ligera2_bed_f_multi: LIGERA2 BED multi: LIght GEnetic Robust Association multiscan...
In OchoaLab/ligera: LIght GEnetic Robust Association

ligera2_bed_f_multi

R Documentation

LIGERA2 BED multi: LIght GEnetic Robust Association multiscan function

Description

This function performs multiple genetic association scans, adding one significant locus per iteration to the model (modeled as a covariate) to increase power in the final model. The function returns a tibble containing association statistics and several intermediates. This version calculates p-values using an F-test, which gives calibrated statistics under both quantitative and binary traits. Compared to ligera(), which uses the faster Wald test (calibrated for quantitative but not binary traits), this F-test version is quite a bit slower, and is optimized for m >> n, so it is a work in progress. This optimized version requires the genotypes to be in a file in BED format.

Usage

ligera2_bed_f_multi(
  file,
  trait,
  mean_kinship,
  q_cut = 0.05,
  one_per_iter = FALSE,
  covar = NULL,
  mem_factor = 0.7,
  mem_lim = NA,
  m_chunk_max = 1000,
  V = 0,
  tol = 1e-15,
  prune_ld = FALSE,
  r2_max = 0.3,
  pos_window = 10000000L
)

Arguments

`file`	The path to the BED file containing the genotypes, potentially excluding the BED extension.
`trait`	The length-`n` trait vector, which may be real valued and contain missing values.
`mean_kinship`	An estimate of the mean kinship produced externally, to ensure internal estimates of kinship are unbiased.
`q_cut`	The q-value threshold to admit new loci into the polygenic model.
`one_per_iter`	If true, only the most significant locus per iteration is added to model of next iteration. Otherwise all significant loci per iteration are added to the model of next iteration.
`covar`	An optional `n`-by-`K` matrix of `K` covariates, aligned with the individuals.
`mem_factor`	Proportion of available memory to use loading and processing genotypes. Ignored if `mem_lim` is not `NA`.
`mem_lim`	Memory limit in GB, used to break up genotype data into chunks for very large datasets. Note memory usage is somewhat underestimated and is not controlled strictly. Default in Linux and Windows is `mem_factor` times the free system memory, otherwise it is 1GB (OSX and other systems).
`m_chunk_max`	Sets the maximum number of loci to process at the time. Actual number of loci loaded may be lower if memory is limiting.
`V`	Algorithm version (0, 1, 2). Experimental features, not worth explaining.
`tol`	Tolerance value passed to conjugate gradient method solver.
`prune_ld`	If `TRUE` (default `FALSE`), at every iteration (including the first one) if there is more than one new locus discovered, the set of loci is pruned by removing correlated loci with the following parameters.
`r2_max`	Maximum squared correlation coefficient between loci (ignored if `prune_ld = FALSE`).
`pos_window`	Window size, in basepairs, for on which to apply pruning (ignored if `prune_ld = FALSE`). If `pos_window > 0`, only pairs of loci in the same chromosome and with positions less than `pos_window` away will be pruned. However, if `pos_window == 0L` then all variants are pruned regardless of chr/pos values.

Details

Suppose there are n individuals and m loci.

Value

A tibble containing the following association statistics from the last scan for non-selected loci. For selected loci, these are the values from the scan before each was added to the model (as after addition they get beta ~= 0 and pval ~= 1).

chr: Chromosome of locus.
id: Locus ID.
posg: Position in genetic distance.
pos: Position in basepairs.
alt: Alternative allele.
ref: Reference allele (counted).
pval: The p-value of the last association scan.
beta: The estimated effect size coefficient for the trait vector at this locus.
f_stat: The F statistic.
df: degrees of freedom: number of non-missing individuals minus number of parameters of full model
qval: The q-value of the last association scan.
sel: the order in which loci were selected, or zero if they were not selected.