thirdclass: Calculate likelihoods for early generation hybrid genotype...

In HIest: Hybrid index estimation

Description

thirdclass uses genetic marker data and parental allele frequencies to calculate the likelihoods for each of the 15 diploid genotype classes possible in the first two generations of admixture (each parental, F1, F2, and each backcross) for each pair of parental lineages.

Usage

thirdclass(G, P, type = "codominant")

Arguments

G	A matrix or data frame of genetic marker data. Each column is a locus. For type="dominant", there should be one row per individual. For type="codominant", each individual is to be represented in consecutive rows (one for each allele).
P	A matrix or data frame with the following columns (order is important!): Locus name, Allele name, P1 allele frequency, P2 allele frequency, P3 allele frequency. For type="dominant", there should be one row per locus, giving the frequencies of the dominant or "1" allele. For type="codominant" there should be a separate row for each allele AND the Allele names should match the data in G.
type	A string representing the data type. The options are "codominant" and "dominant".

Details

The function evaluates genotype classes including only two parental lineages in the context of the possibility of a third parental lineage. It would be straightforward but tedious to extend it to include complex classes such as the offspring of a 1x2 F1 with a 2x3 F1. However, it is not entirely clear how to account for the additional complexity introduced by allowing more classes.

Value

A data matrix with the following columns (one row per individual)

c100000	log-likelihood for genotype class expected for pure parental P1
c010000	log-likelihood for genotype class expected for P2
c001000	log-likelihood for genotype class expected for P3
c000100	log-likelihood for genotype class expected for 1x2 F1 hybrids
c110200	log-likelihood for genotype class expected for 1x2 F2 hybrids
c100100	log-likelihood for genotype class expected for 1x1x2 backcrosses
c010100	log-likelihood for genotype class expected for 1x2x2 backcrosses
c000010	log-likelihood for genotype class expected for 1x3 F1 hybrids
c101020	log-likelihood for genotype class expected for 1x3 F2 hybrids
c100010	log-likelihood for genotype class expected for 1x1x3 backcrosses
c001010	log-likelihood for genotype class expected for 1x3x3 backcrosses
c000001	log-likelihood for genotype class expected for 2x3 F1 hybrids
c011002	log-likelihood for genotype class expected for 2x3 F2 hybrids
c010001	log-likelihood for genotype class expected for 2x2x3 backcrosses
c001001	log-likelihood for genotype class expected for 2x3x3 backcrosses
Best	The class with the highest likelihood of the 15
LLD	The difference in log-likelihood between the best and second-best fit class. This can be used as a criterion for deciding whether the best fit class is enough better to reject the alternatives.

Author(s)

Ben Fitzpatrick

References

Fitzpatrick, B. M. 2012. Estimating ancestry and heterozygosity of hybrids using molecular markers. BMC Evolutionary Biology 12:131. http://www.biomedcentral.com/1471-2148/12/131

See Also

threeway finds maximum likelihood estimates for non-diagnostic marker data. HItest3 compares classification to the continuous model MLE. For conventional two-way hybrid zone analyses, see HIC, HIest, HIclass, HItest.

Examples

	## Not run: 
	## all possible 2-way crosses after 2 generations
G <- rbind(
rep(1,12),rep(1,12),               # parental 1
rep(2,12),rep(2,12),               # parental 2
rep(3,12),rep(3,12),               # parental 3
rep(1,12),rep(2,12),               # 1 x 2 F1
rep(1:2,each=6),rep(1:2,6),        # 1 x 2 F2
rep(1,12),rep(1:2,6),              # 1 x 1 x 2 BC
rep(2,12),rep(1:2,6),              # 1 x 2 x 2 BC
rep(1,12),rep(3,12),               # 1 x 3 F1
rep(c(1,3),each=6),rep(c(1,3),6),  # 1 x 3 F2
rep(1,12),rep(c(1,3),6),           # 1 x 1 x 3 BC
rep(3,12),rep(c(1,3),6),           # 1 x 3 x 3 BC
rep(2,12),rep(3,12),               # 2 x 3 F1
rep(2:3,each=6),rep(2:3,6),        # 2 x 3 F2
rep(3,12),rep(2:3,6),              # 2 x 3 x 3 BC
rep(2,12),rep(2:3,6)               # 2 x 2 x 3 BC
)

P <- data.frame(Locus=rep(1:12,each=3),allele=rep(1:3,12),P1=rep(c(1,0,0),12),
	P2=rep(c(0,1,0),12),P3=rep(c(0,0,1),12))

Est <- HIC3(G,P)
Class <- thirdclass(G,P)
HItest3(Class,Est)

## now for some three-way mixes
G3 <- matrix(1+rbinom(200,2,.5),ncol=10)
Est3 <- HIC3(G3,P)
Class3 <- thirdclass(G3,P)
HItest3(Class3,Est3)   	# usually all classifications will be impossible because all 
						# individuals will have nonzero contributions from each of the 
						# three parentals

## bias toward parental 1
G3 <- matrix(1+rbinom(200,2,.25),ncol=10)
Est3 <- HIC3(G3,P)
Class3 <- thirdclass(G3,P)
HItest3(Class3,Est3) 	# now you might have a few that look like F2's 
						# between 1 and 2 (c110200)

## End(Not run)

HIest documentation built on May 2, 2019, 10:21 a.m.