Mapping Data

Experiment

• Experiment
  TEXT-QTL
• Chromosome
  14
• Reference
  J:245093 Wang RJ, et al., Genetics of Genome-Wide Recombination Rate Evolution in Mice from an Isolated Island. Genetics. 2017 Aug;206(4):1841-1852
• ID
  MGI:6115327

Genes

Gene	Assay Type
Recrq10	visible phenotype
Rec8
Ccnb1ip1

Notes

• Experiment
  Recombination rate is a heritable quantitative trait that evolves despite the fundamentally conserved role that recombination plays in meiosis.
  
  In the current study autosomal recombination was quantified by immunofluorescence cytology in spermatocytes from 240 F2 males ((86-(GI/Wisc x WSB/EiJ)F2, 154-(WSB/EiJ x GI/Wisc)F2)) generated from intercrosses between GI/Wisc-derived mice (Mus musculus domesticus) from Gough Island and the wild-derived inbred strain WSB/EiJ.
  
  Spermatocyte spreads were prepared from 5 WSB/EiJ mice, five GI/Wisc mice and a subset of F2 males to characterize individual recombination rates. Mice were euthanized at ~16 weeks of age and the left testis from each was collected and processed. Cells were selected for scoring based on the morphology of the synaptonemal complex and the robustness of the MLH1 signal.
  
  All mice were genotyped with the Mega Mouse Universal Genotyping Array. Liver tissue from sacrificed mice were extracted and sent to GeneSeek for genotyping. Recombination fractions were converted to map distances with the Carter-Falconer function. The number of crossovers inherited by each individual were estimated by fitting the marker data to a hidden Markov model.
  
  The mean number of MLH1 foci among spermatocytes were calculated for each individual and Haley-Knott regression analysis was used to identify quantitative trait loci (QTL) for the mean MLH1 focus count using data from the 240 F2 individual mice. Thresholds for genome wide significance, at 0.05, were established from 1000 permutation replicates. Effects of cross direction were investigated by adding it as an interactive covariate and by performing separate analyses on each cross direction. Cross direction was also treated as an additive covariate.
  
  Table 1: QTL for the genome wide recombination rate:
  
  QTL Recrq8 (recombination rate in male meiosis QTL 8) mapped to Chromosome 5 peaking at 83.0 Mb (36.9 cM) in a multiple QTL model with a LOD score of 4.61. The 95% confidence interval spanned from 74.3 to 132.5 Mb and accounted for 6.1 % of trait variance. The GI/Wisc allele at this locus acted additively and was associated with an increase in the mean crossover number.
  
  QTL Recrq9 (recombination rate in male meiosis QTL 9) mapped to Chromosome 6 peaking at 81.4 Mb (29.8 cM) in a multiple QTL model with a LOD score of 4.50. The 95% confidence interval spanned from 73.5 to 134.7 Mb and accounted for 5.9 % of trait variance. The GI/Wisc allele at this locus acted additively and was associated with an increase in the mean crossover number.
  
  QTL Recrq10 (recombination rate in male meiosis QTL 10) mapped to Chromosome 14 peaking at 56.5 Mb (20.1 cM) in a multiple QTL model with a LOD score of 7.57. The 95% confidence interval spanned from 28.2 to 60.9 Mb and accounted for 10.4 % of trait variance. The GI/Wisc allele at this locus conferred a reduction in mean crossover number and displayed signs of over dominance.
  
  When cross direction was added to the analysis as an interactive covariate an additional QTL was detected:
  
  QTL Recrq11 (recombination rate in male meiosis QTL 11) mapped to Chromosome 10 peaking at 122.8 Mb (58.0 cM) in a multiple QTL model with a LOD score of 5.12. The 95% confidence interval spanned from 114.5 to 123.8 Mb and accounted for 6.9% of trait variance. The GI/Wisc allele at this locus acted additively and was associated with an increase in the mean crossover number. Analysis results suggested that this QTL was either segregating within the GI/Wisc line, interacting with loci segregating within the GI/Wisc line or interacting with a maternal effect.
  
  Table 2:
  Candidate genes and mutations for these QTL were identified by overlapping the detected intervals (GRCm38/mm10) with whole genome sequencing data and publicly available transcriptomic profiles from spermatocytes. The most promising candidate genes were Rnf212 mapping within the Recrq8 QTL on Chr 5, and Rec8 and Ccnb1ip1 mapping within the Recrq10 QTL on Chr 14.