Mapping Data

Experiment

• Experiment
  TEXT-QTL
• Chromosome
  13
• Reference
  J:268061 Monir MM, et al., beta-composite Interval Mapping for robust QTL analysis. PLoS One. 2018;13(12):e0208234
• ID
  MGI:6316048

Genes

Gene	Assay Type
Kidrq3	visible phenotype
Kidlq2	visible phenotype

Notes

• Experiment
  Interval mapping approaches have been playing significant role for QTL mapping to discover genetic architecture of diseases or traits with molecular markers. Composite interval mapping (CIM) is one of the superior approaches of the interval mapping for discovering both linked and unlinked putative QTL positions. However, estimators of this approach are not robust against phenotypic outliers. In this study, we investigated the performance of Beta-Composite Interval Mapping (BetaCIM) for detecting both linked and unlinked important QTL positions from the robustness points of views.
  
  Performance of this approach depends on the value of tuning parameter Beta. It reduces to the classical CIM approach for Beta --> 0. The authors described and formulated the cross-validation procedure for selecting trait specific optimum value of Beta. It was observed that the optimum value of Beta depends on both amount of contaminated observations and their scatteredness. The BetaCIM approach discovers similar QTL positions as classical IM/CIM in the absence of phenotypic outliers, but gives better results in the presence of phenotypic outliers in terms of detecting true QTL and effects estimation.
  
  The authors formulated the generalized forms of robust QTL analysis and developed an R-package named BetaCIM by implementing this robust approach.
  
  Data from an experiment on multiple traits in the mouse was downloaded from mouse phenome database (https://phenome.jax.org/projects/Feng1). Left and right kidney weight data sets of mouse intercross population (129S1/SvImJ A/J) were analyzed by using BetaCIM, CIM, and IM approaches. There was a total of 336 intercross individuals, aged 8 weeks, genotyped at 91 markers.
  
  For right kidney weight, CIM and BetaCIM provided similar LOD score profiles, and both approaches identified 3 QTL positions:
  
  QTL Kidrq1 (kidney weight, right QTL 1) maps to Chr 9 with a peak LOD score of 5.18 (LODBeta = 5.17) at 42.14 cM with nearby marker rs3676158 (40.876 cM; LOD = 5.16; LODBeta = 5.15).
  
  QTL Kidrq2 (kidney weight, right QTL 2) maps to Chr 10 with a peak LOD score of 3.90 (LODBeta = 3.89) at 67.941 cM (rs3674646).
  
  QTL Kidrq3 (kidney weight, right QTL 3) maps to Chr 13 with a peak LOD score of 3.53 (LODBeta = 3.42) at 45.055 cM (rs3716022).
  
  The IM approach identified similar QTL.
  
  For left kidney weight, there was evidence of one outlying observation, and IM and CIM approaches did not identify any significant QTL.
  However, the BetaCIM approach identified 2 QTL positions:
  
  QTL Kidlq1 (kidney weight, left QTL 1) maps to Chr 5 (12.43 - 21.43 cM) with a peak LODBeta score of 3.78 at 16.550 cM (rs3023765).
  
  QTL Kidlq2 (kidney weight, left QTL 2) maps to Chr 13 (20.30 - 29.30 cM) with a peak LODBeta score of 4.54 at 24.6737 cM (rs3676930).
  
  The most promising candidate gene underlying the Kidlq1 locus is Otof. Individuals with moderate chronic kidney disease display a higher prevalence of hearing loss than those of the same age without CKD. Mice lacking the Otof gene display hearing loss, and mutations in human Otof orthologs are a known cause of neurosensory nonsyndromic recessive deafness, hearing loss.