coancestry: Calculate relatedness and inbreeding coefficients

In related: related: an R package for analyzing pairwise relatedness data based on codominant molecular markers

Description

Implements Jinliang Wang's code for Coancestry, which allows relatedness to be estimated from codominant genetic data using any of seven estimators, and includes options for considering inbreeding and genotyping errors.

Usage

coancestry(genotype.data, error.rates = 0, allele.freqs = NULL, trioml = 0L, 
wang = 0L, lynchli = 0L, lynchrd = 0L, ritland = 0L, quellergt = 0L, dyadml = 0L, 
ci95.num.bootstrap = 100L, trioml.num.reference = 100L, allow.inbreeding = FALSE, 
rng.seed = NULL, working.directory = tempdir(), output.file = FALSE)

Arguments

genotype.data	A data frame containing the genotype data, preferably generated using our readgenotypedata function.
error.rates	Optional. If one error rate across all loci is assummed, use that number. If each locus has a different error rate, create a vector containing the error rate values for each locus, and refer to that vector here.
allele.freqs	Optional. If data were read into R using our readgenotypedata function, then the allele frequency object will be detected automatically. If not, then the object you created should be referred to here.
trioml	Optional. The triadic likelihood relatedness estimate (Wang 2007). If point estimates using this estimator are desired, enter "1", if point estimates and 95% confidence intervals are desired, enter "2".
wang	Optional. The relatedness estimate described in Wang (2002). If point estimates using this estimator are desired, enter "1", if point estimates and 95% confidence intervals are desired, enter "2".
lynchli	Optional. The relatedness estimate described in Li et al. (1993). If point estimates using this estimator are desired, enter "1", if point estimates and 95% confidence intervals are desired, enter "2".
lynchrd	Optional. The relatedness estimate described in Lynch and Ritland (1999). If point estimates using this estimator are desired, enter "1", if point estimates and 95% confidence intervals are desired, enter "2".
ritland	Optional. The relatedness estimate described in Ritland (1996). If point estimates using this estimator are desired, enter "1", if point estimates and 95% confidence intervals are desired, enter "2".
quellergt	Optional. The relatedness estimate described in Queller and Goodnight (1989). If point estimates using this estimator are desired, enter "1", if point estimates and 95% confidence intervals are desired, enter "2".
dyadml	Optional. The dyadic likelihood estimator, described in Milligan (2003). If point estimates using this estimator are desired, enter "1", if point estimates and 95% confidence intervals are desired, enter "2".
ci95.num.bootstrap	Optional. The number of bootstrap iterations to perform to calculate 95% confidence intervals (default = 100).
trioml.num.reference	Optional. The triadic likelihood estimator requires that you specify the number of reference individuals to use for estimating relatedness. Enter that number here. Default = 100.
allow.inbreeding	Optional. A logical where inbreeding should, or should not (default), be considered when estimating relatedness.
rng.seed	Optional. Can manually set the see of the random number generator.
working.directory	Optional. Can indicate what directory files are in, if not in the current directory.
output.file	Optional. Can specify name of the output file prefix (many files will be generated - see below), but can also do this by directing output into an object using standard R commands.

Value

relatedness	A data frame containing all pairwise estimates of relatedness. This will always have 11 columns: (1) an integer for the pair number; (2) the ID for individual #1; (3) the ID for individual #2; (4) the group assignment (see section 3.5 of accompanying vignette); and (5 - 11) for the 7 relatedness estimators - contain values of 0 for estimators not chosen
delta7	A data frame that contains the delta7 estimates for the relatedness estimators that use it (trioml, wang, lynchrd, dyadml). This data frame contains one row for each pairwise comparison, and 8 columns: (1) an integer for the pair number; (2) the ID for individual #1; (3) the ID for individual #2; (4) the group assignment (see section 3.5 of accompanying vignette); and (5 - 8) estimates of delta7 for the 4 relevant estimators, with values of 0 for estimators not chosen.
delta8	A data frame that contains the delta8 estimates for the relatedness estimators that use it (trioml, wang, lynchrd, dyadml). This data frame contains one row for each pairwise comparison, and 8 columns: (1) an integer for the pair number; (2) the ID for individual #1; (3) the ID for individual #2; (4) the group assignment (see section 3.5 of accompanying vignette); and (5 - 8) estimates of delta8 for the 4 relevant estimators, with values of 0 for estimators not chosen.
inbreeding	A data frame that contains the inbreeding estimates for each individual, as used in the relatedness estimates. Only four of the relatedness estimators can account for inbreeding: dyadml, lynchrd, ritland, trioml. This data frame contains one row for each individual, and 5 columns: (1) individual ID; (2-5) inbreeding estimates for the 4 relatedness estimators. Estimators not used will have a 0 in the corresponding column.
relatedness.ci95	If confidence intervals are calculated. A data frame containing the low and high cut-off values for the 95% confidence interval associated with each chosen estimator. This will always have 18 columns: (1) an integer for the pair number; (2) the ID for individual #1; (3) the ID for individual #2; (4) the group assignment (see section 3.5 of accompanying vignette); (5 - 18) for the high and low values associated with each of the 7 relatedness estimators—contain values of 0 for estimators not chosen.
delta7.ci95	If confidence intervals are calculated. A data frame that contains the low and high cut-off values for the 95% confidence interval for the delta7 estimates associated with each chosen estimator that use it (trioml, wang, lynchrd, dyadml). This will always have 12 columns: (1) an integer for the pair number; (2) the ID for individual #1; (3) the ID for individual #2; (4) the group assignment (see section 3.5 of accompanying vignette); (5 - 12) for the high and low values associated with each of the 7 relatedness estimators—contain values of 0 for estimators not chosen.
delta8.ci95	If confidence intervals are calculated. A data frame that contains the low and high cut-off values for the 95% confidence interval for the delta8 estimates associated with each chosen estimator that use it (trioml, wang, lynchrd, dyadml). This will always have 12 columns: (1) an integer for the pair number; (2) the ID for individual #1; (3) the ID for individual #2; (4) the group assignment (see section 3.5 of accompanying vignette); (5 - 12) for the high and low values associated with each of the 7 relatedness estimators—contain values of 0 for estimators not chosen.
inbreeding.ci95	If confidence intervals are calculated. A data frame that contains the low and high cut-off values for the 95% confidence interval for the inbreeding estimates for each individual, as used in the relatedness estimators. Only four of the relatedness estimators can account for inbreeding: dyadml, lynchrd, ritland, trioml. This data frame contains one row for each individual, and 9 columns: (1) individual ID; (2-9) inbreeding estimates for the four relatedness estimators. Estimators not used will have a zero (0) in the corresponding column.

References

Li CC, Weeks DE, Chakravarti A (1993) Similarity of DNA fingerprints due to chance and relatedness. Human Heredity 43: 45-52.

Lynch M, Ritland K (1999) Estimation of pairwise relatedness with molecular markers. Genetics 152: 1753-1766.

Milligan BG (2003) Maximum-likelihood estimation of relatedness. Genetics 163: 1153-1167.

Queller DC, Goodnight KF (1989) Estimating relatedness using molecular markers. Evolution 43: 258-275.

Ritland (1996) Estimators of pairwise relatedness and inbreeding coefficients. Genetical Research 67: 175-186.

Wang J (2002) An estimator of pairwise relatedness using molecular markers. Genetics 160: 1203-1215.

Wang J (2007) Triadic IBD coefficients and applications to estimating pairwise relatedness. Genetical Research 89: 135-153.

Wang J (2011) COANCESTRY: a program for simulating, estimating and analysing relatedness and inbreeding coefficients. Molecular Ecology Resources 11: 141-145.

See Also

readgenotypedata

Examples

	## Not run: 
		#---Read data into R---#
		data(GenotypeData)
		input <- readgenotypedata(GenotypeData)

		#---Calculate Relatedness---#
		output <- coancestry(input$gdata, lynchrd=2, quellergt=2, wang=2)

		#---View Point Estimates---#
		output$relatedness

		#---View 95
		output$relatedness.ci95
	
## End(Not run)	

related documentation built on May 2, 2019, 6:49 p.m.