| Title: | Allele Imputation and Haplotype Reconstruction from Pedigree Databases |
|---|---|
| Description: | Tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker, probability and proportion of missing genotypes, recombination rate, etc). Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited from each parent) and thus imputation and reconstruction results can be deterministically calculated. |
| Authors: | Nathan Medina-Rodriguez and Angelo Santana |
| Maintainer: | Nathan Medina-Rodriguez <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.9.9 |
| Built: | 2024-10-29 06:36:15 UTC |
| Source: | CRAN |
Tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker, probability and proportion of missing genotypes, recombination rate, etc). Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited from each parent), and thus imputation and reconstruction results can be deterministically calculated.
| Package: | alleHap |
| Type: | Package |
| Version: | 0.9.9 |
| Date: | 2017-08-19 |
| Depends: | abind, stats, tools, utils |
| License: | GPL (>=2) |
Nathan Medina-Rodriguez and Angelo Santana
Maintainer: Nathan Medina-Rodriguez <[email protected]>
Medina-Rodriguez, N. Santana A. et al. (2014) alleHap: an efficient algorithm to reconstruct zero-recombinant haplotypes from parent-offspring pedigrees. BMC Bioinformatics, 15, A6 (S-3).
## Generation of 10 simulated families with 2 children per family and 20 markers dataset <- alleSimulator(10,2,20) # List with simulated alleles and haplotypes datasetAlls <- dataset[[1]] # Dataset containing alleles datasetHaps <- dataset[[2]] # Dataset containing haplotypes ## Loading of a dataset in .ped format with alphabetical alleles (A,C,G,T) example1 <- file.path(find.package("alleHap"), "examples", "example1.ped") datasetAlls1 <- alleLoader(example1) ## Loading of a dataset in .ped format with numerical alleles example2 <- file.path(find.package("alleHap"), "examples", "example2.ped") datasetAlls2 <- alleLoader(example2) ## Allele imputation of families with parental missing data datasetAlls <- alleSimulator(10,4,6,missParProb=0.2)[[1]] famsImputed <- alleImputer(datasetAlls) ## Allele imputation of families with offspring missing data datasetAlls <- alleSimulator(10,4,6,missOffProb=0.2)[[1]] famsImputed <- alleImputer(datasetAlls) ## Haplotype reconstruction for 3 families without missing data. simulatedFams <- alleSimulator(3,3,6) (famsAlls <- simulatedFams[[1]]) # Original data famsList <- alleHaplotyper(famsAlls) # List containing families' alleles and haplotypes famsList$reImputedAlls # Re-imputed alleles famsList$haplotypes # Reconstructed haplotypes ## Haplotype reconstruction from a PED file pedFamPath <- file.path(find.package("alleHap"), "examples", "example3.ped") # PED file path pedFamAlls <- alleLoader(pedFamPath,dataSummary=FALSE) pedFamList <- alleHaplotyper(pedFamAlls) pedFamAlls # Original data pedFamList$reImputedAlls # Re-imputed alleles pedFamList$haplotypes # Reconstructed haplotypes## Generation of 10 simulated families with 2 children per family and 20 markers dataset <- alleSimulator(10,2,20) # List with simulated alleles and haplotypes datasetAlls <- dataset[[1]] # Dataset containing alleles datasetHaps <- dataset[[2]] # Dataset containing haplotypes ## Loading of a dataset in .ped format with alphabetical alleles (A,C,G,T) example1 <- file.path(find.package("alleHap"), "examples", "example1.ped") datasetAlls1 <- alleLoader(example1) ## Loading of a dataset in .ped format with numerical alleles example2 <- file.path(find.package("alleHap"), "examples", "example2.ped") datasetAlls2 <- alleLoader(example2) ## Allele imputation of families with parental missing data datasetAlls <- alleSimulator(10,4,6,missParProb=0.2)[[1]] famsImputed <- alleImputer(datasetAlls) ## Allele imputation of families with offspring missing data datasetAlls <- alleSimulator(10,4,6,missOffProb=0.2)[[1]] famsImputed <- alleImputer(datasetAlls) ## Haplotype reconstruction for 3 families without missing data. simulatedFams <- alleSimulator(3,3,6) (famsAlls <- simulatedFams[[1]]) # Original data famsList <- alleHaplotyper(famsAlls) # List containing families' alleles and haplotypes famsList$reImputedAlls # Re-imputed alleles famsList$haplotypes # Reconstructed haplotypes ## Haplotype reconstruction from a PED file pedFamPath <- file.path(find.package("alleHap"), "examples", "example3.ped") # PED file path pedFamAlls <- alleLoader(pedFamPath,dataSummary=FALSE) pedFamList <- alleHaplotyper(pedFamAlls) pedFamAlls # Original data pedFamList$reImputedAlls # Re-imputed alleles pedFamList$haplotypes # Reconstructed haplotypes
By analyzing all possible combinations of a parent-offspring pedigree in which parents may be missing (missParProb>0), as long as one child was genotyped, it is possible an unequivocal reconstruction of many parental haplotypes. When neither parent was genotyped (missParProb==1), also it is possible to reconstruct at least two parental haplotypes in certain cases. Regarding offspring haplotypes, if both parents are completely genotyped (missParProb==0), in majority of cases partial offspring haplotypes may be successfully obtained (missOffProb>0).
alleHaplotyper(data, NAsymbol = "?", alleSep = "", invisibleOutput = TRUE, dataSummary = TRUE)alleHaplotyper(data, NAsymbol = "?", alleSep = "", invisibleOutput = TRUE, dataSummary = TRUE)
data |
Data containing non-genetic and genetic information of families (or PED file path). |
NAsymbol |
Icon which will be placed in the NA values of the haplotypes. |
alleSep |
Icon which will be used as separator of the haplotype alleles. |
invisibleOutput |
Data are not shown by default. |
dataSummary |
A summary of the data is shown by default. |
Re-imputed alleles and haplotypes for each loaded family.
Medina-Rodriguez, N. Santana A. et al. (2014) alleHap: an efficient algorithm to reconstruct zero-recombinant haplotypes from parent-offspring pedigrees. BMC Bioinformatics, 15, A6 (S-3).
## Haplotype reconstruction for 3 families without missing data. simulatedFams <- alleSimulator(3,3,6) (famsAlls <- simulatedFams[[1]]) # Original data famsList <- alleHaplotyper(famsAlls) # List containing families' alleles and haplotypes famsList$reImputedAlls # Re-imputed alleles famsList$haplotypes # Reconstructed haplotypes ## Haplotype reconstruction of a family containing missing data in a parent. infoFam <- data.frame(famID="FAM002",indID=1:6,patID=c(0,0,1,1,1,1), matID=c(0,0,2,2,2,2),sex=c(1,2,1,2,1,2),phenot=c(2,1,1,2,1,2)) Mkrs <- rbind(c(1,4,2,5,3,6),rep(NA,6),c(1,7,2,3,3,2), c(4,7,5,3,6,2),c(1,1,2,2,3,3),c(1,4,2,5,3,6)) colnames(Mkrs) <- c("Mk1_1","Mk1_2","Mk2_1","Mk2_2","Mk3_1","Mk3_2") (family <- cbind(infoFam,Mkrs)) # Original data famList <- alleHaplotyper(family) # List containing family's alleles and haplotypes famList$reImputedAlls # Re-imputed alleles famList$haplotypes # Reconstructed haplotypes ## Haplotype reconstruction from a PED file pedFamPath <- file.path(find.package("alleHap"), "examples", "example3.ped") # PED file path pedFamAlls <- alleLoader(pedFamPath,dataSummary=FALSE) pedFamList <- alleHaplotyper(pedFamAlls) pedFamAlls # Original data pedFamList$reImputedAlls # Re-imputed alleles pedFamList$haplotypes # Reconstructed haplotypes## Haplotype reconstruction for 3 families without missing data. simulatedFams <- alleSimulator(3,3,6) (famsAlls <- simulatedFams[[1]]) # Original data famsList <- alleHaplotyper(famsAlls) # List containing families' alleles and haplotypes famsList$reImputedAlls # Re-imputed alleles famsList$haplotypes # Reconstructed haplotypes ## Haplotype reconstruction of a family containing missing data in a parent. infoFam <- data.frame(famID="FAM002",indID=1:6,patID=c(0,0,1,1,1,1), matID=c(0,0,2,2,2,2),sex=c(1,2,1,2,1,2),phenot=c(2,1,1,2,1,2)) Mkrs <- rbind(c(1,4,2,5,3,6),rep(NA,6),c(1,7,2,3,3,2), c(4,7,5,3,6,2),c(1,1,2,2,3,3),c(1,4,2,5,3,6)) colnames(Mkrs) <- c("Mk1_1","Mk1_2","Mk2_1","Mk2_2","Mk3_1","Mk3_2") (family <- cbind(infoFam,Mkrs)) # Original data famList <- alleHaplotyper(family) # List containing family's alleles and haplotypes famList$reImputedAlls # Re-imputed alleles famList$haplotypes # Reconstructed haplotypes ## Haplotype reconstruction from a PED file pedFamPath <- file.path(find.package("alleHap"), "examples", "example3.ped") # PED file path pedFamAlls <- alleLoader(pedFamPath,dataSummary=FALSE) pedFamList <- alleHaplotyper(pedFamAlls) pedFamAlls # Original data pedFamList$reImputedAlls # Re-imputed alleles pedFamList$haplotypes # Reconstructed haplotypes
By analyzing all possible combinations of a parent-offspring pedigree in which parental and/or offspring genotypes may be missing; as long as one child was genotyped, in certain cases it is possible an unequivocal imputation of the missing genotypes both in parents and children.
alleImputer(data, invisibleOutput = TRUE, dataSummary = TRUE)alleImputer(data, invisibleOutput = TRUE, dataSummary = TRUE)
data |
Data containing the families' identifiers and the corresponding genetic data (or the path of the PED file). |
invisibleOutput |
Data are not shown by default. |
dataSummary |
A summary of the data is shown by default. |
Imputed markers, Homozygosity (HMZ) matrix, marker messages and number of unique alleles per marker.
Medina-Rodriguez, N. Santana A. et al. (2014) alleHap: an efficient algorithm to reconstruct zero-recombinant haplotypes from parent-offspring pedigrees. BMC Bioinformatics, 15, A6 (S-3).
## Imputation of families containing parental missing data simulatedFams <- alleSimulator(10,4,6,missParProb=0.2) famsAlls <- simulatedFams[[1]] # Original data alleImputer(famsAlls) # Imputed alleles (genotypes) ## Imputation of families containing offspring missing data datasetAlls <- alleSimulator(10,4,6,missOffProb=0.2) famsAlls <- simulatedFams[[1]] # Original data alleImputer(famsAlls) # Imputed alleles (genotypes) ## Imputation of a family marker containing missing values in one parent and one child infoFam <- data.frame(famID="FAM03",indID=1:5,patID=c(0,0,1,1,1), matID=c(0,0,2,2,2),sex=c(1,2,1,2,1),phenot=0) mkr <- rbind(father=c(NA,NA),mother=c(1,3),child1=c(1,1),child2=c(2,3),child3=c(NA,NA)) colnames(mkr) <- c("Mkr1_1","Mkr1_2") famMkr <- cbind(infoFam,mkr) # Original data alleImputer(famMkr) # Imputed alleles (genotypes)## Imputation of families containing parental missing data simulatedFams <- alleSimulator(10,4,6,missParProb=0.2) famsAlls <- simulatedFams[[1]] # Original data alleImputer(famsAlls) # Imputed alleles (genotypes) ## Imputation of families containing offspring missing data datasetAlls <- alleSimulator(10,4,6,missOffProb=0.2) famsAlls <- simulatedFams[[1]] # Original data alleImputer(famsAlls) # Imputed alleles (genotypes) ## Imputation of a family marker containing missing values in one parent and one child infoFam <- data.frame(famID="FAM03",indID=1:5,patID=c(0,0,1,1,1), matID=c(0,0,2,2,2),sex=c(1,2,1,2,1),phenot=0) mkr <- rbind(father=c(NA,NA),mother=c(1,3),child1=c(1,1),child2=c(2,3),child3=c(NA,NA)) colnames(mkr) <- c("Mkr1_1","Mkr1_2") famMkr <- cbind(infoFam,mkr) # Original data alleImputer(famMkr) # Imputed alleles (genotypes)
The data to be loaded must be structured in .ped format and families must comprise by parent-offspring pedigrees.
alleLoader(data, invisibleOutput = TRUE, dataSummary = TRUE, missingValues = c(-9, -99))alleLoader(data, invisibleOutput = TRUE, dataSummary = TRUE, missingValues = c(-9, -99))
data |
Data to be loaded. |
invisibleOutput |
Data are not shown by default. |
dataSummary |
A summary of the data is shown by default. |
missingValues |
Specification of the character/numerical values which may be missing. |
Loaded dataset.
Medina-Rodriguez, N. Santana A. et al. (2014) alleHap: an efficient algorithm to reconstruct zero-recombinant haplotypes from parent-offspring pedigrees. BMC Bioinformatics, 15, A6 (S-3).
## Loading of a dataset in .ped format with alphabetical alleles (A,C,G,T) example1 <- file.path(find.package("alleHap"), "examples", "example1.ped") example1Alls <- alleLoader(example1) head(example1Alls) ## Loading of a dataset in .ped format with numerical alleles example2 <- file.path(find.package("alleHap"), "examples", "example2.ped") example2Alls <- alleLoader(example2) head(example2Alls)## Loading of a dataset in .ped format with alphabetical alleles (A,C,G,T) example1 <- file.path(find.package("alleHap"), "examples", "example1.ped") example1Alls <- alleLoader(example1) head(example1Alls) ## Loading of a dataset in .ped format with numerical alleles example2 <- file.path(find.package("alleHap"), "examples", "example2.ped") example2Alls <- alleLoader(example2) head(example2Alls)
Data simulation can be performed taking into account many different factors such as number of families to generate, number of markers (allele pairs), number of different alleles per marker, type of alleles (numeric or character), number of different haplotypes in the population, probability of parent/offspring missing genotypes, proportion of missing genotypes per individual, probability of being affected by disease and recombination rate.
alleSimulator(nFams = 2, nChildren = NULL, nMarkers = 3, numAllperMrk = NULL, chrAlleles = TRUE, nHaplos = 1200, missParProb = 0, missOffProb = 0, ungenotPars = 0, ungenotOffs = 0, phenProb = 0.2, recombRate = 0, invisibleOutput = TRUE)alleSimulator(nFams = 2, nChildren = NULL, nMarkers = 3, numAllperMrk = NULL, chrAlleles = TRUE, nHaplos = 1200, missParProb = 0, missOffProb = 0, ungenotPars = 0, ungenotOffs = 0, phenProb = 0.2, recombRate = 0, invisibleOutput = TRUE)
nFams |
Number of families to generate (integer: 1..1000+) |
nChildren |
Number of children of each family (integer: 1..7 or NULL) |
nMarkers |
Number of markers or allele pairs to generate (integer: 1..1000+) |
numAllperMrk |
Number of different alleles per marker (vector or NULL) |
chrAlleles |
Should alleles be expressed as characters A,C,G,T ? (boolean: FALSE, TRUE) |
nHaplos |
Number of different haplotypes in the population (numeric) |
missParProb |
Probability of parents' missing genotype (numeric: 0..1) |
missOffProb |
Probability of offspring' missing genotype (numeric: 0..1) |
ungenotPars |
Proportion of ungenotyped parents (numeric: 0..1) |
ungenotOffs |
Proportion of ungenotyped offspring (numeric: 0..1) |
phenProb |
Phenotype probability, e.g. being affected by disease (numeric: 0..1) |
recombRate |
Recombination rate (numeric: 0..1) |
invisibleOutput |
Data are not shown by default. |
Families' genotypes and haplotypes.
Medina-Rodriguez, N. Santana A. et al. (2014) alleHap: an efficient algorithm to reconstruct zero-recombinant haplotypes from parent-offspring pedigrees. BMC Bioinformatics, 15, A6 (S-3).
## Generation of 5 simulated families with 2 children per family and 10 markers simulatedFams <- alleSimulator(5,2,10) # List with simulated alleles and haplotypes simulatedFams[[1]] # Alleles (genotypes) of the simulated families simulatedFams[[2]] # Haplotypes of the simulated families## Generation of 5 simulated families with 2 children per family and 10 markers simulatedFams <- alleSimulator(5,2,10) # List with simulated alleles and haplotypes simulatedFams[[1]] # Alleles (genotypes) of the simulated families simulatedFams[[2]] # Haplotypes of the simulated families