Whole genome analysis for QTL/association enrichment
Running...
Version: Enrich S: beta v0.8
Data:
Number of immune capacity traits:
5
Number of QTL / associations found:
51
Number of chromosomes where QTL / associations are found:
18
Chi-squared (χ2) test: are immune capacity traits over-represented on some chromosomes?
Chromosomes
Total χ2
df
p-values
FDR *
Size of χ2
Chromosome 1
0.04900
17
0.998329325823115
0.9999863
Chromosome 2
1.22550
17
0.998329325823115
0.9999863
Chromosome 3
30.63725
17
0.02209903
0.3977825
Chromosome 4
8.28430
17
0.9600839
0.9999863
Chromosome 5
1.22550
17
0.998329325823115
0.9999863
Chromosome 6
5.93135
17
0.9936352
0.9999863
Chromosome 9
8.28430
17
0.9600839
0.9999863
Chromosome 10
1.22550
17
0.998329325823115
0.9999863
Chromosome 11
5.93135
17
0.9936352
0.9999863
Chromosome 12
5.93135
17
0.9936352
0.9999863
Chromosome 13
5.93135
17
0.9936352
0.9999863
Chromosome 15
2.40195
17
0.9999863
0.9999863
Chromosome 16
1.22550
17
0.998329325823115
0.9999863
Chromosome 17
1.22550
17
0.998329325823115
0.9999863
Chromosome 20
1.22550
17
0.998329325823115
0.9999863
Chromosome 21
0.04900
17
0.998329325823115
0.9999863
Chromosome 22
1.22550
17
0.998329325823115
0.9999863
Chromosome 23
17.69610
17
0.408251
0.9999863
Chi-squared (χ2) test: Which of the 5 immune capacity traits are over-represented in the QTLdb
Traits
Total χ2
df
p-values
FDR *
Size of χ2
Change in eosinophil number
10.14287
4
0.03808861
0.0634810167
Immunoglobulin A level
4.6069
4
0.3300594
0.3300594000
Immunoglobulin E level
7.50003
4
0.1117080
0.1396350000
Immunoglobulin G level
26.17498
4
2.917487e-05
0.0001458744
Monocyte number
10.33273
4
0.03518078
0.0634810167
Correlations found between some of these traits for your reference
No correlation data found on these traits
Overall Test
Data
Chi'Square Test
Fisher's Exact Test
Number of chrom.:
18
χ2
=
99.705800
Number of traits:
5
df
=
68
Number of QTLs:
51
p-value
=
0.007368794
FOOT NOTE: * : FDR is short for "false
discovery rate", representing the expected proportion of type I errors. A type I
error is where you incorrectly reject the null hypothesis, i.e. you get a false
positive. It's statistical definition is FDR = E(V/R | R > 0) P(R > 0), where
V = Number of Type I errors (false positives); R = Number of rejected hypotheses.
Benjamini–Hochberg procedure is a practical way to estimate FDR.
Running...
