Fine-mapping genetic associations

Hutchinson, Anna; Asimit, Jennifer; Wallace, Chris

doi:10.1093/hmg/ddaa148

Cited by 42 publications

(39 citation statements)

References 47 publications

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“…-19 H.G., 2020). Widespread LD also poses challenges when trying to distinguish causal variants from their close correlates-an issue which fine-mapping techniques aim to resolve (Broekema et al, 2020;Hutchinson et al, 2020;Schaid et al, 2018). Furthermore, the suggestive genetic risk loci of severe COVID-19 used for analyses herein as well as the identified PheWAS and QTL associations also require validation.…”

Section: Limitations Of the Studymentioning

confidence: 99%

Phenome-wide and expression quantitative trait locus associations of coronavirus disease 2019 genetic risk loci

et al. 2021

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While several genes and clinical traits have been associated with higher risk of severe COVID-19, how host genetic variants may interact with these parameters and contribute to severe disease is still unclear. Herein, we performed phenome-wide association study (PheWAS), tissue and immune-cell specific expression/splicing quantitative trait loci (eQTL/sQTL), and colocalization analyses for genetic risk loci suggestively associated with severe COVID-19 with respiratory failure. Thirteen phenotypes/traits were associated with the severe COVID-19-associated loci at the genome-wide significance threshold, including monocyte counts, fat metabolism traits, and fibrotic idiopathic interstitial pneumonia. In addition, we identified tissue and immune subtype-specific eQTL associations affecting 48 genes, including several that may directly impact host immune responses, colocalized with the severe COVID-19 GWAS associations, and showed altered expression in single-cell transcriptomes. Collectively, our work demonstrates that host genetic variations associated with multiple genes and traits show genetic pleiotropy with severe COVID-19 and may inform disease etiology.

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Section: Limitations Of the Studymentioning

confidence: 99%

Phenome-wide and expression quantitative trait locus associations of coronavirus disease 2019 genetic risk loci

et al. 2021

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“…The RSS-Z model can be derived from the multiple regression model (1) with some additional assumptions [7,29]. In brief, the assumptions are: (i) the correlation of the November 3, 2021 5/33 response, y, with any single variant x j is small; and (ii) R is a good approximation to R. It is also important that the same samples are used to compute z j for each SNP j (using genotype imputation if necessary).…”

Section: Susie With Non-sufficient Summary Statistics: Susie-rssmentioning

confidence: 99%

“…Fine-mapping is the process of narrowing down genetic association signals to a small number of potential causal variants [1][2][3][4], and it plays an important part in the effort to understand the genetic causes of diseases [5,6]. However, fine-mapping is a difficult problem due to the strong and complex correlation patterns ("linkage disequilibrium", or LD) that exist among nearby genetic variants.…”

Section: Introductionmentioning

confidence: 99%

Fine-mapping from summary data with the “Sum of Single Effects” model

Zou

Carbonetto

Gao

et al. 2021

Preprint

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In recent work, Wang et al introduced the “Sum of Single Effects” (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To achieve this we introduce a simple strategy that could be used to extend any individual-level data method to deal with summary data. In essence, this strategy replaces the usual regression likelihood with an analogous likelihood based on summary data, exploiting the close connection between the two. Our strategy also has the benefit of dealing automatically with non-invertible LD matrices, which arise frequently in fine-mapping applications, and can complicate inference. We highlight other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Simulation studies show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data.

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“…Nevertheless, we recommend the following scientific articles as a good source for beginners: "From genome-wide associations to candidate causal variants by statistical fine-mapping", 38 "A practical view of fine-mapping and gene prioritization in the post-genome-wide association era", 39 and "Fine-mapping genetic associations". 54 Conditional association and imputation using summary statistics The aim of performing conditional association and imputation using summary statistics is to evaluate the association between SNPs and biological trait by combining various GWAS summaries from different studies. This method requires a reference population to estimate LD information.…”

Section: Pathway-sub-network-based Approachesmentioning

confidence: 99%

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

et al. 2021

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Genome-wide association studies (GWAS) provide huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis. Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

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Fine-mapping genetic associations

Cited by 42 publications

References 47 publications

Phenome-wide and expression quantitative trait locus associations of coronavirus disease 2019 genetic risk loci

Phenome-wide and expression quantitative trait locus associations of coronavirus disease 2019 genetic risk loci

Fine-mapping from summary data with the “Sum of Single Effects” model

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

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