Pathway analysis by randomization incorporating structure-PARIS: an update

Citrullinated and MMP-degraded vimentin is associated with chronic pulmonary diseases and genetic variants in PADI3/PADI4 and CFH in postmenopausal women

Citrullinated vimentin has been linked to several chronic and autoimmune diseases, but how citrullinated vimentin is associated with disease prevalence and genetic variants in a clinical setting remains unknown. The aim of this study was to obtain a better understanding of the genetic variants and pathologies associated with citrullinated and MMP-degraded vimentin. Patient Registry data, serum samples and genotypes were collected for a total of 4369 Danish post-menopausal women enrolled in the Prospective Epidemiologic and Risk Factor study (PERF). Circulating citrullinated and MMP-degraded vimentin (VICM) was measured. Genome-wide association studies (GWAS) and phenome wide association studies (PheWAS) with levels of VICM were performed. High levels of VICM were significantly associated with the prevalence of chronic pulmonary diseases and death from respiratory and cardiovascular diseases (CVD). GWAS identified 33 single nucleotide polymorphisms (SNPs) with a significant association with VICM. These variants were in the peptidylarginine deiminase 3/4 (PADI3/PADI4) and Complement Factor H (CFH)/KCNT2 gene loci on chromosome 1. Serum levels of VICM, a marker of citrullinated and MMP-degraded vimentin, were associated with chronic pulmonary diseases and genetic variance in PADI3/PADI4 and CFH/ KCNT2. This points to the potential for VICM to be used as an activity marker of both citrullination and inflammation, identifying responders to targeted treatment and patients likely to experience disease progression.

Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration

Background

Age-related macular degeneration (AMD) is a progressive retinal disease contributing to blindness worldwide. Multiple estimates for AMD heritability (h²) exist; however, a substantial proportion of h² is not attributable to known genomic loci. The International AMD Genomics Consortium (IAMDGC) gathered the largest dataset of advanced AMD (ADV) cases and controls available and identified 34 loci containing 52 independent risk variants defining known AMD h². To better define AMD heterogeneity, we used Pathway Analysis by Randomization Incorporating Structure (PARIS) on the IAMDGC data and identified 8 statistical driver genes (SDGs), including 2 novel SDGs not discovered by the IAMDGC. We chose to further investigate these pathway-based risk genes and determine their contribution to ADV h², as well as the differential ADV subtype h².

Methods

We performed genomic-relatedness-based restricted maximum-likelihood (GREML) analyses on ADV, geographic atrophy (GA), and choroidal neovascularization (CNV) subtypes to investigate the h² of genotyped variants on the full DNA array chip, 34 risk loci (n = 2758 common variants), 52 variants from the IAMDGC 2016 GWAS, and the 8 SDGs, specifically the novel 2 SDGs, PPARA and PLCG2.

Results

Via GREML, full chip h² was 44.05% for ADV, 46.37% for GA, and 62.03% for CNV. The lead 52 variants’ h² (ADV: 14.52%, GA: 8.02%, CNV: 13.62%) and 34 loci h² (ADV: 13.73%, GA: 8.81%, CNV: 12.89%) indicate that known variants contribute ~ 14% to ADV h². SDG variants account for a small percentage of ADV, GA, and CNV heritability, but estimates based on the combination of SDGs and the 34 known loci are similar to those calculated for known loci alone. We identified modest epistatic interactions among variants in the 2 SDGs and the 52 IAMDGC variants, including modest interactions between variants in PPARA and PLCG2.

Conclusions

Pathway analyses, which leverage biological relationships among genes in a pathway, may be useful in identifying additional loci that contribute to the heritability of complex disorders in a non-additive manner. Heritability analyses of these loci, especially amongst disease subtypes, may provide clues to the importance of specific genes to the genetic architecture of AMD.

Pathway Analysis Integrating Genome-Wide and Functional Data Identifies PLCG2 as a Candidate Gene for Age-Related Macular Degeneration

Purpose

Age-related macular degeneration (AMD) is the worldwide leading cause of blindness among the elderly. Although genome-wide association studies (GWAS) have identified AMD risk variants, their roles in disease etiology are not well-characterized, and they only explain a portion of AMD heritability.

Methods

We performed pathway analyses using summary statistics from the International AMD Genomics Consortium's 2016 GWAS and multiple pathway databases to identify biological pathways wherein genetic association signals for AMD may be aggregating. We determined which genes contributed most to significant pathway signals across the databases. We characterized these genes by constructing protein-protein interaction networks and performing motif analysis.

Results

We determined that eight genes (C2, C3, LIPC, MICA, NOTCH4, PLCG2, PPARA, and RAD51B) "drive" the statistical signals observed across pathways curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Gene Ontology (GO) databases. We further refined our definition of statistical driver gene to identify PLCG2 as a candidate gene for AMD due to its significant gene-level signals (P < 0.0001) across KEGG, Reactome, GO, and NetPath pathways.

Conclusions

We performed pathway analyses on the largest available collection of advanced AMD cases and controls in the world. Eight genes strongly contributed to significant pathways from the three larger databases, and one gene (PLCG2) was central to significant pathways from all four databases. This is, to our knowledge, the first study to identify PLCG2 as a candidate gene for AMD based solely on genetic burden. Our findings reinforce the utility of integrating in silico genetic and biological pathway data to investigate the genetic architecture of AMD.

Strategies for Pathway Analysis Using GWAS and WGS Data

Single-allele study designs, commonly used in genome-wide association studies (GWAS) as well as the more recently developed whole genome sequencing (WGS) studies, are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p-values are declared statistically significant due to issues arising from multiple testing and type I errors. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, making it difficult to separate true associations from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at the overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios. © 2018 by John Wiley & Sons, Inc.

Testosterone Pathway Genetic Polymorphisms in Relation to Primary Open-Angle Glaucoma: An Analysis in Two Large Datasets

Purpose

Sex hormones may be associated with primary open-angle glaucoma (POAG), although the mechanisms are unclear. We previously observed that gene variants involved with estrogen metabolism were collectively associated with POAG in women but not men; here we assessed gene variants related to testosterone metabolism collectively and POAG risk.

Methods

We used two datasets: one from the United States (3853 cases and 33,480 controls) and another from Australia (1155 cases and 1992 controls). Both datasets contained densely called genotypes imputed to the 1000 Genomes reference panel. We used pathway- and gene-based approaches with Pathway Analysis by Randomization Incorporating Structure (PARIS) software to assess the overall association between a panel of single nucleotide polymorphisms (SNPs) in testosterone metabolism genes and POAG. In sex-stratified analyses, we evaluated POAG overall and POAG subtypes defined by maximum IOP (high-tension [HTG] or normal tension glaucoma [NTG]).

Results

In the US dataset, the SNP panel was not associated with POAG (permuted P = 0.77), although there was an association in the Australian sample (permuted P = 0.018). In both datasets, the SNP panel was associated with POAG in men (permuted P ≤ 0.033) and not women (permuted P ≥ 0.42), but in gene-based analyses, there was no consistency on the main genes responsible for these findings. In both datasets, the testosterone pathway association with HTG was significant (permuted P ≤ 0.011), but again, gene-based analyses showed no consistent driver gene associations.

Conclusions

Collectively, testosterone metabolism pathway SNPs were consistently associated with the high-tension subtype of POAG in two datasets.

GenToS: Use of Orthologous Gene Information to Prioritize Signals from Human GWAS

Genome-wide association studies (GWAS) evaluate associations between genetic variants and a trait or disease of interest free of prior biological hypotheses. GWAS require stringent correction for multiple testing, with genome-wide significance typically defined as association p-value <5*10-8. This study presents a new tool that uses external information about genes to prioritize SNP associations (GenToS). For a given list of candidate genes, GenToS calculates an appropriate statistical significance threshold and then searches for trait-associated variants in summary statistics from human GWAS. It thereby allows for identifying trait-associated genetic variants that do not meet genome-wide significance. The program additionally tests for enrichment of significant candidate gene associations in the human GWAS data compared to the number expected by chance. As proof of principle, this report used external information from a comprehensive resource of genetically manipulated and systematically phenotyped mice. Based on selected murine phenotypes for which human GWAS data for corresponding traits were publicly available, several candidate gene input lists were derived. Using GenToS for the investigation of candidate genes underlying murine skeletal phenotypes in data from a large human discovery GWAS meta-analysis of bone mineral density resulted in the identification of significantly associated variants in 29 genes. Index variants in 28 of these loci were subsequently replicated in an independent GWAS replication step, highlighting that they are true positive associations. One signal, COL11A1, has not been discovered through GWAS so far and represents a novel human candidate gene for altered bone mineral density. The number of observed genes that contained significant SNP associations in human GWAS based on murine candidate gene input lists was much greater than the number expected by chance across several complex human traits (enrichment p-value as low as 10-10). GenToS can be used with any candidate gene list, any GWAS summary file, runs on a desktop computer and is freely available.