Symptom dimensions as alternative phenotypes to address genetic heterogeneity in schizophrenia and bipolar disorder

Abstract

This study introduces a novel way to use the lifetime ratings of symptoms of psychosis, mania and depression in genetic linkage analysis of schizophrenia (SZ) and bipolar disorder (BP). It suggests using a latent class model developed for family data to define more homogeneous symptom subtypes that are influenced by a smaller number of genes that will thus be more easily detectable. In a two-step approach, we proposed: (i) to form homogeneous clusters of subjects based on the symptom dimensions and (ii) to use the information from these homogeneous clusters in linkage analysis. This framework was applied to a unique SZ and BP sample composed of 1278 subjects from 48 large kindreds from the Eastern Quebec population. The results suggest that our strategy has the power to increase linkage signals previously obtained using the diagnosis as phenotype and allows for a better characterization of the linkage signals. This is the case for a linkage signal, which we formerly obtained in chromosome 13q and enhanced using the dimension mania. The analysis also suggests that the methods may detect new linkage signals not previously uncovered by using diagnosis alone, as in chromosomes 2q (delusion), 15q (bizarre behavior), 7p (anhedonia) and 9q (delusion). In the case of the 15q and 2q region, the results coincide with linkage signals detected in other studies. Our results support the view that dissecting phenotypic heterogeneity by modeling symptom dimensions may provide new insights into the genetics of SZ and BP.

TPH2 genotype is associated with PTSD’s avoidance symptoms in Chinese female earthquake survivors

“…The PTSD phenotype is comprised of different types of symptom clusters that may be associated with different psychopathological and biological processes (Asmundson et al, 2004;O'Donnell et al, 2004;Strigo et al, 2010). To reduce the heterogeneity of a complex mental disorder such as PTSD and to increase the chances of identifying distinct contributions by the specific genes, an alternative approach is using more homogeneous symptom clusters as alternative phenotypes in further genetic research (Rietkerk et al, 2008), which demonstrated to be promising (Rietkerk et al, 2008;Labbe et al, 2012;van Veen et al, 2012).…”

<b><i>CTLA4</i></b> and <b><i>CD28</i></b> Gene Polymorphisms with Respect to Affective Symptom Domain in Schizophrenia

“…On the one hand, it can be argued that even though CTLA4 -specific genetic variants do not increase the overall risk for schizophrenia as we have previously shown [20], they can modify the clinical presentation of the illness. This would support the view that dissecting phenotypic heterogeneity by analyzing symptom dimensions across the psychosis spectrum may provide new insights into the genetics of schizophrenia [29]. On the other hand, it can be argued that patients with and without co-occurrence between psychotic and affective symptoms represent two distinct subgroups of schizophrenia patients with different underlying etiopathogenetic mechanisms.…”

“…It has long been argued that due to the high heterogeneity and complexity of schizophrenia, a symptom-based approach may be the only way to elucidate the molecular underpinnings of various pathophysiological processes of specific disease features [26,27,28,29,30]. There are numerous studies showing a significant genetic association with different clinical dimensions [30], subtypes [31], syndromes [32], and even single symptoms [33] of schizophrenia, showing that various clinical phenotypes may be inherited independently and may reflect the effect of individual genes or small sets of genes [26].…”

The genetics of early-onset bipolar disorder: A systematic review

“…Coryell et al, 2013). Such phenotypic features include measures of affective temperament (Greenwood et al, 2012), dimensional analysis of symptoms (Labbe et al, 2012), analysis of multi-dimensional phenotypes like brain structure/function relationships (Schumann, 2014), and endophenotypes like cognitive deficits and resting state brain activity (Gottesman and Gould, 2003; Hall and Smoller, 2010; Hasler et al, 2006; Hasler and Northoff, 2011). One study combined GWAS with fMRI of amygdala activation in a small sample of youth with BD and identified a SNP in the gene DOK5 that accounted for 33% of the variation in amygdala activation in the BD sample compared to 12% of the variance in the controls (Liu et al, 2010).…”