A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations

A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations

As more multi-ancestry GWAS summary data become available, we have developed a comprehensive trans-ancestry pathway analysis framework that effectively utilizes this diverse genetic information. Within this framework, we evaluated various strategies for integrating genetic data at different levels-SNP, gene, and pathway-from multiple ancestry groups. Through extensive simulation studies, we have identified robust strategies that demonstrate superior performance across diverse scenarios. Applying these methods, we analyzed 6,970 pathways for their association with schizophrenia, incorporating data from African, East Asian, and European populations. Our analysis identified over 200 pathways significantly associated with schizophrenia, even after excluding genes near genome-wide significant loci. This approach substantially enhances detection efficiency compared to traditional single-ancestry pathway analysis and the conventional approach that amalgamates single-ancestry pathway analysis results across different ancestry groups. Our framework provides a flexible and effective tool for leveraging the expanding pool of multi-ancestry GWAS summary data, thereby improving our ability to identify biologically relevant pathways that contribute to disease susceptibility.

Genetic drivers and cellular selection of female mosaic X chromosome loss

Mosaic loss of the X chromosome (mLOX) is the most common clonal somatic alteration in leukocytes of female individuals1,2, but little is known about its genetic determinants or phenotypic consequences. Here, to address this, we used data from 883,574 female participants across 8 biobanks; 12% of participants exhibited detectable mLOX in approximately 2% of leukocytes. Female participants with mLOX had an increased risk of myeloid and lymphoid leukaemias. Genetic analyses identified 56 common variants associated with mLOX, implicating genes with roles in chromosomal missegregation, cancer predisposition and autoimmune diseases. Exome-sequence analyses identified rare missense variants in FBXO10 that confer a twofold increased risk of mLOX. Only a small fraction of associations was shared with mosaic Y chromosome loss, suggesting that distinct biological processes drive formation and clonal expansion of sex chromosome missegregation. Allelic shift analyses identified X chromosome alleles that are preferentially retained in mLOX, demonstrating variation at many loci under cellular selection. A polygenic score including 44 allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Our results support a model in which germline variants predispose female individuals to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of clonal expansion.

Unraveling the relationship between histone methylation and nonalcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) poses a significant health challenge in modern societies due to shifts in lifestyle and dietary habits. Its complexity stems from genetic predisposition, environmental influences, and metabolic factors. Epigenetic processes govern various cellular functions such as transcription, chromatin structure, and cell division. In NAFLD, these epigenetic tendencies, especially the process of histone methylation, are intricately intertwined with fat accumulation in the liver. Histone methylation is regulated by different enzymes like methyltransferases and demethylases and influences the expression of genes related to adipogenesis. While early-stage NAFLD is reversible, its progression to severe stages becomes almost irreversible. Therefore, early detection and intervention in NAFLD are crucial, and understanding the precise role of histone methylation in the early stages of NAFLD could be vital in halting or potentially reversing the progression of this disease.

Shared and distinct genetic etiologies for different types of clonal hematopoiesis

Clonal hematopoiesis (CH)-age-related expansion of mutated hematopoietic clones-can differ in frequency and cellular fitness by CH type (e.g., mutations in driver genes (CHIP), gains/losses and copy-neutral loss of chromosomal segments (mCAs), and loss of sex chromosomes). Co-occurring CH raises questions as to their origin, selection, and impact. We integrate sequence and genotype array data in up to 482,378 UK Biobank participants to demonstrate shared genetic architecture across CH types. Our analysis suggests a cellular evolutionary trade-off between different types of CH, with LOY occurring at lower rates in individuals carrying mutations in established CHIP genes. We observed co-occurrence of CHIP and mCAs with overlap at TET2, DNMT3A, and JAK2, in which CHIP precedes mCA acquisition. Furthermore, individuals carrying overlapping CH had high risk of future lymphoid and myeloid malignancies. Finally, we leverage shared genetic architecture of CH traits to identify 15 novel loci associated with leukemia risk.

A multivariate genome-wide association study of psycho-cardiometabolic multimorbidity

Coronary artery disease (CAD), type 2 diabetes (T2D) and depression are among the leading causes of chronic morbidity and mortality worldwide. Epidemiological studies indicate a substantial degree of multimorbidity, which may be explained by shared genetic influences. However, research exploring the presence of pleiotropic variants and genes common to CAD, T2D and depression is lacking. The present study aimed to identify genetic variants with effects on cross-trait liability to psycho-cardiometabolic diseases. We used genomic structural equation modelling to perform a multivariate genome-wide association study of multimorbidity (Neffective = 562,507), using summary statistics from univariate genome-wide association studies for CAD, T2D and major depression. CAD was moderately genetically correlated with T2D (rg = 0.39, P = 2e-34) and weakly correlated with depression (rg = 0.13, P = 3e-6). Depression was weakly correlated with T2D (rg = 0.15, P = 4e-15). The latent multimorbidity factor explained the largest proportion of variance in T2D (45%), followed by CAD (35%) and depression (5%). We identified 11 independent SNPs associated with multimorbidity and 18 putative multimorbidity-associated genes. We observed enrichment in immune and inflammatory pathways. A greater polygenic risk score for multimorbidity in the UK Biobank (N = 306,734) was associated with the co-occurrence of CAD, T2D and depression (OR per standard deviation = 1.91, 95% CI = 1.74-2.10, relative to the healthy group), validating this latent multimorbidity factor. Mendelian randomization analyses suggested potentially causal effects of BMI, body fat percentage, LDL cholesterol, total cholesterol, fasting insulin, income, insomnia, and childhood maltreatment. These findings advance our understanding of multimorbidity suggesting common genetic pathways.

Integrative analysis of individual-level data and high-dimensional summary statistics

MOTIVATION

Researchers usually conduct statistical analyses based on models built on raw data collected from individual participants (individual-level data). There is a growing interest in enhancing inference efficiency by incorporating aggregated summary information from other sources, such as summary statistics on genetic markers' marginal associations with a given trait generated from genome-wide association studies. However, combining high-dimensional summary data with individual-level data using existing integrative procedures can be challenging due to various numeric issues in optimizing an objective function over a large number of unknown parameters.

RESULTS

We develop a procedure to improve the fitting of a targeted statistical model by leveraging external summary data for more efficient statistical inference (both effect estimation and hypothesis testing). To make this procedure scalable to high-dimensional summary data, we propose a divide-and-conquer strategy by breaking the task into easier parallel jobs, each fitting the targeted model by integrating the individual-level data with a small proportion of summary data. We obtain the final estimates of model parameters by pooling results from multiple fitted models through the minimum distance estimation procedure. We improve the procedure for a general class of additive models commonly encountered in genetic studies. We further expand these two approaches to integrate individual-level and high-dimensional summary data from different study populations. We demonstrate the advantage of the proposed methods through simulations and an application to the study of the effect on pancreatic cancer risk by the polygenic risk score defined by BMI-associated genetic markers.

AVAILABILITY AND IMPLEMENTATION

R package is available at https://github.com/fushengstat/MetaGIM.

Population analyses of mosaic X chromosome loss identify genetic drivers and widespread signatures of cellular selection

Mosaic loss of the X chromosome (mLOX) is the most commonly occurring clonal somatic alteration detected in the leukocytes of women, yet little is known about its genetic determinants or phenotypic consequences. To address this, we estimated mLOX in >900,000 women across eight biobanks, identifying 10% of women with detectable X loss in approximately 2% of their leukocytes. Out of 1,253 diseases examined, women with mLOX had an elevated risk of myeloid and lymphoid leukemias and pneumonia. Genetic analyses identified 49 common variants influencing mLOX, implicating genes with established roles in chromosomal missegregation, cancer predisposition, and autoimmune diseases. Complementary exome-sequence analyses identified rare missense variants in FBXO10 which confer a two-fold increased risk of mLOX. A small fraction of these associations were shared with mosaic Y chromosome loss in men, suggesting different biological processes drive the formation and clonal expansion of sex chromosome missegregation events. Allelic shift analyses identified alleles on the X chromosome which are preferentially retained, demonstrating that variation at many loci across the X chromosome is under cellular selection. A novel polygenic score including 44 independent X chromosome allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Collectively our results support a model where germline variants predispose women to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of subsequent clonal expansion.

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.

Hepcidin-regulating iron metabolism genes and pancreatic ductal adenocarcinoma: a pathway analysis of genome-wide association studies

BACKGROUND

Epidemiological studies have suggested positive associations for iron and red meat intake with risk of pancreatic ductal adenocarcinoma (PDAC). Inherited pathogenic variants in genes involved in the hepcidin-regulating iron metabolism pathway are known to cause iron overload and hemochromatosis.

OBJECTIVES

The objective of this study was to determine whether common genetic variation in the hepcidin-regulating iron metabolism pathway is associated with PDAC.

METHODS

We conducted a pathway analysis of the hepcidin-regulating genes using single nucleotide polymorphism (SNP) summary statistics generated from 4 genome-wide association studies in 2 large consortium studies using the summary data-based adaptive rank truncated product method. Our population consisted of 9253 PDAC cases and 12,525 controls of European descent. Our analysis included 11 hepcidin-regulating genes [bone morphogenetic protein 2 (BMP2), bone morphogenetic protein 6 (BMP6), ferritin heavy chain 1 (FTH1), ferritin light chain (FTL), hepcidin (HAMP), homeostatic iron regulator (HFE), hemojuvelin (HJV), nuclear factor erythroid 2-related factor 2 (NRF2), ferroportin 1 (SLC40A1), transferrin receptor 1 (TFR1), and transferrin receptor 2 (TFR2)] and their surrounding genomic regions (±20 kb) for a total of 412 SNPs.

RESULTS

The hepcidin-regulating gene pathway was significantly associated with PDAC (P = 0.002), with the HJV, TFR2, TFR1, BMP6, and HAMP genes contributing the most to the association.

CONCLUSIONS

Our results support that genetic susceptibility related to the hepcidin-regulating gene pathway is associated with PDAC risk and suggest a potential role of iron metabolism in pancreatic carcinogenesis. Further studies are needed to evaluate effect modification by intake of iron-rich foods on this association.

A powerful subset-based method identifies gene set associations and improves interpretation in UK Biobank

Tests of association between a phenotype and a set of genes in a biological pathway can provide insights into the genetic architecture of complex phenotypes beyond those obtained from single-variant or single-gene association analysis. However, most existing gene set tests have limited power to detect gene set-phenotype association when a small fraction of the genes are associated with the phenotype and cannot identify the potentially "active" genes that might drive a gene set-based association. To address these issues, we have developed Gene set analysis Association Using Sparse Signals (GAUSS), a method for gene set association analysis that requires only GWAS summary statistics. For each significantly associated gene set, GAUSS identifies the subset of genes that have the maximal evidence of association and can best account for the gene set association. Using pre-computed correlation structure among test statistics from a reference panel, our p value calculation is substantially faster than other permutation- or simulation-based approaches. In simulations with varying proportions of causal genes, we find that GAUSS effectively controls type 1 error rate and has greater power than several existing methods, particularly when a small proportion of genes account for the gene set signal. Using GAUSS, we analyzed UK Biobank GWAS summary statistics for 10,679 gene sets and 1,403 binary phenotypes. We found that GAUSS is scalable and identified 13,466 phenotype and gene set association pairs. Within these gene sets, we identify an average of 17.2 (max = 405) genes that underlie these gene set associations.

Identification of genetic variants in m6A modification genes associated with pancreatic cancer risk in the Chinese population

N6-Methyladenosine (m⁶A) is the most prevalent modification of RNA in eukaryotes, and is associated with many cellular processes and even the development of cancers. We hypothesized that single-nucleotide polymorphisms (SNPs) in m⁶A modification genes, including its "writers", "erasers" and "readers", might affect the m⁶A functions and associate with the susceptibility to pancreatic ductal adenocarcinoma (PDAC). We first conducted a two-stage case-control study in Chinese population to interrogate all SNPs in 22 m⁶A modification genes. In the discovery stage, a total of 2735 SNPs were genotyped in 980 patients and 1991 controls. Then, the promising SNP was replicated in another independent population consisting of 858 cases and 2084 controls. As a result, we found the rs7495 in 3'UTR of hnRNPC was significantly associated with increased risk of PDAC in both stages (combined odds ratio = 1.22, 95% confidence interval = 1.12-1.32, P = 2.39 × 10^-6). To further reveal the biological function of rs7495 and hnRNPC, we performed a series of biochemical experiments. Luciferase reporter assays indicated that rs7495G allele promoted hnRNPC expression through disrupting a putative binding site for has-miR-183-3p. Cell viability assay demonstrated that knockdown of hnRNPC suppressed the proliferation of PDAC cells. RNA-seq analysis suggested that as an m⁶A "reader", hnRNPC played an important role in RNA biological processes. In conclusion, our findings elucidated that rs7495G could confer higher risk of PDAC via promoting the expression of hnRNPC through a miRNA-mediated manner. These results provided a novel insight into the critical role of m⁶A modification in tumorigenesis.

Understanding Dietary Intervention-Mediated Epigenetic Modifications in Metabolic Diseases

The global prevalence of metabolic disorders, such as obesity, diabetes and fatty liver disease, is dramatically increasing. Both genetic and environmental factors are well-known contributors to the development of these diseases and therefore, the study of epigenetics can provide additional mechanistic insight. Dietary interventions, including caloric restriction, intermittent fasting or time-restricted feeding, have shown promising improvements in patients' overall metabolic profiles (i.e., reduced body weight, improved glucose homeostasis), and an increasing number of studies have associated these beneficial effects with epigenetic alterations. In this article, we review epigenetic changes involved in both metabolic diseases and dietary interventions in primary metabolic tissues (i.e., adipose, liver, and pancreas) in hopes of elucidating potential biomarkers and therapeutic targets for disease prevention and treatment.

Pathway Analysis of Renal Cell Carcinoma Genome-Wide Association Studies Identifies Novel Associations

BACKGROUND

Much of the heritable risk of renal cell carcinoma (RCC) associated with common genetic variation is unexplained. New analytic approaches have been developed to increase the discovery of risk variants in genome-wide association studies (GWAS), including multi-locus testing through pathway analysis.

METHODS

We conducted a pathway analysis using GWAS summary data from six previous scans (10,784 cases and 20,406 controls) and evaluated 3,678 pathways and gene sets drawn from the Molecular Signatures Database. To replicate findings, we analyzed GWAS summary data from the UK Biobank (903 cases and 451,361 controls) and the Genetic Epidemiology Research on Adult Health and Aging cohort (317 cases and 50,511 controls).

RESULTS

We identified 14 pathways/gene sets associated with RCC in both the discovery (P < 1.36 × 10-5, the Bonferroni correction threshold) and replication (P < 0.05) sets, 10 of which include components of the PI3K/AKT pathway. In tests across 2,035 genes in these pathways, associations (Bonferroni corrected P < 2.46 × 10-5 in discovery and replication sets combined) were observed for CASP9, TIPIN, and CDKN2C. The strongest SNP signal was for rs12124078 (P Discovery = 2.6 × 10-5; P Replication = 1.5 × 10-4; P Combined = 6.9 × 10-8), a CASP9 expression quantitative trait locus.

CONCLUSIONS

Our pathway analysis implicates genetic variation within the PI3K/AKT pathway as a source of RCC heritability and identifies several promising novel susceptibility genes, including CASP9, which warrant further investigation.

IMPACT

Our findings illustrate the value of pathway analysis as a complementary approach to analyzing GWAS data.

Genome-Wide Association Study Data Reveal Genetic Susceptibility to Chronic Inflammatory Intestinal Diseases and Pancreatic Ductal Adenocarcinoma Risk

Registry-based epidemiologic studies suggest associations between chronic inflammatory intestinal diseases and pancreatic ductal adenocarcinoma (PDAC). As genetic susceptibility contributes to a large proportion of chronic inflammatory intestinal diseases, we hypothesize that the genomic regions surrounding established genome-wide associated variants for these chronic inflammatory diseases are associated with PDAC. We examined the association between PDAC and genomic regions (±500 kb) surrounding established common susceptibility variants for ulcerative colitis, Crohn's disease, inflammatory bowel disease, celiac disease, chronic pancreatitis, and primary sclerosing cholangitis. We analyzed summary statistics from genome-wide association studies data for 8,384 cases and 11,955 controls of European descent from two large consortium studies using the summary data-based adaptive rank truncated product method to examine the overall association of combined genomic regions for each inflammatory disease group. Combined genomic susceptibility regions for ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis were associated with PDAC at P values < 0.05 (0.0040, 0.0057, 0.011, and 3.4 × 10-6, respectively). After excluding the 20 PDAC susceptibility regions (±500 kb) previously identified by GWAS, the genomic regions for ulcerative colitis, Crohn disease, and inflammatory bowel disease remained associated with PDAC (P = 0.0029, 0.0057, and 0.0098, respectively). Genomic regions for celiac disease (P = 0.22) and primary sclerosing cholangitis (P = 0.078) were not associated with PDAC. Our results support the hypothesis that genomic regions surrounding variants associated with inflammatory intestinal diseases, particularly, ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis are associated with PDAC. SIGNIFICANCE: The joint effects of common variants in genomic regions containing susceptibility loci for inflammatory bowel disease and chronic pancreatitis are associated with PDAC and may provide insights to understanding pancreatic cancer etiology.

An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies

Functionally annotating genetic variations is an essential yet challenging topic in human genetics research. As large consortia including ENCODE and Roadmap Epigenomics Project continue to generate high-throughput transcriptomic and epigenomic data, many computational frameworks have been developed to integrate these experimental data to predict functionality of genetic variations in both protein-coding and noncoding regions. Here, we compare a number of recently developed annotation frameworks for noncoding regions through enrichment analysis on genome-wide association studies (GWASs). We also compare several different strategies to quantify enrichment using GWAS summary statistics. Our analyses highlight the importance of jointly modeling context-specific annotations with genome-wide data in providing statistically powerful and biologically interpretable enrichment for complex disease associations. Our findings provide insights into when and how computational genome annotations may benefit future complex disease studies on the genome-wide scale.

Genetic polymorphisms of IL17A associated with Chagas disease: results from a meta-analysis in Latin American populations

Genetic factors and the immunologic response have been suggested to determine the susceptibility against the infection and the outcome of Chagas disease. In the present study, we analysed three IL17A genetic variants (rs4711998, rs8193036 and rs2275913) regarding the predisposition to Trypanosoma cruzi infection and the development of chronic Chagas cardiomyopathy (CCC) in different Latin American populations. A total of 2,967 individuals from Colombia, Argentina, Bolivia and Brazil, were included in this study. The individuals were classified as seronegative and seropositive for T. cruzi antigens, and this last group were divided into asymptomatic and CCC. For T. cruzi infection susceptibility, the IL17A rs2275913*A showed a significant association in a fixed-effect meta-analysis after a Bonferroni correction (P = 0.016, OR = 1.21, 95%CI = 1.06-1.41). No evidence of association was detected when comparing CCC vs. asymptomatic patients. However, when CCC were compared with seronegative individuals, it showed a nominal association in the meta-analysis (P = 0.040, OR = 1.20, 95%CI = 1.01-1.45). For the IL17A rs4711998 and rs8193036, no association was observed. In conclusion, our results suggest that IL17A rs2275913 plays an important role in the susceptibility to T. cruzi infection and could also be implicated in the development of chronic cardiomyopathy in the studied Latin American population.

Tuberculosis infection and lung adenocarcinoma: Mendelian randomization and pathway analysis of genome-wide association study data from never-smoking Asian women

We investigated whether genetic susceptibility to tuberculosis (TB) influences lung adenocarcinoma development among never-smokers using TB genome-wide association study (GWAS) results within the Female Lung Cancer Consortium in Asia. Pathway analysis with the adaptive rank truncated product method was used to assess the association between a TB-related gene-set and lung adenocarcinoma using GWAS data from 5512 lung adenocarcinoma cases and 6277 controls. The gene-set consisted of 31 genes containing known/suggestive associations with genetic variants from previous TB-GWAS. Subsequently, we followed-up with Mendelian Randomization to evaluate the association between TB and lung adenocarcinoma using three genome-wide significant variants from previous TB-GWAS in East Asians. The TB-related gene-set was associated with lung adenocarcinoma (p = 0.016). Additionally, the Mendelian Randomization showed an association between TB and lung adenocarcinoma (OR = 1.31, 95% CI: 1.03, 1.66, p = 0.027). Our findings support TB as a causal risk factor for lung cancer development among never-smoking Asian women.

Hierarchical structural component model for pathway analysis of common variants

BACKGROUND

Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a small portion of the overall estimated genetic heritability. To address this 'missing heritability' issue, gene- and pathway-based analysis, and biological mechanisms, have been used for many GWAS studies. However, many of these methods often neglect the correlation between genes and between pathways.

METHODS

We constructed a hierarchical component model that considers correlations both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summarizes the common variants of each gene, first at the gene-level, and then analyzes all pathways simultaneously by ridge-type penalization of both the gene and pathway effects on the phenotype. Statistical significance of the gene and pathway coefficients can be examined by permutation tests.

RESULTS

Using the simulation data set of Genetic Analysis Workshop 17 (GAW17), for both binary and continuous phenotypes, we showed that HisCoM-PCA well-controlled type I error, and had a higher empirical power compared to several other methods. In addition, we applied our method to a SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance.

CONCLUSIONS

Our approach has the advantage of providing an intuitive biological interpretation for associations between common variants and phenotypes, via pathway information, potentially addressing the missing heritability conundrum.

Histone demethylase JMJD1C is phosphorylated by mTOR to activate de novo lipogenesis

Fatty acid and triglyceride synthesis increases greatly in response to feeding and insulin. This lipogenic induction involves coordinate transcriptional activation of various enzymes in lipogenic pathway, including fatty acid synthase and glycerol-3-phosphate acyltransferase. Here, we show that JMJD1C is a specific histone demethylase for lipogenic gene transcription in liver. In response to feeding/insulin, JMJD1C is phosphorylated at T505 by mTOR complex to allow direct interaction with USF-1 for recruitment to lipogenic promoter regions. Thus, by demethylating H3K9me2, JMJD1C alters chromatin accessibility to allow transcription. Consequently, JMJD1C promotes lipogenesis in vivo to increase hepatic and plasma triglyceride levels, showing its role in metabolic adaption for activation of the lipogenic program in response to feeding/insulin, and its contribution to development of hepatosteatosis resulting in insulin resistance.

In response to insulin, liver cells increase de novo lipogenesis via the transcription factors USF-1 and SREBP. Here the authors show that USF-1 recruits JMJD1C, after its phosphorylation by mTOR, to lipogenic promoters where JMJD1C demethylates histone H3, contributing to lipogenesis by an epigenetic mechanism.

Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2

Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.

Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

BACKGROUND

Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes.

METHODS

We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided.

RESULTS

We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets.

CONCLUSION

Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.

GScluster: network-weighted gene-set clustering analysis

BACKGROUND

Gene-set analysis (GSA) has been commonly used to identify significantly altered pathways or functions from omics data. However, GSA often yields a long list of gene-sets, necessitating efficient post-processing for improved interpretation. Existing methods cluster the gene-sets based on the extent of their overlap to summarize the GSA results without considering interactions between gene-sets.

RESULTS

Here, we presented a novel network-weighted gene-set clustering that incorporates both the gene-set overlap and protein-protein interaction (PPI) networks. Three examples were demonstrated for microarray gene expression, GWAS summary, and RNA-sequencing data to which different GSA methods were applied. These examples as well as a global analysis show that the proposed method increases PPI densities and functional relevance of the resulting clusters. Additionally, distinct properties of gene-set distance measures were compared. The methods are implemented as an R/Shiny package GScluster that provides gene-set clustering and diverse functions for visualization of gene-sets and PPI networks.

CONCLUSIONS

Network-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis.

UBA6 and Its Bispecific Pathways for Ubiquitin and FAT10

Questions have been raised since the discovery of UBA6 and its significant coexistence with UBE1 in the ubiquitin–proteasome system (UPS). The facts that UBA6 has the dedicated E2 enzyme USE1 and the E1–E2 cascade can activate and transfer both ubiquitin and ubiquitin-like protein FAT10 have attracted a great deal of attention to the regulational mechanisms of the UBA6–USE1 cascade and to how FAT10 and ubiquitin differentiate with each other. This review recapitulates the latest advances in UBA6 and its bispecific UBA6–USE1 pathways for both ubiquitin and FAT10. The intricate networks of UBA6 and its interplays with ubiquitin and FAT10 are briefly reviewed, as are their individual and collective functions in diverse physiological conditions.

GWAS summary-based pathway analysis correcting for the genetic confounding impact of environmental exposures

Genome-wide association study (GWAS)-based pathway association analysis is a powerful approach for the genetic studies of human complex diseases. However, the genetic confounding effects of environment exposure-related genes can decrease the accuracy of GWAS-based pathway association analysis of target diseases. In this study, we developed a pathway association analysis approach, named Mendelian randomization-based pathway enrichment analysis (MRPEA), which was capable of correcting the genetic confounding effects of environmental exposures, using the GWAS summary data of environmental exposures. After analyzing the real GWAS summary data of cardiovascular disease and cigarette smoking, we observed significantly improved performance of MRPEA compared with traditional pathway association analysis (TPAA) without adjusting for environmental exposures. Further, simulation studies found that MRPEA generally outperformed TPAA under various scenarios. We hope that MRPEA could help to fill the gap of TPAA and identify novel causal pathways for complex diseases.

Proper joint analysis of summary association statistics requires the adjustment of heterogeneity in SNP coverage pattern

As meta-analysis results published by consortia of genome-wide association studies (GWASs) become increasingly available, many association summary statistics-based multi-locus tests have been developed to jointly evaluate multiple single-nucleotide polymorphisms (SNPs) to reveal novel genetic architectures of various complex traits. The validity of these approaches relies on the accurate estimate of z-score correlations at considered SNPs, which in turn requires knowledge on the set of SNPs assessed by each study participating in the meta-analysis. However, this exact SNP coverage information is usually unavailable from the meta-analysis results published by GWAS consortia. In the absence of the coverage information, researchers typically estimate the z-score correlations by making oversimplified coverage assumptions. We show through real studies that such a practice can generate highly inflated type I errors, and we demonstrate the proper way to incorporate correct coverage information into multi-locus analyses. We advocate that consortia should make SNP coverage information available when posting their meta-analysis results, and that investigators who develop analytic tools for joint analyses based on summary data should pay attention to the variation in SNP coverage and adjust for it appropriately.

Genetic variation in the Hippo pathway and breast cancer risk in women of African ancestry

Gene expression changes within the Hippo pathway were found to be associated with large tumor size and metastasis in breast cancer. The combined effect of genetic variants in genes of this pathway may have a causal role in breast cancer development. We examined 7086 SNPs that were not highly correlated (r2  < 0.8) in 35 Hippo pathway genes using data from the genome-wide association study of breast cancer from the Root Consortium, which includes 3686 participants of African ancestry from Nigeria, United States of America, and Barbados: 1657 cases (403 estrogen receptor-positive [ER+], 374 ER-) and 2029 controls. Gene-level analyses were conducted using improved AdaJoint test for large-scale genetic association studies adjusting for age, study site and the first four eigenvectors from the principal component analysis. SNP-level analyses were conducted with logistic regression. The Hippo pathway was significantly associated with risk of ER+ breast cancer (pathway-level P = 0.019), with WWC1 (Padj  = 0.04) being the leading gene. The pathway-level significance was lost without WWC1 (P = 0.12). rs147106204 in the WWC1 gene was the most statistically significant SNP after gene-level adjustment for multiple comparisons (OR = 0.53, 95%CI = 0.41-0.70, Padj  = 0.025). We found evidence of an association between genetic variations in the Hippo pathway and ER+ breast cancer. Moreover, WWC1 was identified as the most important genetic susceptibility locus highlighting the importance of genetic epidemiology studies of breast cancer in understudied populations.

Comparison of novel and existing methods for detecting differentially methylated regions

BACKGROUND

Single-probe analyses in epigenome-wide association studies (EWAS) have identified associations between DNA methylation and many phenotypes, but do not take into account information from neighboring probes. Methods to detect differentially methylated regions (DMRs) (clusters of neighboring probes associated with a phenotype) may provide more power to detect associations between DNA methylation and diseases or phenotypes of interest.

RESULTS

We proposed a novel approach, GlobalP, and perform comparisons with 3 methods-DMRcate, Bumphunter, and comb-p-to identify DMRs associated with log triglycerides (TGs) in real GAW20 data before and after fenofibrate treatment. We applied these methods to the summary statistics from an EWAS performed on the methylation data. Comb-p, DMRcate, and GlobalP detected very similar DMRs near the gene CPT1A on chromosome 11 in both the pre- and posttreatment data. In addition, GlobalP detected 2 DMRs before fenofibrate treatment in the genes ETV6 and ABCG1. Bumphunter identified several DMRs on chromosomes 1 and 20, which did not overlap with DMRs detected by other methods.

CONCLUSIONS

Our novel method detected the same DMR identified by two existing methods and detected two additional DMRs not identified by any of the existing methods we compared.

Circadian pathway genetic variation and cancer risk: evidence from genome-wide association studies

BACKGROUND

Dysfunction of the circadian clock and single polymorphisms of some circadian genes have been linked to cancer susceptibility, although data are scarce and findings inconsistent. We aimed to investigate the association between circadian pathway genetic variation and risk of developing common cancers based on the findings of genome-wide association studies (GWASs).

METHODS

Single nucleotide polymorphisms (SNPs) of 17 circadian genes reported by three GWAS meta-analyses dedicated to breast (Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Consortium; cases, n = 15,748; controls, n = 18,084), prostate (Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE) Consortium; cases, n = 14,160; controls, n = 12,724) and lung carcinoma (Transdisciplinary Research In Cancer of the Lung (TRICL) Consortium; cases, n = 12,160; controls, n = 16,838) in patients of European ancestry were utilized to perform pathway analysis by means of the adaptive rank truncated product (ARTP) method. Data were also available for the following subgroups: estrogen receptor negative breast cancer, aggressive prostate cancer, squamous lung carcinoma and lung adenocarcinoma.

RESULTS

We found a highly significant statistical association between circadian pathway genetic variation and the risk of breast (pathway P value = 1.9 × 10^-6; top gene RORA, gene P value = 0.0003), prostate (pathway P value = 4.1 × 10^-6; top gene ARNTL, gene P value = 0.0002) and lung cancer (pathway P value = 6.9 × 10^-7; top gene RORA, gene P value = 2.0 × 10^-6), as well as all their subgroups. Out of 17 genes investigated, 15 were found to be significantly associated with the risk of cancer: four genes were shared by all three malignancies (ARNTL, CLOCK, RORA and RORB), two by breast and lung cancer (CRY1 and CRY2) and three by prostate and lung cancer (NPAS2, NR1D1 and PER3), whereas four genes were specific for lung cancer (ARNTL2, CSNK1E, NR1D2 and PER2) and two for breast cancer (PER1, RORC).

CONCLUSIONS

Our findings, based on the largest series ever utilized for ARTP-based gene and pathway analysis, support the hypothesis that circadian pathway genetic variation is involved in cancer predisposition.

Inherited variation in circadian rhythm genes and risks of prostate cancer and three other cancer sites in combined cancer consortia

Circadian disruption has been linked to carcinogenesis in animal models, but the evidence in humans is inconclusive. Genetic variation in circadian rhythm genes provides a tool to investigate such associations. We examined associations of genetic variation in nine core circadian rhythm genes and six melatonin pathway genes with risk of colorectal, lung, ovarian and prostate cancers using data from the Genetic Associations and Mechanisms in Oncology (GAME-ON) network. The major results for prostate cancer were replicated in the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial, and for colorectal cancer in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). The total number of cancer cases and controls was 15,838/18,159 for colorectal, 14,818/14,227 for prostate, 12,537/17,285 for lung and 4,369/9,123 for ovary. For each cancer site, we conducted gene-based and pathway-based analyses by applying the summary-based Adaptive Rank Truncated Product method (sARTP) on the summary association statistics for each SNP within the candidate gene regions. Aggregate genetic variation in circadian rhythm and melatonin pathways were significantly associated with the risk of prostate cancer in data combining GAME-ON and PLCO, after Bonferroni correction (ppathway  < 0.00625). The two most significant genes were NPAS2 (pgene  = 0.0062) and AANAT (pgene  = 0.00078); the latter being significant after Bonferroni correction. For colorectal cancer, we observed a suggestive association with the circadian rhythm pathway in GAME-ON (ppathway  = 0.021); this association was not confirmed in GECCO (ppathway  = 0.76) or the combined data (ppathway  = 0.17). No significant association was observed for ovarian and lung cancer. These findings support a potential role for circadian rhythm and melatonin pathways in prostate carcinogenesis. Further functional studies are needed to better understand the underlying biologic mechanisms.

Association of breast cancer risk and the mTOR pathway in women of African ancestry in 'The Root' Consortium

Functional studies have elucidated the role of the mammalian target of rapamycin (mTOR) pathway in breast carcinogenesis, but to date, there is a paucity of data on its contribution to breast cancer risk in women of African ancestry. We examined 47628 SNPs in 61 mTOR pathway genes in the genome wide association study of breast cancer in the African Diaspora study (The Root consortium), which included 3686 participants (1657 cases). Pathway- and gene-level analyses were conducted using the adaptive rank truncated product (ARTP) test for 10994 SNPs that were not highly correlated (r2 < 0.8). Odds ratio (OR) and 95% confidence interval (CI) were estimated with logistic regression for each single-nucleotide polymorphism. The mTOR pathway was significantly associated with overall and estrogen receptor-negative (ER-) breast cancer risk (P = 0.003 and 0.03, respectively). PRKAG3 (Padj = 0.0018) and RPS6KA3 (Padj = 0.061) were the leading genes for the associations with overall breast cancer risk and ER- breast cancer risk, respectively. rs190843378 in PRKAG3 was statistically significant after gene-level adjustment for multiple comparisons (OR = 0.50 for each T allele, 95% CI = 0.38-0.66, Padj = 3.6E-05), with a statistical power of 0.914. These results provide new insights on the biological relevance of the mTOR pathway in breast cancer progression and underscore the need for more genetic epidemiology studies of breast cancer in the African Diaspora.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a "Regression with Summary Statistics" (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss.

Genetic variation in the vitamin D related pathway and breast cancer risk in women of African ancestry in the root consortium

The vitamin D related pathway has been evaluated in carcinogenesis but its genetic contribution remains poorly understood. We examined single-nucleotide polymorphisms (SNPs) in the vitamin D related pathway genes using data from a genome-wide association study (GWAS) of breast cancer in the African Diaspora that included 3,686 participants (1,657 cases). Pathway- and gene-level analyses were conducted using the adaptive rank truncated product test. Odds ratios (OR) and 95% confidence intervals (CI) were estimated at SNP-level. After stringent Bonferroni corrections, we observed no significant association between variants in the vitamin D pathway and breast cancer risk at the pathway-, gene-, or SNP-level. In addition, no association was found for either the reported signals from GWASs of vitamin D related traits, or the SNPs within vitamin D receptor (VDR) binding regions. Furthermore, a decrease in genetically predicted 25(OH)D levels by Mendelian randomization was not associated with breast cancer (p = 0.23). However, an association for breast cancer with the pigment synthesis/metabolism pathway almost approached significance (pathway-level p = 0.08), driven primarily by a nonsense SNP rs41302073 in TYRP1, with an OR of 1.54 (95% CI = 1.24-1.91, p_adj  = 0.007). In conclusion, we found no evidence to support an association between vitamin D status and breast cancer risk in women of African ancestry, suggesting that vitamin D is unlikely to have significant effect on breast carcinogenesis. Interestingly, TYRP1 might be related to breast cancer through a non-vitamin D relevant mechanism but further studies are needed.