Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS

Wang, Yunpeng and Thompson, Wesley K and Schork, Andrew J and Holland, Dominic and Chen, Chi-Hua and Bettella, Francesco and Desikan, Rahul S and Li, Wen and Witoelar, Aree and Zuber, Verena and Devor, Anna and Nöthen, Markus M and Rietschel, Marcella and Chen, Qiang and Werge, Thomas and Cichon, Sven and Weinberger, Daniel R and Djurovic, Srdjan and O'Donovan, Michael and Visscher, Peter M and Andreassen, Ole A and Dale, Anders M and Knight, Jo (2016) Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS. PLoS Genetics, 12 (1). ISSN 1553-7390

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Most of the genetic architecture of schizophrenia (SCZ) has not yet been identified. Here, we apply a novel statistical algorithm called Covariate-Modulated Mixture Modeling (CM3), which incorporates auxiliary information (heterozygosity, total linkage disequilibrium, genomic annotations, pleiotropy) for each single nucleotide polymorphism (SNP) to enable more accurate estimation of replication probabilities, conditional on the observed test statistic ("z-score") of the SNP. We use a multiple logistic regression on z-scores to combine information from auxiliary information to derive a "relative enrichment score" for each SNP. For each stratum of these relative enrichment scores, we obtain nonparametric estimates of posterior expected test statistics and replication probabilities as a function of discovery z-scores, using a resampling-based approach that repeatedly and randomly partitions meta-analysis sub-studies into training and replication samples. We fit a scale mixture of two Gaussians model to each stratum, obtaining parameter estimates that minimize the sum of squared differences of the scale-mixture model with the stratified nonparametric estimates. We apply this approach to the recent genome-wide association study (GWAS) of SCZ (n = 82,315), obtaining a good fit between the model-based and observed effect sizes and replication probabilities. We observed that SNPs with low enrichment scores replicate with a lower probability than SNPs with high enrichment scores even when both they are genome-wide significant (p < 5x10-8). There were 693 and 219 independent loci with model-based replication rates ≥80% and ≥90%, respectively. Compared to analyses not incorporating relative enrichment scores, CM3 increased out-of-sample yield for SNPs that replicate at a given rate. This demonstrates that replication probabilities can be more accurately estimated using prior enrichment information with CM3.

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Journal Article
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PLoS Genetics
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19 Jan 2018 11:54
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01 Jul 2020 04:15