Interaction of multiple gene variants and their effects on schizophrenia phenotypes

Cheah, Sern-Yih; Lurie, Janine K.; Lawford, Bruce R.; Young, Ross McD.; Morris, Charles P.; Voisey, Joanne

doi:10.1016/j.comppsych.2016.08.015

Cited by 5 publications

(6 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although our findings provide a rationale for broadening the discussion of EOS differences beyond age alone (≤12 or ≥13), other investigators would need to replicate and validate these findings before they could be considered consistently identifiable “subtypes” of EOS. Cluster analysis has been previously used by numerous investigators searching for ways to harness the heterogeneity of individuals diagnosed with schizophrenia‐spectrum disorders across the life span to provide a useful platform for more effective treatment strategies: cognitive subgroups based on differential brain volumetric reductions and cognitive decline (Weinberg et al, ); interaction of multiple gene variants and their effects on schizophrenia phenotypes (Cheah et al, ); social cognition subgroups (Rocca, Galderisi, Rossi, et al, ); functional outcomes across subgroups of adults (Rocca et al, ); and cognitive profiles of subgroups based on neuropsychological measures (Reser, Allott, Killackey, Farhall, & Cotton, ) among many others over the past two decades.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic clusters associated with an early onset schizophrenia diagnosis among children and adolescents

Jerrell

McIntyre

Deroche

2017

Hum. Psychopharmacol Clin Exp

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Diagnostic clusters associated with an early onset schizophrenia diagnosis among children and adolescents

Jerrell

McIntyre

Deroche

2017

Hum. Psychopharmacol Clin Exp

View full text Add to dashboard Cite

show abstract

“…The evaluation of titles and abstracts resulted in the exclusion of 1231 articles. In total, 61 articles were selected for full-text review, and eight articles [44][45][46][47][48][49][50][51] were excluded due to unclear outcomes, mixed diagnosis of the study population and use of a nonstatistical method of clustering or clustering based on different phenotypes of schizophrenia. Finally, data were extracted from 53 longitudinal and cross-sectional studies.…”

Section: Search Resultsmentioning

confidence: 99%

A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits

et al. 2020

View full text Add to dashboard Cite

To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its symptoms and cognitive deficits. However, a systematic review on this topic is lacking. The objective of this review was to summarize the evidence obtained from longitudinal and cross-sectional data-driven studies in positive and negative symptoms and cognitive deficits in patients with schizophrenia spectrum disorders, their unaffected siblings and healthy controls or individuals from general population. Additionally, we aimed to highlight methodological gaps across studies and point out future directions to optimize the translatability of evidence from data-driven studies. A systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE and Web of Science electronic databases. Both longitudinal and cross-sectional studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data. In this review, 53 studies (19 longitudinal and 34 cross-sectional) that conducted among 17,822 patients, 8729 unaffected siblings and 5520 controls or general population were included. Most longitudinal studies found four trajectories that characterized by stability, progressive deterioration, relapsing and progressive amelioration of symptoms and cognitive function. Cross-sectional studies commonly identified three clusters with low, intermediate (mixed) and high psychotic symptoms and cognitive profiles. Moreover, identified subgroups were predicted by numerous genetic, sociodemographic and clinical factors. Our findings indicate that schizophrenia symptoms and cognitive deficits are heterogeneous, although methodological limitations across studies are observed. Identified clusters and trajectories along with their predictors may be used to base the implementation of personalized treatment and develop a risk prediction model for high-risk individuals with prodromal symptoms.

show abstract

“…These gene-gene interactions may cause network ripple effects among the connected genes that amplify the impact of small individual VAOs. Others have observed a similar phenomenon among risk genes for schizophrenia (Cheah et al 2016;Su et al 2017). Furthermore, this notion is similar to that of Greenspan (2001) who described the functional connectivity of gene networks in terms of exibility and pleiotropy.…”

Section: Discussionmentioning

confidence: 54%

“…Davierwala et al (2005) reported that essential genes are much more interactive than non-essential genes and schizophrenia risk genes are highly enriched for ones that are essential for life and broadly conserved during evolution (Kasap et al 2018). Consequently, we hypothesized that risk genes for schizophrenia are similarly more interactive than random genes, which may amplify the effect sizes of interacting variants (Cheah et al 2016). If so, the ndings would highlight the importance of being able to account for gene-gene interactions in models of genetic liability for heritable psychiatric disorders.…”

Section: Introductionmentioning

confidence: 99%

How variation in risk allele output and gene interactions shape the genetic architecture of schizophrenia

Kasap

Dwyer

2022

Preprint

View full text Add to dashboard Cite

Schizophrenia is a highly heritable polygenic psychiatric disorder. Characterization of its genetic architecture may lead to a better understanding of the overall burden of risk variants and how they determine susceptibility to disease. A major goal of this project was to develop a modeling approach to compare and quantify the relative effects of single nucleotide polymorphisms (SNPs), copy number variants (CNVs) and other factors. We derived a mathematical model for the various genetic contributions based on the probability of expressing a combination of risk variants at a frequency that matched disease prevalence. The model included estimated risk variant allele outputs (VAOs) adjusted for population allele frequency. We hypothesized that schizophrenia risk genes would be more interactive than random genes and we confirmed this relationship. Gene-gene interactions may cause network ripple effects that spread and amplify small individual effects of risk variants. The modeling revealed that the number of risk alleles required to achieve threshold for susceptibility will be determined by the average functional locus output (FLO) associated with a risk allele, the risk allele frequency (RAF), the number of protective variants present and the extent of gene interactions within and between risk loci. The model can account for the quantitative impact of protective variants as well as CNVs on disease susceptibility. The fact that non-affected individuals must carry a non-trivial burden of risk alleles suggests that genetic susceptibility will inevitably reach threshold for schizophrenia at a recurring frequency in the population.

show abstract

Interaction of multiple gene variants and their effects on schizophrenia phenotypes

Abstract: The interaction of specific susceptibility genes is likely to lead to specific clinical sub-phenotypes of schizophrenia. Larger patient cohorts with more extensive clinical data will improve the detection of gene interactions and the resultant schizophrenia clinical phenotypes.

Cited by 5 publications

References 59 publications

Diagnostic clusters associated with an early onset schizophrenia diagnosis among children and adolescents

Diagnostic clusters associated with an early onset schizophrenia diagnosis among children and adolescents

A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits

How variation in risk allele output and gene interactions shape the genetic architecture of schizophrenia

Contact Info

Product

Resources

About