A genetic locus in 7p12.2 associated with treatment resistant schizophrenia

Li, Jiang; Meltzer, Herbert Y.

doi:10.1016/j.schres.2014.08.018

Cited by 23 publications

(13 citation statements)

References 39 publications

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“…A variant on this alternative splicing, in which the non-neuronal variant was spliced to the neuronal acceptor site, has also been suggested in G cells of the rat stomach antrum (Djali et al, 1998), which may indicate cell type-selective plasticity in the control of AADC expression. Recently, a number of cis-acting polymorphisms of AADC have been identified with putative clinical relevance (Li and Meltzer, 2014;Eisenberg et al, 2016), and disease-associated AADC coding variants are also known (Graziano et al, 2015;Kojima et al, 2016;Montioli et al, 2016).…”

Section: Vertebrate Trace Aminesmentioning

confidence: 99%

Trace Amines and Their Receptors

Gainetdinov¹,

Hoener²,

Berry³

2018

Pharmacol Rev

297

287

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Section: Vertebrate Trace Aminesmentioning

confidence: 99%

Trace Amines and Their Receptors

Gainetdinov¹,

Hoener²,

Berry³

2018

Pharmacol Rev

297

287

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“…Other studies Other measures of response that have been assessed with a GWAS methodology include cognitive performance [22], clinically defined treatment resistance [23,24] and antipsychotic dosing [25]. None of these studies identified any SNPs exceeding the established p < 5 × 10 -8 threshold of significance.…”

Section: Yu Et Al (2018): Large Analysis Of Multiple Drugsmentioning

confidence: 99%

A Systematic Review of Genome-Wide Association Studies of Antipsychotic Response

Allen

Bishop

2019

Pharmacogenomics

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Clinical symptom response to antipsychotic medications is highly variable. Genome-wide association studies (GWAS) provide a 'hypothesis-free' method of interrogating the genome for biomarkers of antipsychotic response. We performed a systematic review of GWAS findings for antipsychotic efficacy or effectiveness. 14 studies met our inclusion criteria, ten of which examined antipsychotic response using quantitative rating scales to measure symptom improvement. 15 genome-wide significant loci were identified, seven of which were replicated in other antipsychotic GWAS publications: CNTNAP5, GRID2, GRM7, 8q24 (KCNK9), PCDH7, SLC1A1 and TNIK. Notably, four replicated loci are involved in glutamatergic pathways. Additional validation and evaluation of the biological significance of these markers is warranted. These markers should also be evaluated for clinical utility, especially in the context of other validated pharmacogenomic variants (e.g., CYP450 genes). These findings may generate new avenues for development of novel antipsychotic treatments.

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“…Authors reported a significant association between TRS and several genetic variants in linkage disequilibrium (top hit: rs11030104) located in the brain-derived neurotrophic factor (BDNF) gene. Li and Meltzer (2014) conducted a GWAS on two cohorts of Caucasian patients with ( n = 79 and n = 70) and without ( n = 95 and n = 125) TRS. In this study, treatment resistance was defined as persistence of moderate to severe positive symptoms despite at least two trials of 4–6 weeks with typical or atypical antipsychotics other than clozapine.…”

Section: Genetic Bases Of Trsmentioning

confidence: 99%

“…Although no SNP met the genome-wide significant threshold, interesting results were reported for the rs2237457 variant located in 7p12 ( p value in the combined cohorts: 5.66 × 10 −7 ). This variant is located upstream of the gene encoding L-dopa decarboxylase, a rate-limiting enzyme in the synthesis of trace amines and neurotransmitters, including dopamine (Li and Meltzer, 2014). This result is particularly important based on the fact that a hyperdopaminergic state in the mesolimbic dopamine pathway is thought to play a crucial role in the development of psychotic symptoms according to the dopamine hypothesis of SCZ and that dopamine D2 receptors represent a main target for all antipsychotic drugs (Li et al, 2016).…”

Section: Genetic Bases Of Trsmentioning

confidence: 99%

Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches

Pisanu

Squassina

2019

Front. Pharmacol.

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Schizophrenia (SCZ) is a severe psychiatric disorder affecting approximately 23 million people worldwide. It is considered the eighth leading cause of disability according to the World Health Organization and is associated with a significant reduction in life expectancy. Antipsychotics represent the first-choice treatment in SCZ, but approximately 30% of patients fail to respond to acute treatment. These patients are generally defined as treatment-resistant and are eligible for clozapine treatment. Treatment-resistant patients show a more severe course of the disease, but it has been suggested that treatment-resistant schizophrenia (TRS) may constitute a distinct phenotype that is more than just a more severe form of SCZ. TRS is heritable, and genetics has been shown to play an important role in modulating response to antipsychotics. Important efforts have been put into place in order to better understand the genetic architecture of TRS, with the main goal of identifying reliable predictive markers that might improve the management and quality of life of TRS patients. However, the number of candidate gene and genome-wide association studies specifically focused on TRS is limited, and to date, findings do not allow the disentanglement of its polygenic nature. More recent studies implemented polygenic risk score, gene-based and machine learning methods to explore the genetics of TRS, reporting promising findings. In this review, we present an overview on the genetics of TRS, particularly focusing our discussion on studies implementing polygenic approaches.

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A genetic locus in 7p12.2 associated with treatment resistant schizophrenia

Cited by 23 publications

References 39 publications

Trace Amines and Their Receptors

Trace Amines and Their Receptors

A Systematic Review of Genome-Wide Association Studies of Antipsychotic Response

Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches

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