Burly1 is a mouse QTL for lean body mass that maps to a 0.8-Mb region of chromosome 2
Mammalian Genome, 2018•Springer
To fine map a mouse QTL for lean body mass (Burly1), we used information from intercross,
backcross, consomic, and congenic mice derived from the C57BL/6ByJ (host) and 129P3/J
(donor) strains. The results from these mapping populations were concordant and showed
that Burly1 is located between 151.9 and 152.7 Mb (rs33197365 to rs3700604) on mouse
chromosome 2. The congenic region harboring Burly1 contains 26 protein-coding genes, 11
noncoding RNA elements (eg, lncRNA), and 4 pseudogenes, with 1949 predicted functional …
backcross, consomic, and congenic mice derived from the C57BL/6ByJ (host) and 129P3/J
(donor) strains. The results from these mapping populations were concordant and showed
that Burly1 is located between 151.9 and 152.7 Mb (rs33197365 to rs3700604) on mouse
chromosome 2. The congenic region harboring Burly1 contains 26 protein-coding genes, 11
noncoding RNA elements (eg, lncRNA), and 4 pseudogenes, with 1949 predicted functional …
Abstract
To fine map a mouse QTL for lean body mass (Burly1), we used information from intercross, backcross, consomic, and congenic mice derived from the C57BL/6ByJ (host) and 129P3/J (donor) strains. The results from these mapping populations were concordant and showed that Burly1 is located between 151.9 and 152.7 Mb (rs33197365 to rs3700604) on mouse chromosome 2. The congenic region harboring Burly1 contains 26 protein-coding genes, 11 noncoding RNA elements (e.g., lncRNA), and 4 pseudogenes, with 1949 predicted functional variants. Of the protein-coding genes, 7 have missense variants, including genes that may contribute to lean body weight, such as Angpt41, Slc52c3, and Rem1. Lean body mass was increased by the B6-derived variant relative to the 129-derived allele. Burly1 influenced lean body weight at all ages but not food intake or locomotor activity. However, congenic mice with the B6 allele produced more heat per kilogram of lean body weight than did controls, pointing to a genotype effect on lean mass metabolism. These results show the value of integrating information from several mapping populations to refine the map location of body composition QTLs and to identify a short list of candidate genes.
Springer
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