Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS.
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...
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS Genetics |
| Online Access: | https://doi.org/10.1371/journal.pgen.1005803 |
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| author | Yunpeng Wang Wesley K Thompson Andrew J Schork Dominic Holland Chi-Hua Chen Francesco Bettella Rahul S Desikan Wen Li Aree Witoelar Verena Zuber Anna Devor Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium Enhancing Neuro Imaging Genetics through Meta Analysis Consortium Markus M Nöthen Marcella Rietschel Qiang Chen Thomas Werge Sven Cichon Daniel R Weinberger Srdjan Djurovic Michael O'Donovan Peter M Visscher Ole A Andreassen Anders M Dale |
| author_facet | Yunpeng Wang Wesley K Thompson Andrew J Schork Dominic Holland Chi-Hua Chen Francesco Bettella Rahul S Desikan Wen Li Aree Witoelar Verena Zuber Anna Devor Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium Enhancing Neuro Imaging Genetics through Meta Analysis Consortium Markus M Nöthen Marcella Rietschel Qiang Chen Thomas Werge Sven Cichon Daniel R Weinberger Srdjan Djurovic Michael O'Donovan Peter M Visscher Ole A Andreassen Anders M Dale |
| author_sort | Yunpeng Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-86020116becc4d2e8c2e37ef3b213872 |
| institution | OA Journals |
| issn | 1553-7390 1553-7404 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS Genetics |
| spelling | doaj-art-86020116becc4d2e8c2e37ef3b2138722025-08-20T02:34:12ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042016-01-01121e100580310.1371/journal.pgen.1005803Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS.Yunpeng WangWesley K ThompsonAndrew J SchorkDominic HollandChi-Hua ChenFrancesco BettellaRahul S DesikanWen LiAree WitoelarVerena ZuberAnna DevorBipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics ConsortiumEnhancing Neuro Imaging Genetics through Meta Analysis ConsortiumMarkus M NöthenMarcella RietschelQiang ChenThomas WergeSven CichonDaniel R WeinbergerSrdjan DjurovicMichael O'DonovanPeter M VisscherOle A AndreassenAnders M DaleMost 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.https://doi.org/10.1371/journal.pgen.1005803 |
| spellingShingle | Yunpeng Wang Wesley K Thompson Andrew J Schork Dominic Holland Chi-Hua Chen Francesco Bettella Rahul S Desikan Wen Li Aree Witoelar Verena Zuber Anna Devor Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium Enhancing Neuro Imaging Genetics through Meta Analysis Consortium Markus M Nöthen Marcella Rietschel Qiang Chen Thomas Werge Sven Cichon Daniel R Weinberger Srdjan Djurovic Michael O'Donovan Peter M Visscher Ole A Andreassen Anders M Dale Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS. PLoS Genetics |
| title | Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS. |
| title_full | Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS. |
| title_fullStr | Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS. |
| title_full_unstemmed | Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS. |
| title_short | Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS. |
| title_sort | leveraging genomic annotations and pleiotropic enrichment for improved replication rates in schizophrenia gwas |
| url | https://doi.org/10.1371/journal.pgen.1005803 |
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