Convergent functional genomics of genome‐wide association data for bipolar disorder: Comprehensive identification of candidate genes, pathways and mechanisms

Abstract

Given the mounting convergent evidence implicating many more genes in complex disorders such as bipolar disorder than the small number identified unambiguously by the firstgeneration Genome-Wide Association studies (GWAS) to date, there is a strong need for improvements in methodology. One strategy is to include in the next generation GWAS larger numbers of subjects, and/or to pool independent studies into meta-analyses. We propose and provide proof of principle for the use of a complementary approach, convergent functional genomics (CFG), as a way of mining the existing GWAS datasets for signals that are there already, but did not reach significance using a genetics-only approach. With the CFG approach, the integration of genetics with genomics, of human and animal model data, and of multiple independent lines of evidence converging on the same genes offers a way of extracting signal from noise and prioritizing candidates. In essence our analysis is the most comprehensive integration of genetics and functional genomics to date in the field of bipolar disorder, yielding a series of novel (such as Klf12, Aldh1a1, A2bp1, Ak3l1, Rorb, Rora) and previously known (such as Bdnf, Arntl, Gsk3b, Disc1, Nrg1, Htr2a) candidate genes, blood biomarkers, as well as a comprehensive identification of pathways and mechanisms. These become prime targets for hypothesis driven follow-up studies, new drug development and personalized medicine approaches.

Convergent lines of evidence support CAMKK2 as a schizophrenia susceptibility gene

“…In fact, the CFG method was proved to be a powerful and promising approach to identify the psychosis-associated genes. [53][54][55][56][57] The top schizophrenia genes from these two data sets represent promising candidate genes for schizophrenia. Therefore, it is important to investigate whether CAMKK2 gene is involved in the network that is formed by proteins encoded by top schizophrenia susceptibility genes.…”

Convergent Functional Genomics: what we have learned and can learn about genes, pathways, and mechanisms

“…This approach has been applied, among others, to the study of bipolar disorder (Le-Niculescu et al, 2009b;Niculescu et al, 2000;Ogden et al, 2004) and schizophrenia (Le-Niculescu et al, 2007), as well as to the identification of blood biomarkers (Le-Niculescu et al, 2009a). Human data increase the relevance to the disorder (specificity), whereas animal model data increase the ability to identify the signal (sensitivity).…”

“…The latter method of analysis by itself is proving unwieldy because of the sheer number of common and rare variants involved (complexity) and the diversity (heterogeneity) in the population. A combined approach such as CFG, which is focused on gene-level integration, can harness the present and future body of work in the field to yield a successful outcome, truly reflecting, in essence, a field-wide collaboration (Le- Niculescu et al, 2009b).…”

Convergent functional genomics of psychiatric disorders

“…As such, gene-level analyses are much more likely to be reproducible [Ayalew et al, 2012]. In addition, gene-level analyses permit cross-platforms, cross-methodologies, and cross-species integration [Le- Niculescu et al, 2009a;Patel et al, 2010], particularly with animal models and gene expression studies (Fig. 1), which can help with identification and prioritization of disease-relevant genes.…”

“…Human data provides more specificity and relevance to the human disease. Using a set of mouse experiments as a driving force [Ogden et al, 2004;Le-Niculescu et al, 2008], or using human blood biomarker [Le-Niculescu et al, 2009b] or GWAS data Le-Niculescu et al, 2009a;Patel et al, 2010] as a driving force, such studies have identified and prioritized candidate genes and biomarkers for bipolar disorder that show good reproducibility as well as predictive ability in independent cohorts [Le- Niculescu et al, 2009b;Kurian et al, 2011;Ayalew et al, 2012;Patel et al, 2010].…”