mpp_SIM {mppR}

MPP Simple Interval Mapping

Description

Computes single QTL models along the genome using different models.

Usage

mpp_SIM(mppData, trait = 1, Q.eff = "cr", plot.gen.eff = FALSE, n.cores = 1)

Arguments

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

Q.eff

Character expression indicating the assumption concerning the QTL effects: 1) "cr" for cross-specific; 2) "par" for parental; 3) "anc" for ancestral; 4) "biall" for a bi-allelic. For more details see mpp_SIM. Default = "cr".

plot.gen.eff

Logical value. If plot.gen.eff = TRUE, the function will save the decomposed genetic effects per cross/parent. These results can be plotted with the function plot.QTLprof to visualize a genome-wide decomposition of the genetic effects. This functionality is ony available for the cross-specific, parental and ancestral models. Default value = FALSE.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

Details

The implemented models vary according to the number of alleles assumed at the QTL position and their origin. Four assumptions for the QTL effect are possible.

Concerning the type of QTL effect, the first option is a cross-specific QTL effects model (Q.eff = "cr"). In this model, the QTL effects are assumed to be nested within cross which leads to the estimation of one parameter per cross. The cross-specific model corresponds to the disconnected model described in Blanc et al. 2006.

A second possibility is the parental model (Q.eff = "par"). The parental model assumes one QTL effect (allele) per parent that are independent from the genetic background. This means that QTL coming form parent i has the same effect in all crosses where this parent is used. This model is supposed to produce better estimates of the QTL due to larger sample size when parents are shared between crosses.

In a connected MPP (design_connectivity), if np - 1 < nc, where np is the number of parents and nc the number of crosses, the parental model should be more powerful than the cross-specific model because it estimate a reduced number of QTL parameters. This gain in power will be only true if the assumption of constant parental effect through crosses holds. Calculated with HRT assumption, the parental model corresponds to the connected model presented in Blanc et al. (2006).

The third type of model is the ancestral model (Q.eff = "anc"). This model tries to use genetic relatedness that could exist between parents. Indeed, the parental model assumes that parent are independent which is not the case. Using genetic relatedness between the parents, it is possible group these parents into a reduced number of ancestral cluster. Parents belonging to the same ancestral group are assumed to transmit the same allele (Jansen et al. 2003; Leroux et al. 2014). The ancestral model estimate therefore one QTL effect per ancestral class. Once again, the theoretical expectation is a gain of QTL detection power by the reduction of the number of parameters to estimate. The HRT ancestral model correspond to the linkage desequilibrium linkage analysis (LDLA) models used by Bardol et al. (2013) or Giraud et al. (2014).

The final possibility is the bi-allelic model (Q.eff = "biall"). Bi-allelic genetic predictor are a single vector with value 0, 1 or 2 corresponding to the number of allele copy of the least frequent SNP allele. Relatedness between lines is therefore defined via identical by state (IBS) measurement. This model corresponds to models used for association mapping. For example, it is similar to model B in Wurschum et al. (2012) or association mapping model in Liu et al. (2012).

Value

Return:

SIM

Data.frame of class QTLprof. with five columns : 1) QTL marker names; 2) chromosomes; 3) interger position indicators on the chromosome; 4) positions in centi-Morgan; and 5) -log10(p-val). And if plot.gen.eff = TRUE, p-values of the cross or parental QTL effects.

Author(s)

Vincent Garin

References

Bardol, N., Ventelon, M., Mangin, B., Jasson, S., Loywick, V., Couton, F., ... & Moreau, L. (2013). Combined linkage and linkage disequilibrium QTL mapping in multiple families of maize (Zea mays L.) line crosses highlights complementarities between models based on parental haplotype and single locus polymorphism. Theoretical and applied genetics, 126(11), 2717-2736.

Blanc, G., Charcosset, A., Mangin, B., Gallais, A., & Moreau, L. (2006). Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theoretical and Applied Genetics, 113(2), 206-224.

Giraud, H., Lehermeier, C., Bauer, E., Falque, M., Segura, V., Bauland, C., ... & Moreau, L. (2014). Linkage Disequilibrium with Linkage Analysis of Multiline Crosses Reveals Different Multiallelic QTL for Hybrid Performance in the Flint and Dent Heterotic Groups of Maize. Genetics, 198(4), 1717-1734.

Jansen, R. C., Jannink, J. L., & Beavis, W. D. (2003). Mapping quantitative trait loci in plant breeding populations. Crop Science, 43(3), 829-834.

Leroux, D., Rahmani, A., Jasson, S., Ventelon, M., Louis, F., Moreau, L., & Mangin, B. (2014). Clusthaplo: a plug-in for MCQTL to enhance QTL detection using ancestral alleles in multi-cross design. Theoretical and Applied Genetics, 127(4), 921-933.

Liu, W., Reif, J. C., Ranc, N., Della Porta, G., & Wurschum, T. (2012). Comparison of biometrical approaches for QTL detection in multiple segregating families. Theoretical and Applied Genetics, 125(5), 987-998.

Meuwissen T and Luo, Z. (1992). Computing inbreeding coefficients in large populations. Genetics Selection Evolution, 24(4), 305-313.

Wurschum, T., Liu, W., Gowda, M., Maurer, H. P., Fischer, S., Schechert, A., & Reif, J. C. (2012). Comparison of biometrical models for joint linkage association mapping. Heredity, 108(3), 332-340.

See Also

plot.QTLprof

Examples

# Cross-specific model
######################

data(mppData)

SIM <- mpp_SIM(mppData = mppData, Q.eff = "cr", plot.gen.eff = TRUE)

plot(x = SIM)  
plot(x = SIM, gen.eff = TRUE, mppData = mppData, Q.eff = "cr")


# Bi-allelic model
##################

SIM <- mpp_SIM(mppData = mppData, Q.eff = "biall")

plot(x = SIM, type = "h")