qtlmap
NAME
QTLMap
DESCRIPTION
QTLMap is a software dedicated to the detection of QTL from experimental designs in outbred population.
QTLMap software is developed by the Animal Genetics Division at INRA (French National Institute for Agronomical Research).
The statistical techniques used are linkage analysis (LA) and linkage disequilibrium linkage analysis (LDLA) using interval mapping.
Different versions of the LA are proposed from a quasi Maximum Likelihood approach to a fully linear (regression) model.
The LDLA is a regression approach (Legarra and Fernando, 2009). The population may be sets of half-sib families or mixture of full- and half- sib families.
The computations of Phase and Transmission probabilities are optimized to be rapid and as exact as possible.
QTLMap is able to deal with large numbers of markers (SNP) and traits (eQTL).
The aim of QTLMap developers is to propose various genetic models depending on:
* the number of QTL alleles segregating (biallelic in crosses between monomorphic breeds, biallelic without hypothesis on the origin, multiallelic, haplotype identity),
* the number of QTL segregating (one, two linked, several unlinked),
* the number of traits under the QTL influence. The trait determinism may vary depending on :
- the trait distribution (gaussian trait, survival trait or threshold distribution),
- the interactions between the QTL and fixed effects or other loci,
- the residual variance structure (homo- or heteroskedasticity for half-sib families).
Due to differences with the asymptotical conditions from the chi2 theory, the test statistic significance are evaluated either through numerical approximations,
or through empirical calculations obtained from permutations or simulations under the null hypothesis.
QTLmap is written in fortran 95 and use the OpenMP API (Parallel Programming).
Up to now, the following functionnalities have been implemented :
* QTL detection in half-sib families or mixture of full- and half-sib families
* One or several linked QTL segregating in the population
* Single trait or multiple trait
* Nuisance parameters (e.g. sex, batch, weight...) and their interactions with QTL can be included in the analysis
* Gaussian, discrete or survival (Cox model) data
* Familial heterogeneity of variances (heteroscedasticity)
* Can handle eQTL analyses
* Computation of transmission and phase probabilities adapted to high throughput genotyping (SNP)
* Empirical thresholds are estimated using simulations under the null hypothesis or permutations of trait values
* Computation of power and accuracy of your design or any simulated design
SEE ALSO
http://www.inra.fr/qtlmap