mpp_SIM: MPP Simple Interval Mapping
In vincentgarin/mppR: Multi-Parent Population QTL Analysis
mpp_SIM R Documentation
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")
vincentgarin/mppR documentation built on March 13, 2024, 7:30 p.m.
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| mpp_SIM | R Documentation |
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 |
trait |
|
Q.eff |
|
plot.gen.eff |
|
n.cores |
|
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 |
|
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")
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