Performing post-genome-wide association study analysis: overview, challenges and recommendations

Adam, Yagoub; Samtal, Chaimae; Brandenburg, Jean-Tristan; Falola, Oluwadamilare; Adebiyi, Ezekiel

doi:10.12688/f1000research.53962.1

Cited by 10 publications

(8 citation statements)

References 104 publications

(136 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The application of standard GWA analysis is not possible in this context, as this approach stratifies for the outcome of interest by selecting a subset of the population willing to participate. LD-independent SNPs reaching genome-wide Article https://doi.org/10.1038/s41562-023-01579-9 significance (P < 5 × 10 −8 ) were selected via clumping (clump-kb, 250; clump-r2, 0.1; following standard recommendations 43 ). PhenoScanner 44 , a database of genotype-phenotype associations from existing GWA studies, was used to explore previously identified associations of lead SNPs with other phenotypes.…”

Section: Discussionmentioning

confidence: 99%

Participation bias in the UK Biobank distorts genetic associations and downstream analyses

Schoeler¹,

Speed²,

Porcu³

et al. 2023

Nat Hum Behav

111

View full text Add to dashboard Cite

While volunteer-based studies such as the UK Biobank have become the cornerstone of genetic epidemiology, the participating individuals are rarely representative of their target population. To evaluate the impact of selective participation, here we derived UK Biobank participation probabilities on the basis of 14 variables harmonized across the UK Biobank and a representative sample. We then conducted weighted genome-wide association analyses on 19 traits. Comparing the output from weighted genome-wide association analyses (neffective = 94,643 to 102,215) with that from standard genome-wide association analyses (n = 263,464 to 283,749), we found that increasing representativeness led to changes in SNP effect sizes and identified novel SNP associations for 12 traits. While heritability estimates were less impacted by weighting (maximum change in h2, 5%), we found substantial discrepancies for genetic correlations (maximum change in rg, 0.31) and Mendelian randomization estimates (maximum change in βSTD, 0.15) for socio-behavioural traits. We urge the field to increase representativeness in biobank samples, especially when studying genetic correlates of behaviour, lifestyles and social outcomes.

show abstract

Section: Discussionmentioning

confidence: 99%

Participation bias in the UK Biobank distorts genetic associations and downstream analyses

Schoeler¹,

Speed²,

Porcu³

et al. 2023

Nat Hum Behav

111

View full text Add to dashboard Cite

show abstract

“…Clumping based on Linkage Disequilibrium (LD) is a process used in genetic studies to identify and retain only the most significantly associated variants within a region of linkage disequilibrium (LD) [19, 20]. This is done to reduce redundancy due to the correlation between variants in close proximity to each other.…”

Section: Introductionmentioning

confidence: 99%

EmbedGEM: A framework to evaluate the utility of embeddings for genetic discovery

Mukherjee,

McCaw,

Pei

et al. 2023

Preprint

View full text Add to dashboard Cite

Machine learning derived embeddings are a compressed representation of high content data modalities obtained through deep learning models[1]. Embeddings have been hypothesized to capture detailed information about disease states and have been qualitatively shown to be useful in genetic discovery. Despite their promise, embeddings have some drawbacks: i) they are often confounded by covariates, and ii) their disease relevance is hard to ascertain. In this work we describe a framework to systematically evaluate the utility of embeddings in genetic discovery called EmbedGEM (EmbeddingGeneticEvaluationMethods). Although, motivated by applications to embeddings, EmbedGEM is equally applicable for other multivariate traits as well.EmbedGEM focuses on comparing embeddings along two axes: i) heritability of the embeddings, and ii) ability to identify ‘disease relevant’ variants. We use the number of genome-wide significant signals and mean/median chi-square statistic as a proxy for the heritability of multivariate traits. To evaluate disease relevance, we compute polygenic risk scores for each orthogonalized component of the embedding (or multivariate comparators) and evaluate their association with a held-out set of patients with high-confidence disease traits. While we introduce some relatively straightforward ways to evaluate heritability and disease relevance, we foresee that our framework can be easily extended by adding more metrics.We demonstrate the utility of EmbedGEM by using it to evaluate embedding and non-embedding traits in two separate datasets: i) a synthetic dataset simulated to demonstrate the ability of the framework to correctly rank traits based on their heritability and disease relevance, ii) data from the UK Biobank focused on NAFLD relevant traits. EmbedGEM is implemented in the form of an easy to use Python-based workflow (https://github.com/insitro/EmbedGEM).

show abstract

“…These methods take account of relatedness between individuals with mixed models and different algorithms to improve detection and/or approximations for a very large sample size. Post-association analysis, which may include highly complex methods such as meta analysis considering different GWAS summary statistics, fine-mapping to define causal variants, heritability of phenotype, replication and transferabilty of previous results, annotation, integration of eQTL, and/or calculation of polygenic risk score [ 5 ]. …”

Section: Introductionmentioning

confidence: 99%

“…Post-association analysis, which may include highly complex methods such as meta analysis considering different GWAS summary statistics, fine-mapping to define causal variants, heritability of phenotype, replication and transferabilty of previous results, annotation, integration of eQTL, and/or calculation of polygenic risk score [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

H3AGWAS: a portable workflow for genome wide association studies

et al. 2022

Self Cite

View full text Add to dashboard Cite

Background Genome-wide association studies (GWAS) are a powerful method to detect associations between variants and phenotypes. A GWAS requires several complex computations with large data sets, and many steps may need to be repeated with varying parameters. Manual running of these analyses can be tedious, error-prone and hard to reproduce. Results The H3AGWAS workflow from the Pan-African Bioinformatics Network for H3Africa is a powerful, scalable and portable workflow implementing pre-association analysis, implementation of various association testing methods and post-association analysis of results. Conclusions The workflow is scalable—laptop to cluster to cloud (e.g., SLURM, AWS Batch, Azure). All required software is containerised and can run under Docker or Singularity.

show abstract

Performing post-genome-wide association study analysis: overview, challenges and recommendations

Cited by 10 publications

References 104 publications

Participation bias in the UK Biobank distorts genetic associations and downstream analyses

Participation bias in the UK Biobank distorts genetic associations and downstream analyses

EmbedGEM: A framework to evaluate the utility of embeddings for genetic discovery

H3AGWAS: a portable workflow for genome wide association studies

Contact Info

Product

Resources

About