions belonging to the regions specified in data frame regions.

Examples

library(rehh.data)
data(wgscan.cgu)
regions <- data.frame(CHR = 12, START = 2.88e+7, END = 2.92e+7)
extract_regions(wgscan.cgu, regions)

Plot of unstandardized iHS within frequency bins

Description

Plot of unstandardized iHS within frequency bins.

Usage

freqbinplot(
  x,
  spectrum = FALSE,
  main = NA,
  xlab = "Derived allele frequency",
  ylab = NA,
  xlim = c(0, 1),
  ylim = NULL,
  pch = 20,
  ...
)

Arguments

Details

The plot shows the mean and the quantiles calculated by function ihh2ihs for the unstandardized iHS in each frequency bin. Note that the standardization of iHS is performed bin-wise in order to reduce the frequency-dependence of iHS values (expected under neutrality). An implicit assumption of this procedure is that each bin is dominated by neutral markers.

See Also

ihh2ihs

Examples

library(rehh.data)
data(wgscan.cgu)
#results from a genome scan (44,057 SNPs)
#see ?wgscan.eut and ?wgscan.cgu for details
wgscan.cgu.ihs <- ihh2ihs(wgscan.cgu)
freqbinplot(wgscan.cgu.ihs)

An S4 class to represent a furcation tree on one side of one allele of a focal marker

Description

An S4 class to represent a furcation tree on one side of one allele of a focal marker

Details

A furcation structure consists of two trees ("left" and "right") for each allele of a focal marker. If there are only bi-allelic markers and no missing values, the trees are bifurcating.

Missing values are treated similarly to an extra allele in so far as they cause a furcation. However, the resulting daughter node is marked accordingly and the chromosomes excluded from further calculations. If all chromosomes of a parent node have missing values, the "furcation" is degenerated and yields a single daughter node.

Note that a tree with n leaves can have at most 2n-1 nodes.

In a furcation tree, the leaves do not necessarily represent single chromosomes, either due to multiple missing data or because the first/last marker was reached before all extended haplotypes were distinct.

Slots

node_parent

a vector, representing the tree structure. Each node (number) is assigned its parent node (number).

node_pos

a vector, assigning to each node (number) its position in the chromosome, i.e. at which marker position the furcation occurred.

node_with_missing_data

a vector of type logical. Pseudo-furcations arise due to missing data at a marker. The daughter node (number) is marked accordingly.

label_parent

a vector, that attaches an "extra leave", representing the haplotype number (defined by the order in the haplotype data file) to leaves of the tree. This is necessary because in general not all leaves of the original tree represent a single haplotype/chromosome.

An S4 class representing the complete furcation pattern around a focal marker.

Description

An S4 class representing the complete furcation pattern around a focal marker.

Slots

.Data

a list containing for each allele an object of allelefurcation-class.

mrk.name

the name/identifier of the focal marker.

position

the chromosomal position of the focal marker.

xlim

the range of marker positions.

nhap

the number of haplotypes in the sample.

See Also

calc_furcation

Examples

# copy example files into working directory
make.example.files()
# read first example file
hh <- data2haplohh(hap_file = "example1.hap", map_file = "example1.map", allele_coding = "01")
# remove example files
remove.example.files()
# calculate furcation structure around marker "rs6"
f <- calc_furcation(hh, mrk = "rs6")
# extract left side tree of ancestral allele (which is coded by '0')
f[['0']]@left
# the tree consists of seven nodes, '1' being the root node
# nodes 2 and 3 have the root node as parent, etc.
# the first chromosome is attached as a label node to node 7, etc.
# For comparison, a plot of the complete furcation structure:
plot(f)

class for haplotype length

Description

class for haplotype length

Class "haplohh"

Description

An object of this class contains the information needed for computation of EHH based statistics.

Usage

## S4 method for signature 'haplohh'
chr.name(x)

## S4 method for signature 'haplohh'
positions(x)

## S4 method for signature 'haplohh'
haplo(x)

## S4 method for signature 'haplohh'
nmrk(x)

## S4 method for signature 'haplohh'
mrk.names(x)

## S4 method for signature 'haplohh'
nhap(x)

## S4 method for signature 'haplohh'
hap.names(x)

Arguments

Details

This class is the basis for all calculations done by this package. Note that the matrix in slot haplo has to be of type integer, not numeric. Objects built by versions of rehh up to 2.0.4 coded this matrix as numeric and used a different coding scheme. They can be converted e.g. by haplohh <- update_haplohh(old_haplohh) in order be used with the present version.

Slots

chr.name

name of the chromosome/scaffold to which the markers belong.

positions

vector of type numeric containing the marker positions within the chromosome.

haplo

matrix of type integer containing haplotypes in rows and markers in columns.

See Also

data2haplohh, update_haplohh

Examples

showClass("haplohh")

Translate object of haplohh-class into SweepFinder format

Description

Extract allele frequencies of an object of class haplohh-class and returns a table in SweepFinder input format.

Usage

haplohh2sweepfinder(haplohh, polarized = TRUE, verbose = TRUE)

Arguments

Details

SweepFinder and SweeD are two stand-alone programs which implement the same method to detect selective sweeps using the allele frequency at each site. This function calculates these frequencies from a haplohh-class and returns a table which can be saved into a file (with tabs as separators, without row names and quotes) that can be used as input for the two programs.

Sites with less than two haplotypes genotyped or with more than two alleles are removed. If polarized, sites monomorphic for the ancestral allele are removed, too.

Value

A dataframe with four columns:

• position marker position

• x (absolute) frequency of the alternative (derived) variant

• n number of non-missing genotypes

• folded a flag marking polarization

References

DeGiorgio, M., and, Huber, CD and Hubisz, MJ and, Hellmann, I. and Nielsen, R. (2016) SweepFinder2: increased robustness and flexibility. Bioinformatics 32:1895-1897

Pavlidis, P., D. Zivkovic, A. Stamatakis, and N. Alachiotis, (2013) SweeD: likelihood-based detection of selective sweeps in thousands of genomes. Molecular Biology and Evolution 30: 2224-34.

See Also

haplohh-class, data2haplohh

Examples

#example
# sweepfinder example from vignette
make.example.files()
hh <- data2haplohh("example_sweep_with_recombination.vcf")
haplohh2sweepfinder(hh)
remove.example.files()

Example of an haplohh object

Description

The object contains haplotype data for 140 cattle individuals (280 haplotypes) belonging to the Creole breed from Guadeloupe (CGU) and 1424 markers (mapping to chromosome BTA12).

Usage

data(haplohh_cgu_bta12)

Format

An object of haplohh-class.

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128-3143.

See Also

data2haplohh

Compute XP-EHH

Description

Compute XP-EHH (standardized ratio of iES of two populations).

Usage

ies2xpehh(
  scan_pop1,
  scan_pop2,
  popname1 = NA,
  popname2 = NA,
  min_nhaplo = NA,
  standardize = TRUE,
  include_freq = FALSE,
  p.side = NA,
  p.adjust.method = "none",
  verbose = TRUE
)

Arguments

Details

Log ratio of iES (population 1 over population 2) computed as described in Sabeti et al. (2007). Note that the two data frames are merged on the basis of chromosome and position. Marker names are kept, if they are identical and unique in both data frames.

Since the standardized XP-EHH values follow, if markers evolve predominantly neutrally, approximately a standard Gaussian distribution, it is practical to assign to the values a p-value relative to the null-hypothesis of neutral evolution. The parameter p.side determines if the p-value is assigned to both sides of the distribution or to one side of interest.

Value

The returned value is a data frame with markers in rows and columns for chromosome name, marker position, XP-EHH and, if standardized, p-value in a negative log10 scale. Optionally, allele frequencies are included.

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128-3143.

Sabeti, P.C. et al. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449, 913-918.

See Also

scan_hh, distribplot, manhattanplot

Examples

library(rehh.data)
data(wgscan.cgu) ; data(wgscan.eut)
## results from a genome scan (44,057 SNPs)
##see ?wgscan.eut and ?wgscan.cgu for details
wgscan.xpehh <- ies2xpehh(wgscan.cgu, wgscan.eut, "CGU", "EUT")

Compute iHS

Description

Compute iHS (standardized ratio of iHH values of two alleles).

Usage

ihh2ihs(
  scan,
  freqbin = 0.025,
  min_maf = 0.05,
  min_nhaplo = NA,
  standardize = TRUE,
  include_freq = FALSE,
  right = FALSE,
  alpha = 0.05,
  p.side = NA,
  p.adjust.method = "none",
  verbose = TRUE
)

Arguments

Details

Computes log ratio of iHH of two focal alleles as described in Voight et al. (2006). The standardization is performed within each bins separately because of the frequency-dependence of expected iHS values under neutrality. An implicit assumption of this approach is that each bin is dominated by neutral markers.

Since the standardized iHS values follow, if markers evolve predominantly neutrally, approximately a standard Gaussian distribution, it is practical to assign to the values a p-value relative to the null-hypothesis of neutral evolution. The parameter p.side determines if the p-value is assigned to both sides of the distribution or to one side of interest.

Value

The returned value is a list containing two elements

ihs

a data frame with markers in rows and the columns for chromosome name, marker position, iHS and, if standardized, p-value in a negative log10 scale. Optionally, allele frequencies are included.

frequency.class

a data frame with bins in rows and columns for the number of markers, mean uniHS, standard deviation uniHS, lower quantile uniHS, upper quantile uniHS.

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128-3143.

Voight, B.F. and Kudaravalli, S. and Wen, X. and Pritchard, J.K. (2006). A map of recent positive selection in the human genome. Plos Biology, 4, e72.

See Also

scan_hh, distribplot, freqbinplot, manhattanplot

Examples

library(rehh.data)
data(wgscan.cgu)
#results from a genome scan (44,057 SNPs)
#see ?wgscan.eut and ?wgscan.cgu for details
wgscan.cgu.ihs <- ihh2ihs(wgscan.cgu)

Compute Rsb

Description

Compute Rsb (standardized ratio of inES of two populations).

Usage

ines2rsb(
  scan_pop1,
  scan_pop2,
  popname1 = NA,
  popname2 = NA,
  min_nhaplo = NA,
  standardize = TRUE,
  include_freq = FALSE,
  p.side = NA,
  p.adjust.method = "none",
  verbose = TRUE
)

Arguments

Details

Log ratio of inES (population 1 over population 2) computed as described in Tang et al. (2007). Note that the two data frames are merged on the basis of chromosome and position. Marker names are kept, if they are identical and unique in both data frames.

Since the standardized Rsb values follow, if markers evolve predominantly neutrally, approximately a standard Gaussian distribution, it is practical to assign to the values a p-value relative to the null-hypothesis of neutral evolution. The parameter p.side determines if the p-value is assigned to both sides of the distribution or to one side of interest.

Value

The returned value is a data frame with markers in rows and columns for chromosome name, marker position, Rsb and, if standardized, p-value in a negative log10 scale. Optionally, allele frequencies are included.

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128-3143.

Tang, K. and Thornton, K.R. and Stoneking, M. (2007). A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome. Plos Biology, 7, e171.

See Also

scan_hh, distribplot, manhattanplot

Examples

library(rehh.data)
data(wgscan.cgu) ; data(wgscan.eut)
## results from a genome scan (44,057 SNPs)
##see ?wgscan.eut and ?wgscan.cgu for details
wgscan.rsb <- ines2rsb(wgscan.cgu, wgscan.eut, "CGU", "EUT")

Copy example input files into current working directory

Description

This function copies the following example files to the current working directory:

• example1.hap "example 1" haplotype file in "standard format"

• example1.map "example 1" marker information file

• example1.vcf "example 1" as vcf file

• example2.hap "example 2" haplotype file in "standard format"

• example2.map "example 2" marker information file

• example2.vcf "example 2" as vcf file

• example_neutral.vcf "example neutral evolution" as vcf file

• example_sweep.vcf "example for a selective sweep (without recombination)"

• example_sweep_with_recombination.vcf "example for a selective sweep with recombination

• ms.out output from a small simulation by the program 'ms'

• bta12_cgu.hap an haplotype file in "standard format"

• bta12_cgu.thap an haplotype file in "transposed format"

• bta12_hapguess_switch.out an haplotype file in fastphase output format

• map.inp a marker information file for all bta_cgu markers

Example 1 was used in (Gautier 2017) to explain the various EHH derived statistics calculated by this package. Example 2 is an extension containing multi-allelic markers and missing values.

Examples for neutral data and sweeps are discussed in a supplement of Klassmann (2020).

The bta12 files contain data for 280 haplotypes, originating from 140 individuals belonging to the Creole cattle breed from Guadeloupe, at 1.424 markers mapping to bovine chromosome 12 (BTA12) (Gautier 2011).

Usage

make.example.files()

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128-3143.

Gautier, M., Klassmann, A. and Vitalis, R. (2017). rehh 2.0: a reimplementation of the R package rehh to detect positive selection from haplotype structure. Molecular Ecology Resources, 17, 78-90.

Klassmann, A. and Gautier, M. (2020). Detecting selection using Extended Haplotype Homozygosity-based statistics on unphased or unpolarized data (preprint). https://doi.org/10.22541/au.160405572.29972398/v1

See Also

data2haplohh, remove.example.files

Manhattan plot of iHS, XP-EHH or Rsb over a genome.

Description

Manhattanplot of iHS, XP-EHH or Rsb over a genome.

Usage

manhattanplot(
  data,
  pval = FALSE,
  threshold = c(-2, 2),
  chr.name = NA,
  cr = NULL,
  cr.col = "gray",
  cr.opacity = 0.5,
  cr.lab.cex = 0.6,
  cr.lab.offset = 0,
  cr.lab.pos = "top",
  mrk = NULL,
  mrk.cex = 1,
  mrk.col = "gray",
  mrk.pch = 1,
  mrk.lab.cex = 0.4,
  mrk.lab.pos = 4,
  ignore_sign = FALSE,
  cex = 0.5,
  las = 1,
  pch = 20,
  inset = 5e+06,
  resolution = NULL,
  ...
)

Arguments

Details

The color of chromosomes is taken from the "Graphics Palette", see palette.

If a single chromosome is plotted, a genomic region can be specified by argument xlim.

Other statistics can be plotted as well, although a warning is issued. They must be given by a data.frame with columns CHR and POSITION and the statistic in the third column.

Value

The function returns a plot.

See Also

ihh2ihs, ies2xpehh, ines2rsb, calc_candidate_regions.

Examples

library(rehh.data)
data(wgscan.cgu)
## results from a genome scan (44,057 SNPs)
## see ?wgscan.eut and ?wgscan.cgu for details
wgscan.ihs <- ihh2ihs(wgscan.cgu)
manhattanplot(wgscan.ihs)

Plot EHH around a focal marker

Description

Plot curve of EHH values around a focal marker.

Usage

## S3 method for class 'ehh'
plot(
  x,
  ylim = c(0, 1),
  type = "l",
  main = paste0("EHH around '", x$mrk.name, "'"),
  xlab = "Position",
  ylab = "Extended Haplotype Homozygosity",
  col = c("blue", "red", "violet", "orange"),
  mrk.col = "gray",
  bty = "n",
  legend = NA,
  legend.xy.coords = "automatic",
  ...
)

Arguments

See Also

data2haplohh, calc_ehh, plot.ehhs, scan_hh.

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#computing EHH statistics for the marker "F1205400"
#which displays a strong signal of selection
ehh <- calc_ehh(haplohh_cgu_bta12, mrk = "F1205400")
plot(ehh)

Plot EHHS around a focal marker

Description

Plot curve of EHHS values around a focal marker.

Usage

## S3 method for class 'ehhs'
plot(
  x,
  nehhs = FALSE,
  ylim = c(0, 1),
  type = "l",
  main = paste0("EHHS around '", x$mrk.name, "'"),
  xlab = "Position",
  ylab = "Extended Haplotype Homozygosity per Site",
  bty = "n",
  mrk.col = "gray",
  ...
)

Arguments

See Also

data2haplohh, plot.ehh, calc_ehhs, scan_hh.

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#computing EHHS statisitics for the marker "F1205400"
#which displays a strong signal of selection
ehhs <- calc_ehhs(haplohh_cgu_bta12, mrk = "F1205400")
plot(ehhs)

Plots furcation trees around a focal marker

Description

Plots furcation trees around a focal marker

Usage

## S3 method for class 'furcation'
plot(
  x,
  allele = NA,
  col = c("blue", "red", "violet", "orange"),
  mrk.col = "gray",
  lwd = 0.1,
  hap.names = NULL,
  cex.lab = 1,
  family.lab = "",
  offset.lab = 0.5,
  legend = NA,
  legend.xy.coords = "automatic",
  ...
)

Arguments

See Also

plot.haplen.

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#plotting furcation diagram for both ancestral and derived allele
#from the marker "F1205400"
#which display a strong signal of selection
f <- calc_furcation(haplohh_cgu_bta12, mrk = "F1205400")
plot(f)
plot(f, xlim = c(2e+07,3.5e+07))
plot(f, xlim = c(2.7e+07,3.1e+07))
plot(f, xlim = c(2.7e+07,3.1e+07), hap.names = hap.names(haplohh_cgu_bta12), cex.lab=0.3)

Plot the length of extended haplotypes around a focal marker

Description

Plot the length of extended haplotype around a focal marker.

Usage

## S3 method for class 'haplen'
plot(
  x,
  allele = NA,
  group_by_allele = TRUE,
  order_by_length = FALSE,
  col = c("blue", "red", "violet", "orange"),
  mrk.col = "gray",
  lwd = 1,
  hap.names = NULL,
  cex.lab = 1,
  family.lab = "",
  offset.lab = 0.5,
  pos.lab = "left",
  legend = NA,
  legend.xy.coords = "automatic",
  ...
)

Arguments

See Also

calc_haplen, plot.furcation.

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#plotting length of extended haplotypes for both ancestral and derived allele
#of the marker "F1205400"
#which displays a strong signal of selection
f <- calc_furcation(haplohh_cgu_bta12, mrk = "F1205400")
h <- calc_haplen(f)
plot(h)
plot(h, hap.names = hap.names(haplohh_cgu_bta12), cex.lab = 0.3)

Plot the variants of a haplohh object

Description

Plot the variants of a haplohh object. This method is intended for visualization of very small data sets such as the examples provided by the package.

Usage

## S3 method for class 'haplohh'
plot(
  x,
  mrk = NA,
  allele = NA,
  group_by_allele = FALSE,
  ignore.distances = FALSE,
  col = c("blue", "red", "violet", "orange"),
  linecol = "gray",
  mrk.col = "gray",
  pch = 19,
  cex = 1,
  lwd = 1,
  hap.names = NULL,
  mrk.names = NULL,
  cex.lab.hap = 0.8,
  cex.lab.mrk = 0.8,
  family.lab = "",
  offset.lab.hap = 0.5,
  offset.lab.mrk = 0.25,
  pos.lab.hap = "left",
  pos.lab.mrk = "top",
  srt.hap = 0,
  srt.mrk = 0,
  highlight.mrk = NULL,
  highlight.mrk.col = c("lightgray", "black", "darkgray"),
  ...
)

Arguments

Details

Specifying a haplohh-object with more than 4096 haplotypes or markers produces an error.

See Also

calc_haplen, plot.furcation.

Examples

#example haplohh object
make.example.files()
hh <- data2haplohh(hap_file = "example1.hap",
                   map_file = "example1.map",
                   allele_coding = "01")
plot(hh)
hh <- data2haplohh(hap_file = "example2.hap",
                   map_file = "example2.map",
                   allele_coding = "01",
                   min_perc_geno.mrk = 50)
plot(hh)
remove.example.files()

Remove example files from current working directory.

Description

Remove example files from current working directory.

Usage

remove.example.files()

Details

Removes the files created by make.example.files(). No error is thrown, if files do not exist.

See Also

make.example.files

Compute iHH, iES and inES over a whole chromosome

Description

Compute integrated EHH (iHH), integrated EHHS (iES) and integrated normalized EHHS (inES) for all markers of a chromosome (or linkage group).

Usage

scan_hh(
  haplohh,
  limhaplo = 2,
  limhomohaplo = 2,
  limehh = 0.05,
  limehhs = 0.05,
  phased = TRUE,
  polarized = TRUE,
  scalegap = NA,
  maxgap = NA,
  discard_integration_at_border = TRUE,
  lower_ehh_y_bound = limehh,
  lower_ehhs_y_bound = limehhs,
  interpolate = TRUE,
  threads = 1
)

Arguments

Details

Integrated EHH (iHH), integrated EHHS (iES) and integrated normalized EHHS (inES) are computed for all markers of the chromosome (or linkage group). This function is several times faster as a procedure calling in turn calc_ehh and calc_ehhs for all markers. To perform a whole genome-scan this function needs to be called for each chromosome and results concatenated.

Note that setting limehh or limehhs to zero is likely to reduce power, since even under neutrality a tiny fraction (<<0.05) of extremely long shared haplotypes is expected which, if fully accounted for, would obfuscate the signal at selected sites.

Value

The returned value is a dataframe with markers in rows and the following columns

1. chromosome name

2. position in the chromosome

3. sample frequency of the ancestral / major allele

4. sample frequency of the second-most frequent remaining allele

5. number of evaluated haplotypes at the focal marker for the ancestral / major allele

6. number of evaluated haplotypes at the focal marker for the second-most frequent remaining allele

7. iHH of the ancestral / major allele

8. iHH of the second-most frequent remaining allele

9. iES (used by Sabeti et al 2007)

10. inES (used by Tang et al 2007)

Note that in case of unphased data the evaluation is restricted to haplotypes of homozygous individuals which reduces the power to detect selection, particularly for iHS (for appropriate parameter setting see the main vignette and Klassmann et al (2020)).

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128-3143.

Klassmann, A. and Gautier, M. (2020). Detecting selection using Extended Haplotype Homozygosity-based statistics on unphased or unpolarized data (preprint). https://doi.org/10.22541/au.160405572.29972398/v1

Sabeti, P.C. et al. (2002). Detecting recent positive selection in the human genome from haplotype structure. Nature, 419, 832-837.

Sabeti, P.C. et al. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449, 913-918.

Tang, K. and Thornton, K.R. and Stoneking, M. (2007). A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome. Plos Biology, 7, e171.

Voight, B.F. and Kudaravalli, S. and Wen, X. and Pritchard, J.K. (2006). A map of recent positive selection in the human genome. Plos Biology, 4, e72.

See Also

data2haplohh, calc_ehh, calc_ehhs ihh2ihs,ines2rsb, ies2xpehh

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
scan <- scan_hh(haplohh_cgu_bta12)

Compute iHH, iES and inES over a whole chromosome without cut-offs

Description

Compute integrated EHH (iHH), integrated EHHS (iES) and integrated normalized EHHS (inES) for all markers of a chromosome (or linkage group). This function computes the statistics by a slightly different algorithm than scan_hh: it sidesteps the calculation of EHH and EHHS values and their subsequent integration and consequently no cut-offs relying on these values can be specified. Instead, it computes the (full) lengths of pairwise shared haplotypes and averages them afterwords.

This function is primarily intended for the study of general properties of these statistics using simulated data.

Usage

scan_hh_full(
  haplohh,
  phased = TRUE,
  polarized = TRUE,
  maxgap = NA,
  max_extend = NA,
  discard_integration_at_border = TRUE,
  geometric.mean = FALSE,
  threads = 1
)

Arguments

Details

Integrated EHH (iHH), integrated EHHS (iES) and integrated normalized EHHS (inES) are computed for all markers of the chromosome (or linkage group). This function sidesteps the computation of EHH and EHHS values and their stepwise integration. Instead, the length of all shared haplotypes is computed and afterwords averaged. In the absence of missing values the statistics are identical to those calculated by scan_hh with settings limehh = 0, limehhs = 0, lower_ehh_y_bound = 0 and interpolate = FALSE, yet this function is faster.

Application of a cut-off is necessary for reducing the spurious signals of selection caused by single shared haplotypes of extreme length. Hence, e.g. for human experimental data it might be reasonable to set max_extend to 1 or 2 Mb.

scan_hh computes the statistics iHH_A, ihh_D and iES/inES separately, while this function calculates them simultaneously. Hence, if discard_integration_at_border is set to TRUE and the extension of shared haplotypes reaches a border (i.e. chromosomal boundaries or a gap larger than maxgap), this function discards all statistics.

The handling of missing values is different, too: scan_hh "removes" chromosomes with missing values from further calculations. EHH and EHHS are then calculated for the remaining chromosomes which can accidentally yield an increase in EHH or EHHS. This can not happen with scan_hh_full() which treats each missing value of a marker as if it were a new allele - terminating any shared haplotype, but does changing the set of considered chromosomes. Thus, missing values cause a faster decay of EHH(S) with function scan_hh_full().

Value

The returned value is a dataframe with markers in rows and the following columns

1. chromosome name

2. position in the chromosome

3. sample frequency of the ancestral / major allele

4. sample frequency of the second-most frequent remaining allele

5. number of evaluated haplotypes at the focal marker for the ancestral / major allele

6. number of evaluated haplotypes at the focal marker for the second-most frequent remaining allele

7. iHH of the ancestral / major allele

8. iHH of the second-most frequent remaining allele

9. iES (used by Sabeti et al 2007)

10. inES (used by Tang et al 2007)

Note that in case of unphased data the evaluation is restricted to haplotypes of homozygous individuals which reduces the power to detect selection, particularly for iHS (for appropriate parameter setting see the main vignette and Klassmann et al (2020)).

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128-3143.

Klassmann, A. and Gautier, M. (2020). Detecting selection using Extended Haplotype Homozygosity-based statistics on unphased or unpolarized data (preprint). https://doi.org/10.22541/au.160405572.29972398/v1

Sabeti, P.C. et al. (2002). Detecting recent positive selection in the human genome from haplotype structure. Nature, 419, 832-837.

Sabeti, P.C. et al. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449, 913-918.

Tang, K. and Thornton, K.R. and Stoneking, M. (2007). A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome. Plos Biology, 7, e171.

Voight, B.F. and Kudaravalli, S. and Wen, X. and Pritchard, J.K. (2006). A map of recent positive selection in the human genome. Plos Biology, 4, e72.

See Also

data2haplohh, scan_hh, ihh2ihs, ines2rsb, ies2xpehh

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#using function scan_hh() with no cut-offs
scan <- scan_hh(haplohh_cgu_bta12, discard_integration_at_border = FALSE,
limehh = 0, limehhs = 0, lower_ehh_y_bound = 0, interpolate = FALSE)
#using function scan_hh_full()
scan_full <- scan_hh_full(haplohh_cgu_bta12, discard_integration_at_border = FALSE)
#both yield identical results within numerical precision
all.equal(scan, scan_full)

Subsets object of haplohh-class

Description

Subsets the data of an object of class haplohh-class, meeting certain conditions.

Usage

## S3 method for class 'haplohh'
subset(
  x,
  select.hap = NULL,
  select.mrk = NULL,
  min_perc_geno.hap = NA,
  min_perc_geno.mrk = 100,
  min_maf = NA,
  max_alleles = NA,
  verbose = TRUE,
  ...
)

Arguments

See Also

haplohh-class, data2haplohh

Examples

#example haplohh object (280 haplotypes, 1424 SNPs)
#see ?haplohh_cgu_bta12 for details
data(haplohh_cgu_bta12)
#select subset of first 10 hyplotypes and first 5 markers
subset(haplohh_cgu_bta12, select.hap = 1:10, select.mrk = 1:5)

Update object of class haplohh

Description

Update object of class haplohh-class constructed by rehh versions up to version 2.0.4.

Usage

update_haplohh(haplohh)

Arguments

Details

This function is intended to update haplohh objects that have been built by rehh versions up to 2.0.4. These objects cannot be used in functions of the current version. The following changes have been made to the class definition: The internal representation of the haplotype matrix followed the encoding

• 0 missing value

• 1 ancestral allele

• 2 derived allele

and has been replaced by a vcf-like encoding:

• NA missing value

• 0 ancestral allele

• 1 derived allele.

Furthermore the slots nsnp, snp.name and nhap have been removed and slot position renamed to positions. An update of an old haplohh object is done as follows:

new_haplohh = update_haplohh(old_haplohh).

See Also

haplohh-class, data2haplohh.