scholarly journals Joint analysis of functional genomic data and genome-wide association studies of 18 human traits

2013 ◽  
Author(s):  
Joseph Pickrell
Keyword(s):  
Association Studies ◽  
Regulatory Elements ◽  
Dnase I ◽  
Genome Wide Association ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Platelet Volume ◽  
Dnase I Hypersensitive Sites ◽  
Genome Wide ◽  
Hypersensitive Sites

Annotations of gene structures and regulatory elements can inform genome-wide association studies (GWAS). However, choosing the relevant annotations for interpreting an association study of a given trait remains challenging. We describe a statistical model that uses association statistics computed across the genome to identify classes of genomic element that are enriched or depleted for loci that influence a trait. The model naturally incorporates multiple types of annotations. We applied the model to GWAS of 18 human traits, including red blood cell traits, platelet traits, glucose levels, lipid levels, height, BMI, and Crohn's disease. For each trait, we evaluated the relevance of 450 different genomic annotations, including protein-coding genes, enhancers, and DNase-I hypersensitive sites in over a hundred tissues and cell lines. We show that the fraction of phenotype-associated SNPs that influence protein sequence ranges from around 2% (for platelet volume) up to around 20% (for LDL cholesterol); that repressed chromatin is significantly depleted for SNPs associated with several traits; and that cell type-specific DNase-I hypersensitive sites are enriched for SNPs associated with several traits (for example, the spleen in platelet volume). Finally, by re-weighting each GWAS using information from functional genomics, we increase the number of loci with high-confidence associations by around 5%.

scholarly journals Host Genome-Wide Association Study of Infant Susceptibility to Shigella-Associated Diarrhea

2021 ◽  
Vol 89 (6) ◽  
Author(s):  
Dylan Duchen ◽  
Rashidul Haque ◽  
Laura Chen ◽  
Genevieve Wojcik ◽  
Poonum Korpe ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Diarrheal Disease ◽  
Genetic Model ◽  
Association Studies ◽  
Host Cells ◽  
Genome Wide Association ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Host Genetic

ABSTRACT Shigella is a leading cause of moderate-to-severe diarrhea globally and the causative agent of shigellosis and bacillary dysentery. Associated with 80 to 165 million cases of diarrhea and >13% of diarrheal deaths, in many regions, Shigella exposure is ubiquitous while infection is heterogenous. To characterize host-genetic susceptibility to Shigella-associated diarrhea, we performed two independent genome-wide association studies (GWAS) including Bangladeshi infants from the PROVIDE and CBC birth cohorts in Dhaka, Bangladesh. Cases were infants with Shigella-associated diarrhea (n = 143) and controls were infants with no Shigella-associated diarrhea in the first 13 months of life (n = 446). Shigella-associated diarrhea was identified via quantitative PCR (qPCR) threshold cycle (CT) distributions for the ipaH gene, carried by all four Shigella species and enteroinvasive Escherichia coli. Host GWAS were performed under an additive genetic model. A joint analysis identified protective loci on chromosomes 11 (rs582240, within the KRT18P59 pseudogene; P = 6.40 × 10−8; odds ratio [OR], 0.43) and 8 (rs12550437, within the lincRNA RP11-115J16.1; P = 1.49 × 10−7; OR, 0.48). Conditional analyses identified two previously suggestive loci, a protective locus on chromosome 7 (rs10266841, within the 3′ untranslated region [UTR] of CYTH3; Pconditional = 1.48 × 10−7; OR, 0.44) and a risk-associated locus on chromosome 10 (rs2801847, an intronic variant within MPP7; Pconditional = 8.37 × 10−8; OR, 5.51). These loci have all been indirectly linked to bacterial type 3 secretion system (T3SS) activity, its components, and bacterial effectors delivered into host cells. Host genetic factors that may affect bacterial T3SS activity and are associated with the host response to Shigella-associated diarrhea may provide insight into vaccine and drug development efforts for Shigella-associated diarrheal disease.

scholarly journals Multi-trait genome-wide association study identifies new loci associated with optic disc parameters

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Pieter W. M. Bonnemaijer ◽  
◽  
Elisabeth M. van Leeuwen ◽  
Adriana I. Iglesias ◽  
Puya Gharahkhani ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Open Angle Glaucoma ◽  
Association Studies ◽  
Corneal Thickness ◽  
Genome Wide Association ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Trait Analysis ◽  
Genome Wide

AbstractA new avenue of mining published genome-wide association studies includes the joint analysis of related traits. The power of this approach depends on the genetic correlation of traits, which reflects the number of pleiotropic loci, i.e. genetic loci influencing multiple traits. Here, we applied new meta-analyses of optic nerve head (ONH) related traits implicated in primary open-angle glaucoma (POAG); intraocular pressure and central corneal thickness using Haplotype reference consortium imputations. We performed a multi-trait analysis of ONH parameters cup area, disc area and vertical cup-disc ratio. We uncover new variants; rs11158547 in PPP1R36-PLEKHG3 and rs1028727 near SERPINE3 at genome-wide significance that replicate in independent Asian cohorts imputed to 1000 Genomes. At this point, validation of these variants in POAG cohorts is hampered by the high degree of heterogeneity. Our results show that multi-trait analysis is a valid approach to identify novel pleiotropic variants for ONH.

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies

2006 ◽  
Vol 38 (2) ◽  
pp. 209-213 ◽  
Author(s):  
Andrew D Skol ◽  
Laura J Scott ◽  
Gonçalo R Abecasis ◽  
Michael Boehnke
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Two Stage ◽  
Genome Wide

scholarly journals Bayesian copy number detection and association in large-scale studies

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Stephen Cristiano ◽  
David McKean ◽  
Jacob Carey ◽  
Paige Bracci ◽  
Paul Brennan ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Copy Number ◽  
Large Scale ◽  
Association Studies ◽  
Regulatory Elements ◽  
Bayesian Regression ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Batch Effects ◽  
Genome Wide

Abstract Background Germline copy number variants (CNVs) increase risk for many diseases, yet detection of CNVs and quantifying their contribution to disease risk in large-scale studies is challenging due to biological and technical sources of heterogeneity that vary across the genome within and between samples. Methods We developed an approach called CNPBayes to identify latent batch effects in genome-wide association studies involving copy number, to provide probabilistic estimates of integer copy number across the estimated batches, and to fully integrate the copy number uncertainty in the association model for disease. Results Applying a hidden Markov model (HMM) to identify CNVs in a large multi-site Pancreatic Cancer Case Control study (PanC4) of 7598 participants, we found CNV inference was highly sensitive to technical noise that varied appreciably among participants. Applying CNPBayes to this dataset, we found that the major sources of technical variation were linked to sample processing by the centralized laboratory and not the individual study sites. Modeling the latent batch effects at each CNV region hierarchically, we developed probabilistic estimates of copy number that were directly incorporated in a Bayesian regression model for pancreatic cancer risk. Candidate associations aided by this approach include deletions of 8q24 near regulatory elements of the tumor oncogene MYC and of Tumor Suppressor Candidate 3 (TUSC3). Conclusions Laboratory effects may not account for the major sources of technical variation in genome-wide association studies. This study provides a robust Bayesian inferential framework for identifying latent batch effects, estimating copy number, and evaluating the role of copy number in heritable diseases.

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