Identification of a Putative Quantitative Trait Gene for Resistance to Obesity in Mice Using Transcriptome Analysis and Causal Inference Tests

Abstract

It is still challenging to identify causal genes governing obesity. Pbwg1.5, a quantitative trait locus (QTL) for resistance to obesity, was previously discovered from wild Mus musculus castaneus mice and was fine-mapped to a 2.1-Mb genomic region of mouse chromosome 2, where no known gene with an effect on white adipose tissue (WAT) has been reported. The aim of this study was to identify a strong candidate gene for Pbwg1.5 by an integration approach of transcriptome analysis (RNA-sequencing followed by real-time PCR analysis) and the causal inference test (CIT), a statistical method to infer causal relationships between diplotypes, gene expression and trait values. Body weight, body composition and biochemical traits were measured in F2 mice obtained from an intercross between the C57BL/6JJcl strain and a congenic strain carrying Pbwg1.5 on the C57BL/6JJcl background. The F2 mice showed significant diplotype differences in 12 traits including body weight, WAT weight and serum cholesterol/triglyceride levels. The transcriptome analysis revealed that Ly75, Pla2r1, Fap and Gca genes were differentially expressed in the liver and that Fap, Ifih1 and Grb14 were differentially expressed in WAT. However, CITs indicated statistical evidence that only the liver Ly75 gene mediated between genotype and WAT. Ly75 expression was negatively associated with WAT weight. The results suggested that Ly75 is a putative quantitative trait gene for the obesity-resistant Pbwg1.5 QTL discovered from the wild M. m. castaneus mouse. The finding provides a novel insight into a better understanding of the genetic basis for prevention of obesity.

Adiposity QTLAdip20decomposes into at least four loci when dissected using congenic strains

“…For complex traits, the challenge is now to find 337 new ways to winnow a large number of variants to only a few or to find a way to test many 338 variants and their combinations rapidly. While there are statistical methods to winnow the 339 candidate gene by trying to parse causal relationships among genotype gene expression and 340 trait [83][84][85], ultimately we need direct tests of individual genotypes on traits. 341…”

“…For instance, the 358 microarray approach we used here assumes that causal variation affects gene expression in the 359 gonadal adipose depot of adult mice, but the actual causal variants may not affect gene 360 expression at all or may act in a different tissue or at a different time in development. Obviously, 361 the limitation of microarray experiments is the target tissue selection, and we acknowledge that 362 genotype effects in other tissue could be causal (e.g., liver tissue [84,88]). These points made, 363 there was some convergence among methods.…”

Adiposity QTL Adip20 decomposes into at least four loci when dissected using congenic strains

“…For instance, the microarray approach we used here assumes that causal variation affects gene expression in the gonadal adipose depot of adult mice, but the actual causal variants may not affect gene expression at all or may act in a different tissue or at a different time in development. Obviously, the limitation of microarray experiments is the target tissue selection, and we acknowledge that genotype effects in other tissue could be causal (e.g., liver tissue [ 84 , 88 ]). These points made, there was some convergence among methods.…”

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis

“…Using 3 genotypes, data for 48 quantitative traits (body weight, body composition and biochemical levels), and data for differentially expressed genes ( Table 2 ) measured in a segregating F 2 population obtained from an intercross between SR1 subcongenic and B6 strains, CIT analysis was performed [ 19 ]. None of the four genes differentially expressed in the gonadal fat pad passed all four CIT component tests.…”

“…Exome sequencing (exome-seq) and bioinformatics analysis revealed two candidate genes for each of Pbwg1.12 and Pbwg1.5 [ 18 ]. Finally, by using an integrated approach of mRNA expression analysis and causal analysis inferring causal relationships between genotypes, gene expression and trait values, we succeeded in revealing that Ly75 (lymphocyte antigen 75) is a putative QTG for Pbwg1.5 , though we did not succeed in finding a QTG for Pbwg1.12 [ 19 ].…”