Leveraging genomic annotations and pleiotropic enrichment for improved replication rates in schizophrenia GWAS

Wang, Yunpeng, Thompson, Wesley K., Schork, Andrew J., Holland, Dominic, Chen, Chi-Hua, Bettella, Francesco, Desikan, Rahul S., Li, Wen, Witoelar, Aree, Zuber, Verena, Devor, Anna, Noethen, Markus M., Rietschel, Marcella, Chen, Qiang, Werge, Thomas, Cichon, Sven, Weinberger, Daniel R., Djurovic, Srdjan, O'Donovan, Michael, Visscher, Peter M., Andreassen, Ole A. and Dale, Anders M. (2016) Leveraging genomic annotations and pleiotropic enrichment for improved replication rates in schizophrenia GWAS. Plos Genetics, 12 1: . doi:10.1371/journal.pgen.1005803

Author Wang, Yunpeng
Thompson, Wesley K.
Schork, Andrew J.
Holland, Dominic
Chen, Chi-Hua
Bettella, Francesco
Desikan, Rahul S.
Li, Wen
Witoelar, Aree
Zuber, Verena
Devor, Anna
Noethen, Markus M.
Rietschel, Marcella
Chen, Qiang
Werge, Thomas
Cichon, Sven
Weinberger, Daniel R.
Djurovic, Srdjan
O'Donovan, Michael
Visscher, Peter M.
Andreassen, Ole A.
Dale, Anders M.
Title Leveraging genomic annotations and pleiotropic enrichment for improved replication rates in schizophrenia GWAS
Journal name Plos Genetics   Check publisher's open access policy
ISSN 1553-7404
Publication date 2016-01-25
Year available 2016
Sub-type Article (original research)
DOI 10.1371/journal.pgen.1005803
Open Access Status DOI
Volume 12
Issue 1
Total pages 22
Place of publication San Francisco, United States
Publisher Public Library of Science
Collection year 2017
Language eng
Formatted abstract
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.
Keyword Wide Association
Prior Information
Complex Trait
Genome-wide association studies
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
UQ Diamantina Institute Publications
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