Javascript must be enabled to continue!
GWAS significance thresholds in large cohorts
View through CrossRef
AbstractWhile the p-value threshold of 5.0 × 10−8remains the standard for genome-wide association studies (GWAS) in humans and other species, it still needs to be updated to reflect the current era of large-scale GWAS, where tens of thousands of sample sizes are used to discover genetic associations at loci with smaller minor allele frequencies. In this study, we used a dataset of 348,501 individuals of European ancestry from the UK Biobank to determine the GWAS thresholds required for multiple testing corrections when considering rare and common variants in additive and dominant GWAS models. Additionally, we employed conditional and joint (COJO) analysis to quantify the proportion of false significant hits in the GWAS results for 72 traits in the UK Biobank when applying the traditional GWAS cut-off versus our newly proposed p-value thresholds. Overall, the results indicate that the conventional GWAS significance threshold of 5.0 × 10−8yields a false positive rate of between 20% and 30% in GWAS studies that utilize large sample sizes and less common variants. Instead, a more stringent GWAS p-value threshold of 5.0 × 10−9is needed when rare variants (with minor allele frequency > 0.1%) are included in the association test for both additive and dominance models within the European ancestry population.
Title: GWAS significance thresholds in large cohorts
Description:
AbstractWhile the p-value threshold of 5.
0 × 10−8remains the standard for genome-wide association studies (GWAS) in humans and other species, it still needs to be updated to reflect the current era of large-scale GWAS, where tens of thousands of sample sizes are used to discover genetic associations at loci with smaller minor allele frequencies.
In this study, we used a dataset of 348,501 individuals of European ancestry from the UK Biobank to determine the GWAS thresholds required for multiple testing corrections when considering rare and common variants in additive and dominant GWAS models.
Additionally, we employed conditional and joint (COJO) analysis to quantify the proportion of false significant hits in the GWAS results for 72 traits in the UK Biobank when applying the traditional GWAS cut-off versus our newly proposed p-value thresholds.
Overall, the results indicate that the conventional GWAS significance threshold of 5.
0 × 10−8yields a false positive rate of between 20% and 30% in GWAS studies that utilize large sample sizes and less common variants.
Instead, a more stringent GWAS p-value threshold of 5.
0 × 10−9is needed when rare variants (with minor allele frequency > 0.
1%) are included in the association test for both additive and dominance models within the European ancestry population.
Related Results
Valid inference for machine learning-assisted GWAS
Valid inference for machine learning-assisted GWAS
Abstract
Machine learning (ML) has revolutionized analytical strategies in almost all scientific disciplines including human genetics and genomics. Due to challenge...
Causality between cholelithiasis and ileus: a two-sample Mendelian randomization study
Causality between cholelithiasis and ileus: a two-sample Mendelian randomization study
Abstract
Background: Cholelithiasis is a prevalent digestive ailment in China, prompting extensive research on its association with ileus. However, prior investigations rel...
Linking GWAS to pharmacological treatments for psychiatric disorders
Linking GWAS to pharmacological treatments for psychiatric disorders
Abstract
Importance
Large-scale genome-wide association studies (GWASs) are expected to inform the development of pharmacologic...
e-GRASP: an integrated evolutionary and GRASP resource for exploring disease associations
e-GRASP: an integrated evolutionary and GRASP resource for exploring disease associations
Abstract
Background
Genome-wide association studies (GWAS) have become a mainstay of biological research concerned with d...
Abstract ML-1: Pharmacogenomics in the Quest for Precision Endocrine Therapy of Breast Cancer
Abstract ML-1: Pharmacogenomics in the Quest for Precision Endocrine Therapy of Breast Cancer
Abstract
Endocrine therapy, with SERMs and AIs, is the most important treatment modality for the 70% of patients with ER+ early breast cancer. Clinically, there is m...
Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes
Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes
Abstract
Heterogeneity in definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification er...
Processing genome-wide association studies within a repository of heterogeneous genomic datasets
Processing genome-wide association studies within a repository of heterogeneous genomic datasets
Abstract
Background
Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic va...
Comparison of Methods Utilizing Sex-Specific PRSs Derived From GWAS Summary Statistics
Comparison of Methods Utilizing Sex-Specific PRSs Derived From GWAS Summary Statistics
The polygenic risk score (PRS) is calculated as the weighted sum of an individual’s genotypes and their estimated effect sizes, which is often used to estimate an individual’s gene...

