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The extracted principal components are then analyzed in conjunction with marker data to map the underlying loci.
We have collected marker data to evaluate the continental (i.e., European, Asian, Native American, and African) and within-African ancestry.
First, we used genetic marker data to show that homeologous pairing and recombination are almost negligible in neoallotetraploid Mimulus.
The goal of this study was to use ChIP-Seq-derived histone marker data to identify transcription start sites of miRNAs in Arabidopsis.
We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them.
We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA.
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Some studies have shown benefits of extending the standard models of GS by adding a random effect representing a regression on pedigree information (de los Campos et al. 2009; Crossa et al. 2010); however, the benefits of jointly modeling pedigree and marker data relative to a markers-only model seem to vanish as marker density increases (Vazquez et al. 2010).
In addition, we performed a profiling experiment on a polygenic trait (methionine content) by pooling individual genotypes both on phenotypic and marker data leading to the identification of associated candidate genes linked to the different linkage groups.
Genstat 12 was also used to carry out regressions of the theta scores on the marker data and to estimate the percentage of the variance explained.
The latter was computed from the marker data according to Method 1 of VanRaden (2008).
We also used multi-factorial analysis of the marker data set to analyse the diversity of the Pisum germplasm.
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