rehh (version 3.2.3)

scan_hh_full: 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
)

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)).

Arguments

haplohh

an object of class haplohh (see data2haplohh)

phased

logical. If TRUE (default) chromosomes are expected to be phased. If FALSE, the haplotype data is assumed to consist of pairwise ordered chromosomes belonging to diploid individuals. EHH(S) is then estimated over individuals which are homozygous at the focal marker.

polarized

logical. TRUE by default. If FALSE, use major and minor allele instead of ancestral and derived. If there are more than two alleles then the minor allele refers to the second-most frequent allele.

maxgap

maximum allowed gap in bp between two markers. If exceeded, further calculation of EHH(S) is stopped at the gap (default=NA, i.e. no limitation).

max_extend

maximum distance in bp to extend shared haplotypes away from the focal marker. (default NA, i.e. no limitation).

discard_integration_at_border

logical. If TRUE (default) and computation of any of the statistics reaches first or last marker or a gap larger than maxgap, iHH, iES and inES are set to NA.

geometric.mean

logical. If FALSE (default), the standard arithmetic mean is used to average shared haplotype lengths. If TRUE the geometric mean is used instead (IES values are undefined in this case). Note that usage of the geometric mean has not yet been studied formally and should be considered experimental!

threads

number of threads to parallelize computation

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().

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. (2022). Detecting selection using extended haplotype homozygosity (EHH)-based statistics in unphased or unpolarized data. PLoS One. 17(1):e0262024 tools:::Rd_expr_doi("10.1371/journal.pone.0262024")

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)
#calculate iHH "by hand"
m <- calc_pairwise_haplen(haplohh_cgu_bta12, mrk = "F1205400")
#extract alleles of focal marker
foc_alleles <- haplo(haplohh_cgu_bta12)[, "F1205400"]
#subset matrix to contain only ancestral or derived pairwise haplotype lengths
m_A <- m[foc_alleles == 0, foc_alleles == 0]
m_D <- m[foc_alleles == 1, foc_alleles == 1]
#calculate mean values (the matrices are symmetric and their diagonal is zero)
mean(m_A[upper.tri(m_A)])
mean(m_D[upper.tri(m_D)])
# compare with IHH_A and IHH_D from full scan
scan_full["F1205400", ]