We sought to identify candidate genes for human anxiety disorders starting with a genomewide association study of anxiety phenotypes in outbred mice. We identified the human homologues of the 52 associated murine genes and ranked them for further study using three independent and complementary sources of anxiety-related genetic data: (1) extant linkage and knock-out studies in mice, (2) a meta-analysis of human linkage scans, and (3) a preliminary human GWAS study. The top nine regions containing 14 genes were tested for association in a sample of twin subjects selected for high and low scores on a genetic factor shared across anxiety-related phenotypes. Of these novel candidates, one gene, PPARGC1A, had multiple associated SNPs and overall gene-based significant association.
Population stratification is a potential source of spurious associations for a genetic association study. While we did not have a set of ancestry informative markers genotyped in the twin sample to properly investigate this possibility, several prior analyses suggest that this was not a major source of Type I error (see Supplement
). Type II error is likely more problematic, given the small effect sizes expected for complex genetic phenotypes. Our calculations predicted that the selected twin sample analyzed possessed the power to detect variants that explain ~2% of phenotypic variance, but this is only a rough estimate, as it did not take into account the complex nature of our study.
Another potential source of statistical confounding is the factor score distribution in the NIMH control sample used for GWAS. Like most psychiatric phenotypes, the distribution was quite skewed, with many of the subjects falling under a peak at the lower end of the scores (i.e., unaffected with low neuroticism). Plink’s regression routine makes the usual statistical assumption that the outcomes are normally distributed, which was not satisfied for our factor scores. Transformations provided little improvement. Post-hoc, we re-analyzed 100 randomly selected SNPs for association with the factor scores using permutation testing that does not depend upon such distributional properties, finding very similar p-values to those obtained by regression. This was reassuring, but p-values for some markers in the GWAS might have been biased by this effect.
One potential limitation of any study attempting to synthesize cross-species data is the homology of phenotypes; this is especially problematic for psychiatric phenotypes. While fear and anxiety are evident across mice and men, there are no clear isomorphisms. For this reason, we exploited a wide range of murine fear-related behaviors as well as a human phenotype that tapped into common genetic risk across anxiety-related disorders. However, as Smoller and colleagues have argued (44
), mouse novelty-based tests, like some of the phenotypes utilized to identify the initial set of candidate genes, might find a better human homolog in behavioral inhibition in children rather than anxiety disorders in adults.
Since the time of the initial study planning and data analysis, genotype data for the other approximately half of the MGS control sample has been made publicly available via dbGaP (database of Genotypes and Phenotypes [http://www.ncbi.nlm.nih.gov/gap
], Study Accession: phs000167.v1.p1 “nonGAIN sample”). This provided an opportunity for further replication of our finding (see Supplement for details
). Only trend level association was observed for several SNPs in and around PPARGC1A
(see Table S2 in the Supplement
). We note that, while the nonGAIN sample was convenient due to the availability of phenotypes that overlapped with the twin sample, there are several importance differences between the two samples that affect the power to replicate our initial results. First, their ascertainment and assessment methods differed considerably. Second, the number of subjects in the nonGAIN sample available for analysis was somewhat smaller than for the twin sample. Third, while we utilized factor analysis applied to a similar compliment of internalizing phenotypes in both, the twin sample was selected for extremes
of the common genetic
factor. This results in two important potential differences in power to detect genetic association signals. First, utilizing a genetic versus a phenotypic factor score should provide a measure more directly associated with underlying allelic variants. Second, the twin sample was selected from factor score extremes and, therefore, contains much of the information contained in the larger sample from which it was derived, providing a substantially larger effective sample size.
We note that we were unable to successfully genotype markers in the 7q11.23 region near the gene WBSCR16
. According to HapMap, this is a region with extensive copy number variation, possibly explaining why our SNP assays did not perform well. Looking back at the data in that region from the NIMH sample analysis, while 3 of the 5 SNPs had association p-values < 10-3, they also showed modest violation of HWE, supporting the complexity of that region. The WBSCR16
gene is a potentially interesting candidate for ADs, being one of a group of genes deleted in that region in Williams-Beuren Syndrome (WBS), a multisystem disorder with phenotype consisting of aortic stenosis, mental retardation, visiospatial impairment, and personality traits that include diminished social anxiety. Mice with disruptions of a neighboring, related gene (GTF2IRD1
) have serotonergic alterations in several brain regions and exhibit reduced fear and increased social behaviors (45
This study provides preliminary evidence for PPARGC1A
as a novel candidate gene for anxiety-related psychiatric phenotypes. The PPARGC1A
gene encodes the transcriptional coactivator, peroxisome proliferator-activated receptor gamma coactivator 1alpha (PGC-1α), which plays an important role in normal energy expenditure in peripheral tissue (46
) and has been implicated in metabolic conditions like Type 2 diabetes mellitus (47
). To our knowledge, our study is the first to suggest its association with psychiatric phenotypes, although a related gene, PPARG
, appears in the list of SNPs with clusters of low p-values from the GAIN MDD GWAS (48
). In the brain, PGC-1α is concentrated in GABAergic interneurons, and it may provide neuroprotection by activating genes involved in the metabolism of reactive oxygen species (49
). As indicated in , this gene showed up using the public database search during the prioritization procedure from a knock-out model with anxiety phenotypes. Among various phenotypes described, PGC-1α −/− mice had increased thigmotaxis, a preference for staying at the perimeter walls of an open area or in an enclosed versus exposed area, which is an indicator of increased anxiety (50
). PGC-1α −/− mice are also deficient in the interneuron-specific calcium binding protein, paralbumin, and exhibit signs of GABAergic dysfunction (51
). However, these mice had adverse changes in multiple organ systems (50
), so such a severe genetic change is unlikely to be a reliable predictor of effects from common variants. As with any novel genetic association finding, our identification of PPARGC1A
as an anxiety candidate gene should be considered as tentative until adequate replication is demonstrated.