Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics
Genome-wide Association Studies (GWAS) result in millions of summary statistics (``z-scores'') for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as p...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-02-01
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| Series: | Frontiers in Genetics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00015/full |
| _version_ | 1819180666263175168 |
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| author | Dominic eHolland Dominic eHolland Yunpeng eWang Yunpeng eWang Yunpeng eWang Yunpeng eWang Wesley K Thompson Andrew eSchork Chi-Hua eChen Chi-Hua eChen Min-Tzu eLo Min-Tzu eLo Aree eWitoelar Aree eWitoelar Thomas eWerge Michael eO'Donovan Ole A Andreassen Ole A Andreassen Anders M. Dale Anders M. Dale Anders M. Dale Anders M. Dale |
| author_facet | Dominic eHolland Dominic eHolland Yunpeng eWang Yunpeng eWang Yunpeng eWang Yunpeng eWang Wesley K Thompson Andrew eSchork Chi-Hua eChen Chi-Hua eChen Min-Tzu eLo Min-Tzu eLo Aree eWitoelar Aree eWitoelar Thomas eWerge Michael eO'Donovan Ole A Andreassen Ole A Andreassen Anders M. Dale Anders M. Dale Anders M. Dale Anders M. Dale |
| author_sort | Dominic eHolland |
| collection | DOAJ |
| description | Genome-wide Association Studies (GWAS) result in millions of summary statistics (``z-scores'') for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype and predicting the proportion of chip heritability explainable by genome-wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N=82,315) and putamen volume (N=12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We assess the degree to which effect sizes are over-estimated when based on linear-regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are $10^6$ and $10^5$. The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures. |
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| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-22T22:17:58Z |
| publishDate | 2016-02-01 |
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| series | Frontiers in Genetics |
| spelling | doaj.art-69b87a75249444e8b9ed934b00e2c29e2022-12-21T18:10:44ZengFrontiers Media S.A.Frontiers in Genetics1664-80212016-02-01710.3389/fgene.2016.00015182296Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary StatisticsDominic eHolland0Dominic eHolland1Yunpeng eWang2Yunpeng eWang3Yunpeng eWang4Yunpeng eWang5Wesley K Thompson6Andrew eSchork7Chi-Hua eChen8Chi-Hua eChen9Min-Tzu eLo10Min-Tzu eLo11Aree eWitoelar12Aree eWitoelar13Thomas eWerge14Michael eO'Donovan15Ole A Andreassen16Ole A Andreassen17Anders M. Dale18Anders M. Dale19Anders M. Dale20Anders M. Dale21UCSDUniversity of California at San DiegoUCSDUniversity of OsloOslo University HospitalUniversity of California at San DiegoUniversity of California San DiegoUniversity of California San DiegoUniversity of California at San DiegoUniversity of California San DiegoUniversity of California at San DiegoUniversity of California San DiegoUniversity of OsloOslo University HospitalSct. Hans Hospital and University of CopenhagenSchool of Medicine, Cardiff UniversityUniversity of OsloOslo University HospitalUCSDUniversity of California at San DiegoUniversity of California San DiegoUniversity of California San DiegoGenome-wide Association Studies (GWAS) result in millions of summary statistics (``z-scores'') for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype and predicting the proportion of chip heritability explainable by genome-wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N=82,315) and putamen volume (N=12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We assess the degree to which effect sizes are over-estimated when based on linear-regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are $10^6$ and $10^5$. The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures.http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00015/fullPutamenSchizophreniaGWASeffect sizeSNP discoveryGaussian mixture model |
| spellingShingle | Dominic eHolland Dominic eHolland Yunpeng eWang Yunpeng eWang Yunpeng eWang Yunpeng eWang Wesley K Thompson Andrew eSchork Chi-Hua eChen Chi-Hua eChen Min-Tzu eLo Min-Tzu eLo Aree eWitoelar Aree eWitoelar Thomas eWerge Michael eO'Donovan Ole A Andreassen Ole A Andreassen Anders M. Dale Anders M. Dale Anders M. Dale Anders M. Dale Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics Frontiers in Genetics Putamen Schizophrenia GWAS effect size SNP discovery Gaussian mixture model |
| title | Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics |
| title_full | Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics |
| title_fullStr | Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics |
| title_full_unstemmed | Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics |
| title_short | Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics |
| title_sort | estimating effect sizes and expected replication probabilities from gwas summary statistics |
| topic | Putamen Schizophrenia GWAS effect size SNP discovery Gaussian mixture model |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00015/full |
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