Two-point LOD score
Calculates the two-point LOD scores of a pedigree for the specified markers. The recombination ratio between the disease and marker loci can be either fixed at specific values, or optimized.
lod(x, markers = seq_len(x$nMark), theta = 0, loop_breakers = NULL, max.only = FALSE, verbose = FALSE, tol = 0.01)
x |
a |
markers |
an integer vector denoting which markers to use. |
theta |
either a numeric containing specific recombination ratio(s), or the word 'max', indicating that the recombination ratio should be optimized by the program. |
loop_breakers |
a numeric containing IDs of individuals to be used as
loop breakers. Relevant only if the pedigree has loops. See
|
max.only |
a logical indicating whether only the maximum LOD score should be returned. |
verbose |
a logical: verbose output or not. |
tol |
a numeric passed on to |
The LOD score of a marker is defined as
LOD(θ) = log10 L(θ)/L(0.5)
where L(θ) denotes the likelihood of the observed marker genotypes given a recombination ratio θ between the marker and the disease locus.
If max.only=TRUE, the highest computed LOD score is returned,
as a single number.
Otherwise a linkres object, which is essentially a matrix containing
the LOD scores. The details depend on the other parameters:
If theta is numeric, the matrix has
dimensions length(theta) * length(markers), and the entry in row
t, column m is the lod score of the pedigree for marker
m assuming a recombination rate of t.
If theta='max', the linkres matrix has one column per marker
and two rows: The first containing the LOD score and the second the optimal
recombination ratio for each marker.
If a marker has incompatible values (i.e. if a child of homozygous 1/1
parents has a 2 allele), the corresponding output entries are NaN.
Magnus Dehli Vigeland
x = linkdat(toyped, model=1) res = lod(x) res_theta = lod(x, theta=c(0, 0.1, 0.2, 0.5)) res_max = lod(x, theta='max') stopifnot(all(0.3 == round(c(res, res_theta['0',], res_max['LOD',]), 1))) # bigger pedigree with several markers y = linkdat(dominant) y = setModel(y, model=1, penetrances=c(.001, .9, .99)) lod(y, markers=305:310) lod(y, markers=305:310, theta='max') # Example with pedigree with loops: z = linkdat(twoloops, model=2) # fully penetrant autosomal recessive model. # add SNP for which individuals 15 and 16 are homozygous for the rare allele. m = marker(z, 15:16, c(1,1), alleles=1:2, afreq=c(0.001, 0.999)) z = addMarker(z, m) res1 = lod(z) # manual specification of loop breakers gives same result res2 = lod(z, loop_breakers=c(8,12)) # making the marker triallelic and adding some genotypes. z = modifyMarker(z, marker=1, ids=c(7,9,11,13), genotype=3, alleles=1:3, afreq=c(0.001, 0.499, 0.5)) plot(z, 1) res3 = lod(z) z = modifyMarker(z, marker=1, alleles=1:4, afreq=c(0.001, 0.499, 0.25, 0.25)) res4 = lod(z) stopifnot(all(3 == round(c(res1, res2, res3, res4), 1)))
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