hlaAssocTest: Statistical Association Tests

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/Association.R

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 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

 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       .

HIBAG documentation built on March 24, 2021, 6 p.m.