Whole genome analysis for QTL/association enrichment
Running...
Version: Enrich S: beta v0.8
Data:
Number of milk production & yield traits:
2
Number of QTL / associations found:
84
Number of chromosomes where QTL / associations are found:
19
Chi-squared (χ2) test: are milk production & yield traits over-represented on some chromosomes?
Chromosomes
Total χ2
df
p-values
FDR *
Size of χ2
Chromosome 2
0.15162
18
0.998329325823115
9.999893e-01
Chromosome 3
2.65162
18
0.9999893
9.999893e-01
Chromosome 4
2.65162
18
0.9999893
9.999893e-01
Chromosome 5
2.65162
18
0.9999893
9.999893e-01
Chromosome 6
50.62782
18
6.062914e-05
5.759768e-04
Chromosome 7
5.29448
18
0.9983222
9.999893e-01
Chromosome 8
9.48496
18
0.9474282
9.999893e-01
Chromosome 11
5.29448
18
0.9983222
9.999893e-01
Chromosome 12
5.29448
18
0.9983222
9.999893e-01
Chromosome 14
2.65162
18
0.9999893
9.999893e-01
Chromosome 15
5.29448
18
0.9983222
9.999893e-01
Chromosome 16
5.29448
18
0.9983222
9.999893e-01
Chromosome 17
0.91354
18
0.998329325823115
9.999893e-01
Chromosome 19
295.98496
18
3.221362e-52
6.120588e-51
Chromosome 21
5.29448
18
0.9983222
9.999893e-01
Chromosome 23
2.65162
18
0.9999893
9.999893e-01
Chromosome 25
5.29448
18
0.9983222
9.999893e-01
Chromosome 26
5.29448
18
0.9983222
9.999893e-01
Chromosome 29
0.08020
18
0.998329325823115
9.999893e-01
Chi-squared (χ2) test: Which of the 2 milk production & yield traits are over-represented in the QTLdb
Traits
Total χ2
df
p-values
FDR *
Size of χ2
Lactation length
66.99997
1
2.715113e-16
5.430226e-16
Milk yield
2.48148
1
0.1151938
1.151938e-01
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.:
19
χ2
=
412.857040
Number of traits:
2
df
=
18
Number of QTLs:
84
p-value
=
1.900852e-76
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.