Strategies for Pathway Analysis Using GWAS and WGS Data

From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies?

Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.

Building causal knowledge in behavior genetics

Behavior genetics is a controversial science. For decades, scholars have sought to understand the role of heredity in human behavior and life-course outcomes. Recently, technological advances and the rapid expansion of genomic databases have facilitated the discovery of genes associated with human phenotypes such as educational attainment and substance use disorders. To maximize the potential of this flourishing science, and to minimize potential harms, careful analysis of what it would mean for genes to be causes of human behavior is needed. In this paper, we advance a framework for identifying instances of genetic causes, interpreting those causal relationships, and applying them to advance causal knowledge more generally in the social sciences. Central to thinking about genes as causes is counterfactual reasoning, the cornerstone of causal thinking in statistics, medicine, and philosophy. We argue that within-family genetic effects represent the product of a counterfactual comparison in the same way as average treatment effects (ATEs) from randomized controlled trials (RCTs). Both ATEs from RCTs and within-family genetic effects are shallow causes: They operate within intricate causal systems (non-unitary), produce heterogeneous effects across individuals (non-uniform), and are not mechanistically informative (non-explanatory). Despite these limitations, shallow causal knowledge can be used to improve understanding of the etiology of human behavior and to explore sources of heterogeneity and fade-out in treatment effects.

Genomic architecture of phenotypic extremes in a wild cervid

Identifying the genes underlying fitness-related traits such as body size and male ornamentation can provide tools for conservation and management and are often subject to various selective pressures. Here we performed high-depth whole genome re-sequencing of pools of individuals representing the phenotypic extremes for antler and body size in white-tailed deer (Odocoileus virginianus). Samples were selected from a tissue repository containing phenotypic data for 4,466 male white-tailed deer from Anticosti Island, Quebec, with four pools representing the extreme phenotypes for antler and body size after controlling for age. Our results revealed a largely homogenous population but detected highly divergent windows between pools for both traits, with the mean allele frequency difference of 14% for and 13% for antler and body SNPs in outlier windows, respectively. Genes in outlier antler windows were enriched for pathways associated with cell death and protein metabolism and some of the most differentiated windows included genes associated with oncogenic pathways and reproduction, processes consistent with antler evolution and growth. Genes associated with body size were more nuanced, suggestive of a highly complex trait. Overall, this study revealed the complex genomic make-up of both antler morphology and body size in free-ranging white-tailed deer and identified target loci for additional analyses.

From GWAS to drug screening: repurposing antipsychotics for glioblastoma

BACKGROUND

Glioblastoma is currently an incurable cancer. Genome-wide association studies have demonstrated that 41 genetic variants are associated with glioblastoma and may provide an option for drug development.

METHODS

We investigated FDA-approved antipsychotics for their potential treatment of glioblastoma based on genome-wide association studies data using a 'pathway/gene-set analysis' approach.

RESULTS

The in-silico screening led to the discovery of 12 candidate drugs. DepMap portal revealed that 42 glioma cell lines show higher sensitivities to 12 candidate drugs than to Temozolomide, the current standard treatment for glioblastoma.

CONCLUSION

In particular, cell lines showed significantly higher sensitivities to Norcyclobenzaprine and Protriptyline which were predicted to bind targets to disrupt a certain molecular function such as DNA repair, response to hormones, or DNA-templated transcription, and may lead to an effect on survival-related pathways including cell cycle arrest, response to ER stress, glucose transport, and regulation of autophagy. However, it is recommended that their mechanism of action and efficacy are further determined.

Genetic dissection of complex traits using hierarchical biological knowledge

Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use an ontology-guided ML system to map single nucleotide variants (SNVs) focusing on 6 classic phenotypic traits in natural yeast populations. The 29 identified loci are largely novel and account for ~17% of the phenotypic variance, versus <3% for standard genetic analysis. Representative results show that sensitivity to hydroxyurea is linked to SNVs in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. This work demonstrates a knowledge-based approach to amplifying and interpreting signals in population genetic studies.

Genome-wide association studies (GWAS) have identified many important loci for common diseases and other traits. However, the loci identified by these studies are almost always many steps away from an understanding of underlying biological mechanisms. Here we develop an approach using hierarchical biological knowledge to identify genes and pathways responsible for phenotypic traits. Variants identified by the new method could explain a substantially greater fraction of heritability than previously reported. Moreover, we identified mechanistic pathways by which each causal variant affects cellular function. For example, we find that sensitivity to hydroxyurea is tied to genetic variants in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. The new approach is a potentially transformative concept for understanding the genetic drivers of phenotypic variance, with potential applications in understanding traits in biomedicine and agriculture.

Hogwash: three methods for genome-wide association studies in bacteria

Bacterial genome-wide association studies (bGWAS) capture associations between genomic variation and phenotypic variation. Convergence-based bGWAS methods identify genomic mutations that occur independently multiple times on the phylogenetic tree in the presence of phenotypic variation more often than is expected by chance. This work introduces hogwash, an open source R package that implements three algorithms for convergence-based bGWAS. Hogwash additionally contains two burden testing approaches to perform gene or pathway analysis to improve power and increase convergence detection for related but weakly penetrant genotypes. To identify optimal use cases, we applied hogwash to data simulated with a variety of phylogenetic signals and convergence distributions. These simulated data are publicly available and contain the relevant metadata regarding convergence and phylogenetic signal for each phenotype and genotype. Hogwash is available for download from GitHub.

Machine learning based disease prediction from genotype data

Using results from genome-wide association studies for understanding complex traits is a current challenge. Here we review how genotype data can be used with different machine learning (ML) methods to predict phenotype occurrence and severity from genotype data. We discuss common feature encoding schemes and how studies handle the often small number of samples compared to the huge number of variants. We compare which ML methods are being applied, including recent results using deep neural networks. Further, we review the application of methods for feature explanation and interpretation.

Pathway-based analysis of anthocyanin diversity in diploid potato

Anthocyanin biosynthesis is one of the most studied pathways in plants due to the important ecological role played by these compounds and the potential health benefits of anthocyanin consumption. Given the interest in identifying new genetic factors underlying anthocyanin content we studied a diverse collection of diploid potatoes by combining a genome-wide association study and pathway-based analyses. By using an expanded SNP dataset, we identified candidate genes that had not been associated with anthocyanin variation in potatoes, namely a Myb transcription factor, a Leucoanthocyanidin dioxygenase gene and a vacuolar membrane protein. Importantly, a genomic region in chromosome 10 harbored the SNPs with strongest associations with anthocyanin content in GWAS. Some of these SNPs were associated with multiple anthocyanin compounds and therefore could underline the existence of pleiotropic genes or anthocyanin biosynthetic clusters. We identified multiple anthocyanin homologs in this genomic region, including four transcription factors and five enzymes that could be governing anthocyanin variation. For instance, a SNP linked to the phenylalanine ammonia-lyase gene, encoding the first enzyme in the phenylpropanoid biosynthetic pathway, was associated with all of the five anthocyanins measured. Finally, we combined a pathway analysis and GWAS of other agronomic traits to identify pathways related to anthocyanin biosynthesis in potatoes. We found that methionine metabolism and the production of sugars and hydroxycinnamic acids are genetically correlated to anthocyanin biosynthesis. The results contribute to the understanding of anthocyanins regulation in potatoes and can be used in future breeding programs focused on nutraceutical food.

Common Variation in Cytoskeletal Genes is Associated with Conotruncal Heart Defects

There is strong evidence for a genetic contribution to non-syndromic congenital heart defects (CHDs). However, exome- and genome-wide studies conducted at the variant and gene-level have identified few genome-wide significant CHD-related genes. Gene-set analyses are a useful complement to such studies and candidate gene-set analyses of rare variants have provided insight into the genetics of CHDs. However, similar analyses have not been conducted using data on common genetic variants. Consequently, we conducted common variant analyses of 15 CHD candidate gene-sets, using data from two common types of CHDs: conotruncal heart defects (1431 cases) and left ventricular outflow tract defects (509 cases). After Bonferroni correction for evaluation of multiple gene-sets, the cytoskeletal gene-set was significantly associated with conotruncal heart defects (β_S = 0.09; 95% confidence interval (CI) 0.03–0.15). This association was stronger when analyses were restricted to the sub-set of cytoskeletal genes that have been observed to harbor rare damaging genotypes in at least two CHD cases (β_S = 0.32, 95% CI 0.08–0.56). These findings add to the evidence linking cytoskeletal genes to CHDs and suggest that, for cytoskeletal genes, common variation may contribute to the risk of CHDs.

Attitudes among South African university staff and students towards disclosing secondary genetic findings

The present study represents an initial step in understanding diverse academic perspectives on the disclosure of secondary findings (SFs) from genetic research conducted in Africa. Using an online survey completed by 674 university students and academic staff in South Africa, we elicited attitudes towards the return of SFs. Latent class analysis (LCA) was performed to classify sub-groups of participants according to their overall attitudes to returning SFs. We did not find substantial differences in attitudes towards the return of findings between staff and students. Overall, respondents were in favour of the return of SFs in genetics research, depending on the type. The majority of survey respondents (80%) indicated that research participants should be given the option of deciding whether to have genetic SFs returned. LCA revealed that the largest group (53%) comprised individuals with more favourable attitudes to the return of SFs in genetics research. Those with less favourable attitudes comprised only 4% of the sample. This study provides important insights that may, together with further empirical evidence, inform the development of research guidelines and policy to assist healthcare professionals and researchers.

Functional genomics, genetic risk profiling and cell phenotypes in neurodegenerative disease

Human genetics provides unbiased insights into the causes of human disease, which can be used to create a foundation for effective ways to more accurately diagnose patients, stratify patients for more successful clinical trials, discover and develop new therapies, and ultimately help patients choose the safest and most promising therapeutic option based on their risk profile. But the process for translating basic observations from human genetics studies into pathogenic disease mechanisms and treatments is laborious and complex, and this challenge has particularly slowed the development of interventions for neurodegenerative disease. In this review, we discuss the many steps in the process, the important considerations at each stage, and some of the latest tools and technologies that are available to help investigators translate insights from human genetics into diagnostic and therapeutic strategies that will lead to the sort of advances in clinical care that make a difference for patients.

Genome-Wide Association Study and Pathway Analysis for Heterophil/Lymphocyte (H/L) Ratio in Chicken

Disease control and prevention have been critical factors in the dramatic growth of the poultry industry. Disease resistance in chickens can be improved through genetic selection for immunocompetence. The heterophil/lymphocyte ratio (H/L) in the blood reflects the immune system status of chickens. Our objective was to conduct a genome-wide association study (GWAS) and pathway analysis to identify possible biological mechanisms involved in H/L traits. In this study, GWAS for H/L was performed in 1317 Cobb broilers to identify significant single-nucleotide polymorphisms (SNPs) associated with H/L. Eight SNPs (p < 1/8068) reached a significant level of association. The significant SNP on GGA 19 (chicken chromosome 19) was in the gene for complement C1q binding protein (C1QBP). The wild-type and mutant individuals showed significant differences in H/L at five identified SNPs (p < 0.05). According to the results of pathway analysis, nine associated pathways (p < 0.05) were identified. By combining GWAS with pathway analysis, we found that all SNPs after QC explained 12.4% of the phenotypic variation in H/L, and 52 SNPs associated with H/L explained as much as 9.7% of the phenotypic variation in H/L. Our findings contribute to understanding of the genetic regulation of H/L and provide theoretical support.

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.

Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning

The genetic effect explains the causality from genetic mutations to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a deep association kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects where existing approaches fail. When applied to four real-world GWAS datasets including cancers and schizophrenia, our DAK discovered potential casual pathways, including the association between dilated cardiomyopathy pathway and schizophrenia.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

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

Osteoporosis is a common complex human disease. Until now, large-scale genome-wide 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.

Changes in the Composition of the Gut Microbiota and the Blood Transcriptome in Preterm Infants at Less than 29 Weeks Gestation Diagnosed with Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a common chronic lung condition in preterm infants that results in abnormal lung development and leads to considerable morbidity and mortality, making BPD one of the most common complications of preterm birth. We employed RNA sequencing and 16S rRNA gene sequencing to profile gene expression in blood and the composition of the fecal microbiota in infants born at <29 weeks gestational age and diagnosed with BPD in comparison to those of preterm infants that were not diagnosed with BPD. 16S rRNA gene sequencing, performed longitudinally on 255 fecal samples collected from 50 infants in the first months of life, identified significant differences in the relative levels of abundance of Klebsiella, Salmonella, Escherichia/Shigella, and Bifidobacterium in the BPD infants in a manner that was birth mode dependent. Transcriptome sequencing (RNA-Seq) analysis revealed that more than 400 genes were upregulated in infants with BPD. Genes upregulated in BPD infants were significantly enriched for functions related to red blood cell development and oxygen transport, while several immune-related pathways were downregulated. We also identified a gene expression signature consistent with an enrichment of immunosuppressive CD71+ early erythroid cells in infants with BPD. Intriguingly, genes that were correlated in their expression with the relative abundances of specific taxa in the microbiota were significantly enriched for roles in the immune system, suggesting that changes in the microbiota might influence immune gene expression systemically.IMPORTANCE Bronchopulmonary dysplasia (BPD) is a serious inflammatory condition of the lung and is the most common complication associated with preterm birth. A large body of evidence now suggests that the gut microbiota can influence immunity and inflammation systemically; however, the role of the gut microbiota in BPD has not been evaluated to date. Here, we report that there are significant differences in the gut microbiota of infants born at <29 weeks gestation and subsequently diagnosed with BPD, which are particularly pronounced when infants are stratified by birth mode. We also show that erythroid and immune gene expression levels are significantly altered in BPD infants. Interestingly, we identified an association between the composition of the microbiota and immune gene expression in blood in early life. Together, these findings suggest that the composition of the microbiota may influence the risk of developing BPD and, more generally, may shape systemic immune gene expression.

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.