CRAN now hosts the packages
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:
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
\[ G \sim N(0, K, V_g) \]
\[ E \sim N(0, I, V_e) \]
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)