Converging Evidence for Epistasis between ANK3 and Potassium Channel Gene KCNQ2 in Bipolar Disorder.

Genome-wide association studies (GWAS) have implicated ANK3 as a susceptibility gene for bipolar disorder (BP). We examined whether epistasis with ANK3 may contribute to the "missing heritability" in BP. We first identified via the STRING database 14 genes encoding proteins with prior biological evidence that they interact molecularly with ANK3. We then tested for statistical evidence of interactions between SNPs in these genes in association with BP in a discovery GWAS dataset and two replication GWAS datasets. The most significant interaction in the discovery GWAS was between SNPs in ANK3 and KCNQ2 (p = 3.18 × 10(-8)). A total of 31 pair-wise interactions involving combinations between two SNPs from KCNQ2 and 16 different SNPs in ANK3 were significant after permutation. Of these, 28 pair-wise interactions were significant in the first replication GWAS. None were significant in the second replication GWAS, but the two SNPs from KCNQ2 were found to significantly interact with five other SNPs in ANK3, suggesting possible allelic heterogeneity. KCNQ2 forms homo- and hetero-meric complexes with KCNQ3 that constitute voltage-gated potassium channels in neurons. ANK3 is an adaptor protein that, through its interaction with KCNQ2 and KCNQ3, directs the localization of this channel in the axon initial segment (AIS). At the AIS, the KCNQ2/3 complex gives rise to the M-current, which stabilizes the neuronal resting potential and inhibits repetitive firing of action potentials. Thus, these channels act as "dampening" components and prevent neuronal hyperactivity. The interactions between ANK3 and KCNQ2 merit further investigation, and if confirmed, may motivate a new line of research into a novel therapeutic target for BP.

TileProbe: modeling tiling array probe effects using publicly available data.

MOTIVATION: Individual probes on an Affymetrix tiling array usually behave differently. Modeling and removing these probe effects are critical for detecting signals from the array data. Current data processing techniques either require control samples or use probe sequences to model probe-specific variability, such as with MAT. Although the MAT approach can be applied without control samples, residual probe effects continue to distort the true biological signals. RESULTS: We propose TileProbe, a new technique that builds upon the MAT algorithm by incorporating publicly available data sets to remove tiling array probe effects. By using a large number of these readily available arrays, TileProbe robustly models the residual probe effects that MAT model cannot explain. When applied to analyzing ChIP-chip data, TileProbe performs consistently better than MAT across a variety of analytical conditions. This shows that TileProbe resolves the issue of probe-specific effects more completely. AVAILABILITY: http://www.biostat.jhsph.edu/ approximately hji/cisgenome/index_files/tileprobe.htm.