qpadm: Estimate admixture weights

In uqrmaie1/admixtools: Inferring demographic history from genetic data

Estimate admixture weights

Description

qpadm models a target population as a mixture of left (source) populations, given a set of right (outgroup) populations. It can be used to estimate whether the left populations explain all genetic variation in the target population, relative to the right populations, and to estimate admixture proportions of the left populations to the target population.

Usage

qpadm(
  data,
  left,
  right,
  target,
  f4blocks = NULL,
  fudge = 1e-04,
  fudge_twice = FALSE,
  boot = FALSE,
  getcov = TRUE,
  constrained = FALSE,
  return_f4 = FALSE,
  cpp = TRUE,
  verbose = TRUE,
  ...
)

Arguments

data	The input data in the form of: A 3d array of blocked f2 statistics, output of f2_from_precomp or extract_f2 A directory with f2 statistics The prefix of a genotype file
left	Left populations (sources)
right	Right populations (outgroups)
target	Target population
f4blocks	Instead of f2 blocks, f4 blocks can be supplied. This is used by qpadm_multi
fudge	Value added to diagonal matrix elements before inverting
fudge_twice	Setting this to TRUE should result in p-values that better match those in the original qpAdm program
boot	If FALSE (the default), block-jackknife resampling will be used to compute standard errors. Otherwise, block-bootstrap resampling will be used to compute standard errors. If boot is an integer, that number will specify the number of bootstrap resamplings. If boot = TRUE, the number of bootstrap resamplings will be equal to the number of SNP blocks.
getcov	Compute weights covariance. Setting getcov = FALSE will speed up the computation.
constrained	Constrain admixture weights to be non-negative
return_f4	Return f4-statistics
cpp	Use C++ functions. Setting this to FALSE will be slower but can help with debugging.
verbose	Print progress updates
...	If data is the prefix of genotype files, additional arguments will be passed to f4blockdat_from_geno

Value

qpadm returns a list with up to four data frames describing the model fit:

1. weights: A data frame with estimated admixture proportions where each row is a left population.

2. f4: A data frame with estimated and fitted f4-statistics

3. rankdrop: A data frame describing model fits with different ranks, including p-values for the overall fit and for nested models (comparing two models with rank difference of one). A model with L left populations and R right populations has an f4-matrix of dimensions (L-1)*(R-1). If no two left population form a clade with respect to all right populations, this model will have rank (L-1)*(R-1).

   • f4rank: Tested rank

   • dof: Degrees of freedom of the chi-squared null distribution: (L-1-f4rank)*(R-1-f4rank)

   • chisq: Chi-sqaured statistic, obtained as ⁠E'QE⁠, where E is the difference between estimated and fitted f4-statistics, and Q is the f4-statistic covariance matrix.

   • p: p-value obtained from chisq as pchisq(chisq, df = dof, lower.tail = FALSE)

   • dofdiff: Difference in degrees of freedom between this model and the model with one less rank

   • chisqdiff: Difference in chi-squared statistics

   • p_nested: p-value testing whether the difference between two models of rank difference 1 is significant

4. popdrop: A data frame describing model fits with different populations. Note that all models with fewer populations use the same set of SNPs as the first model.

   • pat: A binary code indicating which populations are present in this model. A 1 represents dropped populations. The full model is all zeros.

   • wt: Number of populations dropped

   • dof: Degrees of freedom of the chi-squared null distribution: (L-1-f4rank)*(R-1-f4rank)

   • chisq: Chi-sqaured statistic, obtained as ⁠E'QE⁠, where E is the difference between estimated and fitted f4-statistics, and Q is the f4-statistic covariance matrix.

   • p: p-value obtained from chisq as pchisq(chisq, df = dof, lower.tail = FALSE)

   • f4rank: Tested rank

   • feasible: A model is feasible if all weights fall between 0 and 1

   • ⁠<population name>⁠: The weights for each population in this model

References

Haak, W. et al. (2015) Massive migration from the steppe was a source for Indo-European languages in Europe. Nature (SI 10)

See Also

qpwave, lazadm

Examples

left = c('Altai_Neanderthal.DG', 'Vindija.DG')
right = c('Chimp.REF', 'Mbuti.DG', 'Russia_Ust_Ishim.DG', 'Switzerland_Bichon.SG')
target = 'Denisova.DG'
qpadm(example_f2_blocks, left, right, target)
## Not run: 
# The original ADMIXTOOLS qpAadm program has an option called "allsnps"
# that selects different SNPs for each f4-statistic, which is
# useful when working with sparse genotype data.
# To get the same behavior in ADMIXTOOLS 2, supply the genotype data prefix
# and set `allsnps = TRUE`
qpadm("/my/geno/prefix", left, right, target, allsnps = TRUE)

## End(Not run)

uqrmaie1/admixtools documentation built on March 20, 2024, 8:24 a.m.