In the current study 17 subcongenic strains from the B6.Cg-Pbwg1 (B6.Cg-(D2Mit33-D2Mit38)) congenic strain were developed to search for and fine map QTL affecting body weight and growth to the wild derived genomic region of B6.Cg-Pbwg1. By phenotypic characterization of several developed subcongenic strains and F2 segregating populations obtained from intercrosses between subcongenic and background strains the genomic regions of closely linked QTL with opposite effects on body weight were physically delineated.
To develop subcongenic strains, B6.Cg-Pbwg1 mice were crossed to C57BL/JJcl (B6) mice purchased from Clea Japan (Tokyo, Japan), and the F1 mice obtained were intercrossed to generate F2 mice. Since most of the previously identified QTL were clustered in the interval between D2Mit270 and D2Mit205, subcongenic strains covering this interval were selectively phenotyped. The F2 mice obtained were genotyped for 6 of 28 microsatellite markers on Chr 2. Recombinant mice obtained were used as founders for the development of 15 subcongenic strains (B6.Cg-Pbwg1/SR1 to B6.Cg-Pbwg1/SR15). In the second phase, two subcongenic strains (B6.Cg-Pbwg1/SR18 and B6.Cg-Pbwg1/SR19) were developed from descendants obtained from crosses between B6 and each of the B6.Cg-Pbwg1/SR8 and B6.Cg-Pbwg1/SR3 strains. The castaneus donor regions of the subcongenic strains developed were validated to be fixed by genotyping all markers on corresponding donor regions [Sup Fig. 1].
All pups were weaned at 3 weeks of age and fed standard chow. Body weight was measured at 1, 3, 6, 10 and 13 weeks of age in the B6 strain and the homozygous congenic and subcongenic strains and for the F2 segregating populations; weight gains were also calculated. The data was systematically analyzed using a linear mixed model of the statisitical discovery software JMP version 6.0.3.
Since the growth and body composition QTL were closely linked and clustered around two markers, D2Mit270 and D2Mit205, six subcongenic strains with overlapping and nonoverlapping donor regions were carefully selected [Fig.1] from the 17 subcongenic strains.
The body weights of B6.Cg-Pbwg1/SR8 mice were significantly lower at 6 and 13 weeks of age compared to those of B6.Cg-Pbwg1/SR3 . Since B6.Cg-Pbwg1/SR3 and B6.Cg- Pbwg1/SR8 strains had an overlapping interval between D2Mit7 and D2Mit270, it was concluded that the castaneus allele at a QTL within the interval of D2Mit270 and D2Mit472 decreased body weight at 6-13 weeks of age.
B6.Cg-Pbwg1/SR11, B6.Cg-Pbwg1/SR12, and B6.Cg-Pbwg1/SR14 showed significantly higher body weight gain at 13 weeks of age than B6 did. The body weight gain of B6.Cg-Pbwg1/SR8 was significantly lower than that of B6.Cg-Pbwg1/SR11. However, it was not significantly different between B6.Cg-Pbwg1/SR12 and B6.Cg-Pbwg1/ SR14; thus, it was concluded that a QTL affecting body weight gain at 13 weeks of age resided between D2Mit205 and D2Mit182.
To confirm the phenotypic effects of the above two QTL, Pbwg1.11 and Pbwg1.12, an independent study was performed using two F2 segregating populations produced from intercrosses between B6 and each of the two subcongenic strains, B6.Cg-Pbwg1/SR8 (containing Pbwg1.11) and B6.Cg- Pbwg1/SR14 (containing Pbwg1.12).
When the results of subcongenic comparisons and F2 analysis were taken together, it was concluded that at least two distinct QTL with opposite effects on body weight were very closely linked on mouse Chr 2. One was Pbwg1.11 (postnatal body weight growth 1.11), whose wild-derived castaneus allele reduced body weight at 6-13 weeks of age and mapped to an approximate 8.9-Mb interval between D2Mit270 and D2Mit472, and the other was Pbwg1.12 (postnatal body weight growth 1.12), whose castaneus allele increased body weight at 6-13 weeks of age and mapped to an approximate 3.6-Mb interval between D2Mit205 and D2Mit182.
Using Positional Medline (http://omicspace.riken.jp/PosMed/), a database search engine that ranks positional candidate genes based on published literature (Kobayashi and Toyoda 2008; Yoshida et al. 2009). Nr4a2, Kcnj3, Gpd2, Itgb6, and Acvr1 were ranked as the top five candidate genes for the Pbwg1.11 QTL, and Grb14, Galnt3, Scn9a, Fign, and Scn7a were ranked as the top five candidate genes for the Pbwg1.12 QTL [Sup Table 4].