snpgdsPairIBD: Calculate Identity-By-Descent (IBD) Coefficients
In SNPRelate: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Calculate the three IBD coefficients (k0, k1, k2) for non-inbred
individual pairs by Maximum Likelihood Estimation (MLE) or PLINK
Method of Moment (MoM).
Usage
1
2
3
4
Arguments
geno1
the SNP genotypes for the first individual,
0 – BB, 1 – AB, 2 – AA, other values – missing
geno2
the SNP genotypes for the second individual,
0 – BB, 1 – AB, 2 – AA, other values – missing
allele.freq
the allele frequencies
method
"EM", "downhill.simplex", "MoM" or "Jacquard", see details
kinship.constraint
if TRUE, constrict IBD coefficients
($k_0,k_1,k_2$) in the genealogical region ($2 k_0 k_1 >= k_2^2$)
max.niter
the maximum number of iterations
reltol
relative convergence tolerance; the algorithm stops if
it is unable to reduce the value of log likelihood by a factor of
$reltol * (abs(log likelihood with the initial parameters) + reltol)$
at a step.
coeff.correct
TRUE by default, see details
out.num.iter
if TRUE, output the numbers of iterations
verbose
if TRUE, show information
Details
If method = "MoM", then PLINK Method of Moment without a
allele-count-based correction factor is conducted. Otherwise, two numeric
approaches for maximum likelihood estimation can be used: one is
Expectation-Maximization (EM) algorithm, and the other is Nelder-Mead method
or downhill simplex method. Generally, EM algorithm is more robust than
downhill simplex method. "Jacquard" refers to the estimation of nine
Jacquard's coefficients.
If coeff.correct is TRUE, the final point that is found by
searching algorithm (EM or downhill simplex) is used to compare the six points
(fullsib, offspring, halfsib, cousin, unrelated), since any numeric approach
might not reach the maximum position after a finit number of steps. If any of
these six points has a higher value of log likelihood, the final point will be
replaced by the best one.
Value
Return a data.frame:
k0
IBD coefficient, the probability of sharing ZERO IBD
k1
IBD coefficient, the probability of sharing ONE IBD
loglik
the value of log likelihood
niter
the number of iterations
Author(s)
Xiuwen Zheng
References
Milligan BG. 2003. Maximum-likelihood estimation of relatedness.
Genetics 163:1153-1167.
Weir BS, Anderson AD, Hepler AB. 2006.
Genetic relatedness analysis: modern data and new challenges.
Nat Rev Genet. 7(10):771-80.
Choi Y, Wijsman EM, Weir BS. 2009.
Case-control association testing in the presence of unknown relationships.
Genet Epidemiol 33(8):668-78.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D,
Maller J, Sklar P, de Bakker PIW, Daly MJ & Sham PC. 2007.
PLINK: a toolset for whole-genome association and population-based linkage
analysis. American Journal of Human Genetics, 81.
See Also
snpgdsPairIBDMLELogLik, snpgdsIBDMLE,
snpgdsIBDMLELogLik, snpgdsIBDMoM
Examples
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# open an example dataset (HapMap)
genofile <- snpgdsOpen(snpgdsExampleFileName())
YRI.id <- read.gdsn(index.gdsn(genofile, "sample.id"))[
read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))=="YRI"]
# SNP pruning
set.seed(10)
snpset <- snpgdsLDpruning(genofile, sample.id=YRI.id, maf=0.05,
missing.rate=0.05)
snpset <- unname(sample(unlist(snpset), 250))
# the number of samples
n <- 25
# specify allele frequencies
RF <- snpgdsSNPRateFreq(genofile, sample.id=YRI.id, snp.id=snpset,
with.id=TRUE)
summary(RF$AlleleFreq)
subMLE <- snpgdsIBDMLE(genofile, sample.id=YRI.id[1:n], snp.id=RF$snp.id,
allele.freq=RF$AlleleFreq)
subMoM <- snpgdsIBDMoM(genofile, sample.id=YRI.id[1:n], snp.id=RF$snp.id,
allele.freq=RF$AlleleFreq)
subJac <- snpgdsIBDMLE(genofile, sample.id=YRI.id[1:n], snp.id=RF$snp.id,
allele.freq=RF$AlleleFreq, method="Jacquard")
########################
# genotype matrix
mat <- snpgdsGetGeno(genofile, sample.id=YRI.id[1:n], snp.id=snpset,
snpfirstdim=TRUE)
rv <- NULL
for (i in 2:n)
{
rv <- rbind(rv, snpgdsPairIBD(mat[,1], mat[,i], RF$AlleleFreq, "EM"))
print(snpgdsPairIBDMLELogLik(mat[,1], mat[,i], RF$AlleleFreq,
relatedness="unrelated", verbose=TRUE))
}
rv
summary(rv$k0 - subMLE$k0[1, 2:n])
summary(rv$k1 - subMLE$k1[1, 2:n])
# ZERO
rv <- NULL
for (i in 2:n)
rv <- rbind(rv, snpgdsPairIBD(mat[,1], mat[,i], RF$AlleleFreq, "MoM"))
rv
summary(rv$k0 - subMoM$k0[1, 2:n])
summary(rv$k1 - subMoM$k1[1, 2:n])
# ZERO
rv <- NULL
for (i in 2:n)
rv <- rbind(rv, snpgdsPairIBD(mat[,1], mat[,i], RF$AlleleFreq, "Jacquard"))
rv
summary(rv$D1 - subJac$D1[1, 2:n])
summary(rv$D2 - subJac$D2[1, 2:n])
# ZERO
# close the genotype file
snpgdsClose(genofile)
Example output
Loading required package: gdsfmt
SNPRelate -- supported by Streaming SIMD Extensions 2 (SSE2)
SNP pruning based on LD:
Excluding 365 SNPs on non-autosomes
Excluding 1,646 SNPs (monomorphic: TRUE, MAF: 0.05, missing rate: 0.05)
# of samples: 93
# of SNPs: 7,077
using 1 thread
sliding window: 500,000 basepairs, Inf SNPs
|LD| threshold: 0.2
method: composite
Chromosome 1: 62.29%, 446/716
Chromosome 2: 62.67%, 465/742
Chromosome 3: 59.93%, 365/609
Chromosome 4: 64.23%, 361/562
Chromosome 5: 62.37%, 353/566
Chromosome 6: 59.82%, 338/565
Chromosome 7: 63.14%, 298/472
Chromosome 8: 57.58%, 281/488
Chromosome 9: 62.98%, 262/416
Chromosome 10: 60.46%, 292/483
Chromosome 11: 63.09%, 282/447
Chromosome 12: 62.76%, 268/427
Chromosome 13: 63.08%, 217/344
Chromosome 14: 63.83%, 180/282
Chromosome 15: 63.74%, 167/262
Chromosome 16: 62.23%, 173/278
Chromosome 17: 65.70%, 136/207
Chromosome 18: 59.40%, 158/266
Chromosome 19: 68.33%, 82/120
Chromosome 20: 66.38%, 152/229
Chromosome 21: 61.11%, 77/126
Chromosome 22: 57.76%, 67/116
5,420 markers are selected in total.
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.05376 0.23790 0.46774 0.48641 0.74597 0.94624
Identity-By-Descent analysis (MLE) on genotypes:
Excluding 8,838 SNPs (non-autosomes or non-selection)
Excluding 0 SNP (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
# of samples: 25
# of SNPs: 250
using 1 thread
Specifying allele frequencies, mean: 0.486, sd: 0.284
MLE IBD: the sum of all selected genotypes (0,1,2) = 6112
MLE IBD: Wed Apr 14 08:21:08 2021 0%
MLE IBD: Wed Apr 14 08:21:09 2021 100%
IBD analysis (PLINK method of moment) on genotypes:
Excluding 8,838 SNPs (non-autosomes or non-selection)
Excluding 0 SNP (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
# of samples: 25
# of SNPs: 250
using 1 thread
Specifying allele frequencies, mean: 0.486, sd: 0.284
*** A correction factor based on allele count is not used, since the allele frequencies are specified.
PLINK IBD: the sum of all selected genotypes (0,1,2) = 6112
Wed Apr 14 08:21:09 2021 (internal increment: 65536)
[..................................................] 0%, ETC: ---
[==================================================] 100%, completed, 0s
Wed Apr 14 08:21:09 2021 Done.
Identity-By-Descent analysis (MLE) on genotypes:
Excluding 8,838 SNPs (non-autosomes or non-selection)
Excluding 0 SNP (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
# of samples: 25
# of SNPs: 250
using 1 thread
Specifying allele frequencies, mean: 0.486, sd: 0.284
MLE IBD: the sum of all selected genotypes (0,1,2) = 6112
MLE IBD: Wed Apr 14 08:21:09 2021 0%
MLE IBD: Wed Apr 14 08:21:09 2021 100%
Genotype matrix: 250 SNPs X 25 samples
[1] -370.7482
[1] -402.2141
[1] -383.7897
[1] -377.9084
[1] -381.3139
[1] -397.5581
[1] -378.3344
[1] -370.703
[1] -376.103
[1] -377.7911
[1] -375.5425
[1] -373.13
[1] -383.6992
[1] -393.5194
[1] -371.9843
[1] -369.6468
[1] -374.5139
[1] -377.841
[1] -387.5622
[1] -377.1646
[1] -377.4659
[1] -375.2204
[1] -372.0639
[1] -379.816
k0 k1 loglik niter
1 1.0000000 0.000000e+00 -370.7482 340
2 0.9278497 4.490042e-02 -401.8323 674
3 0.9320171 2.171748e-04 -382.7851 637
4 0.9757904 1.433990e-04 -377.8073 587
5 1.0000000 0.000000e+00 -381.3139 550
6 1.0000000 0.000000e+00 -397.5581 272
7 1.0000000 0.000000e+00 -378.3344 344
8 0.9430855 3.402422e-05 -369.9385 238
9 1.0000000 0.000000e+00 -376.1030 611
10 0.9179167 5.186286e-02 -377.3652 714
11 0.9630939 2.182349e-03 -375.3065 1000
12 0.0000000 1.000000e+00 -348.6070 147
13 1.0000000 0.000000e+00 -383.6992 743
14 0.9061298 6.031602e-02 -393.0712 437
15 0.8641232 1.358768e-01 -371.6073 210
16 0.7981697 2.018303e-01 -368.8638 177
17 1.0000000 0.000000e+00 -374.5139 164
18 1.0000000 0.000000e+00 -377.8410 118
19 0.9500700 4.992932e-02 -387.5143 313
20 0.9949179 2.725808e-04 -377.1602 629
21 0.9710880 3.137421e-05 -377.3199 343
22 0.9097788 2.643432e-04 -373.9321 305
23 0.7913741 2.086257e-01 -371.1589 157
24 1.0000000 0.000000e+00 -379.8160 156
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
k0 k1 loglik niter
1 0.7532082 0.24679185 NaN 0
2 0.7855774 0.21442258 NaN 0
3 0.7103556 0.28964437 NaN 0
4 0.6865533 0.31344674 NaN 0
5 0.5769952 0.42300478 NaN 0
6 1.0000000 0.00000000 NaN 0
7 0.6434333 0.35656665 NaN 0
8 0.9096684 0.01573758 NaN 0
9 0.6943081 0.30569185 NaN 0
10 0.5581343 0.44186567 NaN 0
11 0.5404675 0.45953254 NaN 0
12 0.0000000 1.00000000 NaN 0
13 0.5435959 0.45640406 NaN 0
14 0.7883793 0.19911987 NaN 0
15 0.3378116 0.66218843 NaN 0
16 0.3005318 0.69946817 NaN 0
17 0.9637291 0.03627094 NaN 0
18 1.0000000 0.00000000 NaN 0
19 0.6715518 0.32844817 NaN 0
20 0.8490238 0.13702670 NaN 0
21 0.7706416 0.22935836 NaN 0
22 0.8687864 0.00000000 NaN 0
23 0.4312604 0.56873956 NaN 0
24 0.7080179 0.29198213 NaN 0
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
D1 D2 D3 D4 D5
1 5.624046e-37 1.180280e-20 4.904718e-21 1.677608e-11 5.609379e-11
2 1.599776e-55 1.503765e-27 7.079950e-35 6.262253e-19 1.625399e-18
3 1.072335e-40 4.267261e-24 5.654489e-26 3.201097e-16 2.342972e-13
4 1.329931e-31 6.653634e-22 4.747074e-20 9.030593e-14 5.531960e-15
5 5.885350e-50 2.609011e-32 2.161310e-25 6.454104e-19 1.012404e-02
6 1.538934e-61 3.872675e-18 2.647038e-31 3.315840e-16 2.139932e-24
7 1.343589e-34 3.488172e-23 1.680912e-18 6.893508e-12 5.160654e-18
8 2.349188e-43 5.205905e-20 3.894035e-30 1.812634e-18 9.555617e-19
9 5.986137e-31 2.918346e-21 7.377681e-18 4.993721e-13 1.567748e-15
10 3.538317e-34 3.287673e-27 2.209391e-19 1.586568e-17 1.173310e-12
11 3.581253e-37 2.001565e-34 6.198208e-23 1.017490e-17 9.002649e-23
12 2.197678e-92 3.064865e-136 7.025180e-47 2.326243e-62 1.855975e-05
13 3.276195e-112 8.125742e-63 3.412096e-46 5.285428e-28 1.114704e-04
14 4.852213e-79 3.798155e-37 5.596519e-37 5.027177e-29 7.222682e-17
15 2.827713e-66 4.657999e-53 2.856851e-25 4.390683e-21 8.227261e-06
16 2.574573e-53 1.343953e-47 6.051528e-22 9.472207e-23 3.519872e-17
17 5.840299e-68 5.261274e-26 9.255782e-40 2.157719e-21 6.128667e-25
18 4.758757e-49 4.322460e-21 1.081958e-40 5.913304e-20 1.755982e-19
19 4.309697e-45 1.176392e-28 1.438804e-17 1.130813e-16 1.620374e-21
20 8.802271e-43 2.137563e-20 2.466227e-21 2.128369e-14 5.266146e-16
21 2.912346e-35 2.903154e-19 3.196077e-22 1.909580e-12 4.410476e-18
22 4.784547e-62 7.600208e-27 9.232315e-35 1.717683e-21 1.201433e-25
23 3.815788e-61 2.498728e-47 5.921616e-33 3.367568e-23 1.155295e-11
24 8.430738e-35 2.120598e-18 2.968306e-17 7.510117e-09 2.776526e-12
D6 D7 D8 loglik niter
1 1.159093e-08 2.164057e-11 2.917311e-06 -331.5457 90
2 1.359681e-07 2.030707e-10 1.310150e-05 -354.4143 151
3 8.297192e-08 2.262973e-10 5.180403e-06 -331.0238 115
4 2.604857e-09 2.548209e-09 5.092313e-06 -336.9514 111
5 1.703514e-05 1.583926e-22 4.689546e-08 -340.3995 165
6 1.058149e-05 4.430545e-14 6.757829e-09 -355.8108 135
7 2.058527e-11 5.171751e-10 4.469748e-06 -349.2330 105
8 9.060776e-06 1.604413e-09 5.581030e-07 -328.1605 145
9 1.057860e-08 3.945948e-10 4.859482e-06 -336.6887 108
10 7.127443e-07 1.262448e-12 6.518920e-06 -328.9219 133
11 2.652194e-16 4.287636e-08 1.299948e-05 -335.7984 153
12 1.096117e-13 9.705827e-32 8.786320e-01 -300.4332 370
13 4.482075e-02 6.237102e-49 1.878718e-09 -350.1596 296
14 7.369247e-05 2.501202e-18 3.839098e-08 -342.5152 234
15 4.648563e-08 7.165893e-28 9.071040e-06 -329.6142 178
16 3.911998e-10 3.056952e-19 2.581633e-05 -325.9074 195
17 2.377998e-05 4.652730e-27 2.109743e-13 -330.5680 179
18 2.192231e-05 4.916291e-23 3.503103e-13 -338.2142 175
19 7.350363e-09 5.310814e-12 1.194814e-05 -349.8404 146
20 2.879301e-06 5.136261e-12 1.328897e-06 -331.9516 109
21 4.795682e-10 5.542997e-08 3.508234e-06 -338.1645 100
22 4.913266e-10 9.012403e-06 1.046127e-05 -332.1700 190
23 4.893518e-11 1.624286e-17 2.286308e-05 -322.7322 187
24 1.638351e-07 2.251153e-14 1.143161e-06 -350.8069 71
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
Calculate the three IBD coefficients (k0, k1, k2) for non-inbred individual pairs by Maximum Likelihood Estimation (MLE) or PLINK Method of Moment (MoM).
Usage
1 2 3 4 |
Arguments
geno1 |
the SNP genotypes for the first individual, 0 – BB, 1 – AB, 2 – AA, other values – missing |
geno2 |
the SNP genotypes for the second individual, 0 – BB, 1 – AB, 2 – AA, other values – missing |
allele.freq |
the allele frequencies |
method |
"EM", "downhill.simplex", "MoM" or "Jacquard", see details |
kinship.constraint |
if TRUE, constrict IBD coefficients ($k_0,k_1,k_2$) in the genealogical region ($2 k_0 k_1 >= k_2^2$) |
max.niter |
the maximum number of iterations |
reltol |
relative convergence tolerance; the algorithm stops if it is unable to reduce the value of log likelihood by a factor of $reltol * (abs(log likelihood with the initial parameters) + reltol)$ at a step. |
coeff.correct |
|
out.num.iter |
if TRUE, output the numbers of iterations |
verbose |
if TRUE, show information |
Details
If method = "MoM", then PLINK Method of Moment without a
allele-count-based correction factor is conducted. Otherwise, two numeric
approaches for maximum likelihood estimation can be used: one is
Expectation-Maximization (EM) algorithm, and the other is Nelder-Mead method
or downhill simplex method. Generally, EM algorithm is more robust than
downhill simplex method. "Jacquard" refers to the estimation of nine
Jacquard's coefficients.
If coeff.correct is TRUE, the final point that is found by
searching algorithm (EM or downhill simplex) is used to compare the six points
(fullsib, offspring, halfsib, cousin, unrelated), since any numeric approach
might not reach the maximum position after a finit number of steps. If any of
these six points has a higher value of log likelihood, the final point will be
replaced by the best one.
Value
Return a data.frame:
k0 |
IBD coefficient, the probability of sharing ZERO IBD |
k1 |
IBD coefficient, the probability of sharing ONE IBD |
loglik |
the value of log likelihood |
niter |
the number of iterations |
Author(s)
Xiuwen Zheng
References
Milligan BG. 2003. Maximum-likelihood estimation of relatedness. Genetics 163:1153-1167.
Weir BS, Anderson AD, Hepler AB. 2006. Genetic relatedness analysis: modern data and new challenges. Nat Rev Genet. 7(10):771-80.
Choi Y, Wijsman EM, Weir BS. 2009. Case-control association testing in the presence of unknown relationships. Genet Epidemiol 33(8):668-78.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ & Sham PC. 2007. PLINK: a toolset for whole-genome association and population-based linkage analysis. American Journal of Human Genetics, 81.
See Also
snpgdsPairIBDMLELogLik, snpgdsIBDMLE,
snpgdsIBDMLELogLik, snpgdsIBDMoM
Examples
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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | # open an example dataset (HapMap)
genofile <- snpgdsOpen(snpgdsExampleFileName())
YRI.id <- read.gdsn(index.gdsn(genofile, "sample.id"))[
read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))=="YRI"]
# SNP pruning
set.seed(10)
snpset <- snpgdsLDpruning(genofile, sample.id=YRI.id, maf=0.05,
missing.rate=0.05)
snpset <- unname(sample(unlist(snpset), 250))
# the number of samples
n <- 25
# specify allele frequencies
RF <- snpgdsSNPRateFreq(genofile, sample.id=YRI.id, snp.id=snpset,
with.id=TRUE)
summary(RF$AlleleFreq)
subMLE <- snpgdsIBDMLE(genofile, sample.id=YRI.id[1:n], snp.id=RF$snp.id,
allele.freq=RF$AlleleFreq)
subMoM <- snpgdsIBDMoM(genofile, sample.id=YRI.id[1:n], snp.id=RF$snp.id,
allele.freq=RF$AlleleFreq)
subJac <- snpgdsIBDMLE(genofile, sample.id=YRI.id[1:n], snp.id=RF$snp.id,
allele.freq=RF$AlleleFreq, method="Jacquard")
########################
# genotype matrix
mat <- snpgdsGetGeno(genofile, sample.id=YRI.id[1:n], snp.id=snpset,
snpfirstdim=TRUE)
rv <- NULL
for (i in 2:n)
{
rv <- rbind(rv, snpgdsPairIBD(mat[,1], mat[,i], RF$AlleleFreq, "EM"))
print(snpgdsPairIBDMLELogLik(mat[,1], mat[,i], RF$AlleleFreq,
relatedness="unrelated", verbose=TRUE))
}
rv
summary(rv$k0 - subMLE$k0[1, 2:n])
summary(rv$k1 - subMLE$k1[1, 2:n])
# ZERO
rv <- NULL
for (i in 2:n)
rv <- rbind(rv, snpgdsPairIBD(mat[,1], mat[,i], RF$AlleleFreq, "MoM"))
rv
summary(rv$k0 - subMoM$k0[1, 2:n])
summary(rv$k1 - subMoM$k1[1, 2:n])
# ZERO
rv <- NULL
for (i in 2:n)
rv <- rbind(rv, snpgdsPairIBD(mat[,1], mat[,i], RF$AlleleFreq, "Jacquard"))
rv
summary(rv$D1 - subJac$D1[1, 2:n])
summary(rv$D2 - subJac$D2[1, 2:n])
# ZERO
# close the genotype file
snpgdsClose(genofile)
|
Example output
Loading required package: gdsfmt
SNPRelate -- supported by Streaming SIMD Extensions 2 (SSE2)
SNP pruning based on LD:
Excluding 365 SNPs on non-autosomes
Excluding 1,646 SNPs (monomorphic: TRUE, MAF: 0.05, missing rate: 0.05)
# of samples: 93
# of SNPs: 7,077
using 1 thread
sliding window: 500,000 basepairs, Inf SNPs
|LD| threshold: 0.2
method: composite
Chromosome 1: 62.29%, 446/716
Chromosome 2: 62.67%, 465/742
Chromosome 3: 59.93%, 365/609
Chromosome 4: 64.23%, 361/562
Chromosome 5: 62.37%, 353/566
Chromosome 6: 59.82%, 338/565
Chromosome 7: 63.14%, 298/472
Chromosome 8: 57.58%, 281/488
Chromosome 9: 62.98%, 262/416
Chromosome 10: 60.46%, 292/483
Chromosome 11: 63.09%, 282/447
Chromosome 12: 62.76%, 268/427
Chromosome 13: 63.08%, 217/344
Chromosome 14: 63.83%, 180/282
Chromosome 15: 63.74%, 167/262
Chromosome 16: 62.23%, 173/278
Chromosome 17: 65.70%, 136/207
Chromosome 18: 59.40%, 158/266
Chromosome 19: 68.33%, 82/120
Chromosome 20: 66.38%, 152/229
Chromosome 21: 61.11%, 77/126
Chromosome 22: 57.76%, 67/116
5,420 markers are selected in total.
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.05376 0.23790 0.46774 0.48641 0.74597 0.94624
Identity-By-Descent analysis (MLE) on genotypes:
Excluding 8,838 SNPs (non-autosomes or non-selection)
Excluding 0 SNP (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
# of samples: 25
# of SNPs: 250
using 1 thread
Specifying allele frequencies, mean: 0.486, sd: 0.284
MLE IBD: the sum of all selected genotypes (0,1,2) = 6112
MLE IBD: Wed Apr 14 08:21:08 2021 0%
MLE IBD: Wed Apr 14 08:21:09 2021 100%
IBD analysis (PLINK method of moment) on genotypes:
Excluding 8,838 SNPs (non-autosomes or non-selection)
Excluding 0 SNP (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
# of samples: 25
# of SNPs: 250
using 1 thread
Specifying allele frequencies, mean: 0.486, sd: 0.284
*** A correction factor based on allele count is not used, since the allele frequencies are specified.
PLINK IBD: the sum of all selected genotypes (0,1,2) = 6112
Wed Apr 14 08:21:09 2021 (internal increment: 65536)
[..................................................] 0%, ETC: ---
[==================================================] 100%, completed, 0s
Wed Apr 14 08:21:09 2021 Done.
Identity-By-Descent analysis (MLE) on genotypes:
Excluding 8,838 SNPs (non-autosomes or non-selection)
Excluding 0 SNP (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
# of samples: 25
# of SNPs: 250
using 1 thread
Specifying allele frequencies, mean: 0.486, sd: 0.284
MLE IBD: the sum of all selected genotypes (0,1,2) = 6112
MLE IBD: Wed Apr 14 08:21:09 2021 0%
MLE IBD: Wed Apr 14 08:21:09 2021 100%
Genotype matrix: 250 SNPs X 25 samples
[1] -370.7482
[1] -402.2141
[1] -383.7897
[1] -377.9084
[1] -381.3139
[1] -397.5581
[1] -378.3344
[1] -370.703
[1] -376.103
[1] -377.7911
[1] -375.5425
[1] -373.13
[1] -383.6992
[1] -393.5194
[1] -371.9843
[1] -369.6468
[1] -374.5139
[1] -377.841
[1] -387.5622
[1] -377.1646
[1] -377.4659
[1] -375.2204
[1] -372.0639
[1] -379.816
k0 k1 loglik niter
1 1.0000000 0.000000e+00 -370.7482 340
2 0.9278497 4.490042e-02 -401.8323 674
3 0.9320171 2.171748e-04 -382.7851 637
4 0.9757904 1.433990e-04 -377.8073 587
5 1.0000000 0.000000e+00 -381.3139 550
6 1.0000000 0.000000e+00 -397.5581 272
7 1.0000000 0.000000e+00 -378.3344 344
8 0.9430855 3.402422e-05 -369.9385 238
9 1.0000000 0.000000e+00 -376.1030 611
10 0.9179167 5.186286e-02 -377.3652 714
11 0.9630939 2.182349e-03 -375.3065 1000
12 0.0000000 1.000000e+00 -348.6070 147
13 1.0000000 0.000000e+00 -383.6992 743
14 0.9061298 6.031602e-02 -393.0712 437
15 0.8641232 1.358768e-01 -371.6073 210
16 0.7981697 2.018303e-01 -368.8638 177
17 1.0000000 0.000000e+00 -374.5139 164
18 1.0000000 0.000000e+00 -377.8410 118
19 0.9500700 4.992932e-02 -387.5143 313
20 0.9949179 2.725808e-04 -377.1602 629
21 0.9710880 3.137421e-05 -377.3199 343
22 0.9097788 2.643432e-04 -373.9321 305
23 0.7913741 2.086257e-01 -371.1589 157
24 1.0000000 0.000000e+00 -379.8160 156
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
k0 k1 loglik niter
1 0.7532082 0.24679185 NaN 0
2 0.7855774 0.21442258 NaN 0
3 0.7103556 0.28964437 NaN 0
4 0.6865533 0.31344674 NaN 0
5 0.5769952 0.42300478 NaN 0
6 1.0000000 0.00000000 NaN 0
7 0.6434333 0.35656665 NaN 0
8 0.9096684 0.01573758 NaN 0
9 0.6943081 0.30569185 NaN 0
10 0.5581343 0.44186567 NaN 0
11 0.5404675 0.45953254 NaN 0
12 0.0000000 1.00000000 NaN 0
13 0.5435959 0.45640406 NaN 0
14 0.7883793 0.19911987 NaN 0
15 0.3378116 0.66218843 NaN 0
16 0.3005318 0.69946817 NaN 0
17 0.9637291 0.03627094 NaN 0
18 1.0000000 0.00000000 NaN 0
19 0.6715518 0.32844817 NaN 0
20 0.8490238 0.13702670 NaN 0
21 0.7706416 0.22935836 NaN 0
22 0.8687864 0.00000000 NaN 0
23 0.4312604 0.56873956 NaN 0
24 0.7080179 0.29198213 NaN 0
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
D1 D2 D3 D4 D5
1 5.624046e-37 1.180280e-20 4.904718e-21 1.677608e-11 5.609379e-11
2 1.599776e-55 1.503765e-27 7.079950e-35 6.262253e-19 1.625399e-18
3 1.072335e-40 4.267261e-24 5.654489e-26 3.201097e-16 2.342972e-13
4 1.329931e-31 6.653634e-22 4.747074e-20 9.030593e-14 5.531960e-15
5 5.885350e-50 2.609011e-32 2.161310e-25 6.454104e-19 1.012404e-02
6 1.538934e-61 3.872675e-18 2.647038e-31 3.315840e-16 2.139932e-24
7 1.343589e-34 3.488172e-23 1.680912e-18 6.893508e-12 5.160654e-18
8 2.349188e-43 5.205905e-20 3.894035e-30 1.812634e-18 9.555617e-19
9 5.986137e-31 2.918346e-21 7.377681e-18 4.993721e-13 1.567748e-15
10 3.538317e-34 3.287673e-27 2.209391e-19 1.586568e-17 1.173310e-12
11 3.581253e-37 2.001565e-34 6.198208e-23 1.017490e-17 9.002649e-23
12 2.197678e-92 3.064865e-136 7.025180e-47 2.326243e-62 1.855975e-05
13 3.276195e-112 8.125742e-63 3.412096e-46 5.285428e-28 1.114704e-04
14 4.852213e-79 3.798155e-37 5.596519e-37 5.027177e-29 7.222682e-17
15 2.827713e-66 4.657999e-53 2.856851e-25 4.390683e-21 8.227261e-06
16 2.574573e-53 1.343953e-47 6.051528e-22 9.472207e-23 3.519872e-17
17 5.840299e-68 5.261274e-26 9.255782e-40 2.157719e-21 6.128667e-25
18 4.758757e-49 4.322460e-21 1.081958e-40 5.913304e-20 1.755982e-19
19 4.309697e-45 1.176392e-28 1.438804e-17 1.130813e-16 1.620374e-21
20 8.802271e-43 2.137563e-20 2.466227e-21 2.128369e-14 5.266146e-16
21 2.912346e-35 2.903154e-19 3.196077e-22 1.909580e-12 4.410476e-18
22 4.784547e-62 7.600208e-27 9.232315e-35 1.717683e-21 1.201433e-25
23 3.815788e-61 2.498728e-47 5.921616e-33 3.367568e-23 1.155295e-11
24 8.430738e-35 2.120598e-18 2.968306e-17 7.510117e-09 2.776526e-12
D6 D7 D8 loglik niter
1 1.159093e-08 2.164057e-11 2.917311e-06 -331.5457 90
2 1.359681e-07 2.030707e-10 1.310150e-05 -354.4143 151
3 8.297192e-08 2.262973e-10 5.180403e-06 -331.0238 115
4 2.604857e-09 2.548209e-09 5.092313e-06 -336.9514 111
5 1.703514e-05 1.583926e-22 4.689546e-08 -340.3995 165
6 1.058149e-05 4.430545e-14 6.757829e-09 -355.8108 135
7 2.058527e-11 5.171751e-10 4.469748e-06 -349.2330 105
8 9.060776e-06 1.604413e-09 5.581030e-07 -328.1605 145
9 1.057860e-08 3.945948e-10 4.859482e-06 -336.6887 108
10 7.127443e-07 1.262448e-12 6.518920e-06 -328.9219 133
11 2.652194e-16 4.287636e-08 1.299948e-05 -335.7984 153
12 1.096117e-13 9.705827e-32 8.786320e-01 -300.4332 370
13 4.482075e-02 6.237102e-49 1.878718e-09 -350.1596 296
14 7.369247e-05 2.501202e-18 3.839098e-08 -342.5152 234
15 4.648563e-08 7.165893e-28 9.071040e-06 -329.6142 178
16 3.911998e-10 3.056952e-19 2.581633e-05 -325.9074 195
17 2.377998e-05 4.652730e-27 2.109743e-13 -330.5680 179
18 2.192231e-05 4.916291e-23 3.503103e-13 -338.2142 175
19 7.350363e-09 5.310814e-12 1.194814e-05 -349.8404 146
20 2.879301e-06 5.136261e-12 1.328897e-06 -331.9516 109
21 4.795682e-10 5.542997e-08 3.508234e-06 -338.1645 100
22 4.913266e-10 9.012403e-06 1.046127e-05 -332.1700 190
23 4.893518e-11 1.624286e-17 2.286308e-05 -322.7322 187
24 1.638351e-07 2.251153e-14 1.143161e-06 -350.8069 71
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
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