One way to understand polygenic associations for a complex trait is if
the implicated genetic variants are in genes that comprise a biological pathway.
Gene-set analysis includes evaluation of genetic variants in genes that are
grouped based on their interacting role in biological pathways (biological
pathway analysis) and genes that share similar cellular functions (functional
gene-set analysis).
We used JAG (Joint Association of Genetic variants,http://ctglab.nl/software ) to conduct gene-set analyses. This
method has previously been applied to the International Schizophrenia Consortium
data by Lips et al. 94 (link) JAG
tests for the association of specified gene-sets with schizophrenia as applied
to individual-level genotype data which tends to be more powerful than using
summary statistics. JAG constructs a test-statistic for each gene-set. JAG
includes both self-contained and competitive tests. These two approaches
evaluate different null hypotheses. Statistical significance
(Pself and
Pcomp) are determined using permutation. First, the
self-contained test evaluates the null hypothesis that a defined set of genes is
not associated with schizophrenia while accounting for the some of the
properties of the SNPs being studied (e.g., LD structure). Second, the
competitive test evaluates whether a specific set of genes has evidence for
stronger associations with schizophrenia than randomly selected sets of control
genes (with the latter matched to the former using the same effective number of
SNPs per gene-set). Thus, a competitive test is of the null hypothesis is that
these genes are not more strongly associated than a similar but
randomly-selected set of genes. That is, the comparison is more one to the
average degree of association across genes. The principal comparison is the
competitive test, and we present self-contained tests for completeness.
Competitive gene-set tests are more appropriate for a polygenic disease like
schizophrenia because they explicitly prioritize gene-sets that show a greater
average degree of association, over and above the polygenic background, rather
than prioritizing larger but more weakly-enriched gene-sets (as self-contained
tests would tend to do).
the implicated genetic variants are in genes that comprise a biological pathway.
Gene-set analysis includes evaluation of genetic variants in genes that are
grouped based on their interacting role in biological pathways (biological
pathway analysis) and genes that share similar cellular functions (functional
gene-set analysis).
We used JAG (Joint Association of Genetic variants,
method has previously been applied to the International Schizophrenia Consortium
data by Lips et al. 94 (link) JAG
tests for the association of specified gene-sets with schizophrenia as applied
to individual-level genotype data which tends to be more powerful than using
summary statistics. JAG constructs a test-statistic for each gene-set. JAG
includes both self-contained and competitive tests. These two approaches
evaluate different null hypotheses. Statistical significance
(Pself and
Pcomp) are determined using permutation. First, the
self-contained test evaluates the null hypothesis that a defined set of genes is
not associated with schizophrenia while accounting for the some of the
properties of the SNPs being studied (e.g., LD structure). Second, the
competitive test evaluates whether a specific set of genes has evidence for
stronger associations with schizophrenia than randomly selected sets of control
genes (with the latter matched to the former using the same effective number of
SNPs per gene-set). Thus, a competitive test is of the null hypothesis is that
these genes are not more strongly associated than a similar but
randomly-selected set of genes. That is, the comparison is more one to the
average degree of association across genes. The principal comparison is the
competitive test, and we present self-contained tests for completeness.
Competitive gene-set tests are more appropriate for a polygenic disease like
schizophrenia because they explicitly prioritize gene-sets that show a greater
average degree of association, over and above the polygenic background, rather
than prioritizing larger but more weakly-enriched gene-sets (as self-contained
tests would tend to do).
