Pathway-Based Genome-Wide Association Studies for Plasma Triglycerides in Obese Females and Normal-Weight Controls

Pathway-based analysis as an alternative approach can provide complementary information to single-marker genome-wide association studies (GWASs), which always ignore the epistasis and does not have sufficient power to find rare variants. In this study, using genotypes from a genome-wide association study (GWAS), pathway-based association studies were carried out by a modified Gene Set Enrichment Algorithm (GSEA) method (GenGen) for triglyceride in 1028 unrelated European-American extremely obese females (BMI≥35kg/m²) and normal-weight controls (BMI<25kg/m²), and another pathway association analysis (ICSNPathway) was also used to verify the GenGen result in the same data. The GO0009110 pathway (vitamin anabolism) was among the strongest associations with triglyceride (empirical P<0.001); the result remained significant after FDR correction (P = 0.022). MMAB, an obesity-related locus, included in this pathway. The ABCG1 and BCL6 gene was found in several triglyceride-related pathways (empirical P<0.05), which were also replicated by ICSNPathway (empirical P<0.05, FDR<0.05). We also performed single-marked GWAS using PLINK for TG levels (log-transformed). Significant associations were found between ASTN2 gene SNPs and plasma triglyceride levels (rs7035794, P = 2.24×10⁻¹⁰). Our study suggested that vitamin anabolism pathway, BCL6 gene pathways and ASTN2 gene may contribute to the genetic variation of plasma triglyceride concentrations.     

Replication of Genome Wide Association Studies of Alcohol Dependence: Support for Association with Variation in ADH1C

Genome-wide association studies (GWAS) have revealed many single nucleotide polymorphisms (SNPs) associated with complex traits. Although these studies frequently fail to identify statistically significant associations, the top association signals from GWAS may be enriched for true associations. We therefore investigated the association of alcohol dependence with 43 SNPs selected from association signals in the first two published GWAS of alcoholism. Our analysis of 808 alcohol-dependent cases and 1,248 controls provided evidence of association of alcohol dependence with SNP rs1614972 in the ADH1C gene (unadjusted p = 0.0017). Because the GWAS study that originally reported association of alcohol dependence with this SNP [1] included only men, we also performed analyses in sex-specific strata. The results suggest that this SNP has a similar effect in both sexes (men: OR (95%CI) = 0.80 (0.66, 0.95); women: OR (95%CI) = 0.83 (0.66, 1.03)). We also observed marginal evidence of association of the rs1614972 minor allele with lower alcohol consumption in the non-alcoholic controls (p = 0.081), and independently in the alcohol-dependent cases (p = 0.046). Despite a number of potential differences between the samples investigated by the prior GWAS and the current study, data presented here provide additional support for the association of SNP rs1614972 in ADH1C with alcohol dependence and extend this finding by demonstrating association with consumption levels in both non-alcoholic and alcohol-dependent populations. Further studies should investigate the association of other polymorphisms in this gene with alcohol dependence and related alcohol-use phenotypes.     

From SNPs to genes: disease association at the gene level

Interpreting Genome-Wide Association Studies (GWAS) at a gene level is an important step towards understanding the molecular processes that lead to disease. In order to incorporate prior biological knowledge such as pathways and protein interactions in the analysis of GWAS data it is necessary to derive one measure of association for each gene. We compare three different methods to obtain gene-wide test statistics from Single Nucleotide Polymorphism (SNP) based association data: choosing the test statistic from the most significant SNP; the mean test statistics of all SNPs; and the mean of the top quartile of all test statistics. We demonstrate that the gene-wide test statistics can be controlled for the number of SNPs within each gene and show that all three methods perform considerably better than expected by chance at identifying genes with confirmed associations. By applying each method to GWAS data for Crohn's Disease and Type 1 Diabetes we identified new potential disease genes.     

Large-scale pathway-based analysis of bladder cancer genome-wide association data from five studies of European background

Pathway analysis of genome-wide association studies (GWAS) offer a unique opportunity to collectively evaluate genetic variants with effects that are too small to be detected individually. We applied a pathway analysis to a bladder cancer GWAS containing data from 3,532 cases and 5,120 controls of European background (n = 5 studies). Thirteen hundred and ninety-nine pathways were drawn from five publicly available resources (Biocarta, Kegg, NCI-PID, HumanCyc, and Reactome), and we constructed 22 additional candidate pathways previously hypothesized to be related to bladder cancer. In total, 1421 pathways, 5647 genes and ∼90,000 SNPs were included in our study. Logistic regression model adjusting for age, sex, study, DNA source, and smoking status was used to assess the marginal trend effect of SNPs on bladder cancer risk. Two complementary pathway-based methods (gene-set enrichment analysis [GSEA], and adapted rank-truncated product [ARTP]) were used to assess the enrichment of association signals within each pathway. Eighteen pathways were detected by either GSEA or ARTP at P≤0.01. To minimize false positives, we used the I² statistic to identify SNPs displaying heterogeneous effects across the five studies. After removing these SNPs, seven pathways (‘Aromatic amine metabolism’ [P_GSEA = 0.0100, P_ARTP = 0.0020], ‘NAD biosynthesis’ [P_GSEA = 0.0018, P_ARTP = 0.0086], ‘NAD salvage’ [P_ARTP = 0.0068], ‘Clathrin derived vesicle budding’ [P_ARTP = 0.0018], ‘Lysosome vesicle biogenesis’ [P_GSEA = 0.0023, P_ARTP<0.00012], ’Retrograde neurotrophin signaling’ [P_GSEA = 0.00840], and ‘Mitotic metaphase/anaphase transition’ [P_GSEA = 0.0040]) remained. These pathways seem to belong to three fundamental cellular processes (metabolic detoxification, mitosis, and clathrin-mediated vesicles). Identification of the aromatic amine metabolism pathway provides support for the ability of this approach to identify pathways with established relevance to bladder carcinogenesis.     

Characterizing associations and SNP-environment interactions for GWAS-identified prostate cancer risk markers--results from BPC3

Genome-wide association studies (GWAS) have identified multiple single nucleotide polymorphisms (SNPs) associated with prostate cancer risk. However, whether these associations can be consistently replicated, vary with disease aggressiveness (tumor stage and grade) and/or interact with non-genetic potential risk factors or other SNPs is unknown. We therefore genotyped 39 SNPs from regions identified by several prostate cancer GWAS in 10,501 prostate cancer cases and 10,831 controls from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3). We replicated 36 out of 39 SNPs (P-values ranging from 0.01 to 10⁻²⁸). Two SNPs located near KLK3 associated with PSA levels showed differential association with Gleason grade (rs2735839, P = 0.0001 and rs266849, P = 0.0004; case-only test), where the alleles associated with decreasing PSA levels were inversely associated with low-grade (as defined by Gleason grade <8) tumors but positively associated with high-grade tumors. No other SNP showed differential associations according to disease stage or grade. We observed no effect modification by SNP for association with age at diagnosis, family history of prostate cancer, diabetes, BMI, height, smoking or alcohol intake. Moreover, we found no evidence of pair-wise SNP-SNP interactions. While these SNPs represent new independent risk factors for prostate cancer, we saw little evidence for effect modification by other SNPs or by the environmental factors examined.     

Pathway-Based Analysis Using Genome-wide Association Data from a Korean Non-Small Cell Lung Cancer Study

Pathway-based analysis, used in conjunction with genome-wide association study (GWAS) techniques, is a powerful tool to detect subtle but systematic patterns in genome that can help elucidate complex diseases, like cancers. Here, we stepped back from genetic polymorphisms at a single locus and examined how multiple association signals can be orchestrated to find pathways related to lung cancer susceptibility. We used single-nucleotide polymorphism (SNP) array data from 869 non-small cell lung cancer (NSCLC) cases from a previous GWAS at the National Cancer Center and 1,533 controls from the Korean Association Resource project for the pathway-based analysis. After mapping single-nucleotide polymorphisms to genes, considering their coding region and regulatory elements (±20 kbp), multivariate logistic regression of additive and dominant genetic models were fitted against disease status, with adjustments for age, gender, and smoking status. Pathway statistics were evaluated using Gene Set Enrichment Analysis (GSEA) and Adaptive Rank Truncated Product (ARTP) methods. Among 880 pathways, 11 showed relatively significant statistics compared to our positive controls (P_GSEA≤0.025, false discovery rate≤0.25). Candidate pathways were validated using the ARTP method and similarities between pathways were computed against each other. The top-ranked pathways were ABC Transporters (P_GSEA<0.001, P_ARTP = 0.001), VEGF Signaling Pathway (P_GSEA<0.001, P_ARTP = 0.008), G1/S Check Point (P_GSEA = 0.004, P_ARTP = 0.013), and NRAGE Signals Death through JNK (P_GSEA = 0.006, P_ARTP = 0.001). Our results demonstrate that pathway analysis can shed light on post-GWAS research and help identify potential targets for cancer susceptibility.     

GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm

Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.     

Replication of the 4p16 Susceptibility Locus in Congenital Heart Disease in Han Chinese Populations

Congenital heart disease (CHD) is the most common form of congenital human birth anomalies and a leading cause of perinatal and infant mortality. Some studies including our published genome-wide association study (GWAS) of CHD have indicated that genetic variants may contribute to the risk of CHD. Recently, Cordell et al. published a GWAS of multiple CHD phenotypes in European Caucasians and identified 3 susceptibility loci (rs870142, rs16835979 and rs6824295) for ostium secundum atrial septal defect (ASD) at chromosome 4p16. However, whether these loci at 4p16 confer the predisposition to CHD in Chinese population is unclear. In the current study, we first analyzed the associations between these 3 single nucleotide polymorphisms (SNPs) at 4p16 and CHD risk by using our existing genome-wide scan data and found all of the 3 SNPs showed significant associations with ASD in the same direction as that observed in Cordell’s study, but not with other subtypes- ventricular septal defect (VSD) and ASD combined VSD. As these 3 SNPs were in high linkage disequilibrium (LD) in Chinese population, we selected one SNP with the lowest P value in our GWAS scan (rs16835979) to perform a replication study with additional 1,709 CHD cases with multiple phenotypes and 1,962 controls. The significant association was also observed only within the ASD subgroup, which was heterogeneous from other disease groups. In combined GWAS and replication samples, the minor allele of rs16835979 remained significant association with the risk of ASD (OR = 1.22, 95% CI = 1.08–1.38, P = 0.001). Our findings suggest that susceptibility loci of ASD identified from Cordell’s European GWAS are generalizable to Chinese population, and such investigation may provide new insights into the roles of genetic variants in the etiology of different CHD phenotypes.     

Evaluation of Replication of Variants Associated with Genetic Risk of Otitis Media

The first Genome Wide Association Study (GWAS) of otitis media (OM) found evidence of association in the Western Australian Pregnancy Cohort (Raine) study, but lacked replication in an independent OM population. The aim of this study was to investigate association at these loci in our family-based sample of chronic otitis media with effusion and recurrent otitis media (COME/ROM). Autosomal SNPs were selected from the Raine OM GWAS results. SNPs from the Raine cohort GWAS genotyped in our GWAS of COME/ROM had P-values ranging from P = 0.06–0.80. After removal of SNPs previously genotyped in our GWAS of COME/ROM (N = 21) and those that failed Fluidigm assay design (N = 1), 26 SNPs were successfully genotyped in 716 individuals from our COME/ROM family population. None of the SNP associations replicated in our family-based population (unadjusted P = 0.03–0.93). Replication in an independent sample would confirm that these represent novel OM loci, and that further investigation is warranted.     

Dementia Revealed: Novel Chromosome 6 Locus for Late-Onset Alzheimer Disease Provides Genetic Evidence for Folate-Pathway Abnormalities

Genome-wide association studies (GWAS) of late-onset Alzheimer disease (LOAD) have consistently observed strong evidence of association with polymorphisms in APOE. However, until recently, variants at few other loci with statistically significant associations have replicated across studies. The present study combines data on 483,399 single nucleotide polymorphisms (SNPs) from a previously reported GWAS of 492 LOAD cases and 496 controls and from an independent set of 439 LOAD cases and 608 controls to strengthen power to identify novel genetic association signals. Associations exceeding the experiment-wide significance threshold () were replicated in an additional 1,338 cases and 2,003 controls. As expected, these analyses unequivocally confirmed APOE''s risk effect (rs2075650, ). Additionally, the SNP rs11754661 at 151.2 Mb of chromosome 6q25.1 in the gene MTHFD1L (which encodes the methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like protein) was significantly associated with LOAD (; Bonferroni-corrected P = 0.022). Subsequent genotyping of SNPs in high linkage disequilibrium () with rs11754661 identified statistically significant associations in multiple SNPs (rs803424, P = 0.016; rs2073067, P = 0.03; rs2072064, P = 0.035), reducing the likelihood of association due to genotyping error. In the replication case-control set, we observed an association of rs11754661 in the same direction as the previous association at P = 0.002 ( in combined analysis of discovery and replication sets), with associations of similar statistical significance at several adjacent SNPs (rs17349743, P = 0.005; rs803422, P = 0.004). In summary, we observed and replicated a novel statistically significant association in MTHFD1L, a gene involved in the tetrahydrofolate synthesis pathway. This finding is noteworthy, as MTHFD1L may play a role in the generation of methionine from homocysteine and influence homocysteine-related pathways and as levels of homocysteine are a significant risk factor for LOAD development.     

Evidence for more than one Parkinson's disease-associated variant within the HLA region

Parkinson''s disease (PD) was recently found to be associated with HLA in a genome-wide association study (GWAS). Follow-up GWAS''s replicated the PD-HLA association but their top hits differ. Do the different hits tag the same locus or is there more than one PD-associated variant within HLA? We show that the top GWAS hits are not correlated with each other (0.00≤r²≤0.15). Using our GWAS (2000 cases, 1986 controls) we conducted step-wise conditional analysis on 107 SNPs with P<10⁻³ for PD-association; 103 dropped-out, four remained significant. Each SNP, when conditioned on the other three, yielded P_SNP1 = 5×10⁻⁴, P_SNP2 = 5×10⁻⁴, P_SNP3 = 4×10⁻³ and P_SNP4 = 0.025. The four SNPs were not correlated (0.01≤r²≤0.20). Haplotype analysis (excluding rare SNP2) revealed increasing PD risk with increasing risk alleles from OR = 1.27, P = 5×10⁻³ for one risk allele to OR = 1.65, P = 4×10⁻⁸ for three. Using additional 843 cases and 856 controls we replicated the independent effects of SNP1 (P_{conditioned-on-SNP4} = 0.04) and SNP4 (P_{conditioned-on-SNP1} = 0.04); SNP2 and SNP3 could not be replicated. In pooled GWAS and replication, SNP1 had OR_{conditioned-on-SNP4} = 1.23, P_{conditioned-on-SNP4} = 6×10⁻⁷; SNP4 had OR_{conditioned-on-SNP1} = 1.18, P_{conditioned-on-SNP1} = 3×10⁻³; and the haplotype with both risk alleles had OR = 1.48, P = 2×10⁻¹². Genotypic OR increased with the number of risk alleles an individual possessed up to OR = 1.94, P = 2×10⁻¹¹ for individuals who were homozygous for the risk allele at both SNP1 and SNP4. SNP1 is a variant in HLA-DRA and is associated with HLA-DRA, DRB5 and DQA2 gene expression. SNP4 is correlated (r² = 0.95) with variants that are associated with HLA-DQA2 expression, and with the top HLA SNP from the IPDGC GWAS (r² = 0.60). Our findings suggest more than one PD-HLA association; either different alleles of the same gene, or separate loci.     

How Genome-Wide SNP-SNP Interactions Relate to Nasopharyngeal Carcinoma Susceptibility

This study is the first to use genome-wide association study (GWAS) data to evaluate the multidimensional genetic architecture underlying nasopharyngeal cancer. Since analysis of data from GWAS confirms a close and consistent association between elevated risk for nasopharyngeal carcinoma (NPC) and major histocompatibility complex class 1 genes, our goal here was to explore lesser effects of gene-gene interactions. We conducted an exhaustive genome-wide analysis of GWAS data of NPC, revealing two-locus interactions occurring between single nucleotide polymorphisms (SNPs), and identified a number of suggestive interaction loci which were missed by traditional GWAS analyses. Although none of the interaction pairs we identified passed the genome-wide Bonferroni-adjusted threshold for significance, using independent GWAS data from the same population (Stage 2), we selected 66 SNP pairs in 39 clusters with P<0.01. We identified that in several chromosome regions, multiple suggestive interactions group to form a block-like signal, effectively reducing the rate of false discovery. The strongest cluster of interactions involved the CREB5 gene and a SNP rs1607979 on chromosome 17q22 (P = 9.86×10⁻¹¹) which also show trans-expression quantitative loci (eQTL) association in Chinese population. We then detected a complicated cis-interaction pattern around the NPC-associated HLA-B locus, which is immediately adjacent to copy-number variations implicated in male susceptibility for NPC. While it remains to be seen exactly how and to what degree SNP-SNP interactions such as these affect susceptibility for nasopharyngeal cancer, future research on these questions holds great promise for increasing our understanding of this disease’s genetic etiology, and possibly also that of other gene-related cancers.     

全基因组关联研究通路分析方法现状

全基因组关联研究(genome-wide association study, GWAS)自2005年首次发表以来已不断增进人们对疾病遗传机制的认识,结合系统生物学并改进统计分析方法是对GWAS数据进行深度挖掘的重要途径。通路分析(pathway analysis)将GWAS所检测的遗传变异根据一定的生物学含义组合为集合进行分析,有利于发现对疾病单独效应小却在通路中相互关联的遗传变异,更有利于进行生物学解释。当前通路分析在GWAS数据上已有较为广泛的应用并取得初步成果。与此同时,通路分析的统计方法仍在不断发展。本文旨在介绍现有直接以SNP为对象的GWAS通路分析算法,根据方法中是否采用核函数分为非核算法和核算法两大类,其中非核算法主要包括基因功能富集分析(gene set enrichment analysis, GSEA)和分层贝叶斯优取(hierarchical Bayes prioritization, HBP),核算法包括线性核(linear kernel, LIN)、状态认证核(identity-by-status kernel, IBS)和尺度不变核(powered exponential kernel)。通过介绍这些方法的计算原理和优缺点,以期为新算法的构建提供更好的思路,为GWAS领域研究方法的选择提供参考。     

Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data

Genome-wide association studies (GWAS) have become increasingly common due to advances in technology and have permitted the identification of differences in single nucleotide polymorphism (SNP) alleles that are associated with diseases. However, while typical GWAS analysis techniques treat markers individually, complex diseases (cancers, diabetes, and Alzheimers, amongst others) are unlikely to have a single causative gene. Thus, there is a pressing need for multi-SNP analysis methods that can reveal system-level differences in cases and controls. Here, we present a novel multi-SNP GWAS analysis method called Pathways of Distinction Analysis (PoDA). The method uses GWAS data and known pathway-gene and gene-SNP associations to identify pathways that permit, ideally, the distinction of cases from controls. The technique is based upon the hypothesis that, if a pathway is related to disease risk, cases will appear more similar to other cases than to controls (or vice versa) for the SNPs associated with that pathway. By systematically applying the method to all pathways of potential interest, we can identify those for which the hypothesis holds true, i.e., pathways containing SNPs for which the samples exhibit greater within-class similarity than across classes. Importantly, PoDA improves on existing single-SNP and SNP-set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects. This permits PoDA to reveal pathways in which epistatic interactions drive risk. In this paper, we detail the PoDA method and apply it to two GWAS: one of breast cancer and the other of liver cancer. The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility. PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level.     

Use of Allele-Specific FAIRE to Determine Functional Regulatory Polymorphism Using Large-Scale Genotyping Arrays

Following the widespread use of genome-wide association studies (GWAS), focus is turning towards identification of causal variants rather than simply genetic markers of diseases and traits. As a step towards a high-throughput method to identify genome-wide, non-coding, functional regulatory variants, we describe the technique of allele-specific FAIRE, utilising large-scale genotyping technology (FAIRE-gen) to determine allelic effects on chromatin accessibility and regulatory potential. FAIRE-gen was explored using lymphoblastoid cells and the 50,000 SNP Illumina CVD BeadChip. The technique identified an allele-specific regulatory polymorphism within NR1H3 (coding for LXR-α), rs7120118, coinciding with a previously GWAS-identified SNP for HDL-C levels. This finding was confirmed using FAIRE-gen with the 200,000 SNP Illumina Metabochip and verified with the established method of TaqMan allelic discrimination. Examination of this SNP in two prospective Caucasian cohorts comprising 15,000 individuals confirmed the association with HDL-C levels (combined beta = 0.016; p = 0.0006), and analysis of gene expression identified an allelic association with LXR-α expression in heart tissue. Using increasingly comprehensive genotyping chips and distinct tissues for examination, FAIRE-gen has the potential to aid the identification of many causal SNPs associated with disease from GWAS.     

A Novel MMP12 Locus Is Associated with Large Artery Atherosclerotic Stroke Using a Genome-Wide Age-at-Onset Informed Approach

Genome-wide association studies (GWAS) have begun to identify the common genetic component to ischaemic stroke (IS). However, IS has considerable phenotypic heterogeneity. Where clinical covariates explain a large fraction of disease risk, covariate informed designs can increase power to detect associations. As prevalence rates in IS are markedly affected by age, and younger onset cases may have higher genetic predisposition, we investigated whether an age-at-onset informed approach could detect novel associations with IS and its subtypes; cardioembolic (CE), large artery atherosclerosis (LAA) and small vessel disease (SVD) in 6,778 cases of European ancestry and 12,095 ancestry-matched controls. Regression analysis to identify SNP associations was performed on posterior liabilities after conditioning on age-at-onset and affection status. We sought further evidence of an association with LAA in 1,881 cases and 50,817 controls, and examined mRNA expression levels of the nearby genes in atherosclerotic carotid artery plaques. Secondly, we performed permutation analyses to evaluate the extent to which age-at-onset informed analysis improves significance for novel loci. We identified a novel association with an MMP12 locus in LAA (rs660599; p = 2.5×10⁻⁷), with independent replication in a second population (p = 0.0048, OR(95% CI) = 1.18(1.05–1.32); meta-analysis p = 2.6×10⁻⁸). The nearby gene, MMP12, was significantly overexpressed in carotid plaques compared to atherosclerosis-free control arteries (p = 1.2×10⁻¹⁵; fold change = 335.6). Permutation analyses demonstrated improved significance for associations when accounting for age-at-onset in all four stroke phenotypes (p<0.001). Our results show that a covariate-informed design, by adjusting for age-at-onset of stroke, can detect variants not identified by conventional GWAS.     

META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies

Introduction

Gene-set analysis (GSA) methods are used as complementary approaches to genome-wide association studies (GWASs). The single marker association estimates of a predefined set of genes are either contrasted with those of all remaining genes or with a null non-associated background. To pool the p-values from several GSAs, it is important to take into account the concordance of the observed patterns resulting from single marker association point estimates across any given gene set. Here we propose an enhanced version of Fisher’s inverse χ²-method META-GSA, however weighting each study to account for imperfect correlation between association patterns.

Simulation and Power

We investigated the performance of META-GSA by simulating GWASs with 500 cases and 500 controls at 100 diallelic markers in 20 different scenarios, simulating different relative risks between 1 and 1.5 in gene sets of 10 genes. Wilcoxon’s rank sum test was applied as GSA for each study. We found that META-GSA has greater power to discover truly associated gene sets than simple pooling of the p-values, by e.g. 59% versus 37%, when the true relative risk for 5 of 10 genes was assume to be 1.5. Under the null hypothesis of no difference in the true association pattern between the gene set of interest and the set of remaining genes, the results of both approaches are almost uncorrelated. We recommend not relying on p-values alone when combining the results of independent GSAs.

Application

We applied META-GSA to pool the results of four case-control GWASs of lung cancer risk (Central European Study and Toronto/Lunenfeld-Tanenbaum Research Institute Study; German Lung Cancer Study and MD Anderson Cancer Center Study), which had already been analyzed separately with four different GSA methods (EASE; SLAT, mSUMSTAT and GenGen). This application revealed the pathway GO0015291 “transmembrane transporter activity” as significantly enriched with associated genes (GSA-method: EASE, p = 0.0315 corrected for multiple testing). Similar results were found for GO0015464 “acetylcholine receptor activity” but only when not corrected for multiple testing (all GSA-methods applied; p≈0.02).     

A Comparison of Multivariate Genome-Wide Association Methods

Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three quantitative traits and one bi-allelic quantitative trait locus (QTL), and varied the number of traits associated with the QTL (explained variance 0.1%), minor allele frequency of the QTL, residual correlation between the traits, and the sign of the correlation induced by the QTL relative to the residual correlation. We compared the power of the methods using empirically fixed significance thresholds (α = 0.05). Our results showed that the multivariate methods implemented in PLINK, SNPTEST, MultiPhen and BIMBAM performed best for the majority of the tested scenarios, with a notable increase in power for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between traits are weak.     

Genome-Wide Association Study Implicates Chromosome 9q21.31 as a Susceptibility Locus for Asthma in Mexican Children

Many candidate genes have been studied for asthma, but replication has varied. Novel candidate genes have been identified for various complex diseases using genome-wide association studies (GWASs). We conducted a GWAS in 492 Mexican children with asthma, predominantly atopic by skin prick test, and their parents using the Illumina HumanHap 550 K BeadChip to identify novel genetic variation for childhood asthma. The 520,767 autosomal single nucleotide polymorphisms (SNPs) passing quality control were tested for association with childhood asthma using log-linear regression with a log-additive risk model. Eleven of the most significantly associated GWAS SNPs were tested for replication in an independent study of 177 Mexican case–parent trios with childhood-onset asthma and atopy using log-linear analysis. The chromosome 9q21.31 SNP rs2378383 (p = 7.10×10⁻⁶ in the GWAS), located upstream of transducin-like enhancer of split 4 (TLE4), gave a p-value of 0.03 and the same direction and magnitude of association in the replication study (combined p = 6.79×10⁻⁷). Ancestry analysis on chromosome 9q supported an inverse association between the rs2378383 minor allele (G) and childhood asthma. This work identifies chromosome 9q21.31 as a novel susceptibility locus for childhood asthma in Mexicans. Further, analysis of genome-wide expression data in 51 human tissues from the Novartis Research Foundation showed that median GWAS significance levels for SNPs in genes expressed in the lung differed most significantly from genes not expressed in the lung when compared to 50 other tissues, supporting the biological plausibility of our overall GWAS findings and the multigenic etiology of childhood asthma.