xmu_gsem_expand_snp: Expand SNP Summary Statistics into a Full Covariance Matrix

In umx: Structural Equation Modeling and Twin Modeling in R

Expand SNP Summary Statistics into a Full Covariance Matrix

Description

Takes a baseline covariance structure (covstruc) of traits and incorporates the summary statistics of a single SNP to produce a combined (k+1) \times (k+1) covariance matrix (S) and corresponding asymptotic covariance matrix (V).

Usage

xmu_gsem_expand_snp(
  covstruc,
  beta_i,
  se_i,
  varSNP_i,
  varSNPSE2,
  GC = "standard",
  coords = NULL
)

Arguments

covstruc	A list containing the base trait covariance matrix ⁠$S⁠ and its asymptotic covariance ⁠$V⁠.
beta_i	Numeric vector of length $k$. The GWAS betas (effects) of the SNP on the $k$ traits.
se_i	Numeric vector of length $k$. The GWAS standard errors for the SNP on the $k$ traits.
varSNP_i	Numeric. The variance of the SNP (typically 2 \times MAF \times (1 - MAF)).
varSNPSE2	Numeric. The asymptotic variance of the SNP's variance estimate.
GC	Character. Genomic control method applied to the cross-trait sampling covariances. Options are "standard", "conserv", or "none".
coords	Optional matrix mapping coordinates for the asymptotic covariance matrix.

Value

A list containing:

• S: The expanded (k+1) \times (k+1) covariance matrix.

• V: The expanded ((k+1)(k+2)/2) \times ((k+1)(k+2)/2) asymptotic covariance matrix.

umx documentation built on Oct. 11, 2026, 5:07 p.m.