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Paradoxically, molecular traits, such as gene expression, methylation, protein or metabolite levels, typically have a lower heritability, but sometimes individual genetic variants show much higher effect sizes on these traits.
The evolution of complex molecular traits such as disulphide bridges often requires multiple mutations.
Molecular traits such as mRNA expression levels, metabolite concentrations, and protein levels are often highly correlated and the Q-method is not adequate in these situations.
As molecular phylogenetics has been unable to satisfactorily resolve the issue, others have explored alternative molecular traits, such as the conservation of gene order.
Within these families, genera are defined by other molecular traits, such as the cos or pac sites, terminal redundancy and circular permutation of the genome, concatemer formation, modified bases, and the presence of DNA polymerase or RNA polymerase genes.
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However, as more information becomes available at the molecular level, traits such as these could play a more important role in arachnid classification.
First, we are developing new methods to map genetic variants affecting molecular-level traits (such as DNA methylation and transcription factor binding) throughout the genome, which do not require any individual-level genotyping or phenotyping.
The elements of influenza pandemic risk assessment that are most amenable to computational prediction are those that correspond to well-defined, quantifiable molecular-scale traits such as receptor-binding preference, antiviral susceptibility, antigenicity of HA and NA, and possibly T-cell epitopes.
For example, Hittel and Storey (2001) have used this approach to study the molecular basis of traits, such as hibernation, not present in model species.
The ability to type molecular traits of pathogens, such as surface proteins or highly variable DNA segments, allowed the characterization of sufficient strain-to-strain variation to determine when transmission of disease occurred [ 2] as well as surveillance of the frequencies of different strain types over time [ 3].
Comparative genomics has emerged as a promising means of unravelling the molecular networks underlying complex traits such as drought tolerance.
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