Mapping Data

Experiment

• Experiment
  TEXT-Congenic
• Chromosome
  6
• Reference
  J:161397 Shao H, et al., Analyzing complex traits with congenic strains. Mamm Genome. 2010 Jun;21(5-6):276-86
• ID
  MGI:5776376

Genes

Gene
Bmiq1
Bmiq2
Bmiq3
Bmiq4

Notes

• Reference
  Sequential congenic segment analysis:
  
  The authors propose an alternative to 'common segment' congenic strain analysis to identify QTL. They have proposed calling the alternative method 'sequential' anaylsis. It is based on a unique principal of QTL analysis where each strain, corresponding to a single genotype, is tested individually for QTL effects rather than testing the congenic panel collectively for common effects across heterogeneous backgrounds.
  
  The sequential method is based on comparing phenotypes for sequential pairs of congenic strains, beginning with the strain with the shortest congenic segment and the host strain, and then in a stepwise fashion to strains with progressively longer, overlapping congenic segments. If the phenotypes for the strain with the shortest congenic segment and the host strain differ significantly, the conclusion is that at least one QTL maps to the congenic segment. Next, the congenic strain with the next longer, overlapping segment is compared to the previous congenic strain. If the introduced segment has a QTL the phenotypes for the first and second congenic strains will differ significantly, assigning a QTL to the chromosome segment that differs between the two strains. The process is repeated until each strain in the panel has been tested once and only once.
  
  A panel of 15 congenic strains was derived from the C57BL/6J-Chr6^A/J/NaJ chromosome substitution strain (CSS-A6);
  a panel of 9 congenic strains was derived from the C57BL/6J-Chr10^A/J/NaJ chromosome substitution strain (CSS-A10); and
  a panel of 7 congenic strains derived from C57BL/6J-Chr13^A/J/NaJ chromosome substitution strains (CSS-A13). Each panel collectively spans the length of the chromosome, and the congenic segments are bounded on one end by a telomere, except of 6C15 and 13C25 strains.
  
• Experiment
  For the CSS-A6 and the CSS-A10 congenic panels five traits related to diet-induced obesity and metabolic disease were studied. Males from the two congenic panels as well as the C57BL/6J host and the A/J donor strains were weaned at 3 weeks of age. At 35 days old they were placed on either a high fat, simple-carbohydrate diet or a low-fat complex-carbohydrate diet for app 100 days; at which point they were weighed and various metabolic traits were measured. Body mass index (BMI) was the focus for the CSS-A6 panel and blood gluscose (GLU) and insulin levels as well as HOMA for the CSS-A10 panel. Timing of vaginal puberty (age of vaginal opening, VO) and body weight (BW) at VO for females were the focus of the CSS-A13 strain and the seven congenic strains in the CSS-A13 panel.
  
  The sequential method provided unambiguous evidence for four BMI QTL on Chromosome 6:
  
  On strain 6C2 (B6.A-(D6Mit138-D6Mit159)/6C2Na) the BMI was significantly less than that for 6C1 (B6.A-(6cen-D6Mit138)/6C1Na), demonstrating that a QTL, Bmiq1 (body mass index QTL 1) mapped between markers 4.5 Mb and 29.8 Mb in the congenic segment that differed between these two strains.
  
  The BMI for 6C3 (B6.A-(D6Mit138-D6Mit223)/6C3Na) was significantly greater than that for 6C2, indicating that a second QTL, Bmiq2 (body mass index QTL 2) was located in the interval between markers at 29.8 and 45.5 Mb that was unique to 6C3.
  
  Comparing BMIs for 6C4 (B6.A-(D6Mit138-D6Mit274)/6C4Na) and 6C3 (B6.A-(D6Mit138-D6Mit223)/6C3Na) revealed a third QTL, Bmiq3 (body mass index QTL 3) between markers at 45.5 and 55.3 Mb on congenic segment 6C4.
  
  The BMI for 6C12 (B6.A-(D6Mit284-D6Mit15)/6C12Na) was significantly less than that for 6C13 (D6Mit254-D6Mit15)/6C13Na), indicating a fourth QTL Bmiq4 (body mass index QTL 4) between markers at 93 and 126 Mb.
  
  Comparison p values for Bmiq1-4 Fig. 1, A.
  
  With these results the authors conclude that the sequential method performs better than the common-segment, interval mapping and multiple linear regression methods.