CRAN now hosts the packages `qtl2pleio`

and `gemma2`

. `qtl2pleio`

performs a d-variate, d-QTL scan over a select genomic region. `gemma2`

is used by `qtl2pleio`

for the inference of multivariate variance components.

They can be installed with:

`install.packages("qtl2pleio")`

The statistical model that `qtl2pleio`

fits for each d-tuple of markers (or pseudomarkers) is

\[ vec(Y) = Xvec(B) + vec(G) + vec(E) \] where \(Y\) is a n by d matrix of d traits (for each of n subjects), X is a dn by df block-diagonal matrix of founder allele probabilities, B is a f by d matrix of allele effects for each of d traits, G is a n by d matrix of polygenic random effects, and E is a n by d matrix of random errors. We assume that

\[ G \sim N(0, K, V_g) \]

and

\[ E \sim N(0, I, V_e) \]

We further assume that \(G\) and \(E\) are independent. \(K\) is an estimated kinship matrix.

In a recent G3 article, we developed our test of pleiotropy vs. separate QTL and applied it to two behavioral traits measured on a cohort of Diversity Outbred mice.

`qtl2pleio`

“plays nicely” with `qtl2`

and other R packages from the `qtl2`

ecosystem by using the same structures for genotype data, phenotype data, and covariates.

We’re currently investigating our pleiotropy test in the context of expression trait hotspots.

(Last modified: 2020-01-12 13:51:26)