Leveraging genome-wide association and clinical data in revealing schizophrenia subgroups

Yin, Liangying; Cheung, Eric F.C.; Chen, Ronald Yuk-Lun; Wong, Emily H. M.; Sham, Pak-Chung; So, Hon‐Cheong

doi:10.1016/j.jpsychires.2018.09.010

Cited by 14 publications

(13 citation statements)

References 43 publications

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“…Genome-wide association studies (GWASs) can discover novel and unexpected candidate loci in an unbiased manner. Previous GWAS analyses found that the SLC6A4 gene was not associated with schizophrenia 90–93. A comparison of 12 single-disorder GWAS meta-analyses suggested no overlap in significant genetic variants identified from the different studies 21.…”

Section: Discussionmentioning

confidence: 93%

Association between the <em>SLC6A4</em> gene and schizophrenia: an updated meta-analysis

Xu¹,

Wang²,

Yao³

2018

NDT

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Background: In order to explore the association between the SLC6A4 gene and the risk of schizophrenia, an updated meta-analysis was conducted using a total of 46 scientific articles. Methods: Through a literature search, papers studied included 35 articles on serotonin-transporter-linked polymorphic region (5-HTTLPR) with 8,752 cases and 10,610 controls, 17 articles on second intron variable number of tandem repeats with 7,284 cases and 8,544 controls, four studies on rs1042173 with 1,351 cases and 2,101 controls, and four studies on rs140700 with 1,770 cases and 2,386 controls. Pooled, subgroup, and sensitivity analyses were performed, and the results were visualized by forest and funnel plots. Results: An association between 5-HTTLPR and the risk of schizophrenia was not found, except for an Indian subgroup analysis (P z =0.014, OR =1.749, 95% CI =1.120-2.731). A 10 repeats/12 repeats (10R/12R) genotype was a protective factor against schizophrenia (P z =0.020, OR =0.789, 95% CI =0.646-0.963), but a 12R/12R genotype was a risk factor for schizophrenia (P z =0.004, OR =1.936, 95% CI =1.238-3.029) in the pooled analyses. In Caucasians, a GG genotype of rs1042173 may be a risk factor for schizophrenia (P z =0.006, OR =1.299, 95% CI =1.079-1.565). No association was found between rs140700 and the risk for schizophrenia. Conclusion: Through meta-analysis, we were able to gain insight into previously reported associations between SLC6A4 polymorphism and schizophrenia.

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Section: Discussionmentioning

confidence: 93%

Association between the <em>SLC6A4</em> gene and schizophrenia: an updated meta-analysis

Xu¹,

Wang²,

Yao³

2018

NDT

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“…However, in a cluster analysis context, we argue that population stratification is usually not a major problem, particularly from a clinical point of view. We have discussed this issue in detail in our previous work 8 'valid' as long as it is applied to a similar population. However, if we only wish to reveal the genes contributing to the disease subgroups, the genetic variants identified may not have direct biological relevance to the studied disease under this condition.…”

Section: Discussionmentioning

confidence: 99%

“…While an alternative approach is to perform pre-screening for a subset of more promising variants before cluster analysis, the choice of the significance threshold for SNP inclusion is often arbitrary. In addition, our previous work also showed inferior performance of a pre-screening approach compared to modelling all SNPs 8 . Also, it has been argued recently that a very large number of genetic variants, or even the majority of the genome, may be associated with complex diseases 11 .…”

Section: Introductionmentioning

confidence: 91%

“…Cleynen et al 6 performed disease subtyping on Crohn's disease based on 46 single nucleotide polymorphisms (SNPs) extracted from a GWAS and found modest differences in clinical variables among the subgroups. 3 In a more recent work 8 , we have presented the first application of whole-genome SNP data and clinical variables for subtyping a complex disease. We studied schizophrenia (SCZ), a highly heterogeneous psychiatric disorder.…”

Section: Introductionmentioning

confidence: 99%

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Uncovering complex disease subtypes by integrating clinical data and imputed transcriptome from genome-wide association studies: Applications in psychiatry and cardiovascular medicine

Yin

Chau

Sham

et al. 2019

Preprint

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Classifying patients into clinically and biologically homogenous subgroups will facilitate the understanding of disease pathophysiology and development of more targeted prevention and intervention strategies.Traditionally, disease subtyping is based on clinical characteristics alone, however disease subtypes identified by such an approach may not conform exactly to the underlying biological mechanisms. Very few studies have integrated genomic profiles (such as those from GWAS) with clinical symptoms for disease subtyping.In this study, we proposed a novel analytic framework capable of finding subgroups of complex diseases by leveraging both GWAS-predicted gene expression levels and clinical data by a multi-view bicluster analysis. This approach connects SNPs to genes via their effects on expression, hence the analysis is more biologically relevant and interpretable than a pure SNP-based analysis. Transcriptome of different tissues can also be readily modelled. We also proposed various new evaluation or validation metrics, such as a newly modified 'prediction strength' measure to assess generalization of clustering performance. The proposed framework was applied to derive subtypes for schizophrenia, and to stratify subjects into different levels of cardiometabolic risks.Our framework was able to subtype schizophrenia patients with diverse prognosis and treatment response.We also applied the framework to the Northern Finland Cohort (NFBC) 1966 dataset, and identified high-and low cardiometabolic risk subgroups in a gender-stratified analysis. Our results suggest a more data-driven and biologically-informed approach to defining metabolic syndrome. The prediction strength was over 80%, 2 suggesting that the cluster model generalizes well to new datasets. Moreover, we found that the genes 'blindly' selected by the cluster algorithm are significantly enriched for known susceptibility genes discovered in GWAS of schizophrenia and cardiovascular diseases, providing further support to the validity of our approach. The proposed framework may be applied to any complex diseases, and opens up a new approach to patient stratification.

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“…In a recent work, 8 we have presented how to employ whole-genome SNP data and clinical variables for subtyping a complex disease. We studied schizophrenia (SCZ), a highly heterogeneous psychiatric disorder.…”

Section: Introductionmentioning

confidence: 99%

Integrating Clinical Data and Imputed Transcriptome from GWAS to Uncover Complex Disease Subtypes: Applications in Psychiatry and Cardiology

Yin

Chau

Sham

et al. 2019

The American Journal of Human Genetics

Self Cite

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Classifying subjects into clinically and biologically homogeneous subgroups will facilitate the understanding of disease pathophysiology and development of targeted prevention and intervention strategies. Traditionally, disease subtyping is based on clinical characteristics alone, but subtypes identified by such an approach may not conform exactly to the underlying biological mechanisms. Very few studies have integrated genomic profiles (e.g., those from GWASs) with clinical symptoms for disease subtyping. Here we proposed an analytic framework capable of finding complex diseases subgroups by leveraging both GWAS-predicted gene expression levels and clinical data by a multi-view bicluster analysis. This approach connects SNPs to genes via their effects on expression, so the analysis is more biologically relevant and interpretable than a pure SNP-based analysis. Transcriptome of different tissues can also be readily modeled. We also proposed various evaluation metrics for assessing clustering performance. Our framework was able to subtype schizophrenia subjects into diverse subgroups with different prognosis and treatment response. We also applied the framework to the Northern Finland Birth Cohort (NFBC) 1966 dataset and identified high and low cardiometabolic risk subgroups in a gender-stratified analysis. The prediction strength by cross-validation was generally greater than 80%, suggesting good stability of the clustering model. Our results suggest a more data-driven and biologically informed approach to defining metabolic syndrome and subtyping psychiatric disorders. Moreover, we found that the genes ''blindly'' selected by the algorithm are significantly enriched for known susceptibility genes discovered in GWASs of schizophrenia or cardiovascular diseases. The proposed framework opens up an approach to subject stratification.

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Leveraging genome-wide association and clinical data in revealing schizophrenia subgroups

Cited by 14 publications

References 43 publications

Association between the <em>SLC6A4</em> gene and schizophrenia: an updated meta-analysis

Association between the <em>SLC6A4</em> gene and schizophrenia: an updated meta-analysis

Uncovering complex disease subtypes by integrating clinical data and imputed transcriptome from genome-wide association studies: Applications in psychiatry and cardiovascular medicine

Integrating Clinical Data and Imputed Transcriptome from GWAS to Uncover Complex Disease Subtypes: Applications in Psychiatry and Cardiology

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