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However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking.
Most complex phenotypes arise as a result of the interplay between genetics and environment.
Longevity and healthy aging are among the most complex phenotypes studied to date.
It can be imagined that the underlying genetic architecture for most complex phenotypes is far more complicated than any mathematical models could fully accommodate.
However, as most complex phenotypes are now thought to be influenced by a myriad of factors of small effect [ 53, 54], more subtle differences in DNA methylation levels may be important.
This phenomenon is of the utmost importance to the exploration of the genetic basis of complex phenotypes, as most complex phenotypes have been found to be affected by numerous loci, rather than a single locus (McClellan and King, 2010).
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Genetic association studies are considered to be an effective approach towards the detection of SNPs with moderate effects, as in most common diseases with complex phenotypes.
Conceptually related to mass mating, genome shuffling is one of the most recent techniques to improve complex phenotypes in microorganisms in a fast and relatively easy manner (see Gong et al., 2009).
This research strategy is based on the supposition that it is easier to reliably identify the neural correlates of simple behavioural states and traits than the complex phenotypes typical of most mental disorders.
In humans, genetic interactions are involved in many complex phenotypes and they contribute to most genetic disorders, but the organization of the underlying networks is largely unknown [2], [3].
Bridging the gap between statistical and biological epistasis is an essential next step towards understanding the genetics of complex phenotypes such as Alzheimer's disease, since most phenotypes in complex organisms involve epistasis.
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