CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

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Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

Genome Medicine ◽

10.1186/s13073-021-00857-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Shuquan Rao ◽

Yao Yao ◽

Daniel E. Bauer

Keyword(s):

Genome Editing ◽

Genetic Variants ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Functional Studies ◽

Functional Genetics ◽

Genome Wide ◽

Causal Variants ◽

Experimental Approaches

AbstractGenome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

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Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

10.1101/133132 ◽

2017 ◽

Cited By ~ 8

Author(s):

Dominic Holland ◽

Oleksandr Frei ◽

Rahul Desikan ◽

Chun-Chieh Fan ◽

Alexey A. Shadrin ◽

...

Keyword(s):

Association Studies ◽

Causal Snps ◽

Reference Panel ◽

Causal Effects ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Common Variants ◽

Genome Wide ◽

Causal Variants

AbstractEstimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from ≃ 2 × 10−5to ≃ 4 × 10−3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.Author SummaryThere are ~10 million common variants in the genome of humans with European ancestry. For any particular phenotype a number of these variants will have some causal effect. It is of great interest to be able to quantify the number of these causal variants and the strength of their effect on the phenotype.Genome wide association studies (GWAS) produce very noisy summary statistics for the association between subsets of common variants and phenotypes. For any phenotype, these statistics collectively are difficult to interpret, but buried within them is the true landscape of causal effects. In this work, we posit a probability distribution for the causal effects, and assess its validity using simulations. Using a detailed reference panel of ~11 million common variants – among which only a small fraction are likely to be causal, but allowing for non-causal variants to show an association with the phenotype due to correlation with causal variants – we implement an exact procedure for estimating the number of causal variants and their mean strength of association with the phenotype. We find that, across different phenotypes, both these quantities – whose product allows for lower bound estimates of heritability – vary by orders of magnitude.

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Statistical fine-mapping and summary statistics imputation in genome-wide association studies

10.14711/thesis-991012710761503412 ◽

2019 ◽

Author(s):

Yu Pu

Keyword(s):

Fine Mapping ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Genome Wide

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Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2017.08.012 ◽

2017 ◽

Vol 101 (4) ◽

pp. 539-551 ◽

Cited By ~ 67

Author(s):

Christian Benner ◽

Aki S. Havulinna ◽

Marjo-Riitta Järvelin ◽

Veikko Salomaa ◽

Samuli Ripatti ◽

...

Keyword(s):

Fine Mapping ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Genome Wide ◽

Genomic Regions

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Causal variants in autoimmune disease: a commentary on a recent published fine-mapping algorithm analysis in genome-wide association studies study

Annals of Translational Medicine ◽

10.21037/atm.2017.02.26 ◽

2017 ◽

Vol 5 (6) ◽

pp. 151-151 ◽

Cited By ~ 3

Author(s):

Jiunn-Diann Lin ◽

Chao-Wen Cheng

Keyword(s):

Autoimmune Disease ◽

Fine Mapping ◽

Association Studies ◽

Genome Wide Association ◽

Algorithm Analysis ◽

Genome Wide Association Studies ◽

Mapping Algorithm ◽

Genome Wide ◽

Causal Variants

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Identification of Novel Genome-Wide Associations for Oral Inflammatory Traits

10.21203/rs.3.rs-104530/v1 ◽

2020 ◽

Author(s):

Yanjiao Jin ◽

Jie Yang ◽

Shuyue Zhang ◽

Jin Li ◽

Songlin Wang

Keyword(s):

Candidate Genes ◽

Immune Regulation ◽

Functional Annotation ◽

Inflammatory Diseases ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Protein Protein Interaction ◽

Genome Wide ◽

Causal Variants

Abstract Background: Oral diseases impact the majority of the world’s population. The following traits are common in oral inflammatory diseases: mouth ulcers, painful gums, bleeding gums, loose teeth, and toothache. Despite the prevalence of genome-wide association studies, the associations between these traits and common genomic variants, and whether pleiotropic loci are shared by some of these traits remain poorly understood. Methods: In this work, we conducted multi-trait joint analyses based on the summary statistics of genome-wide association studies of these five oral inflammatory traits from the UK Biobank, each of which is comprised of over 10,000 cases and over 300,000 controls. We estimated the genetic correlations between the five traits. We conducted fine-mapping and functional annotation based on multi-omics data to better understand the biological functions of the potential causal variants at each locus. To identify the pathways in which the candidate genes were mainly involved, we applied gene-set enrichment analysis, and further performed protein-protein interaction (PPI) analyses.Results: We identified 39 association signals that surpassed genome-wide significance, including three that were shared between two or more oral inflammatory traits, consistent with a strong correlation. Among these genome-wide significant loci, two were novel for both painful gums and toothache. We performed fine-mapping and identified causal variants at each novel locus. Further functional annotation based on multi-omics data suggested IL10 and IL12A/TRIM59 as potential candidate genes at the novel pleiotropic loci, respectively. Subsequent analyses of pathway enrichment and protein-protein interaction networks suggested the involvement of candidate genes at genome-wide significant loci in immune regulation.Conclusions: Our results highlighted the importance of immune regulation in the pathogenesis of oral inflammatory diseases. Some common immune-related pleiotropic loci or genetic variants are shared by multiple oral inflammatory traits. These findings will be beneficial for risk prediction, prevention, and therapy of oral inflammatory diseases.

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