Genetic Factor Analysis

Usage

gfa_fit(
  Z_hat = NULL,
  N = NULL,
  N_case = NULL,
  pop_prev = NULL,
  B_hat = NULL,
  S = NULL,
  R = NULL,
  params = gfa_default_parameters(),
  method = c("fixed_factors", "random_effect"),
  mode = c("z-score", "b-std"),
  no_wrapup = FALSE,
  F_init = NULL,
  fix_F = FALSE
)

Arguments

Z_hat: A matrix of z-scores with rows for variants and columns for traits.
N: Vector of sample sizes length equal to number of traits. If not provided, N will default to the vector of 1's and the factor matrix will be returned on the "z-score scale".
B_hat: A matrix of GWAS effect estimates. B_hat is an alternative to Z_hat (only provide one of these). If using B_hat, you must also provide S.
S: If using B_hat, provide the corresponding matrix of standard errors.
R: Estimated residual correlation matrix. This can be produced for example using R_ldsc or R_pt.
params: List of parameters. For most users this can be left at the default values. See details.
method: Either "fixed_factors" or "random_effect". "random_effect" is experimental. See details.
mode: Either "z-score" or "b-std". See details.

Value

A list with elements L_hat and F_hat for estimated variant-factor and factor-trait effects, gfa_pve which contains the proportion of heritability explained by each factor, and some other objects useful internally.

Details

The method argument can be either fixed_factors or random_effect. These are different methods for fitting the GFA model. The random_effect method requires installing a fork of the flashier package using `install_github("jean997/flashier", ref = "random-effect")`. Most users can stick with the fixed_factors method.

The mode option tells GFA to either fit using z-scores as the outcome and then convert the factor matrix to the standardized scale (mode = "z-score") or to fit directly using standardized effect estimates as the outcome (mode = "b-std"). These give very similar results and the z-score mode is faster and so recommended.

The params list includes the following elements which can be modified. Most users will not need to modify any of these, except possibly `max_iter`.

kmax: Maximum number of factors. Defaults to twice the number of traits

cond_num: Maximum allowable condition number for R. Defaults to 1e5.

max_iter: Maximum number of iterations. Defaults to 1000.

extrapolate: Passed to flashier, defaults to TRUE

ebnm_fn_F and ebnm_fn_L: Prior families for factors and loadings. Defaults to point-normal.

init_fn: Flashier initialization function.