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Joint mapping of quantitative-trait loci (QTLs) for multiple correlated traits plays an important role in unraveling the genetic architecture of complex traits.
The second approach is to jointly analyze multiple correlated traits.
The age-stratified results suggest that QTLs expressed over long periods of time and affecting multiple, correlated traits may be identified using genome scans and variance-component analysis to help detect early and/or late gene expression.
Studying the quantitative genetics of multiple correlated traits requires a joint modeling approach (Walsh and Blows 2009).
The diversity of implicated processes underscores that complex field environments can impose selection on multiple correlated traits.
To promote adaptation, selection on protein length variation must overcome deleterious effects of multiple correlated traits (pleiotropy).
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For example, graph-guided fused lasso analyzed multiple traits simultaneously by considering a network of multiple traits to find genetic markers with pleiotropic effects that affect multiple-correlated traits jointly (Kim and Xing, 2009).
However, the underlying factors linking multiple correlated life-history traits to molecular evolutionary rates remain unclear.
Finally, GFlasso is designed to identify additive effects of multiple genetic loci on correlated traits, and thus, the effects of multiple eQTL hotspots on each gene were additive.
Previous studies in fine mapping and map-based cloning have found that QTLs and genes can exhibit pleiotropic effects on multiple traits, and phenotypically correlated traits are often mapped together [ 30].
Selective breeding for a single social trait, the amount of surplus pollen stored in the nest (pollen hoarding) revealed a phenotypic architecture of correlated traits at multiple levels of biological organization in facultatively sterile female worker honeybees.
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multiple correlated processes
multiple correlated items
multiple correlated responses
multiple correlated SNPs
multiple correlated markers
multiple correlated descriptors
multiple unique traits
multiple correlated distributions
multiple correlated VMs
multiple correlated phenotypes
multiple correlated exposures
multiple correlated risks
multiple correlated sources
multiple correlated tests
multiple correlated signals
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