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Boosting the power of schizophrenia genetics by leveraging new statistical tools.

ABSTRACT: Genome-wide association studies (GWAS) have identified a large number of gene variants associated with schizophrenia, but these variants explain only a small portion of the heritability. It is becoming increasingly clear that schizophrenia is influenced by many genes, most of which have effects too small to be identified using traditional GWAS statistical methods. By applying recently developed Empirical Bayes statistical approaches, we have demonstrated that functional genic elements show differential contribution to phenotypic variance, with some elements (regulatory regions and exons) showing strong enrichment for association with schizophrenia. Applying related methods, we also showed abundant genetic overlap (pleiotropy) between schizophrenia and other phenotypes, including bipolar disorder, cardiovascular disease risk factors, and multiple sclerosis. We estimated the number of gene variants with effects in schizophrenia and bipolar disorder to be approximately 1.2%. By applying our novel statistical framework, we dramatically improved gene discovery and detected a large number of new gene loci associated with schizophrenia that have not yet been identified with standard GWAS methods. Utilizing independent schizophrenia substudies, we showed that these new loci have high replication rates in de novo samples, indicating that they likely represent true schizophrenia risk genes. The new statistical tools provide a powerful approach for uncovering more of the missing heritability of schizophrenia and other complex disorders. In conclusion, the highly polygenic architecture of schizophrenia strongly suggests the utility of research approaches that recognize schizophrenia neuropathology as a complex dynamic system, with many small gene effects integrated in functional networks.

SUBMITTER: Andreassen OA

PROVIDER: S-EPMC3885310 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Boosting the power of schizophrenia genetics by leveraging new statistical tools.

Andreassen Ole A OA Thompson Wesley K WK Dale Anders M AM

Schizophrenia bulletin 20131206 1

Genome-wide association studies (GWAS) have identified a large number of gene variants associated with schizophrenia, but these variants explain only a small portion of the heritability. It is becoming increasingly clear that schizophrenia is influenced by many genes, most of which have effects too small to be identified using traditional GWAS statistical methods. By applying recently developed Empirical Bayes statistical approaches, we have demonstrated that functional genic elements show diffe ...[more]

PMID: 24319118

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Project description:A key factor in designing randomized clinical trials is the sample size required to achieve a particular level of power to detect the benefit of a treatment. Sample size calculations depend upon the expected benefits of a treatment (effect size), the accuracy of measurement of the primary outcome, and the level of power specified by the investigators. In this study, we show that radiomic models, which leverage complex brain MRI patterns and machine learning, can be utilized in clinical trials with protocols that incorporate baseline MR imaging to significantly increase statistical power to detect treatment effects. Akin to the historical control paradigm, we propose to utilize a radiomic prediction model to generate a pseudo-control sample for each individual in the trial of interest. Because the variability of expected outcome across patients can mask our ability to detect treatment effects, we can increase the power to detect a treatment effect in a clinical trial by reducing that variability through using radiomic predictors as surrogates. We illustrate this method with simulations based on data from two cohorts in different neurologic diseases, Alzheimer's disease and glioblastoma multiforme. We present sample size requirements across a range of effect sizes using conventional analysis and models that include a radiomic predictor. For our Alzheimer's disease cohort, at an effect size of 0.35, total sample size requirements for 80% power declined from 246 to 212 for the endpoint cognitive decline. For our glioblastoma multiforme cohort, at an effect size of 1.65 with the endpoint survival time, total sample size requirements declined from 128 to 74. This methodology can decrease the required sample sizes by as much as 50%, depending on the strength of the radiomic predictor. The power of this method grows with increased accuracy of radiomic prediction, and furthermore, this method is most helpful when treatment effect sizes are small. Neuroimaging biomarkers are a powerful and increasingly common suite of tools that are, in many cases, highly predictive of disease outcomes. Here, we explore the possibility of using MRI-based radiomic biomarkers for the purpose of improving statistical power in clinical trials in the contexts of brain cancer and prodromal Alzheimer's disease. These methods can be applied to a broad range of neurologic diseases using a broad range of predictors of outcome to make clinical trials more efficient.

Dataset Information

Boosting the power of schizophrenia genetics by leveraging new statistical tools.

Publications

Boosting the power of schizophrenia genetics by leveraging new statistical tools.

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