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

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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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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Fine mapping of breast cancer genome-wide association studies loci in women of African ancestry identifies novel susceptibility markers

Carcinogenesis ◽

10.1093/carcin/bgt090 ◽

2013 ◽

Vol 34 (7) ◽

pp. 1520-1528 ◽

Cited By ~ 21

Author(s):

Y. Zheng ◽

T. O. Ogundiran ◽

A. G. Falusi ◽

K. L. Nathanson ◽

E. M. John ◽

...

Keyword(s):

Breast Cancer ◽

Fine Mapping ◽

Association Studies ◽

African Ancestry ◽

Cancer Genome ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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The genomics of autoimmune disease in the era of genome-wide association studies and beyond

Autoimmunity Reviews ◽

10.1016/j.autrev.2011.10.003 ◽

2012 ◽

Vol 11 (4) ◽

pp. 267-275 ◽

Cited By ~ 45

Author(s):

Christopher J. Lessard ◽

John A. Ice ◽

Indra Adrianto ◽

Graham B. Wiley ◽

Jennifer A. Kelly ◽

...

Keyword(s):

Autoimmune Disease ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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Identification of ST3AGL4, MFHAS1, CSNK2A2 and CD226 as loci associated with systemic lupus erythematosus (SLE) and evaluation of SLE genetics in drug repositioning

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2018-213093 ◽

2018 ◽

Vol 77 (7) ◽

pp. 1078-1084 ◽

Cited By ~ 10

Author(s):

Yong-Fei Wang ◽

Yan Zhang ◽

Zhengwei Zhu ◽

Ting-You Wang ◽

David L Morris ◽

...

Keyword(s):

Systemic Lupus Erythematosus ◽

Fine Mapping ◽

Lupus Erythematosus ◽

Drug Repositioning ◽

Association Studies ◽

Genome Wide Association Studies ◽

Systemic Lupus ◽

Genome Wide ◽

Causal Variants

ObjectivesSystemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic component in its pathogenesis. Through genome-wide association studies (GWAS), we recently identified 10 novel loci associated with SLE and uncovered a number of suggestive loci requiring further validation. This study aimed to validate those loci in independent cohorts and evaluate the role of SLE genetics in drug repositioning.MethodsWe conducted GWAS and replication studies involving 12 280 SLE cases and 18 828 controls, and performed fine-mapping analyses to identify likely causal variants within the newly identified loci. We further scanned drug target databases to evaluate the role of SLE genetics in drug repositioning.ResultsWe identified three novel loci that surpassed genome-wide significance, including ST3AGL4 (rs13238909, pmeta=4.40E-08), MFHAS1 (rs2428, pmeta=1.17E-08) and CSNK2A2 (rs2731783, pmeta=1.08E-09). We also confirmed the association of CD226 locus with SLE (rs763361, pmeta=2.45E-08). Fine-mapping and functional analyses indicated that the putative causal variants in CSNK2A2 locus reside in an enhancer and are associated with expression of CSNK2A2 in B-lymphocytes, suggesting a potential mechanism of association. In addition, we demonstrated that SLE risk genes were more likely to be interacting proteins with targets of approved SLE drugs (OR=2.41, p=1.50E-03) which supports the role of genetic studies to repurpose drugs approved for other diseases for the treatment of SLE.ConclusionThis study identified three novel loci associated with SLE and demonstrated the role of SLE GWAS findings in drug repositioning.

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Haplotype-based genome wide association study using a novel SNP-set method : RAINBOW

10.1101/612028 ◽

2019 ◽

Cited By ~ 1

Author(s):

Kosuke Hamazaki ◽

Hiroyoshi Iwata

Keyword(s):

Haplotype Block ◽

Rare Variants ◽

Association Studies ◽

False Positives ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Multiple Alleles ◽

Genome Wide ◽

Causal Variants ◽

Novel Snp

AbstractBackgroundDiffculty in detecting rare variants is one of the problems in conventional genome wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set GWAS method named “RAINBOW” and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes.ResultsWe prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. The results of the comparison indicated that the proposed method was able to better control false positives than the others. The proposed method was also excellent at detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset. Moreover, the proposed method showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, especially when the causal variants were located very close to each other and the directions of their effects were opposite.ConclusionThe proposed method, RAINBOW, is especially superior in controlling false positives, detecting causal variants, and detecting nearby causal variants with opposite effects. By using the SNP-set approach as the proposed method, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as the R package and is available at https://github.com/KosukeHamazaki/RAINBOW.

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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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Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections

10.1101/073056 ◽

2016 ◽

Cited By ~ 1

Author(s):

Chao Tian ◽

Bethann S. Hromatka ◽

Amy K Kiefer ◽

Nicholas Eriksson ◽

Joyce Y Tung ◽

...

Keyword(s):

Fine Mapping ◽

Association Studies ◽

Genome Wide Association ◽

European Ancestry ◽

Multiple Infections ◽

Genome Wide Association Studies ◽

Missense Mutations ◽

Genome Wide ◽

Tract Infections ◽

Common Infections

ABSTRACTWe performed 23 genome-wide association studies for common infections, including chickenpox, shingles, cold sores, mononucleosis, mumps, hepatitis B, plantar warts, positive tuberculosis test results, strep throat, scarlet fever, pneumonia, bacterial meningitis, yeast infections, urinary tract infections, tonsillectomy, childhood ear infections, myringotomy, measles, hepatitis A, rheumatic fever, common colds, rubella and chronic sinus infection, in more than 200,000 individuals of European ancestry. For the first time, genome-wide significant associations (P< 5 × 10−8) were identified for many common infections. The associations were mapped to genes with key roles in acquired and innate immunity(HLA, IFNA21, FUT2, ST3GAL4, ABO, IFNL4, LCE3E, DSG1, LTBR, MTMR3, TNFRSF13B, TNFSF13B, NFKB1, CD40) and in regulation of embryonic developmental process(TBX1, FGF, FOXA1 and FOXN1).Several missense mutations were also identified (inLCE5A, DSG1, FUT2, TBX1, CDHR3, PLG, TNFRSF13B, FOXA1, SH2B3, ST5andFOXN1). Missense mutations inFUT2andTBX1were implicated in multiple infections. We applied fine-mapping analysis to dissect associations in the human leukocyte antigen region, which suggested important roles of specific amino acid polymorphisms in the antigen-binding clefts. Our findings provide an important step toward dissecting the host genetic architecture of response to common infections.

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Causal Haplotype Block Identification in Plant Genome-Wide Association Studies

10.1101/2021.10.28.466332 ◽

2021 ◽

Author(s):

Xing Wu ◽

Wei Jiang ◽

Christopher Fragoso ◽

Jing Huang ◽

Geyu Zhou ◽

...

Keyword(s):

Fine Mapping ◽

Complex Traits ◽

Haplotype Block ◽

Association Studies ◽

Crop Improvement ◽

Plant Genome ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Haplotype Blocks ◽

Genome Wide

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) used in many GWAS that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects. In plants, the relatively small population size in GWAS and the high genetic diversity found many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to infer the causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, and GMMAT in both simulation and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in simulations with high polygenicity. Moreover, it resulted in higher mapping resolution, especially in regions of high LD, by identifying small causal blocks in the larger haplotype block. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA results, and its average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved mapping resolution to facilitate crop improvement.

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