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

Abstract

BackgroundShifts 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.ResultsHere, 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.ConclusionsOur 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.

Estimation of Additive, Dominance, and Imprinting Genetic Variance Using Genomic Data

“…In this study, the trait with the greatest proportion of phenotypic variance explained by imprinting effects was backfat (0.017 in the Landrace, 0.029 in the Large White, and 0.020 in the Pietrain population), although the estimates were still quite low, in comparison with the amount of narrow-sense heritability and the proportion of phenotypic variance explained by dominance effects. In mice, a gene expression QTL mapping study for body composition traits showed that imprinting QTL accounted for only a limited amount of the phenotypic variance (<2.50%) for most traits ( Cheng et al 2013 ). In a pedigree-based study in pigs, it was shown that 5–7% of the phenotypic variance of backfat and 1–4% of growth rate was explained by paternal imprinting ( De Vries et al 1994 ).…”

“…However, the estimation of heritability via the use of additive models does not only capture additive gene action but can potentially also capture part of the dominance effects and epistatic interactions ( Hill et al 2008 ; Falconer and Mackay 1996 ). In addition, traditional additive models ignore imprinting effects, which also are expected to contribute to the genetic architecture and evolution of complex traits ( Lawson et al 2013 ; Cheng et al 2013 ). Therefore, the proportion of phenotypic variation that is explained by all genetic effects and how much of the total genetic variation is actually due to additive effects is still unclear in modern genetics ( Vinkhuyzen et al 2013 ).…”

TheMstncmpt-Dl1abcmutation impairs secretion of promyostatin

“…In harmony with the difference between Compact mice and myostatin-null mice, the spectra of modifier loci identified in myostatin-null mice [33,34] are markedly different from those defined using Compact mice [12,13,25,26]: in the case of myostatin-null mice twelve Quantitative Trait Loci were identified on mouse chromosomes 1,3, 6, 7 that significantly interacted with the myostatin genotype [34], whereas in the case of compact mice hypermuscularity was associated with several unrelated markers, markers on chromosomes 16 and X showing the strongest association [12,13]. 0.85 GMQE score*: 0.55 *GMQE scores (Global Model Quality Estimation score, ranging between 0-1) combine properties from the target-template alignment and reflect the expected accuracy of a model built with that alignment and template.…”

Gene Co-Expression Network Analysis Provides Novel Insights into Myostatin Regulation at Three Different Mouse Developmental Timepoints

“…The identification of Atp2a2 (a.k.a. Serca2a ) as the top-ranked RIF2 gene at d35 is particularly noteworthy, because it was previously identified as a potential modifier of MSTN in a large F2 mouse study [ 47 ]. The ATP2A2 calcium pump affects sarcoplasmic reticulum function and muscle fiber type.…”

“…The ATP2A2 calcium pump affects sarcoplasmic reticulum function and muscle fiber type. Transgenic Atp2a2 mice exhibit cardiac hypertrophy [ 47 – 50 ]. Furthermore, pharmacological inhibition of SMAD2 in cardiomyocytes indicates that TGFβ/SMAD2 signaling regulates ATP2A2 function [ 51 ].…”