Strategies for Pathway Analysis Using GWAS and WGS Data

White, Marquitta J.; Yaspan, Brian L.; Veatch, Olivia J.; Goddard, Pagé C.; Risse-Adams, Oona; Contreras, María G.

doi:10.1002/cphg.79

Cited by 39 publications

(33 citation statements)

References 54 publications

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“…However, pinpointing the causative variation is more di cult. To reveal the mechanism through which a mutation site affects a phenotype and to perform subsequent functional research, GWAS joint analysis on multiple genomic levels can be used and biological pathway analysis can be applied to detect the superposition of multiple minor genes by examining genes involved in the same biological pathway, thus enabling deeper mining of GWAS data [10][11][12][13]. With the development of genome sequencing technology and the continuous improvement of statistical methods, GWAS is expected to be more e ciently applied to gene identi cation for important traits in livestock and poultry and to play an increasingly important role in animal breeding.…”

Section: Introductionmentioning

confidence: 99%

Pathway-based analysis enables deeper mining of GWAS data on H/L in chickens

Wang¹,

Zhu²,

Wen³

et al. 2020

Preprint

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Background Heterophils are refers to white blood cells with phagocytosis and killing functions in the avian immune system, These cells identify and kill pathogenic microorganisms through precise regulatory mechanisms. As a simple index, the heterophil/lymphocyte ratio (H/L) in the blood reflects the immune system status of chickens. H/L is a complex trait affected by multiple genetic loci, but single nucleotide polymorphisms (SNPs) significantly associated with traits can usually explain only part of the phenotypic variation. Combining a genome-wide association study (GWAS) with pathway analysis can improve understanding of the biological pathways affecting traits. Our objective was to conduct a SNP- and pathway-based analysis to identify possible biological mechanisms involved in H/L traits. Methods GWAS for H/L was performed in 1,317 Cobb broilers to identify significant single nucleotide polymorphisms (SNPs) associated with H/L. Eight SNPs (P< 1/8,068) reached a significant level of association. The following results were obtained through a series of analyses, including pathway-based association analysis and analysis of the proportion of phenotypic variance explained by SNPs. Results On the basis of GWAS analysis results, one associated SNP (5% genome-wide significance (6.80E-6,0.05/8,068)) on GGA 1(chicken chromosome 1) and seven suggestively associated SNPs with a trend toward significance(1.24E-4, 1/8,068) on GGAs 1, 7, 13 and 19 were detected. The significant SNP on GGA 19 was in Complement C1q Binding Protein (C1QBP). The wild-type and mutant individuals showed significant differences in H/L at five loci (P< 0.05). According to the results of pathway-based analysis, nine associated pathways (P < 0.05) were identified, including small cell lung cancer, proteoglycans in cancer, sulfur relay system, pathways in cancer, gastric acid secretion and purine metabolism. By combining GWAS with pathway analysis, we found that all SNPs after QC explained 12.4% of the phenotypic variation in H/L and 53 SNPs associated with H/L explained as much as 9.7% of the phenotypic variation in H/L. Conclusions Our findings contribute to understanding of the genetic regulation of H/L and provide theoretical support.

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Section: Introductionmentioning

confidence: 99%

Pathway-based analysis enables deeper mining of GWAS data on H/L in chickens

Wang¹,

Zhu²,

Wen³

et al. 2020

Preprint

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“…Hence, our current pathway analysis findings are consistent with those using 813 BMD risk genes (data now shown). However, selection of different thresholds may have different findings, as described in recent studies (Yoon et al, 2018;Ierodiakonou et al, 2019;White et al, 2019). In summary, two studies reported regulation-of-autophagy and other eight significant pathways (Zhang et al, 2010;Lee et al, 2012).…”

Section: Discussionmentioning

confidence: 84%

“…In future, we will verify our findings using other gene and pathway based methods. Third, we selected the top 5% significant signals from the genebased test for following pathway analysis, as did in recent studies (Yoon et al, 2018;Ierodiakonou et al, 2019;White et al, 2019). Using this strategy, we selected 700 BMD risk genes with P < 0.04239.…”

Section: Discussionmentioning

confidence: 99%

Analyzing Genome-Wide Association Study Dataset Highlights Immune Pathways in Lip Bone Mineral Density

et al. 2020

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Osteoporosis is a common complex human disease. Until now, large-scale genomewide association studies (GWAS) using single genetic variant have reported some novel osteoporosis susceptibility variants. However, these risk variants only explain a small proportion of osteoporosis genetic risk, and most genetic risk is largely unknown. Interestingly, the pathway analysis method has been used in investigation of osteoporosis mechanisms and reported some novel pathways. Until now, it remains unclear whether there are other risk pathways involved in BMD. Here, we selected a lip BMD GWAS with 301,019 SNPs in 5,858 Europeans, and conducted a gene-based analysis (SET SCREEN TEST) and a pathway-based analysis (WebGestalt). On the gene level, BMD susceptibility genes reported by previous GWAS were identified to be the top 10 significant signals. On the pathway level, we identified 27 significant KEGG pathways. Three immune pathways including T cell receptor signaling pathway (hsa04660), complement and coagulation cascades (hsa04610), and intestinal immune network for IgA production (hsa04672) are ranked the top three significant signals. Evidence from the PubMed and Google Scholar databases further supports our findings. In summary, our findings provide complementary information to these nine risk pathways.

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“…Pathway analysis is a common post-GWAS approach that groups loci into meaningful groups, such as mapping SNPS to pathways(13,14). Analyzing aggregated loci can improve both the interpretability of GWAS results and improve power to detect associations(13,14). However, post-GWAS pathway analysis may not surmount the high stringency of convergence based bGWAS approaches.…”

Section: Introductionmentioning

confidence: 99%

hogwash: Three Methods for Genome-Wide Association Studies in Bacteria

Saund

Snitkin

2020

Preprint

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17Bacterial genome-wide association studies (bGWAS) capture associations between genomic 18 variation and phenotypic variation. Convergence based bGWAS methods identify genomic 19 mutations that arise more often in the presence of phenotypic variation than is expected by 20 chance. This work introduces hogwash, an open source R package that implements three 21 algorithms for convergence based bGWAS. Hogwash additionally contains a novel grouping tool 22 to perform gene-or pathway-analysis to improve power and increase convergence detection for 23 related but weakly penetrant genotypes. To identify optimal use cases we applied hogwash to 24 data simulated with a variety of phylogenetic signals and convergence distributions. These 25 simulated data are publicly available and contain the relevant metadata regarding convergence 26 and phylogenetic signal for each phenotype and genotype. Hogwash is available for download 27 from GitHub. 28 29 DATA SUMMARY 30 1. hogwash is available from GitHub under the MIT license 31 (https://github.com/katiesaund/hogwash) and can be installed using the R command 32 devtools::install_github("katiesaund/hogwash") 33 2. The simulated data used in this manuscript and the code to generate it are available 34 from GitHub 35 (https://github.com/katiesaund/simulate_data_for_convergence_based_bGWAS) 36 37 IMPACT STATEMENT 38We introduce hogwash, an R package with three methods for bacterial genome-wide 39 association studies. There are two methods for handling binary phenotypes, including an 40 implementation of PhyC(1), as well as one method for handling continuous phenotypes. We 41 formulate two novel indices quantifying the relationship between phenotype convergence and 42 genotype convergence on a phylogenetic tree, one for binary phenotypes and one for 43 continuous phenotypes. These indices shape an intuitive understanding for the ability of 44 hogwash to detect significant intersections of phenotype convergence and genotype 45 convergence and how to interpret hogwash outputs.46 47 INTRODUCTION 48 Bacterial Genome-Wide Association Studies 49 Bacterial genome-wide association studies (bGWAS) infer statistical associations between 50 genotypes and phenotypes. Seminal bGWAS papers identified novel variants associated with 51 antibiotic resistance in M. tuberculosis and host specificity in Campylobacter(1,2). Since then, 52there have been numerous applications of bGWAS that have further highlighted the potential of 53 this approach to identify genetic pathways underlying phenotypic variation and provide insights 54 into the evolution of phenotypes of interest. Association studies can use various genetic data 55 types including single nucleotide polymorphisms (SNPs), k-mers, copy number variants, 56 accessory genes, insertions, and deletions. To improve the power and interpretability of bGWAS 57 inclusion criteria or weighting can be applied to these variants based on predicted functional 58 impact, membership in pathways of interest, or other user preferences (3,4). Differences 59 between ...

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Strategies for Pathway Analysis Using GWAS and WGS Data

Cited by 39 publications

References 54 publications

Pathway-based analysis enables deeper mining of GWAS data on H/L in chickens

Pathway-based analysis enables deeper mining of GWAS data on H/L in chickens

Analyzing Genome-Wide Association Study Dataset Highlights Immune Pathways in Lip Bone Mineral Density

hogwash: Three Methods for Genome-Wide Association Studies in Bacteria

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