relatedness_mat

Given a matrix of genotypes, this function estimates the genetic relatedness matrix (GRM,
also known as the RRM, see Hayes et al. 2009, tools:::Rd_expr_doi("10.1017/S0016672308009981")) among
the subjects: \(\frac{1}{p}(XX^T)\), where X is standardized.

Fits penalized linear mixed models that correct for
unobserved confounding factors. 'plmmr' infers and corrects for the
presence of unobserved confounding effects such as population
stratification and environmental heterogeneity. It then fits a linear
model via penalized maximum likelihood. Originally designed for the
multivariate analysis of single nucleotide polymorphisms (SNPs)
measured in a genome-wide association study (GWAS), 'plmmr' eliminates
the need for subpopulation-specific analyses and post-analysis p-value
adjustments. Functions for the appropriate processing of 'PLINK'
files are also supplied. For examples, see the package homepage
<https://pbreheny.github.io/plmmr/>.

Patrick Breheny

plmmr

Penalized Linear Mixed Models for Correlated Data

Tabitha K. Peter

Anna C. Reisetter

Yujing Lu

Oscar A. Rysavy

Patrick J. Breheny

relatedness_mat function

<dl><dt>X</dt>
<dd>An n x p numeric matrix of genotypes (from fully-imputed data). Can be a filebacked <code>big.matrix</code> object.
Note: This matrix should not include non-genetic features.</dd>
<dt>std</dt>
<dd>Logical: should <code>X</code> be standardized? If you set this to FALSE, you should have a good reason for doing so,
as standardization is a best practice.</dd></dl>

Arguments

Calculate a relatedness matrix — relatedness_mat

<dl>

<dt>X</dt>
<dd>An n x p numeric matrix of genotypes (from fully-imputed data). Can be a filebacked <code>big.matrix</code> object.
Note: This matrix should not include non-genetic features.</dd>


<dt>std</dt>
<dd>Logical: should <code>X</code> be standardized? If you set this to FALSE, you should have a good reason for doing so,
as standardization is a best practice.</dd>

</dl>

relatedness_mat: Calculate a relatedness matrix

Description

Usage

Value

Arguments

Examples