LDpred2. Tutorial at https://privefl.github.io/bigsnpr/articles/LDpred2.html.

```
snp_ldpred2_inf(corr, df_beta, h2)
snp_ldpred2_grid(
corr,
df_beta,
grid_param,
burn_in = 50,
num_iter = 100,
ncores = 1,
return_sampling_betas = FALSE,
ind.corr = cols_along(corr)
)
snp_ldpred2_auto(
corr,
df_beta,
h2_init,
vec_p_init = 0.1,
burn_in = 500,
num_iter = 200,
sparse = FALSE,
verbose = FALSE,
report_step = num_iter + 1L,
allow_jump_sign = TRUE,
shrink_corr = 1,
use_MLE = TRUE,
p_bounds = c(1e-05, 1),
alpha_bounds = c(-1.5, 0.5),
ind.corr = cols_along(corr),
ncores = 1
)
```

- corr
Sparse correlation matrix as an SFBM. If

`corr`

is a dsCMatrix or a dgCMatrix, you can use`as_SFBM(corr)`

.- df_beta
A data frame with 3 columns:

`$beta`

: effect size estimates`$beta_se`

: standard errors of effect size estimates`$n_eff`

: either tha GWAS sample size(s) when estimating`beta`

for a continuous trait, or in the case of a binary trait, this is`4 / (1 / n_control + 1 / n_case)`

; in the case of a meta-analysis, you should sum the effective sample sizes of each study instead of using the total numbers of cases and controls, see doi:10.1016/j.biopsych.2022.05.029 ; when using a mixed model, the effective sample size needs to be adjusted as well, see doi:10.1016/j.xhgg.2022.100136 .

- h2
Heritability estimate.

- grid_param
A data frame with 3 columns as a grid of hyper-parameters:

`$p`

: proportion of causal variants`$h2`

: heritability (captured by the variants used)`$sparse`

: boolean, whether a sparse model is sought They can be run in parallel by changing`ncores`

.

- burn_in
Number of burn-in iterations.

- num_iter
Number of iterations after burn-in.

- ncores
Number of cores used. Default doesn't use parallelism. You may use

`nb_cores()`

.- return_sampling_betas
Whether to return all sampling betas (after burn-in)? This is useful for assessing the uncertainty of the PRS at the individual level (see doi:10.1101/2020.11.30.403188 ). Default is

`FALSE`

(only returns the averaged final vectors of betas). If`TRUE`

, only one set of parameters is allowed.- ind.corr
Indices to "subset"

`corr`

, as if this was run with`corr[ind.corr, ind.corr]`

instead. No subsetting by default.- h2_init
Heritability estimate for initialization.

- vec_p_init
Vector of initial values for p. Default is

`0.1`

.- sparse
In LDpred2-auto, whether to also report a sparse solution by running LDpred2-grid with the estimates of p and h2 from LDpred2-auto, and sparsity enabled. Default is

`FALSE`

.- verbose
Whether to print "p // h2" estimates at each iteration. Disabled when parallelism is used.

- report_step
Step to report sampling betas (after burn-in and before unscaling). Nothing is reported by default. If using

`num_iter = 200`

and`report_step = 20`

, then 10 vectors of sampling betas are reported (as a sparse matrix with 10 columns).- allow_jump_sign
Whether to allow for effects sizes to change sign in consecutive iterations? Default is

`TRUE`

(normal sampling). You can use`FALSE`

to force effects to go through 0 first before changing sign. Setting this parameter to`FALSE`

could be useful to prevent instability (oscillation and ultimately divergence) of the Gibbs sampler. This would also be useful for accelerating convergence of chains with a large initial value for p.- shrink_corr
Shrinkage multiplicative coefficient to apply to off-diagonal elements of the correlation matrix. Default is

`1`

(unchanged). You can use e.g.`0.95`

to add a bit of regularization.- use_MLE
Whether to use maximum likelihood estimation (MLE) to estimate alpha and the variance component (since v1.11.4), or assume that alpha is -1 and estimate the variance of (scaled) effects as h2/(m*p), as it was done in earlier versions of LDpred2-auto (e.g. in v1.10.8). Default is

`TRUE`

, which should provide a better model fit, but might also be less robust.- p_bounds
Boundaries for the estimates of p (the polygenicity). Default is

`c(1e-5, 1)`

. You can use the same value twice to fix p.- alpha_bounds
Boundaries for the estimates of \(\alpha\). Default is

`c(-1.5, 0.5)`

. You can use the same value twice to fix \(\alpha\).

`snp_ldpred2_inf`

: A vector of effects, assuming an infinitesimal model.

`snp_ldpred2_grid`

: A matrix of effect sizes, one vector (column)
for each row of `grid_param`

. Missing values are returned when strong
divergence is detected. If using `return_sampling_betas`

, each column
corresponds to one iteration instead (after burn-in).

`snp_ldpred2_auto`

: A list (over `vec_p_init`

) of lists with

`$beta_est`

: vector of effect sizes (on the allele scale); note that missing values are returned when strong divergence is detected`$beta_est_sparse`

(only when`sparse = TRUE`

): sparse vector of effect sizes`$postp_est`

: vector of posterior probabilities of being causal`$corr_est`

, the "imputed" correlations between variants and phenotypes, which can be used for post-QCing variants by comparing those to`with(df_beta, beta / sqrt(n_eff * beta_se^2 + beta^2))`

`$sample_beta`

: sparse matrix of sampling betas (see parameter`report_step`

),*not*on the allele scale, for which you need to multiply by`with(df_beta, sqrt(n_eff * beta_se^2 + beta^2))`

`$path_p_est`

: full path of p estimates (including burn-in); useful to check convergence of the iterative algorithm`$path_h2_est`

: full path of h2 estimates (including burn-in); useful to check convergence of the iterative algorithm`$path_alpha_est`

: full path of alpha estimates (including burn-in); useful to check convergence of the iterative algorithm`$h2_est`

: estimate of the (SNP) heritability (also see coef_to_liab)`$p_est`

: estimate of p, the proportion of causal variants`$alpha_est`

: estimate of alpha, the parameter controlling the relationship between allele frequencies and expected effect sizes`$h2_init`

and`$p_init`

: input parameters, for convenience

For reproducibility, `set.seed()`

can be used to ensure that two runs of
LDpred2 give the exact same results (since v1.10).