Biological and practical implications of genome-wide association study of schizophrenia using Bayesian variable selection

Benazir Rowe,Jingchun Chen,Amei Amei,Xiangning Chen,Zuoheng Wang

doi:10.1038/s41537-019-0088-6

Benazir Rowe, Jingchun Chen + Show 3 more

Open Access

https://doi.org/10.1038/s41537-019-0088-6

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Abstract

Genome-wide association studies (GWAS) have identified over 100 loci associated with schizophrenia. Most of these studies test genetic variants for association one at a time. In this study, we performed GWAS of the molecular genetics of schizophrenia (MGS) dataset with 5334 subjects using multivariate Bayesian variable selection (BVS) method Posterior Inference via Model Averaging and Subset Selection (piMASS) and compared our results with the previous univariate analysis of the MGS dataset. We showed that piMASS can improve the power of detecting schizophrenia-associated SNPs, potentially leading to new discoveries from existing data without increasing the sample size. We tested SNPs in groups to allow for local additive effects and used permutation test to determine statistical significance in order to compare our results with univariate method. The previous univariate analysis of the MGS dataset revealed no genome-wide significant loci. Using the same dataset, we identified a single region that exceeded the genome-wide significance. The result was replicated using an independent Swedish Schizophrenia Case–Control Study (SSCCS) dataset. Based on the SZGR 2.0 database we found 63 SNPs from the best performing regions that are mapped to 27 genes known to be associated with schizophrenia. Overall, we demonstrated that piMASS could discover association signals that otherwise would need a much larger sample size. Our study has important implication that reanalyzing published datasets with BVS methods like piMASS might have more power to discover new risk variants for many diseases without new sample collection, ascertainment, and genotyping.

Highlights

Schizophrenia is a severe psychiatric disorder with an estimated global lifetime prevalence of 0.4−0.75% with no significant differences across urban, rural, and mixed sites or genders.[1,2]While being a low prevalence disorder, it has substantial societal burden.[3]
Since the report of the major histocompatibility complex (MHC) locus on chromosome 6 in 2009,5 the number of schizophrenia-associated genetic loci has risen to 5 loci in 20116 and to 108 loci in 2014.7 This increase in the number of significant loci could be partially explained by the increase in the sample size of the studies that led to improvement in the statistical power of the association tests
Table 1) are among the single nucleotide polymorphisms (SNPs) with top 1% highest posterior inclusion probabilities (PIPs) and mapped to gene NTRK3 in the SchiZophrenia Gene Resource database, SZGR 2.0, a comprehensive database of variants and genes reported to have an association with schizophrenia.[14]

Summary

Introduction

Schizophrenia is a severe psychiatric disorder with an estimated global lifetime prevalence of 0.4−0.75% with no significant differences across urban, rural, and mixed sites or genders.[1,2]While being a low prevalence disorder, it has substantial societal burden.[3]. Since the report of the major histocompatibility complex (MHC) locus on chromosome 6 in 2009,5 the number of schizophrenia-associated genetic loci has risen to 5 loci in 20116 and to 108 loci in 2014.7 This increase in the number of significant loci could be partially explained by the increase in the sample size of the studies that led to improvement in the statistical power of the association tests. These studies suggest that common variants usually have small to medium effects that makes them hard to reach the typical GWAS significance threshold (P = 5 × 10−8). The application of regression methods on set of genetic variants with appropriate prior specification may have the potential to uncover the largely hidden heritability

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