Body composition and gene expression QTL mapping in mice reveals imprinting and interaction effects

BMC Genetics

James M Reecy³  Jack CM Dekkers³  Richard G Tait³  Mary Sue Mayes³  Angela Cánovas²  Satyanarayana Rachagani¹  Ye Cheng³ 

[1] Current address: Department of Biochemistry and Molecular Biology, Department of Pathology and Microbiology, Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA;Genètica i Millora Animal, Institute de Recercai Tecnologia Agroalimentària, Lleida, Spain;Department of Animal Science, Iowa State University, 2255 Kildee, Ames, IA, USA

关键词: Mouse;    Interaction;    Imprinting;    Myostatin;    Body composition;    QTL mapping;    eQTL mapping;   

Others  :  1086441
DOI  :  10.1186/1471-2156-14-103

 received in 2013-07-02, accepted in 2013-10-22,  发布年份 2013

【 摘 要 】

Background

Shifts in body composition, such as accumulation of body fat, can be a symptom of many chronic human diseases; hence, efforts have been made to investigate the genetic mechanisms that underlie body composition. For example, a few quantitative trait loci (QTL) have been discovered using genome-wide association studies, which will eventually lead to the discovery of causal mutations that are associated with tissue traits. Although some body composition QTL have been identified in mice, limited research has been focused on the imprinting and interaction effects that are involved in these traits. Previously, we found that Myostatin genotype, reciprocal cross, and sex interacted with numerous chromosomal regions to affect growth traits.

Results

Here, we report on the identification of muscle, adipose, and morphometric phenotypic QTL (pQTL), translation and transcription QTL (tQTL) and expression QTL (eQTL) by applying a QTL model with additive, dominance, imprinting, and interaction effects. Using an F2 population of 1000 mice derived from the Myostatin-null C57BL/6 and M16i mouse lines, six imprinted pQTL were discovered on chromosomes 6, 9, 10, 11, and 18. We also identified two IGF1 and two Atp2a2 eQTL, which could be important trans-regulatory elements. pQTL, tQTL and eQTL that interacted with Myostatin, reciprocal cross, and sex were detected as well. Combining with the additive and dominance effect, these variants accounted for a large amount of phenotypic variation in this study.

Conclusions

Our study indicates that both imprinting and interaction effects are important components of the genetic model of body composition traits. Furthermore, the integration of eQTL and traditional QTL mapping may help to explain more phenotypic variation than either alone, thereby uncovering more molecular details of how tissue traits are regulated.

【 授权许可】

   
2013 Cheng et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 3.

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