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PLoS Genet. 2016 Jan 25;12(1):e1005803. doi: 10.1371/journal.pgen.1005803. eCollection 2016 Jan.

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

Author information

1
NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
2
Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
3
Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America.
4
Multimodal Imaging Laboratory, University of California at San Diego, La Jolla, California, United States of America.
5
Department of Psychiatry, University of California, San Diego, La Jolla, California, United States of America.
6
Department of Cognitive Sciences, University of California at San Diego, La Jolla, California, United States of America.
7
Department of Radiology, University of California, San Diego, La Jolla, California, United States of America.
8
Institute of Human Genetics, University of Bonn, Bonn, Germany.
9
Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Mannheim, Germany.
10
Lieber Institute for Brain Development, Baltimore, Maryland, United States of America.
11
Institute of Biological Psychiatry, MHC, Sct. Hans Hospital and University of Copenhagen, Copenhagen, Denmark.
12
Department of Biomedicine, University of Basel, Basel, Switzerland.
13
Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.
14
MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom.
15
The Queensland Brain Institute, The University of Queensland, Brisbane, Australia.
16
University of Queensland Diamantina Institute, University of Queensland, Translational Research Institute (TRI), Brisbane, Australia.

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.

PMID:
26808560
PMCID:
PMC4726519
DOI:
10.1371/journal.pgen.1005803
[Indexed for MEDLINE]
Free PMC Article

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