Discussion
We demonstrate that both SCZ and BPD have a theoretical basis for a
diagnosis in response to the presence of certain AKAP11 variants;
however, when applied practically to observed diagnosis frequencies, the
possibility that SCZ has a correlation with these variants is weak. One
potential reason for this discrepancy is disagreements in the
eukaryogenesis hypothesis \citep{Koonin2006}. Current literature presents
contradictions in the evolution of modern eukaryotic cells: some suggest
that the ability for eukaryotic mRNA splicing is a consequence of intron
interruption within genes, while others suggest that intron lineage had
no effect on eukaryotic evolution \citep*{Belshaw2006}.
Previous studies on the AKAP11 correlation with mental disease
focused solely on protein–coding exons and their role in disease
pathogenesis. On the contrary, our study chose to adopt a more outward
approach which focused on the gene variants as a whole rather than
solely exon interaction. These inherent differences in approach
represent differing results from eukaryogenesis hypotheses in that we
considered introns and exons equally, though others have excluded
introns from analysis. Subsequent research must therefore be conducted
to further investigate genetic modelling to provide a more comprehensive
understanding of the underlying assumptions made in both models.
However, we obtained perfect overlap between SCZ and BPD when we apply
high–risk variants to their expected amino acid frequencies, indicating
of a strong correlation between AKAP11 and both of these
diseases. This suggests that the altered bonding of AKAP11 variants to group A–kinase anchor proteins results in a higher
probability of SCZ and BPD diagnosis. Overall, we find that the
correlation to AKAP11 is robust with BPD and moderate with SCZ.
The reason for a less significant effect when SCZ is applied to the
Canadian population may be due to limitations in our model. For
example, variables in the genome annotations of each variant likely
influenced our findings. Namely, the results of the regression are
based primarily on the consequence genome annotation as defined by
Sequence Ontology to classify each variant. While it is an accepted
annotation tool, improvements are still being suggested to this
annotation at the current date \citep{Eilbeck2005}. As seen in
Fig. \ref{378760}, there is a vast discrepancy between the probabilities
that each variant will be placed in Class 4 between SCZ and BPD (as
evidenced by the extreme scaling differences of the relative
probabilities of both diseases). Such a large variance in the class
probabilities between SCZ and BPD is likely indicative that there may be
differences in genome annotation, which is required for both theoretical
and practical statistical testing.
Similarly, the study is limited by the fact that only the AKAP11 variants found in SCHEMA and BipEx were included in the analysis. There
were many differences between which variants were described in each data
set. Without identical data sets, it is impossible to get a complete
analysis of the entire AKAP11 gene; as a result, both diseases
have different data inputs for the logistic regression. This therefore
reduces the accuracy of comparing the results from two different
regression tests to a single data set of the Canadian population.
The results of this statistical model have far–reaching implications.
Individuals with a diagnosis of either disorder would require highly
individualized treatment (\citealp{Goldberg2019}; \citealp{Vieta2010}) rather than a
universalized system which assumes complete similarity between SCZ and
BPD. With respect to legislative consequences, it would be beneficial
to invest in a more genetic approach to combating mental diseases. For
example, the genome editing CRISPR–Cas–9 system has several
applications, including mental illnesses such as schizophrenia and
bipolar I disorder. Using such a method, precise and highly targeted
edits of potentially threatening genetic alleles \citep*{Michael2021}, specifically AKAP11. It is also necessary to better
subsidize treatment facilities which specifically target at–risk
demographics for these diseases. We find that males under 30 and
females between 50 and 55 may be particularly vulnerable, and this could
aid in creating an improved system of finding and treating individuals
affected with SCZ and BPD, respectively. Lastly, the efficiency of our
methods could be improved for future research through a computer code
(see Appendix) that compares data sets for high–risk AKAP11 variants
and demographic diagnoses. This would allow for a more practical
application of data for the global population rather than just the
Canadian population.
Since our results suggest a strong correlation between AKAP11 and
BPD and a slight correlation with SCZ, further research aimed at the
genetic predispositions of mental disease will allow for more confident
applications of genetic patterns as it relates to SCZ and BPD.