Genomic data in lmt
lmt can account for genomic data via genomic relationship matrices $$G$$ and single step relationship matrices $$H$$ where ssGBLUP[1], ssGTBLUP[2], and ssSNPBLUP[3] are supported. lmt accepts plain marker data and will calculate all necessary derivatives required by the model. Genotypes can be scaled using average or marker specific allele content variance (2pq), and allele frequencies are either calculated from the data, or read from user specified input files. Futher, if requested $$G$$ can be adjusted to fit $$A$$[4], even for models where $$G$$ is not build(e.g. ssSNP-BLUP).
Genetic Groups
If the pedigree constituting $$A$$ contains phantom parents, genetic groups are automatically fitted for all different Single-Step co-variance structures. Alternatively, genetic groups can be fitted as an extra factor.
Single-Step SNP-BLUP
lmt's Single-Step SNP-BLUP model uses the approach described in Liu and Goddard 2014Cite error: Closing </ref> missing for <ref> tag
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[1]
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[2]
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- ↑ 1.0 1.1 Christensen et al.; Genomic prediction when some animals are not genotyped; Genetics Selection Evolution; 2010
- ↑ 2.0 2.1 Mäntysaari et al.;Efficient single-step genomic evaluation for a multibreed beef cattle population having many genotyped animals; Journal of Animal Science;2017
- ↑ 3.0 3.1 Liu et al.;A single-step genomic model with direct estimation of marker effects; Journal of Dairy Science;2014
- ↑ Cite error: Invalid
<ref>tag; no text was provided for refs namedChristensen2012 - ↑ Westell et al.; Genetic Groups in an Animal Model; Journal of Dairy Science; 1988