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The vast majority of lost DNA disrupted parts of the genome that control how genes are expressed.
Similar to QTLs controlling metabolite accumulation, regions in the genome that control variation in the transcript level for a given gene (eQTL) can be identified [18] [21].
These quantitative trait locus (QTL) mapping techniques have been successful in identifying regions of the genome that control phenotypic variation, but have been less productive when it comes to the identification of causative functional DNA variants or, more importantly, how these variants act at the molecular level to drive phenotypes [1].
There are hundreds of genes in the nuclear genome that control chloroplast biogenesis or function, and mutants in these genes manifest as albino, pale, or yellow-leaf phenotypes.
However, in peanut there is an almost complete lack of knowledge of the regions of the Arachis genome that control disease resistance.
A prediction of our hypothesis is that we should be able to find areas of the rice genome that control both constitutive expression of defense-related genes and partial resistance.
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DNA methylation is a critical epigenetic modification of the genome that controls many biologic processes, including embryonic development, X chromosome inactivation, imprinting, and gene expression [ 45, 46].
One method for reducing the number of false positives is to require that the area of the genome that controls the transcription of a candidate gene also controls variation in the phenotype of interest.
Moreover, there is intrinsic sequence information in a large fraction of the genome that controls nucleosome positioning despite the absence of any detectable sequence motif (Segal and Widom 2009).
Wu and group are among the first to develop statistical models and algorithms for characterizing maternal-zygotic effects of quantitative trait loci (QTLs) on seed development [25], [27], [28], [50], and further used these models to map genome-genome interactive QTLs that control endosperm traits in rice [26] and maize [49].
A better understanding of the size and abundance of open reading frames (ORFS) in whole genomes may shed light on the factors that control genome complexity.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com