Research
Print page Print page
Switch language
The Capital Region of Denmark - a part of Copenhagen University Hospital
Published

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

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Wang, Y, Thompson, WK, Schork, AJ, Holland, D, Chen, C-H, Bettella, F, Desikan, RS, Li, HW, Witoelar, A, Zuber, V, Devor, A, Nöthen, MM, Rietschel, M, Chen, Q, Werge, T, Cichon, S, Weinberger, DR, Djurovic, S, O'Donovan, M, Visscher, PM, Andreassen, OA, Dale, AM & Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium 2016, 'Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS' P L o S Genetics (Online), vol. 12, no. 1, pp. e1005803. https://doi.org/10.1371/journal.pgen.1005803

APA

Wang, Y., Thompson, W. K., Schork, A. J., Holland, D., Chen, C-H., Bettella, F., ... Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium (2016). Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS. P L o S Genetics (Online), 12(1), e1005803. https://doi.org/10.1371/journal.pgen.1005803

CBE

Wang Y, Thompson WK, Schork AJ, Holland D, Chen C-H, Bettella F, Desikan RS, Li HW, Witoelar A, Zuber V, Devor A, Nöthen MM, Rietschel M, Chen Q, Werge T, Cichon S, Weinberger DR, Djurovic S, O'Donovan M, Visscher PM, Andreassen OA, Dale AM, Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. 2016. Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS. P L o S Genetics (Online). 12(1):e1005803. https://doi.org/10.1371/journal.pgen.1005803

MLA

Vancouver

Author

Wang, Yunpeng ; Thompson, Wesley K ; Schork, Andrew J ; Holland, Dominic ; Chen, Chi-Hua ; Bettella, Francesco ; Desikan, Rahul S ; Li, Hong Wen ; Witoelar, Aree ; Zuber, Verena ; Devor, Anna ; Nöthen, 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 ; Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. / Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS. In: P L o S Genetics (Online). 2016 ; Vol. 12, No. 1. pp. e1005803.

Bibtex

@article{6b76d518f4144eb99fffb0f673d8827e,
title = "Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS",
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.",
author = "Yunpeng Wang and Thompson, {Wesley K} and Schork, {Andrew J} and Dominic Holland and Chi-Hua Chen and Francesco Bettella and Desikan, {Rahul S} and Li, {Hong Wen} and Aree Witoelar and Verena Zuber and Anna Devor and N{\"o}then, {Markus M} and Marcella Rietschel and Qiang Chen and Thomas Werge and Sven Cichon and Weinberger, {Daniel R} and Srdjan Djurovic and Michael O'Donovan and Visscher, {Peter M} and Andreassen, {Ole A} and Dale, {Anders M} and {Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium} and Hansen, {Thomas Folkmann}",
year = "2016",
month = "1",
doi = "10.1371/journal.pgen.1005803",
language = "English",
volume = "12",
pages = "e1005803",
journal = "P L o S Genetics (Online)",
issn = "1553-7404",
publisher = "Public Library of Science",
number = "1",

}

RIS

TY - JOUR

T1 - Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS

AU - Wang, Yunpeng

AU - Thompson, Wesley K

AU - Schork, Andrew J

AU - Holland, Dominic

AU - Chen, Chi-Hua

AU - Bettella, Francesco

AU - Desikan, Rahul S

AU - Li, Hong Wen

AU - Witoelar, Aree

AU - Zuber, Verena

AU - Devor, Anna

AU - Nöthen, Markus M

AU - Rietschel, Marcella

AU - Chen, Qiang

AU - Werge, Thomas

AU - Cichon, Sven

AU - Weinberger, Daniel R

AU - Djurovic, Srdjan

AU - O'Donovan, Michael

AU - Visscher, Peter M

AU - Andreassen, Ole A

AU - Dale, Anders M

AU - Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium

AU - Hansen, Thomas Folkmann

PY - 2016/1

Y1 - 2016/1

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pgen.1005803

DO - 10.1371/journal.pgen.1005803

M3 - Journal article

VL - 12

SP - e1005803

JO - P L o S Genetics (Online)

JF - P L o S Genetics (Online)

SN - 1553-7404

IS - 1

ER -

ID: 46043324