Further mapping of quantitative trait loci for postnatal growth in an intersubspecific backcross of wild Mus musculus castaneus and C57BL/6J mice

Abstract

We performed a quantitative trait locus (QTL) analysis of eight body weights recorded weekly from 3 weeks to 10 weeks after birth and two weight gains recorded between 3 weeks and 6 weeks, and between 6 weeks and 10 weeks in an inter-sub-specific backcross population of wild Mus musculus castaneus mice captured in the Philippines and the common inbred strain C57BL/6J ( M. musculus domesticus ), to elucidate the complex genetic architecture of body weight and growth. Interval mapping identified 17 significant QTLs with main effects on 11 chromosomes. In particular, the main effect of the most potent QTL on proximal chromosome 2 increased linearly with age, whereas other QTLs exerted effects on either the early or late growth period. Surprisingly, although wild mice displayed 60% of the body size of their C57BL/6J counterparts, the wild-derived allele enhanced growth at two QTLs. Interestingly, five of the 17 main-effect QTLs identified had significant epistatic interaction effects. Five new epistatic QTLs with no main effects were identified on different chromosomes or regions. For one pair of epistatic QTLs, mice that were heterozygous for the wild-derived allele at one QTL and homozygous for that allele at another QTL exhibited the most rapid growth in all four possible genotypic combinations. Out of the identified QTLs, several showed significant sex-specific effects.

Detection of Quantitative Trait Loci Causing Abnormal Spermatogenesis and Reduced Testis Weight in the Small Testis (Smt) Mutant Mouse

“…When this assumption is violated, the contrast between alleles from the Smt and MOM will reduce, leading to a reduction in power to detect QTLs, as described in a previous QTL study using non-inbred wild mice [15]. Simple interval mapping (SIM) of both raw data and transformed data for all five traits were performed following the method of Ishikawa et al [14] with the computer software Map Manager QTXb20, which is based on a regression method [20], to detect QTLs with main effects and epistatic interaction effects. Furthermore, composite interval mapping (CIM) of transformed data was performed with the computer software Windows QTL Cartographer 2.0 [29], as described by Ishikawa et al [14].…”

“…Simple interval mapping (SIM) of both raw data and transformed data for all five traits were performed following the method of Ishikawa et al [14] with the computer software Map Manager QTXb20, which is based on a regression method [20], to detect QTLs with main effects and epistatic interaction effects. Furthermore, composite interval mapping (CIM) of transformed data was performed with the computer software Windows QTL Cartographer 2.0 [29], as described by Ishikawa et al [14]. CIM can perform QTLs mapping more precisely than SIM because it can control spurious ghost loci by controlling the genetic background containing the other QTLs [34].…”

“…As described by Ishikawa et al [14], two significance tests for total and interaction effects of a marker pair were performed to detect QTLs with epistatic interaction effects on transformed data for five traits. The genome-wide 5% and 1% significance levels for total effects, determined by the permutation test of the software Map Manager QTX, were 7.9-9.7 and 11.0-16.0, respectively.…”

Development and Characterization of a Congenic Strain Carrying Pbwg12, a Growth QTL on Mouse Chromosome 12

“…In previous genome-wide QTL studies by our groups, 24 QTLs for body weight and growth at 3-10 weeks after birth were mapped on 13 mouse chromosomes in an intersubspecific backcross population between wild Mus musculus castaneus mice captured in the Philippines and C57BL/6J, a common inbred strain [5,7,8]. Among the 24 QTLs mapped, the Pbwg12 QTL located on chromosome 12 does not have a main effect on body weight but interacts epistatically with Pbwg1 on chromosome 2, a potent main-effect QTL [5]. This interaction effect increases linearly with advance of age and accounts for up to 9% of the total phenotypic variance.…”

“…Furthermore, we phenotypically characterized the congenic strain developed in comparison with C57BL/6J to confirm whether Pbwg12 has a main effect on body weight or not, and to fine-map new QTLs for growth and body composition traits, such as internal organ weight, in the introgressed congenic region. Body composition traits were not investigated in our previous genome-wide QTL studies [5,7,8].…”