scholarly journals Genetic susceptibility to SLE: new insights from fine mapping and genome-wide association studies

2009 ◽  
Vol 10 (5) ◽  
pp. 285-290 ◽  
Author(s):  
Isaac T. W. Harley ◽  
Kenneth M. Kaufman ◽  
Carl D. Langefeld ◽  
John B. Harley ◽  
Jennifer A. Kelly
Keyword(s):  
Genetic Susceptibility ◽  
Fine Mapping ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

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

2019 ◽  
Author(s):  
Jianhua Wang ◽  
Dandan Huang ◽  
Yao Zhou ◽  
Hongcheng Yao ◽  
Huanhuan Liu ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Credible Sets ◽  
Causal Variants

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.

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

2013 ◽  
Vol 34 (7) ◽  
pp. 1520-1528 ◽  
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

scholarly journals Genetic susceptibility markers for a breast-colorectal cancer phenotype: Exploratory results from genome-wide association studies

PLoS ONE ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. e0196245
Author(s):  
Mala Pande ◽  
Aron Joon ◽  
Abenaa M. Brewster ◽  
Wei V. Chen ◽  
John L. Hopper ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Genetic Susceptibility ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Cancer Phenotype

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

2016 ◽  
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.

scholarly journals Causal Haplotype Block Identification in Plant Genome-Wide Association Studies

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.

Sign in / Sign up

Export Citation Format

Share Document