Meta-analysis of multiple genome-wide association studies (GWAS) has become an effective approach for detecting single nucleotide polymorphism (SNP) associations with complex traits. However, it is difficult to integrate the readily accessible SNP-level summary statistics from a meta-analysis into more powerful multi-marker testing procedures, which generally require individual-level genetic data. We developed a general procedure called Summary based Adaptive Rank Truncated Product (sARTP) for conducting gene and pathway meta-analysis that uses only SNP-level summary statistics in combination with genotype correlation estimated from a panel of individual-level genetic data. We demonstrated the validity and power advantage of sARTP through empirical and simulated data. We conducted a comprehensive pathway-based meta-analysis with sARTP on type 2 diabetes (T2D) by integrating SNP-level summary statistics from two large studies consisting of 19,809 T2D cases and 111,181 controls with European ancestry. Among 4,713 candidate pathways from which genes in neighborhoods of 170 GWAS established T2D loci were excluded, we detected 43 T2D globally significant pathways (with Bonferroni corrected p-values < 0. 05), which included the insulin signaling pathway and T2D pathway defined by KEGG, as well as the pathways defined according to specific gene expression patterns on pancreatic adenocarcinoma, hepatocellular carcinoma, and bladder carcinoma. Using summary data from 8 eastern Asian T2D GWAS with 6,952 cases and 11,865 controls, we showed 7 out of the 43 pathways identified in European populations remained to be significant in eastern Asians at the false discovery rate of 0. 1. We created an R package and a web-based tool for sARTP with the capability to analyze pathways with thousands of genes and tens of thousands of SNPs.
Genome-wide association study (GWAS) has become a very effective way to identify common genetic variants underlying various complex traits [1]. The most commonly used approach to analyze GWAS data is the single-locus test, which evaluates one single nucleotide polymorphism (SNP) at a time. Despite the enormous success of the single-locus analysis in GWAS, proportions of genetic heritability explained by already identified variants for most complex traits still remain small [2]. It is increasingly recognized that the multi-locus test, such as gene-based analysis and pathway (or gene-set) analysis, can be potentially more powerful than the single-locus analysis, and shed new light on the genetic architecture of complex traits [3,4]. The pathway analysis jointly tests the association between an outcome and SNPs within a set of genes compiled in a pathway according to existing biological knowledge [4]. Although the marginal effect of a single SNP might be too weak to be detectable by the single-locus test, accumulated association evidence from all signal-bearing SNPs within a pathway could be strong enough to be picked up by the pathway analysis if this pathway is enriched with outcome-associated SNPs. Various pathway analysis procedures have been proposed in the literature, with the assumption that researchers could have full access to individual-level genotype data [5–9]. In practice, pathway analysis usually utilizes data from a single resource with limited sample size, as it can be challenging to obtain and manage individual-level GWAS data from multiple resources. As a result, pathway analysis often fails to identify new findings beyond what have already been discovered by the single-locus tests. To maximize the chance of discovering novel outcome-associated variants by increasing sample size, a number of consortia have been formed to conduct single-locus meta-analysis on data across multiple GWAS [10–14]. The single-locus meta-analysis aggregates easily accessible SNP-level summary statistics from multiple studies. Similarly, the pathway-based meta-analysis [15–21] that integrates the same type of summary data across participating studies could provide us a greater opportunity for detecting novel pathway associations. Future association studies focusing on identified pathways would have a much-reduced multiple-comparison burden in searching for novel variants with main or complicated nonlinear joint effects on the outcome of interest. In this paper, we developed a pathway-based meta-analysis procedure by extending the adaptive rank truncated product (ARTP) pathway analysis procedure [9], which was originally developed for analyzing individual-level genotype data. The new procedure, called Summary based ARTP (sARTP), accepts input from SNP-level summary statistics, with their correlations estimated from a panel of reference samples with individual-level genotype data, such as the ones from the 1000 Genomes Project [22,23]. This idea was initially used in conducting gene-based meta-analysis [24,25] or conditional test [26]. As will be shown in the Results Section, sARTP usually has a power advantage over its competitors. In addition, sARTP is specifically designed for conducting pathway-based meta-analysis using SNP-level summary statistics from multiple studies. In real applications (e. g. , the type 2 diabetes example described below), it is very common that different studies could have genotypes measured or imputed on different sets of SNPs. As a result, the sample size used in the pathway-based meta-analysis on each SNP can be quite different. Ignoring the difference in sample sizes across SNPs in a pathway-based meta-analysis would generate biased testing results. Pathway analysis generally targets two types of null hypotheses [4], including the competitive null hypothesis [15,16,18–20], i. e. , the genes in a pathway of interest are no more associated with the outcome than any other genes outside this pathway, and the self-contained null hypothesis [17,21], i. e. , none of the genes in a pathway of interest is associated with the outcome. The sARTP procedure focuses on the self-contained null hypothesis, as our main goal is to identify outcome-associated genes or loci. Also, as pointed out by [27], tests for the competitive null hypothesis often assume that genotype measured at different genes are independent when evaluating the association significance level. This assumption, which is generally invalid in practice, is unnecessary for sARTP when testing the self-contained null hypothesis. One may refer to [27] and [4] for more discussion and comparison of these two types of hypotheses. The pathways defined in many public databases can consist of thousands of genes and tens of thousands of SNPs. To make the procedure applicable to large pathways, or pathways with high statistical significance, we implement sARTP with efficient and parallelizable algorithms, and adopt the direct simulation approach (DSA) [28] to evaluate the significance of the pathway association. We demonstrated the validity and power advantage of sARTP through simulated and empirical data. We applied sARTP to conduct a pathway-based meta-analysis on the association between type 2 diabetes (T2D) and 4,713 candidate pathways defined in the Molecular Signatures Database (MSigDB) v5. 0. The analysis used SNP-level summary statistics from two sources with European ancestry. One is generated from the Diabetes Genetics Replication and Meta-analysis (DIAGRAM) consortium [13], which consists of 12,171 T2D cases and 56,862 controls across 12 GWAS. The other one is based on a T2D GWAS with 7,638 T2D cases and 54,319 controls that were extracted from the Genetic Epidemiology Research on Aging (GERA) study [29,30]. The novel T2D-associated pathways detected in the European population were further examined in Asians using summary data generated by the Asian Genetic Epidemiology Network (AGEN) consortium meta-analysis, which combined 8 GWAS of T2D with a total of 6,952 and 11,865 controls from eastern Asian populations [10].
Here we describe the proposed method sARTP for assessing the association between a dichotomous outcome and a pre-defined pathway consisting of J genes. The same procedure can be applied to study a quantitative outcome with minor modifications.
Firstly, we conducted a simulation study to evaluate the empirical size of sARTP and MsARTP. Secondly, we compared empirical powers of different strategies for carrying out pathway-based meta-analysis that integrated summary statistics from multiple studies. We also evaluated whether results from sARTP were consistent with the ones from MsARTP. Thirdly, we compared our method to the recently developed method aSPUsPath [8] that can be used for pathway-based meta-analysis. We used the R package, aSPU (version 1. 39), with the default settings given in [8,17] to conduct the aSPUsPath test. To demonstrate the consistency between results obtained by sARTP using SNP-level summary statistics and the ones by ARTP using individual-level genotype data, we compared pathway analysis results from three different procedures on the 4,713 candidate pathways using the GERA GWAS data. Details on how those 4,713 pathways were pre-processed are given in the Results of T2D Pathway Analysis Section. We applied sARTP to the SNP-level summary statistics generated from the GERA study, using either an internal or an external reference panel. We also obtained the pathway p-values by directly applying the ARTP method to the individual-level GERA GWAS data. Fig 1 shows the comparison among p-values from these three analyses, and demonstrates that all three approaches can generate very consistent results.
The URLs for data and software presented herein are as follows: DIAbetes Genetics Replication And Meta-analysis (DIAGRAMv3), http: //diagram-consortium. org/ Genetic Epidemiology Research on Aging (GERA, dbGaP Study Accession: phs000674. v1. p1), http: //www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi? study_id=phs000674. v1. p1 Molecular Signatures Database (C2: curated gene sets), http: //software. broadinstitute. org/gsea/msigdb/collections. jsp#C2 BioMart (Homo sapiens genes NCBI36 and GRCh37. p13), http: //feb2014. archive. ensembl. org/ IMPUTE2, https: //mathgen. stats. ox. ac. uk/impute/impute_v2. html GWAS Catalog, http: //www. ebi. ac. uk/gwas/ 1000 Genomes Project (Phase 3, v5,2013/05/02), ftp: //ftp. 1000genomes. ebi. ac. uk/vol1/ftp/release/20130502/ aSPU, https: //cran. r-project. org/web/packages/aSPU/index. html GTEx Portal v6, http: //gtexportal. org/home/ GeneCards Human Gene Database, http: //www. genecards. org/ Ingenuity Pathway Analysis, http: //www. ingenuity. com/ LocusZoom, http: //locuszoom. sph. umich. edu/locuszoom/ ARTP2 package, https: //cran. r-project. org/web/packages/ARTP2/ Web-based tool of ARTP2, http: //analysistools. nci. nih. gov/pathway/
As GWAS continue to grow in sample size, it is evident that these studies need to be utilized more effectively for detecting individual susceptibility variants, and more importantly, to provide insight into global genetic architecture of complex traits. Towards this goal, identifying association with respect to a collection of variants in biological pathways can be particularly insightful for understanding how networks of genes might be affecting pathophysiology of diseases. Here we present a new pathway analysis procedure that can be conducted using summary-level association statistics, which have become the main vehicle for performing meta-analysis of individual genetic variants across studies in large consortia. Through simulation studies we showed the proposed method was more powerful than the existing state-of-art method. We carried out a comprehensive pathway analysis of 4,713 candidate pathways on their association with T2D using two large studies with European ancestry and identified 43 T2D-associated pathways. Further examinations of those 43 pathways in 8 Asian studies showed that some pathways were trans-ethnically associated with T2D. This analysis clearly highlights novel T2D-associated pathways beyond what has been known from single-variant association analysis reported from largest GWAS to date. We also identify a novel locus for T2D in the European populations at chromosome 17q21 (rs1058018, p = 3. 06 × 10−8).
genome-wide association studies
medicine and health sciences
variant genotypes
carcinomas
cancers and neoplasms
gastrointestinal tumors
liver diseases
genetic mapping
epidemiological methods and statistics
oncology
research design
mathematics
statistics (mathematics)
test statistics
genome analysis
gastroenterology and hepatology
research and analysis methods
case-control studies
mathematical and statistical techniques
epidemiology
statistical methods
hepatocellular carcinoma
epidemiological statistics
heredity
meta-analysis
genetics
biology and life sciences
physical sciences
genomics
genomics statistics
computational biology
human genetics
A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations