scholarly journals Estimation of effect size distribution from genome-wide association studies and implications for future discoveries

2010 ◽  
Vol 42 (7) ◽  
pp. 570-575 ◽  
Author(s):  
Ju-Hyun Park ◽  
Sholom Wacholder ◽  
Mitchell H Gail ◽  
Ulrike Peters ◽  
Kevin B Jacobs ◽  
...  
Keyword(s):  
Size Distribution ◽  
Effect Size ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Effect Size Distribution

scholarly journals Semi-parametric empirical Bayes factor for genome-wide association studies

2021 ◽  
Author(s):  
Junji Morisawa ◽  
Takahiro Otani ◽  
Jo Nishino ◽  
Ryo Emoto ◽  
Kunihiko Takahashi ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Size Distribution ◽  
Effect Size ◽  
Empirical Bayes ◽  
Bayes Factor ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Effect Size Distribution

AbstractBayes factor analysis has the attractive property of accommodating the risks of both false negatives and false positives when identifying susceptibility gene variants in genome-wide association studies (GWASs). For a particular SNP, the critical aspect of this analysis is that it incorporates the probability of obtaining the observed value of a statistic on disease association under the alternative hypotheses of non-null association. An approximate Bayes factor (ABF) was proposed by Wakefield (Genetic Epidemiology 2009;33:79–86) based on a normal prior for the underlying effect-size distribution. However, misspecification of the prior can lead to failure in incorporating the probability under the alternative hypothesis. In this paper, we propose a semi-parametric, empirical Bayes factor (SP-EBF) based on a nonparametric effect-size distribution estimated from the data. Analysis of several GWAS datasets revealed the presence of substantial numbers of SNPs with small effect sizes, and the SP-EBF attributed much greater significance to such SNPs than the ABF. Overall, the SP-EBF incorporates an effect-size distribution that is estimated from the data, and it has the potential to improve the accuracy of Bayes factor analysis in GWASs.

scholarly journals Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

2020 ◽  
Author(s):  
Jack W. O’Sullivan ◽  
John P. A. Ioannidis
Keyword(s):  
Effect Size ◽  
Association Studies ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Single Nucleotide ◽  
Genome Wide ◽  
The Uk ◽  
Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

scholarly journals Reproducibility in the UK biobank of genome-wide significant signals discovered in earlier genome-wide association studies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jack W. O’Sullivan ◽  
John P. A. Ioannidis
Keyword(s):  
Effect Size ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Uk Biobank ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Genome Wide ◽  
The Uk

AbstractWith the establishment of large biobanks, discovery of single nucleotide variants (SNVs, also known as single nucleotide polymorphisms (SNVs)) associated with various phenotypes has accelerated. An open question is whether genome-wide significant SNVs identified in earlier genome-wide association studies (GWAS) are replicated in later GWAS conducted in biobanks. To address this, we examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, “replication” GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNVs (of which 6289 reached P < 5e−8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0%; although lower for binary than quantitative phenotypes (58.1% versus 94.8% respectively). There was a 18.0% decrease in SNV effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNV effect size, phenotype trait (binary or quantitative), and discovery P value, we built and validated a model that predicted SNV replication with area under the Receiver Operator Curve = 0.90. While non-replication may reflect lack of power rather than genuine false-positives, these results provide insights about which discovered associations are likely to be replicated across subsequent GWAS.

scholarly journals Ancestry-specific association mapping in admixed populations

2015 ◽  
Author(s):  
Line Skotte ◽  
Emil Jørsboe ◽  
Thorfinn Sand Korneliussen ◽  
Ida Moltke ◽  
Anders Albrechtsen
Keyword(s):  
Effect Size ◽  
Genetic Variants ◽  
Statistical Power ◽  
Association Studies ◽  
Causal Variant ◽  
Genome Wide Association ◽  
Reasonable Assumption ◽  
Genome Wide Association Studies ◽  
Association Testing ◽  
Genome Wide

AbstractDuring the last decade genome–wide association studies have proven to be a powerful approach to identifying disease-causing variants. However, for admixed populations, most current methods for association testing are based on the assumption that the effect of a genetic variant is the same regardless of its ancestry. This is a reasonable assumption for a causal variant, but may not hold for the genetic variants that are tested in genome–wide association studies, which are usually not causal. The effects of non-causal genetic variants depend on how strongly their presence correlate with the presence of the causal variant, which may vary between ancestral populations because of different linkage disequilibrium patterns and allele frequencies.Motivated by this, we here introduce a new statistical method for association testing in recently admixed populations, where the effect size is allowed to depend on the ancestry of a given allele. Our method does not rely on accurate inference of local ancestry, yet using simulations we show that in some scenarios it gives a dramatic increase in statistical power to detect associations. In addition, the method allows for testing for difference in effect size between ancestral populations, which can be used to help determine if a SNP is causal. We demonstrate the usefulness of the method on data from the Greenlandic population.

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