Dear all,
I have a genetic map constructed for the F2 cross and a corresponding phenotype data for parents and progeny. The problem with the phenotype data (it is the measurements of 5 biochemical substances) that is not normally distributed and some of the substances are depenedent observations. I currently use the composite interval mapping (CIM) approach for each of the traits in r/qtl software but still, I am not sure that this one is relevant since I did not find any evidence that this approach works well with non-normally distributed data. However, CIM still finds the loci that we expect. But I did not find anything better.
Could you please suggest some practices on how to perform QTL mapping on dependent and non-normally distributed traits. And please share your opinion if the usage of composite interval mapping is appropriate in my case.
Hi Rim Gubaev you need to normalize your data prior to the analysis in R. there are many functions in R do that but this depend on the type of your data,
Rim Gubaev Simple data transformation may change significance of linkadge, but will not remove the factor that has caused skeweness and covariation between the measured parameters. You can make several assamptions about nature of non-normal distribition /covariation of your data and test them within more complex model, like Malosetti M, Voltas J, Romagosa I, Ullrich SE, Van Eeuwijk FA. Mixed models including environmental covariables for studying QTL by environment interaction. Euphytica. 2004 Jun 1;137(1):139-45. In my experience, skewed segregation in F2 was caused by presense of nuclear and cytoplasmic genes interaction, lethal recessive alleles, selection agains highly susceptible male gametes during pollination (too cold or too hot weather).
This paper may be helpful: A Nonparametric Approach for Mapping Quantitative Trait Loci
Article Kruglyak L, Lander ES. A nonparametric approach for mapping ...
If your population size is small (less than 200), QTL effects can be overestimated using CIM
A block bootstrap may be an approach here. This has been used in other areas to analyse non normally distributed dependent data.
A second approach would be to use a Bayesian model to examine the data. The downside here is that this requires a theorectical model of the linkage. This may not be doable. Bootstrapping is model free.
The usual parametric methods are not applicable as these require independence. Some of non parametric methods might be applicable but again most of these require independence.
A Markov model system might be an alternative but this would depend on the linkages. .
Hey Rim Gubaev, If your phenotypic data is asymmetrical (non normal distributed) then you have transfer the arithmetic mean to geometric mean. You might observed the normal distribution to the geometric mean. Rim Gubaev
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