Description Usage Arguments Details Value Author(s) References See Also Examples
ibdPlot
produces an IBD plot showing observed
identity by descent values color coded by expected relationship.
Theoretical boundaries for fullsibling, seconddegree, and
thirddegree relatives are plotted in orange.
ibdAreasDraw
overlays relationship areas for IBD analysis on
the plot.
ibdAssignRelatedness
identifies observed relatives.
ibdAssignRelatednessKing
identifies observed relatives using the kinship coefficients and IBS0 estimates from the KING model.
kingIBS0FSCI
returns a confidence interval for expected IBS0 for full siblings.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ibdPlot(k0, k1, alpha=0.05, relation=NULL, color=NULL,
rel.lwd=2, rel.draw=c("FS", "Deg2", "Deg3"), ...)
ibdAreasDraw(alpha=0.05, m=0.04, po.w=0.1, po.h=0.1,
dup.w=0.1, dup.h=0.1, un.w=0.25, un.h=0.25, rel.lwd=2,
xcol=c("cyan","red","blue","lightgreen","magenta","black"))
ibdAssignRelatedness(k0, k1, alpha=0.05, m=0.04, po.w=0.1, po.h=0.1,
dup.w=0.1, dup.h=0.1, un.w=0.25, un.h=0.25)
ibdAssignRelatednessKing(ibs0, kc, cut.kc.dup=1/(2^(3/2)),
cut.kc.fs=1/(2^(5/2)), cut.kc.deg2=1/(2^(7/2)),
cut.kc.deg3=1/(2^(9/2)), cut.ibs0.err=0.003)
kingIBS0FSCI(freq, alpha=0.01)

k0 
A vector of k0 values. 
k1 
A vector of k1 values. 
kc 
A vector of kinship coefficient values (KING model). 
ibs0 
A vector of IBS0 values (KING model). 
alpha 
significance level  finds 100(1alpha)% prediction intervals for second and third degree relatives and 100(1alpha)% prediction ellipse for full siblings. 
relation 
A vector of relationships. Recognized values are "PO"=parent/offspring, "FS"=full siblings, "HS"=half siblings, "Av"=avuncular, "GpGc"=grandparentgrandchild, "Deg2"=any seconddegree, "FC"=first cousins, "HAv"=halfavuncular, "Deg3"=any third degree, "U"=unrelated, and "Q"=unknown. 
color 
A vector of colors for (k0,k1) points. 
rel.lwd 
Line width for theoretical fullsib, Deg2, and Deg3 boundaries. 
rel.draw 
Which theoretical boundaries to plot: one or more of
"FS" (fullsib), "Deg2" (seconddegree), "Deg3" (thirddegree). If

... 
Other graphical parameters to pass to 
m 
width of rectangle along diagonal line 
po.w 
width of parentoffspring rectangle 
po.h 
height of parentoffspring rectangle 
dup.w 
width of duplicate rectangle 
dup.h 
height of duplicate rectangle 
un.w 
width of unrelated rectangle 
un.h 
height of unrelated rectangle 
xcol 
colors for parentoffspring, fullsib, Deg2, Deg3, dup & unrelated areas 
cut.kc.dup 
Kinship coefficient threshold for dividing duplicates and first degree relatives. 
cut.kc.fs 
Kinship coefficient threshold for dividing full siblings and second degree relatives. 
cut.kc.deg2 
Kinship coefficient threshold for dividing second and third degree relatives. 
cut.kc.deg3 
Kinship coefficient threshold for dividing third degree relatives and unrelated. 
cut.ibs0.err 
IBS0 threshold for dividing parentoffsprings pairs from other relatives. Should be 0, but is usually slightly higher due to genotyping errors. 
freq 
vector of allele frequencies at the variants used in the KING analysis 
ibdPlot
produces an IBD plot showing observed identity by descent
values color coded by expected relationship, typically as deduced from
pedigree data. Points are plotted according to their corresponding
value in the color
vector, and the relation
vector is used
to make the plot legend. In addition to the relationships listed above,
any relationships output from pedigreePairwiseRelatedness
will be recognized.
Theoretical boundary for fullsibs is indicated by ellipse and boundaries for second and third degree intervals are indicated in orange. For fullsibs, 100(1alpha)% prediction ellipse is based on assuming bivariate normal distribution with known mean and covariance matrix. For second degree (half siblings, avuncular, grandparentgrandchild) and third degree (first cousins), 100(1alpha)% prediction intervals for k1 are based on assuming normal distribution with known mean and variance, computed as in Hill and Weir (2011).
ibdAreasDraw
overlays relationship areas on the plot to help
with analyzing observed relationships.
ibdAssignRelatedness
identifies relatives based on their (k0, k1) coordinates.
ibdAssignRelatednessKing
identifies relatives based on their (ibs0, kc) coordinates (KING model).
kingIBS0FSCI
uses allele frequencies to find the confidence interval for expected IBS0 for full siblings.
We condition on the three possible IBD states: IBD0, IBD1, or IBD2; but P(AA, aa  IBD1) = P(AA, aa  IBD2) = 0.
P(IBS0  FS) = P(AA, aa  FS) + P(aa, AA  FS) = P(AA, aa  IBD0)P(IBD0) + P(aa, AA  IBD0)P(IBD0) = p^2*q^2*k0 + q^2*p^2*k0 = 2*p^2*q^2*k0
Knowing a pair's value of k0 and the allele frequencies at each variant, we can get an estimate of P(IBS0  FS) by averaging across all the markers used in the analysis. Siblings have k0 = 0.25 on average.
ibdAssignRelatedness
and ibdAssignRelatednessKing
return a vector of relationships with
values "Dup"=duplicate, "PO"=parentoffspring, "FS"=full sibling,
"Deg2"=second degree, "Deg3"=third degree, "U"=unrelated, and
"Q"=unknown.
kingIBS0FSCI
returns a vector with 3 values: the point estimate, the lower bound of the CI, the upper bound of the CI
Cathy Laurie, Cecelia Laurie, Adrienne Stilp, Matthew Conomos
Hill, W.G. and B.S. Weir, Variation in actual relationship as a consequence of mendelian sampling and linkage, Genet. Res. Camb. (2011), 93, 4764.
Manichaikul, A., Mychaleckyj J.C., Rich S.S., Daly K., Sale M., and Chen W.M., Robust relationship inference in genomewide association studies, Bioinformatics (2010), 26(22), 28672873.
relationsMeanVar
,
pedigreePairwiseRelatedness
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  k0 < c(0, 0, 0.25, 0.5, 0.75, 1)
k1 < c(0, 1, 0.5, 0.5, 0.25, 0)
exp.rel < c("Dup", "PO", "FS", "HS", "FC", "U")
ibdPlot(k0, k1, relation=exp.rel)
ibdAreasDraw()
obs.rel < ibdAssignRelatedness(k0, k1)
kc < c(0.5, 0.25, 0.25, 0.125, 0.063, 0)
ibs0 < c(0, 0, 0.25, 0.5, 0.75, 1)
obs.rel.king < ibdAssignRelatednessKing(ibs0, kc)
library(SNPRelate)
library(GWASdata)
gdsfile < system.file("extdata", "illumina_geno.gds", package="GWASdata")
gds < snpgdsOpen(gdsfile)
ibd < snpgdsIBDKING(gds)
ibd < snpgdsIBDSelection(ibd, kinship.cutoff=1/32)
snpgdsClose(gds)
gds < GdsGenotypeReader(gdsfile)
data(illuminaScanADF)
genoData < GenotypeData(gds, scanAnnot=illuminaScanADF)
freq < alleleFrequency(genoData)
close(genoData)
fs < kingIBS0FSCI(freq[,"MAF"])
plot(ibd$IBS0, ibd$kinship)
abline(v=fs)
obs.rel.king < ibdAssignRelatednessKing(ibd$IBS0, ibd$kinship,
cut.ibs0.err=fs["LL"])

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