hlaAssocTest: Statistical Association Tests
In HIBAG: HLA Genotype Imputation with Attribute Bagging
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
Perform statistical association tests via Pearson's Chi-squared test,
Fisher's exact test and logistic regressions.
Usage
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## S3 method for class 'hlaAlleleClass'
hlaAssocTest(hla, formula, data,
model=c("dominant", "additive", "recessive", "genotype"),
model.fit=c("glm"), prob.threshold=NaN, use.prob=FALSE, showOR=FALSE,
verbose=TRUE, ...)
## S3 method for class 'hlaAASeqClass'
hlaAssocTest(hla, formula, data,
model=c("dominant", "additive", "recessive", "genotype"),
model.fit=c("glm"), prob.threshold=NaN, use.prob=FALSE, showOR=FALSE,
show.all=FALSE, verbose=TRUE, ...)
Arguments
hla
an object of hlaAlleleClass
formula
an object of class "formula" (or one that can be
coerced to that class): a symbolic description of the model to be
fitted, e.g., y ~ 1, y ~ h + a
data
an optional data frame, list or environment containing the
variables in the model. If not found in data, the variables
are taken from environment(formula)
model
dominant, additive, recessive or genotype models:
"dominant" is default
model.fit
"glm" – generalized linear regression
prob.threshold
the probability threshold to exclude individuals
with low confidence scores
use.prob
if TRUE, use the posterior probabilities as weights
in glm models
showOR
show odd ratio (OR) instead of log OR if TRUE
show.all
if TRUE, show both significant and non-significant
results; if FALSE, only show significant results
verbose
if TRUE, show information
...
optional arguments to glm or nlme call
Details
model description (given a specific HLA allele h)
dominant [-/-] vs. [-/h,h/h] (0 vs. 1 in design matrix)
additive [-] vs. [h] in Chi-squared and Fisher's exact test,
the allele dosage in regressions (0: -/-, 1: -/h, 2: h/h)
recessive [-/-,-/h] vs. [h/h] (0 vs. 1 in design matrix)
genotype [-/-], [-/h], [h/h] (0 vs. 1 in design matrix)
In allelic associations, Chi-squared and Fisher exact tests are preformed
on the cross tabulation, which is constructed according to the specified
model (dominant, additive, recessive and gneotype).
In amino acid associations, Fisher exact test is performed on a cross
tabulation with the numbers of each amino acid stratified by response variable
(e.g., disease status).
In linear and logistic regressions, 95% confidence intervals are
calculated based on asymptotic normality. The option use.prob=TRUE might
be useful in the sensitivity analysis.
Value
Return a data.frame with
[-]
the number of haplotypes not carrying the specified HLA allele
[h]
the number of haplotype carrying the specified HLA allele
%.[-], ...
case/disease proportion in the group [-], ...
[-/-]
the number of individuals or haplotypes not carrying the
specified HLA allele
[-/h]
the number of individuals or haplotypes carrying one
specified HLA allele
[-/h]
the number of individuals or haplotypes carrying two
specified HLA alleles
[-/h,h/h]
the number of individuals or haplotypes carrying one or
two specified HLA alleles
[-/-,-/h]
the number of individuals or haplotypes carrying at most
one specified HLA allele
%.[-/-], ...
case/disease proportion in the group [-/-], ...
avg.[-/-], ...
outcome average in the group [-/-], ...
chisq.st
the value the chi-squared test statistic
chisq.p
the p-value for the Chi-squared test
fisher.p
the p-value for the Fisher's exact test
h.est
the coefficient estimate of HLA allele
h.25%, h.75%
the 95% confidence interval for HLA allele
h.pval
p value for HLA allele
Author(s)
Xiuwen Zheng
See Also
hlaConvSequence, summary.hlaAASeqClass
Examples
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hla.id <- "A"
hla <- hlaAllele(HLA_Type_Table$sample.id,
H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
locus=hla.id, assembly="hg19")
set.seed(1000)
n <- nrow(hla$value)
dat <- data.frame(case = c(rep(0, n/2), rep(1, n/2)), y = rnorm(n),
pc1 = rnorm(n))
hlaAssocTest(hla, case ~ 1, data=dat)
hlaAssocTest(hla, case ~ 1, data=dat, model="additive")
hlaAssocTest(hla, case ~ 1, data=dat, model="recessive")
hlaAssocTest(hla, case ~ 1, data=dat, model="genotype")
hlaAssocTest(hla, y ~ 1, data=dat)
hlaAssocTest(hla, y ~ 1, data=dat, model="genotype")
hlaAssocTest(hla, case ~ h, data=dat)
hlaAssocTest(hla, case ~ h + pc1, data=dat)
hlaAssocTest(hla, case ~ h + pc1, data=dat, showOR=TRUE)
hlaAssocTest(hla, y ~ h, data=dat)
hlaAssocTest(hla, y ~ h + pc1, data=dat)
hlaAssocTest(hla, y ~ h + pc1, data=dat, showOR=TRUE)
hlaAssocTest(hla, case ~ h, data=dat, model="additive")
hlaAssocTest(hla, case ~ h, data=dat, model="recessive")
hlaAssocTest(hla, case ~ h, data=dat, model="genotype")
Example output
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.3
Supported by Streaming SIMD Extensions (SSE2) [64-bit]
dominant model:
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042*
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000
02:01 25 35 52.0 48.6 0.0000 1.000 1.000
02:06 59 1 50.8 0.0 0.0000 1.000 1.000
03:01 51 9 49.0 55.6 0.0000 1.000 1.000
11:01 55 5 50.9 40.0 0.0000 1.000 1.000
23:01 58 2 50.0 50.0 0.0000 1.000 1.000
24:03 59 1 50.8 0.0 0.0000 1.000 1.000
25:01 55 5 52.7 20.0 0.8727 0.350 0.353
26:01 57 3 52.6 0.0 1.4035 0.236 0.237
29:02 56 4 51.8 25.0 0.2679 0.605 0.612
31:01 57 3 49.1 66.7 0.0000 1.000 1.000
32:01 56 4 46.4 100.0 2.4107 0.121 0.112
68:01 57 3 52.6 0.0 1.4035 0.236 0.237
additive model:
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p
01:01 95 25 50.5 48.0 0.0000 1.000 1.000
02:01 77 43 48.1 53.5 0.1450 0.703 0.704
02:06 119 1 50.4 0.0 0.0000 1.000 1.000
03:01 111 9 49.5 55.6 0.0000 1.000 1.000
11:01 115 5 50.4 40.0 0.0000 1.000 1.000
23:01 117 3 50.4 33.3 0.0000 1.000 1.000
24:02 109 11 46.8 81.8 3.6030 0.058 0.053
24:03 119 1 50.4 0.0 0.0000 1.000 1.000
25:01 115 5 51.3 20.0 0.8348 0.361 0.364
26:01 117 3 51.3 0.0 1.3675 0.242 0.244
29:02 116 4 50.9 25.0 0.2586 0.611 0.619
31:01 117 3 49.6 66.7 0.0000 1.000 1.000
32:01 116 4 48.3 100.0 2.3276 0.127 0.119
68:01 117 3 51.3 0.0 1.3675 0.242 0.244
recessive model:
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p
01:01 59 1 50.8 0 0.000 1.000 1.000
02:01 52 8 46.2 75 1.298 0.255 0.254
02:06 60 0 50.0 . . . .
03:01 60 0 50.0 . . . .
11:01 60 0 50.0 . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000
24:02 60 0 50.0 . . . .
24:03 60 0 50.0 . . . .
25:01 60 0 50.0 . . . .
26:01 60 0 50.0 . . . .
29:02 60 0 50.0 . . . .
31:01 60 0 50.0 . . . .
32:01 60 0 50.0 . . . .
68:01 60 0 50.0 . . . .
genotype model:
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
dominant model:
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p
01:01 36 24 -0.14684 -0.117427 0.909
02:01 25 35 -0.32331 -0.000618 0.190
02:06 59 1 -0.14024 0.170057 .
03:01 51 9 -0.05600 -0.583178 0.147
11:01 55 5 -0.19188 0.489815 0.287
23:01 58 2 -0.15400 0.413687 0.281
24:02 49 11 -0.10486 -0.269664 0.537
24:03 59 1 -0.11409 -1.373118 .
25:01 55 5 -0.12237 -0.274749 0.742
26:01 57 3 -0.12473 -0.331558 0.690
29:02 56 4 -0.13044 -0.199941 0.789
31:01 57 3 -0.10097 -0.783003 0.607
32:01 56 4 -0.07702 -0.947791 0.092
68:01 57 3 -0.16915 0.512457 0.196
genotype model:
[-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p
01:01 36 23 1 -0.14684 -0.08833 -0.78655 0.784
02:01 25 27 8 -0.32331 -0.02341 0.07631 0.446
02:06 59 1 0 -0.14024 0.17006 . 0.756
03:01 51 9 0 -0.05600 -0.58318 . 0.138
11:01 55 5 0 -0.19188 0.48981 . 0.137
23:01 58 1 1 -0.15400 0.10762 0.71975 0.663
24:02 49 11 0 -0.10486 -0.26966 . 0.618
24:03 59 1 0 -0.11409 -1.37312 . 0.205
25:01 55 5 0 -0.12237 -0.27475 . 0.742
26:01 57 3 0 -0.12473 -0.33156 . 0.725
29:02 56 4 0 -0.13044 -0.19994 . 0.892
31:01 57 3 0 -0.10097 -0.78300 . 0.243
32:01 56 4 0 -0.07702 -0.94779 . 0.086
68:01 57 3 0 -0.16915 0.51246 . 0.243
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.792e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -5.851e-16
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.372e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.624e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.418e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 2.236e-15
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.495e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.170e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.282e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.771e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
h.2.5% h.97.5% h.pval
24:02 0.1585 3.4251 0.032*
-----
01:01 -1.0330 1.0330 1.000
02:01 -1.1643 0.8899 0.793
02:06 -2868.1268 2836.9268 0.991
03:01 -1.1624 1.6872 0.718
11:01 -2.3074 1.4237 0.643
23:01 -2.8192 2.8192 1.000
24:03 -2868.1268 2836.9268 0.991
25:01 -3.7498 0.7588 0.194
26:01 -2731.9621 2698.6192 0.990
29:02 -3.4931 1.1530 0.324
31:01 -1.7277 3.1842 0.561
32:01 -3859.2763 3894.6947 0.993
68:01 -2731.9621 2698.6192 0.990
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.793e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -2.268e-04
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.370e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.562e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.686e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.451e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 -3.062e-03
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.501e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.189e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.289e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.781e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.673e+01
h.2.5% h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 0.1587 3.4264 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 -1.0334 1.0330 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 -1.1652 0.8913 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 -2868.1460 2836.9076 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 -1.1813 1.7185 0.717 0.011958 -0.5044 0.5283 0.964
11:01 -2.3225 1.4322 0.642 0.008025 -0.5026 0.5186 0.975
23:01 -2.8348 2.8287 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 -2868.1286 2836.9250 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 -3.7579 0.7568 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 -2731.8901 2698.5450 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 -3.5309 1.1526 0.320 0.033234 -0.4796 0.5461 0.899
31:01 -1.7274 3.1851 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 -3845.6317 3881.2510 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 -2721.2124 2687.7497 0.990 -0.086589 -0.6512 0.4781 0.764
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 6.005e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 9.998e-01
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 8.720e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 1.647e-07
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 1.308e+00
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 6.407e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 9.969e-01
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 1.676e-07
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 2.230e-01
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.744e-08
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 3.045e-01
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 2.073e+00
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 5.428e+07
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.416e-08
h.2.5%_OR h.97.5%_OR h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 1.17200 30.766 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 0.35579 2.809 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 0.31185 2.438 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 0.00000 Inf 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 0.30687 5.576 0.717 0.011958 -0.5044 0.5283 0.964
11:01 0.09803 4.188 0.642 0.008025 -0.5026 0.5186 0.975
23:01 0.05873 16.923 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 0.00000 Inf 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 0.02333 2.131 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 0.00000 Inf 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 0.02928 3.167 0.320 0.033234 -0.4796 0.5461 0.899
31:01 0.17774 24.171 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 0.00000 Inf 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 0.00000 Inf 0.990 -0.086589 -0.6512 0.4781 0.764
Linear regression (dominant model) with 60 individuals:
glm(y ~ h, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.02941 -0.4805 0.5393
02:01 25 35 -0.32331 -0.000618 0.190 0.32269 -0.1772 0.8226
02:06 59 1 -0.14024 0.170057 . 0.31030 -1.6397 2.2603
03:01 51 9 -0.05600 -0.583178 0.147 -0.52718 -1.2136 0.1592
11:01 55 5 -0.19188 0.489815 0.287 0.68170 -0.2051 1.5685
23:01 58 2 -0.15400 0.413687 0.281 0.56768 -0.8165 1.9518
24:02 49 11 -0.10486 -0.269664 0.537 -0.16481 -0.8091 0.4795
24:03 59 1 -0.11409 -1.373118 . -1.25903 -3.1835 0.6655
25:01 55 5 -0.12237 -0.274749 0.742 -0.15237 -1.0555 0.7507
26:01 57 3 -0.12473 -0.331558 0.690 -0.20683 -1.3519 0.9383
29:02 56 4 -0.13044 -0.199941 0.789 -0.06950 -1.0709 0.9319
31:01 57 3 -0.10097 -0.783003 0.607 -0.68203 -1.8149 0.4508
32:01 56 4 -0.07702 -0.947791 0.092 -0.87077 -1.8470 0.1054
68:01 57 3 -0.16915 0.512457 0.196 0.68161 -0.4512 1.8145
h.pval
01:01 0.910
02:01 0.211
02:06 0.756
03:01 0.138
11:01 0.137
23:01 0.425
24:02 0.618
24:03 0.205
25:01 0.742
26:01 0.725
29:02 0.892
31:01 0.243
32:01 0.086
68:01 0.243
Linear regression (dominant model) with 60 individuals:
glm(y ~ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Linear regression (dominant model) with 60 individuals:
glm(y ~ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Logistic regression (additive model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5%
24:02 109 11 46.8 81.8 3.6030 0.058 0.053 1.7918 0.1585
-----
01:01 95 25 50.5 48.0 0.0000 1.000 1.000 -0.1207 -1.0843
02:01 77 43 48.1 53.5 0.1450 0.703 0.704 0.2137 -0.5289
02:06 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
03:01 111 9 49.5 55.6 0.0000 1.000 1.000 0.2624 -1.1624
11:01 115 5 50.4 40.0 0.0000 1.000 1.000 -0.4418 -2.3074
23:01 117 3 50.4 33.3 0.0000 1.000 1.000 -0.4323 -2.3435
24:03 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
25:01 115 5 51.3 20.0 0.8348 0.361 0.364 -1.4955 -3.7498
26:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
29:02 116 4 50.9 25.0 0.2586 0.611 0.619 -1.1701 -3.4931
31:01 117 3 49.6 66.7 0.0000 1.000 1.000 0.7282 -1.7277
32:01 116 4 48.3 100.0 2.3276 0.127 0.119 17.7092 -3859.2763
68:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
h.97.5% h.pval
24:02 3.4251 0.032*
-----
01:01 0.8430 0.806
02:01 0.9563 0.573
02:06 2836.9268 0.991
03:01 1.6872 0.718
11:01 1.4237 0.643
23:01 1.4789 0.658
24:03 2836.9268 0.991
25:01 0.7588 0.194
26:01 2698.6192 0.990
29:02 1.1530 0.324
31:01 3.1842 0.561
32:01 3894.6947 0.993
68:01 2698.6192 0.990
Logistic regression (recessive model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p h.est
01:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
02:01 52 8 46.2 75 1.298 0.255 0.254 1.253
02:06 60 0 50.0 . . . . .
03:01 60 0 50.0 . . . . .
11:01 60 0 50.0 . . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
24:02 60 0 50.0 . . . . .
24:03 60 0 50.0 . . . . .
25:01 60 0 50.0 . . . . .
26:01 60 0 50.0 . . . . .
29:02 60 0 50.0 . . . . .
31:01 60 0 50.0 . . . . .
32:01 60 0 50.0 . . . . .
68:01 60 0 50.0 . . . . .
h.2.5% h.97.5% h.pval
01:01 -2868.1268 2836.927 0.991
02:01 -0.4379 2.943 0.146
02:06 . . .
03:01 . . .
11:01 . . .
23:01 -2868.1268 2836.927 0.991
24:02 . . .
24:03 . . .
25:01 . . .
26:01 . . .
29:02 . . .
31:01 . . .
32:01 . . .
68:01 . . .
Logistic regression (genotype model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
h1.est h1.2.5% h1.97.5% h1.pval h2.est h2.2.5% h2.97.5%
24:02 1.79176 0.1585 3.4251 0.032* . . .
-----
01:01 0.08701 -0.9600 1.1340 0.871 -15.566 -2868.0929 2836.961
02:01 -0.45474 -1.5524 0.6430 0.417 1.019 -0.7637 2.801
02:06 -15.59997 -2868.1268 2836.9268 0.991 . . .
03:01 0.26236 -1.1624 1.6872 0.718 . . .
11:01 -0.44183 -2.3074 1.4237 0.643 . . .
23:01 16.56607 -4686.4552 4719.5873 0.994 -16.566 -4719.5873 4686.455
24:03 -15.59997 -2868.1268 2836.9268 0.991 . . .
25:01 -1.49549 -3.7498 0.7588 0.194 . . .
26:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
29:02 -1.17007 -3.4931 1.1530 0.324 . . .
31:01 0.72824 -1.7277 3.1842 0.561 . . .
32:01 17.70917 -3859.2763 3894.6947 0.993 . . .
68:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
h2.pval
24:02 .
-----
01:01 0.991
02:01 0.263
02:06 .
03:01 .
11:01 .
23:01 0.994
24:03 .
25:01 .
26:01 .
29:02 .
31:01 .
32:01 .
68:01 .
HIBAG documentation built on March 24, 2021, 6 p.m.
R Package Documentation
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
Perform statistical association tests via Pearson's Chi-squared test, Fisher's exact test and logistic regressions.
Usage
1 2 3 4 5 6 7 8 9 10 | ## S3 method for class 'hlaAlleleClass'
hlaAssocTest(hla, formula, data,
model=c("dominant", "additive", "recessive", "genotype"),
model.fit=c("glm"), prob.threshold=NaN, use.prob=FALSE, showOR=FALSE,
verbose=TRUE, ...)
## S3 method for class 'hlaAASeqClass'
hlaAssocTest(hla, formula, data,
model=c("dominant", "additive", "recessive", "genotype"),
model.fit=c("glm"), prob.threshold=NaN, use.prob=FALSE, showOR=FALSE,
show.all=FALSE, verbose=TRUE, ...)
|
Arguments
hla |
an object of |
formula |
an object of class |
data |
an optional data frame, list or environment containing the
variables in the model. If not found in |
model |
dominant, additive, recessive or genotype models:
|
model.fit |
"glm" – generalized linear regression |
prob.threshold |
the probability threshold to exclude individuals with low confidence scores |
use.prob |
if |
showOR |
show odd ratio (OR) instead of log OR if |
show.all |
if |
verbose |
if TRUE, show information |
... |
optional arguments to |
Details
| model | description (given a specific HLA allele h) |
| dominant | [-/-] vs. [-/h,h/h] (0 vs. 1 in design matrix) |
| additive | [-] vs. [h] in Chi-squared and Fisher's exact test, the allele dosage in regressions (0: -/-, 1: -/h, 2: h/h) |
| recessive | [-/-,-/h] vs. [h/h] (0 vs. 1 in design matrix) |
| genotype | [-/-], [-/h], [h/h] (0 vs. 1 in design matrix) |
In allelic associations, Chi-squared and Fisher exact tests are preformed on the cross tabulation, which is constructed according to the specified model (dominant, additive, recessive and gneotype).
In amino acid associations, Fisher exact test is performed on a cross tabulation with the numbers of each amino acid stratified by response variable (e.g., disease status).
In linear and logistic regressions, 95% confidence intervals are
calculated based on asymptotic normality. The option use.prob=TRUE might
be useful in the sensitivity analysis.
Value
Return a data.frame with
[-] |
the number of haplotypes not carrying the specified HLA allele |
[h] |
the number of haplotype carrying the specified HLA allele |
%.[-], ... |
case/disease proportion in the group [-], ... |
[-/-] |
the number of individuals or haplotypes not carrying the specified HLA allele |
[-/h] |
the number of individuals or haplotypes carrying one specified HLA allele |
[-/h] |
the number of individuals or haplotypes carrying two specified HLA alleles |
[-/h,h/h] |
the number of individuals or haplotypes carrying one or two specified HLA alleles |
[-/-,-/h] |
the number of individuals or haplotypes carrying at most one specified HLA allele |
%.[-/-], ... |
case/disease proportion in the group [-/-], ... |
avg.[-/-], ... |
outcome average in the group [-/-], ... |
chisq.st |
the value the chi-squared test statistic |
chisq.p |
the p-value for the Chi-squared test |
fisher.p |
the p-value for the Fisher's exact test |
h.est |
the coefficient estimate of HLA allele |
h.25%, h.75% |
the 95% confidence interval for HLA allele |
h.pval |
p value for HLA allele |
Author(s)
Xiuwen Zheng
See Also
hlaConvSequence, summary.hlaAASeqClass
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 | hla.id <- "A"
hla <- hlaAllele(HLA_Type_Table$sample.id,
H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
locus=hla.id, assembly="hg19")
set.seed(1000)
n <- nrow(hla$value)
dat <- data.frame(case = c(rep(0, n/2), rep(1, n/2)), y = rnorm(n),
pc1 = rnorm(n))
hlaAssocTest(hla, case ~ 1, data=dat)
hlaAssocTest(hla, case ~ 1, data=dat, model="additive")
hlaAssocTest(hla, case ~ 1, data=dat, model="recessive")
hlaAssocTest(hla, case ~ 1, data=dat, model="genotype")
hlaAssocTest(hla, y ~ 1, data=dat)
hlaAssocTest(hla, y ~ 1, data=dat, model="genotype")
hlaAssocTest(hla, case ~ h, data=dat)
hlaAssocTest(hla, case ~ h + pc1, data=dat)
hlaAssocTest(hla, case ~ h + pc1, data=dat, showOR=TRUE)
hlaAssocTest(hla, y ~ h, data=dat)
hlaAssocTest(hla, y ~ h + pc1, data=dat)
hlaAssocTest(hla, y ~ h + pc1, data=dat, showOR=TRUE)
hlaAssocTest(hla, case ~ h, data=dat, model="additive")
hlaAssocTest(hla, case ~ h, data=dat, model="recessive")
hlaAssocTest(hla, case ~ h, data=dat, model="genotype")
|
Example output
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.3
Supported by Streaming SIMD Extensions (SSE2) [64-bit]
dominant model:
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042*
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000
02:01 25 35 52.0 48.6 0.0000 1.000 1.000
02:06 59 1 50.8 0.0 0.0000 1.000 1.000
03:01 51 9 49.0 55.6 0.0000 1.000 1.000
11:01 55 5 50.9 40.0 0.0000 1.000 1.000
23:01 58 2 50.0 50.0 0.0000 1.000 1.000
24:03 59 1 50.8 0.0 0.0000 1.000 1.000
25:01 55 5 52.7 20.0 0.8727 0.350 0.353
26:01 57 3 52.6 0.0 1.4035 0.236 0.237
29:02 56 4 51.8 25.0 0.2679 0.605 0.612
31:01 57 3 49.1 66.7 0.0000 1.000 1.000
32:01 56 4 46.4 100.0 2.4107 0.121 0.112
68:01 57 3 52.6 0.0 1.4035 0.236 0.237
additive model:
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p
01:01 95 25 50.5 48.0 0.0000 1.000 1.000
02:01 77 43 48.1 53.5 0.1450 0.703 0.704
02:06 119 1 50.4 0.0 0.0000 1.000 1.000
03:01 111 9 49.5 55.6 0.0000 1.000 1.000
11:01 115 5 50.4 40.0 0.0000 1.000 1.000
23:01 117 3 50.4 33.3 0.0000 1.000 1.000
24:02 109 11 46.8 81.8 3.6030 0.058 0.053
24:03 119 1 50.4 0.0 0.0000 1.000 1.000
25:01 115 5 51.3 20.0 0.8348 0.361 0.364
26:01 117 3 51.3 0.0 1.3675 0.242 0.244
29:02 116 4 50.9 25.0 0.2586 0.611 0.619
31:01 117 3 49.6 66.7 0.0000 1.000 1.000
32:01 116 4 48.3 100.0 2.3276 0.127 0.119
68:01 117 3 51.3 0.0 1.3675 0.242 0.244
recessive model:
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p
01:01 59 1 50.8 0 0.000 1.000 1.000
02:01 52 8 46.2 75 1.298 0.255 0.254
02:06 60 0 50.0 . . . .
03:01 60 0 50.0 . . . .
11:01 60 0 50.0 . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000
24:02 60 0 50.0 . . . .
24:03 60 0 50.0 . . . .
25:01 60 0 50.0 . . . .
26:01 60 0 50.0 . . . .
29:02 60 0 50.0 . . . .
31:01 60 0 50.0 . . . .
32:01 60 0 50.0 . . . .
68:01 60 0 50.0 . . . .
genotype model:
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
dominant model:
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p
01:01 36 24 -0.14684 -0.117427 0.909
02:01 25 35 -0.32331 -0.000618 0.190
02:06 59 1 -0.14024 0.170057 .
03:01 51 9 -0.05600 -0.583178 0.147
11:01 55 5 -0.19188 0.489815 0.287
23:01 58 2 -0.15400 0.413687 0.281
24:02 49 11 -0.10486 -0.269664 0.537
24:03 59 1 -0.11409 -1.373118 .
25:01 55 5 -0.12237 -0.274749 0.742
26:01 57 3 -0.12473 -0.331558 0.690
29:02 56 4 -0.13044 -0.199941 0.789
31:01 57 3 -0.10097 -0.783003 0.607
32:01 56 4 -0.07702 -0.947791 0.092
68:01 57 3 -0.16915 0.512457 0.196
genotype model:
[-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p
01:01 36 23 1 -0.14684 -0.08833 -0.78655 0.784
02:01 25 27 8 -0.32331 -0.02341 0.07631 0.446
02:06 59 1 0 -0.14024 0.17006 . 0.756
03:01 51 9 0 -0.05600 -0.58318 . 0.138
11:01 55 5 0 -0.19188 0.48981 . 0.137
23:01 58 1 1 -0.15400 0.10762 0.71975 0.663
24:02 49 11 0 -0.10486 -0.26966 . 0.618
24:03 59 1 0 -0.11409 -1.37312 . 0.205
25:01 55 5 0 -0.12237 -0.27475 . 0.742
26:01 57 3 0 -0.12473 -0.33156 . 0.725
29:02 56 4 0 -0.13044 -0.19994 . 0.892
31:01 57 3 0 -0.10097 -0.78300 . 0.243
32:01 56 4 0 -0.07702 -0.94779 . 0.086
68:01 57 3 0 -0.16915 0.51246 . 0.243
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.792e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -5.851e-16
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.372e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.624e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.418e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 2.236e-15
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.495e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.170e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.282e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.771e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
h.2.5% h.97.5% h.pval
24:02 0.1585 3.4251 0.032*
-----
01:01 -1.0330 1.0330 1.000
02:01 -1.1643 0.8899 0.793
02:06 -2868.1268 2836.9268 0.991
03:01 -1.1624 1.6872 0.718
11:01 -2.3074 1.4237 0.643
23:01 -2.8192 2.8192 1.000
24:03 -2868.1268 2836.9268 0.991
25:01 -3.7498 0.7588 0.194
26:01 -2731.9621 2698.6192 0.990
29:02 -3.4931 1.1530 0.324
31:01 -1.7277 3.1842 0.561
32:01 -3859.2763 3894.6947 0.993
68:01 -2731.9621 2698.6192 0.990
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.793e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -2.268e-04
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.370e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.562e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.686e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.451e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 -3.062e-03
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.501e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.189e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.289e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.781e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.673e+01
h.2.5% h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 0.1587 3.4264 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 -1.0334 1.0330 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 -1.1652 0.8913 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 -2868.1460 2836.9076 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 -1.1813 1.7185 0.717 0.011958 -0.5044 0.5283 0.964
11:01 -2.3225 1.4322 0.642 0.008025 -0.5026 0.5186 0.975
23:01 -2.8348 2.8287 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 -2868.1286 2836.9250 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 -3.7579 0.7568 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 -2731.8901 2698.5450 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 -3.5309 1.1526 0.320 0.033234 -0.4796 0.5461 0.899
31:01 -1.7274 3.1851 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 -3845.6317 3881.2510 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 -2721.2124 2687.7497 0.990 -0.086589 -0.6512 0.4781 0.764
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 6.005e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 9.998e-01
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 8.720e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 1.647e-07
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 1.308e+00
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 6.407e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 9.969e-01
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 1.676e-07
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 2.230e-01
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.744e-08
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 3.045e-01
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 2.073e+00
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 5.428e+07
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.416e-08
h.2.5%_OR h.97.5%_OR h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 1.17200 30.766 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 0.35579 2.809 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 0.31185 2.438 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 0.00000 Inf 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 0.30687 5.576 0.717 0.011958 -0.5044 0.5283 0.964
11:01 0.09803 4.188 0.642 0.008025 -0.5026 0.5186 0.975
23:01 0.05873 16.923 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 0.00000 Inf 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 0.02333 2.131 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 0.00000 Inf 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 0.02928 3.167 0.320 0.033234 -0.4796 0.5461 0.899
31:01 0.17774 24.171 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 0.00000 Inf 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 0.00000 Inf 0.990 -0.086589 -0.6512 0.4781 0.764
Linear regression (dominant model) with 60 individuals:
glm(y ~ h, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.02941 -0.4805 0.5393
02:01 25 35 -0.32331 -0.000618 0.190 0.32269 -0.1772 0.8226
02:06 59 1 -0.14024 0.170057 . 0.31030 -1.6397 2.2603
03:01 51 9 -0.05600 -0.583178 0.147 -0.52718 -1.2136 0.1592
11:01 55 5 -0.19188 0.489815 0.287 0.68170 -0.2051 1.5685
23:01 58 2 -0.15400 0.413687 0.281 0.56768 -0.8165 1.9518
24:02 49 11 -0.10486 -0.269664 0.537 -0.16481 -0.8091 0.4795
24:03 59 1 -0.11409 -1.373118 . -1.25903 -3.1835 0.6655
25:01 55 5 -0.12237 -0.274749 0.742 -0.15237 -1.0555 0.7507
26:01 57 3 -0.12473 -0.331558 0.690 -0.20683 -1.3519 0.9383
29:02 56 4 -0.13044 -0.199941 0.789 -0.06950 -1.0709 0.9319
31:01 57 3 -0.10097 -0.783003 0.607 -0.68203 -1.8149 0.4508
32:01 56 4 -0.07702 -0.947791 0.092 -0.87077 -1.8470 0.1054
68:01 57 3 -0.16915 0.512457 0.196 0.68161 -0.4512 1.8145
h.pval
01:01 0.910
02:01 0.211
02:06 0.756
03:01 0.138
11:01 0.137
23:01 0.425
24:02 0.618
24:03 0.205
25:01 0.742
26:01 0.725
29:02 0.892
31:01 0.243
32:01 0.086
68:01 0.243
Linear regression (dominant model) with 60 individuals:
glm(y ~ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Linear regression (dominant model) with 60 individuals:
glm(y ~ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Logistic regression (additive model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5%
24:02 109 11 46.8 81.8 3.6030 0.058 0.053 1.7918 0.1585
-----
01:01 95 25 50.5 48.0 0.0000 1.000 1.000 -0.1207 -1.0843
02:01 77 43 48.1 53.5 0.1450 0.703 0.704 0.2137 -0.5289
02:06 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
03:01 111 9 49.5 55.6 0.0000 1.000 1.000 0.2624 -1.1624
11:01 115 5 50.4 40.0 0.0000 1.000 1.000 -0.4418 -2.3074
23:01 117 3 50.4 33.3 0.0000 1.000 1.000 -0.4323 -2.3435
24:03 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
25:01 115 5 51.3 20.0 0.8348 0.361 0.364 -1.4955 -3.7498
26:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
29:02 116 4 50.9 25.0 0.2586 0.611 0.619 -1.1701 -3.4931
31:01 117 3 49.6 66.7 0.0000 1.000 1.000 0.7282 -1.7277
32:01 116 4 48.3 100.0 2.3276 0.127 0.119 17.7092 -3859.2763
68:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
h.97.5% h.pval
24:02 3.4251 0.032*
-----
01:01 0.8430 0.806
02:01 0.9563 0.573
02:06 2836.9268 0.991
03:01 1.6872 0.718
11:01 1.4237 0.643
23:01 1.4789 0.658
24:03 2836.9268 0.991
25:01 0.7588 0.194
26:01 2698.6192 0.990
29:02 1.1530 0.324
31:01 3.1842 0.561
32:01 3894.6947 0.993
68:01 2698.6192 0.990
Logistic regression (recessive model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p h.est
01:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
02:01 52 8 46.2 75 1.298 0.255 0.254 1.253
02:06 60 0 50.0 . . . . .
03:01 60 0 50.0 . . . . .
11:01 60 0 50.0 . . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
24:02 60 0 50.0 . . . . .
24:03 60 0 50.0 . . . . .
25:01 60 0 50.0 . . . . .
26:01 60 0 50.0 . . . . .
29:02 60 0 50.0 . . . . .
31:01 60 0 50.0 . . . . .
32:01 60 0 50.0 . . . . .
68:01 60 0 50.0 . . . . .
h.2.5% h.97.5% h.pval
01:01 -2868.1268 2836.927 0.991
02:01 -0.4379 2.943 0.146
02:06 . . .
03:01 . . .
11:01 . . .
23:01 -2868.1268 2836.927 0.991
24:02 . . .
24:03 . . .
25:01 . . .
26:01 . . .
29:02 . . .
31:01 . . .
32:01 . . .
68:01 . . .
Logistic regression (genotype model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
h1.est h1.2.5% h1.97.5% h1.pval h2.est h2.2.5% h2.97.5%
24:02 1.79176 0.1585 3.4251 0.032* . . .
-----
01:01 0.08701 -0.9600 1.1340 0.871 -15.566 -2868.0929 2836.961
02:01 -0.45474 -1.5524 0.6430 0.417 1.019 -0.7637 2.801
02:06 -15.59997 -2868.1268 2836.9268 0.991 . . .
03:01 0.26236 -1.1624 1.6872 0.718 . . .
11:01 -0.44183 -2.3074 1.4237 0.643 . . .
23:01 16.56607 -4686.4552 4719.5873 0.994 -16.566 -4719.5873 4686.455
24:03 -15.59997 -2868.1268 2836.9268 0.991 . . .
25:01 -1.49549 -3.7498 0.7588 0.194 . . .
26:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
29:02 -1.17007 -3.4931 1.1530 0.324 . . .
31:01 0.72824 -1.7277 3.1842 0.561 . . .
32:01 17.70917 -3859.2763 3894.6947 0.993 . . .
68:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
h2.pval
24:02 .
-----
01:01 0.991
02:01 0.263
02:06 .
03:01 .
11:01 .
23:01 0.994
24:03 .
25:01 .
26:01 .
29:02 .
31:01 .
32:01 .
68:01 .
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