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Angiogenesis QTL on Mouse Chromosome 8 Colocalizes with Differential ?-Defensin Expression.


ABSTRACT: Identification of genetic factors that modify complex traits is often complicated by gene-environment interactions that contribute to the observed phenotype. In model systems, the phenotypic outcomes quantified are typically traits that maximize observed variance, which in turn, should maximize the detection of quantitative trait loci (QTL) in subsequent mapping studies. However, when the observed trait is dependent on multiple interacting factors, it can complicate genetic analysis, reducing the likelihood that the modifying mutation will ultimately be found. Alternatively, by focusing on intermediate phenotypes of a larger condition, we can reduce a model's complexity, which will, in turn, limit the number of QTL that contribute to variance. We used a novel method to follow angiogenesis in mice that reduces environmental variance by measuring endothelial cell growth from culture of isolated skin biopsies that varies depending on the genetic source of the tissue. This method, in combination with a backcross breeding strategy, is intended to reduce genetic complexity and limit the phenotypic effects to fewer modifier loci. We determined that our approach was an efficient means to generate recombinant progeny and used this cohort to map a novel s.c. angiogenesis QTL to proximal mouse chromosome (Chr.) 8 with suggestive QTL on Chr. 2 and 7. Global mRNA expression analysis of samples from parental reference strains revealed ?-defensins as potential candidate genes for future study.

SUBMITTER: Smith J 

PROVIDER: S-EPMC4365986 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Angiogenesis QTL on Mouse Chromosome 8 Colocalizes with Differential β-Defensin Expression.

Smith Jason J   Liu Fang F   Beyer Barbara B   Morales Krista K   Reilly Andrew A   Cole Richard R   Herron Bruce J BJ  

Journal of biomolecular techniques : JBT 20150701 2


Identification of genetic factors that modify complex traits is often complicated by gene-environment interactions that contribute to the observed phenotype. In model systems, the phenotypic outcomes quantified are typically traits that maximize observed variance, which in turn, should maximize the detection of quantitative trait loci (QTL) in subsequent mapping studies. However, when the observed trait is dependent on multiple interacting factors, it can complicate genetic analysis, reducing th  ...[more]

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