Pathway analysis by adaptive combination of P-values

Statin use is not associated with inflammation among Chilean women of Mapuche and non-Mapuche ancestry with gallstones

Aim: Statins are associated with lower risk of gallstones due to anti-inflammatory effects. We assessed whether statins impact circulating inflammation among Chilean women with gallstones. Materials & methods: 200 Mapuche women were matched on statin use and age to 200 non-Mapuche women in the Chile Biliary Longitudinal Study. We analyzed 92 inflammatory biomarkers using multivariable-adjusted regression models, random forests and pathway analyses. Results: Statins were not significantly associated with any inflammation marker when women were analyzed jointly or stratified by ancestry. No significant associations were found through random forest methods and pathway analyses. Discussion: We did not find significant associations between statin use and inflammation markers in women with gallstones, suggesting that statins do not reduce inflammation once gallstones have formed.

Cauchy combination omnibus test for normality

Testing whether data are from a normal distribution is a traditional problem and is of great concern for data analyses. The normality is the premise of many statistical methods, such as t-test, Hotelling T2 test and ANOVA. There are numerous tests in the literature and the commonly used ones are Anderson-Darling test, Shapiro-Wilk test and Jarque-Bera test. Each test has its own advantageous points since they are developed for specific patterns and there is no method that consistently performs optimally in all situations. Since the data distribution of practical problems can be complex and diverse, we propose a Cauchy Combination Omnibus Test (CCOT) that is robust and valid in most data cases. We also give some theoretical results to analyze the good properties of CCOT. Two obvious advantages of CCOT are that not only does CCOT have a display expression for calculating statistical significance, but extensive simulation results show its robustness regardless of the shape of distribution the data comes from. Applications to South African Heart Disease and Neonatal Hearing Impairment data further illustrate its practicability.

Comparative Analysis of the Humoral Immune Response to the EBV Proteome across EBV-Related Malignancies

BACKGROUND

Epstein-Barr virus (EBV) is linked to multiple cancers, including classical Hodgkin lymphoma (cHL), endemic Burkitt lymphoma (eBL), nasopharyngeal carcinoma (NPC), and extranodal natural killer/T-cell lymphoma (NKTCL).

METHODS

Anti-EBV IgG and IgA antibody responses targeting 202 sequences from 86 EBV proteins were measured using the same EBV whole proteome array across four case-control studies investigating EBV-positive cHL, eBL, NPC, and NKTCL (407 cases/620 controls). We grouped EBV-targeted antibodies into pathways by immunoglobulin type (IgA and IgG) and life-cycle stage (latent, immediate early lytic, early lytic, late lytic, and glycoprotein) and evaluated their association with each cancer type. In an additional analysis, we focused on the subset of 46 individual antibodies representing the top candidates for each cancer and compared their associations across the four cancer types using multivariable linear regression models.

RESULTS

IgA antibody responses targeting all EBV life-cycle stages were associated with NPC but limited to anti-early lytic stage for cHL. NPC and eBL were associated with IgG antibodies across the viral life cycle; cHL with antibodies in the early lytic, late lytic and glycoprotein stages; and NKTCL with antibodies in the latent, immediate early lytic and early lytic phases. EBNA3A, BBLF1, BDLF4, and BLRF2 IgG antibodies were associated with all cancer types.

CONCLUSIONS

Our observed similarities and differences across four EBV-associated cancers may inform EBV-related oncogenesis.

IMPACT

Understanding the comparative humoral immune response across EBV-related cancers may aid in identifying shared etiologic roles of EBV proteins and inform unique pathogenic processes for each cancer.

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.

Simultaneous detection of novel genes and SNPs by adaptive p-value combination

Combining SNP p-values from GWAS summary data is a promising strategy for detecting novel genetic factors. Existing statistical methods for the p-value-based SNP-set testing confront two challenges. First, the statistical power of different methods depends on unknown patterns of genetic effects that could drastically vary over different SNP sets. Second, they do not identify which SNPs primarily contribute to the global association of the whole set. We propose a new signal-adaptive analysis pipeline to address these challenges using the omnibus thresholding Fisher’s method (oTFisher). The oTFisher remains robustly powerful over various patterns of genetic effects. Its adaptive thresholding can be applied to estimate important SNPs contributing to the overall significance of the given SNP set. We develop efficient calculation algorithms to control the type I error rate, which accounts for the linkage disequilibrium among SNPs. Extensive simulations show that the oTFisher has robustly high power and provides a higher balanced accuracy in screening SNPs than the traditional Bonferroni and FDR procedures. We applied the oTFisher to study the genetic association of genes and haplotype blocks of the bone density-related traits using the summary data of the Genetic Factors for Osteoporosis Consortium. The oTFisher identified more novel and literature-reported genetic factors than existing p-value combination methods. Relevant computation has been implemented into the R package TFisher to support similar data analysis.

Association between Immunologic Markers and Cirrhosis in Individuals from a Prospective Chronic Hepatitis C Cohort

Background: Chronic hepatitis C virus (HCV) infection can affect immune response and inflammatory pathways, leading to severe liver diseases such as cirrhosis and hepatocellular carcinoma (HCC). Methods: In a prospective cohort of chronically HCV-infected individuals, we sampled 68 individuals who developed cirrhosis, 91 controls who did not develop cirrhosis, and 94 individuals who developed HCC. Unconditional odds ratios (ORs) from polytomous logistic regression models and canonical discriminant analyses (CDAs) were used to compare categorical (C) baseline plasma levels for 102 markers in individuals who developed cirrhosis vs. controls and those who developed HCC vs. cirrhosis. Leave-one-out cross validation was used to produce receiver operating characteristic curves to assess predictive ability of markers. Lastly, biological pathways were assessed in association with cirrhotic development compared to controls. Results: After multivariable adjustment, DEFA-1 (OR: C2v.C1 = 7.73; p < 0.0001), ITGAM (OR: C2v.C1 = 4.03; p = 0.0002), SCF (OR: C4v.C1 = 0.19; p-trend = 0.0001), and CCL11 (OR: C4v.C1 = 0.31; p-trend= 0.002) were all associated with development of cirrhosis compared to controls; these markers, together with clinical/demographics variables, improved prediction of cirrhosis from 55.7% (in clinical/demographic-only model) to 74.9% accuracy. A twelve-marker model based on CDA results further increased prediction of cirrhosis to 88.0%. While six biological pathways were found to be associated with cirrhosis, cell adhesion was the only pathway associated with cirrhosis after Bonferroni correction. In contrast to cirrhosis, DEFA-1 and ITGAM levels were inversely associated with HCC risk. Conclusions: Pending validation, these findings highlight the important role of immunological markers in predicting HCV-related cirrhosis even 11 years post-enrollment.

Interpretable network-guided epistasis detection

Background

Detecting epistatic interactions at the gene level is essential to understanding the biological mechanisms of complex diseases. Unfortunately, genome-wide interaction association studies involve many statistical challenges that make such detection hard. We propose a multi-step protocol for epistasis detection along the edges of a gene-gene co-function network. Such an approach reduces the number of tests performed and provides interpretable interactions while keeping type I error controlled. Yet, mapping gene interactions into testable single-nucleotide polymorphism (SNP)-interaction hypotheses, as well as computing gene pair association scores from SNP pair ones, is not trivial.

Results

Here we compare 3 SNP-gene mappings (positional overlap, expression quantitative trait loci, and proximity in 3D structure) and use the adaptive truncated product method to compute gene pair scores. This method is non-parametric, does not require a known null distribution, and is fast to compute. We apply multiple variants of this protocol to a genome-wide association study dataset on inflammatory bowel disease. Different configurations produced different results, highlighting that various mechanisms are implicated in inflammatory bowel disease, while at the same time, results overlapped with known disease characteristics. Importantly, the proposed pipeline also differs from a conventional approach where no network is used, showing the potential for additional discoveries when prior biological knowledge is incorporated into epistasis detection.

Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar's test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

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.

Powerful p-value combination methods to detect incomplete association

Meta-analyses increase statistical power by combining statistics from multiple studies. Meta-analysis methods have mostly been evaluated under the condition that all the data in each study have an association with the given phenotype. However, specific experimental conditions in each study or genetic heterogeneity can result in "unassociated statistics" that are derived from the null distribution. Here, we show that power of conventional meta-analysis methods rapidly decreases as an increasing number of unassociated statistics are included, whereas the classical Fisher's method and its weighted variant (wFisher) exhibit relatively high power that is robust to addition of unassociated statistics. We also propose another robust method based on joint distribution of ordered p-values (ordmeta). Simulation analyses for t-test, RNA-seq, and microarray data demonstrated that wFisher and ordmeta, when only a small number of studies have an association, outperformed existing meta-analysis methods. We performed meta-analyses of nine microarray datasets (prostate cancer) and four association summary datasets (body mass index), where our methods exhibited high biological relevance and were able to detect genes that the-state-of-the-art methods missed. The metapro R package that implements the proposed methods is available from both CRAN and GitHub ( http://github.com/unistbig/metapro ).

A novel test by combining the maximum and minimum values among a large number of dependent Z-scores with application to genome wide association study

In this article, we propose a novel test via combining the maximum and minimum values among a large number of dependent Z-scores for testing the hypothesis with sparse signals. The proposed test employs the information about different signs of maximum and minimum Z-scores and thus power is gained. Its asymptotic null distribution is derived under the null hypothesis and some regular conditions. Extensive simulation studies are conducted to show the advantages of the proposed test by comparing with two existing ones. A real application to the lipids genome wide association study further shows its performances.

ASYMPTOTICALLY INDEPENDENT U-STATISTICS IN HIGH-DIMENSIONAL TESTING

Many high-dimensional hypothesis tests aim to globally examine marginal or low-dimensional features of a high-dimensional joint distribution, such as testing of mean vectors, covariance matrices and regression coefficients. This paper constructs a family of U-statistics as unbiased estimators of the ℓ p -norms of those features. We show that under the null hypothesis, the U-statistics of different finite orders are asymptotically independent and normally distributed. Moreover, they are also asymptotically independent with the maximum-type test statistic, whose limiting distribution is an extreme value distribution. Based on the asymptotic independence property, we propose an adaptive testing procedure which combines p-values computed from the U-statistics of different orders. We further establish power analysis results and show that the proposed adaptive procedure maintains high power against various alternatives.

Circadian genes polymorphisms, night work and prostate cancer risk: Findings from the EPICAP study

Over the past two decades, several studies have attempted to understand the hypothesis that disrupting the circadian rhythm may promote the development of cancer. Some have suggested that night work and some circadian genes polymorphisms are associated with cancer, including prostate cancer. Our study aims to test the hypothesis that prostate cancer risk among night workers may be modulated by genetic polymorphisms in the circadian pathway genes based on data from the EPICAP study, a population-based case-control study including 1511 men (732 cases/779 controls) with genotyped data. We estimated odds ratio (ORs) and P values of the association between prostate cancer and circadian gene variants using logistic regression models. We tested the interaction between circadian genes variants and night work indicators that were significantly associated with prostate cancer at pathway, gene and SNP levels. Analyses were also stratified by each of these night work indicators and by cancer aggressiveness. The circadian pathway was significantly associated with aggressive prostate cancer among night workers (P = .004), particularly for men who worked at night for <20 years (P = .0002) and those who performed long night shift (>10 hours, P = .001). At the gene level, we observed among night workers significant associations between aggressive prostate cancer and ARNTL, NPAS2 and RORA. At the SNP-level, no significant association was observed. Our findings provide some clues of a potential modulating effect of circadian genes in the relationship between night work and prostate cancer. Further investigation is warranted to confirm these findings and to better elucidate the biological pathways involved.

Genome-wide pathway-based quantitative multiple phenotypes analysis

For complex diseases, genome-wide pathway association studies have become increasingly promising. Currently, however, pathway-based association analysis mainly focus on a single phenotype, which may insufficient to describe the complex diseases and physiological processes. This work proposes a combination model to evaluate the association between a pathway and multiple phenotypes and to reduce the run time based on asymptotic results. For a single phenotype, we propose a semi-supervised maximum kernel-based U-statistics (mSKU) method to assess the pathway-based association analysis. For multiple phenotypes, we propose the fisher combination function with dependent phenotypes (FC) to transform the p-values between the pathway and each marginal phenotype individually to achieve pathway-based multiple phenotypes analysis. With real data from the Alzheimer Disease Neuroimaging Initiative (ADNI) study and Human Liver Cohort (HLC) study, the FC-mSKU method allows us to specify which pathways are specific to a single phenotype or contribute to common genetic constructions of multiple phenotypes. If we only focus on single-phenotype tests, we may miss some findings for etiology studies. Through extensive simulation studies, the FC-mSKU method demonstrates its advantages compared with its counterparts.

A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies

BACKGROUND

Pathway analysis allows joint consideration of multiple SNPs belonging to multiple genes, which in turn belong to a biologically defined pathway. This type of analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway.

METHODS

We develop a Bayesian hierarchical model by fully modeling the 3-level hierarchy, namely, SNP-gene-pathway that is naturally inherent in the structure of the pathways, unlike the currently used ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. To deal with the high dimensionality, we regularize the regression coefficients through an appropriate choice of priors. The model is fit using a combination of iteratively weighted least squares and expectation-maximization algorithms to estimate the posterior modes and their standard errors. A normal approximation is used for inference.

RESULTS

We conduct simulations to study the proposed method and find that our method has higher power than some standard approaches in several settings for identifying pathways with multiple modest-sized variants. We illustrate the method by analyzing data from two genome-wide association studies on breast and renal cancers.

CONCLUSION

Our method can be helpful in detecting pathway association.

Integration of GWAS and eQTL Analysis to Identify Risk Loci and Susceptibility Genes for Gastric Cancer

Genome-wide association studies (GWAS) have identified several susceptibility loci for gastric cancer (GC), but the majority of identified single-nucleotide polymorphisms (SNPs) fall within the non-coding region and are likely to exert their biological function by modulating gene expression. To systematically estimate expression-associated SNPs (eSNPs) that confer genetic predisposition to GC, we evaluated the associations of 314,203 stomach tissue-specific eSNPs with GC risk in three GWAS datasets (2,631 cases and 4,373 controls). Subsequently, we conducted a gene-based analysis to calculate the cumulative effect of eSNPs through sequence kernel association combined test and Sherlock integrative analysis. At the SNP-level, we identified two novel variants (rs836545 at 7p22.1 and rs1892252 at 6p22.2) associated with GC risk. The risk allele carriers of rs836545-T and rs1892252-G exhibited higher expression levels of DAGLB (P = 3.70 × 10^–18) and BTN3A2 (P = 3.20 × 10^–5), respectively. Gene-based analyses identified DAGLB and FBXO43 as novel susceptibility genes for GC. DAGLB and FBXO43 were significantly overexpressed in GC tissues than in their adjacent tissues (P = 5.59 × 10^–7 and P = 3.90 × 10^–6, respectively), and high expression level of these two genes was associated with an unfavorable prognosis of GC patients (P = 1.30 × 10^–7 and P = 7.60 × 10^–3, respectively). Co-expression genes with these two novel genes in normal stomach tissues were significantly enriched in several cancer-related pathways, including P53, MAPK and TGF-beta pathways. In summary, our findings confirm the importance of eSNPs in dissecting the genetic basis of GC, and the identified eSNPs and relevant genes will provide new insight into the genetic and biological basis for the mechanism of GC development.

Statistical Method Based on Bayes-Type Empirical Score Test for Assessing Genetic Association with Multilocus Genotype Data

Simultaneous testing of multiple genetic variants for association is widely recognized as a valuable complementary approach to single-marker tests. As such, principal component regression (PCR) has been found to have competitive power. We focus on exploring a robust test for an unknown genetic mode of all SNPs, an unknown Hardy-Weinberg equilibrium (HWE) in a population, and a large number of all SNPs. First, we propose a new global test by means of the use of codominant codes for all markers and PCR. The new global test is built on an empirical Bayes-type score statistic for testing marginal associations with each single marker. The new global test gains power by robustly exploiting the Hardy-Weinberg equilibrium in the control population and effectively using linkage disequilibrium among test markers. The new global test reduces to PCR when the genotype for each marker is coded as the number of minor alleles. This connection lends insight into the power of the new global test relative to PCR and some other popular multimarker test methods. Second, we propose a robust test method based on the new global test and the ordinary PCR test built on a prospective score statistic for testing marginal associations with each single marker when the genotype for each marker is coded as the number of minor alleles by taking the minimum p value of these two tests. Finally, through extensive simulation studies and analysis of the association between pancreatic cancer and some genes of interest, we show that the proposed robust test method has desirable power and can often identify association signals that may be missed by existing methods.

Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges

Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.

Polymorphisms in the CTLA4 promoter sequence are associated with canine hypoadrenocorticism

Background

Canine hypoadrenocorticism is an immune-mediated endocrinopathy that shares both clinical and pathophysiological similarities with Addison’s disease in humans. Several dog breeds are overrepresented in the disease population, suggesting that a genetic component is involved, although this is likely to be polygenic. Previous research has implicated CTLA4 as a potential susceptibility gene. CTLA4 is an important regulator of T cell function and polymorphisms/mutations in CTLA4 have been associated with a number of autoimmune phenotypes in both humans and rodent models of autoimmunity. The aim of the current study was to undertake a case:control association study of CTLA4 promotor polymorphisms in three dog breeds, cocker spaniels, springer spaniels and West Highland white terriers (WHWT).

Results

Polymorphisms in the CTLA4 promoter were determined by PCR and sequence-based typing. There were significant associations with three promoter haplotypes in cocker spaniels (p = 0.003). A series of SNPs were also associated with hypoadrenocorticism in cocker spaniels and springer spaniels, including polymorphisms in predicted NFAT and SP1 transcription factor binding sites.

Conclusions

This study provides further evidence that CTLA4 promotor polymorphisms are associated with this complex genetic disease and supports an immune mediated aetiopathogenesis of canine hypoadrenocorticism.

Post genome-wide association analysis: dissecting computational pathway/network-based approaches

Over thousands of genetic associations to diseases have been identified by genome-wide association studies (GWASs), which conceptually is a single-marker-based approach. There are potentially many uses of these identified variants, including a better understanding of the pathogenesis of diseases, new leads for studying underlying risk prediction and clinical prediction of treatment. However, because of inadequate power, GWAS might miss disease genes and/or pathways with weak genetic or strong epistatic effects. Driven by the need to extract useful information from GWAS summary statistics, post-GWAS approaches (PGAs) were introduced. Here, we dissect and discuss advances made in pathway/network-based PGAs, with a particular focus on protein-protein interaction networks that leverage GWAS summary statistics by combining effects of multiple loci, subnetworks or pathways to detect genetic signals associated with complex diseases. We conclude with a discussion of research areas where further work on summary statistic-based methods is needed.

Detecting Weak Signals by Combining Small P-Values in Genetic Association Studies

We approach the problem of combining top-ranking association statistics or P-values from a new perspective which leads to a remarkably simple and powerful method. Statistical methods, such as the rank truncated product (RTP), have been developed for combining top-ranking associations, and this general strategy proved to be useful in applications for detecting combined effects of multiple disease components. To increase power, these methods aggregate signals across top ranking single nucleotide polymorphisms (SNPs), while adjusting for their total number assessed in a study. Analytic expressions for combined top statistics or P-values tend to be unwieldy, which complicates interpretation and practical implementation and hinders further developments. Here, we propose the augmented rank truncation (ART) method that retains main characteristics of the RTP but is substantially simpler to implement. ART leads to an efficient form of the adaptive algorithm, an approach where the number of top ranking SNPs is varied to optimize power. We illustrate our methods by strengthening previously reported associations of μ-opioid receptor variants with sensitivity to pain.

Vitamin D-Related Genes, Blood Vitamin D Levels and Colorectal Cancer Risk in Western European Populations

Higher circulating 25-hydroxyvitamin D levels (25(OH)D) have been found to be associated with lower risk for colorectal cancer (CRC) in prospective studies. Whether this association is modified by genetic variation in genes related to vitamin D metabolism and action has not been well studied in humans. We investigated 1307 functional and tagging single-nucleotide polymorphisms (SNPs; individually, and by gene/pathway) in 86 vitamin D-related genes in 1420 incident CRC cases matched to controls from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. We also evaluated the association between these SNPs and circulating 25(OH)D in a subset of controls. We confirmed previously reported CRC risk associations between SNPs in the VDR, GC, and CYP27B1 genes. We also identified additional associations with 25(OH)D, as well as CRC risk, and several potentially novel SNPs in genes related to vitamin D transport and action (LRP2, CUBN, NCOA7, and HDAC9). However, none of these SNPs were statistically significant after Benjamini-Hochberg (BH) multiple testing correction. When assessed by a priori defined functional pathways, tumor growth factor β (TGFβ) signaling was associated with CRC risk (P ≤ 0.001), with most statistically significant genes being SMAD7 (PBH = 0.008) and SMAD3 (PBH = 0.008), and 18 SNPs in the vitamin D receptor (VDR) binding sites (P = 0.036). The 25(OH)D-gene pathway analysis suggested that genetic variants in the genes related to VDR complex formation and transcriptional activity are associated with CRC depending on 25(OH)D levels (interaction P = 0.041). Additional studies in large populations and consortia, especially with measured circulating 25(OH)D, are needed to confirm our findings.

Multiple rare and common variants in APOB gene locus associated with oxidatively modified low-density lipoprotein levels

Oxidatively modified low-density lipoproteins (oxLDL) play an important role in the occurrence and progression of atherosclerosis. To identify the genetic factors influencing the oxLDL levels, we have genotyped 776 DNA samples of Russian individuals for 196,725 single-nucleotide polymorphisms (SNPs) using the Cardio-MetaboChip (Illumina, USA) and conducted genome-wide association study (GWAS). Fourteen common variants in the locus including APOB gene were significantly associated with the oxLDL levels (P < 2.18 × 10⁻⁷). These variants explained only 6% of the variation in the oxLDL levels. Then, we assessed the contribution of rare coding variants of APOB gene to the oxLDL levels. Individuals with the extreme oxLDL levels (48 with the lowest and 48 with the highest values) were selected for targeted sequencing of the region including APOB gene. To evaluate the contribution of the SNPs to the oxLDL levels we used various statistical methods for the association analysis of rare variants: WST, SKAT, and SKAT-O. We revealed that both synonymous and nonsynonymous SNPs affected the oxLDL levels. For the joint analysis of the rare and common variants, we conducted the SKAT-C testing and found a group of 15 SNPs significantly associated with the oxLDL levels (P = 2.14 × 10⁻⁹). Our results indicate that the oxLDL levels depend on both common and rare variants of the APOB gene.

Association of Selenoprotein and Selenium Pathway Genotypes with Risk of Colorectal Cancer and Interaction with Selenium Status

Selenoprotein genetic variations and suboptimal selenium (Se) levels may contribute to the risk of colorectal cancer (CRC) development. We examined the association between CRC risk and genotype for single nucleotide polymorphisms (SNPs) in selenoprotein and Se metabolic pathway genes. Illumina Goldengate assays were designed and resulted in the genotyping of 1040 variants in 154 genes from 1420 cases and 1421 controls within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Multivariable logistic regression revealed an association of 144 individual SNPs from 63 Se pathway genes with CRC risk. However, regarding the selenoprotein genes, only TXNRD1 rs11111979 retained borderline statistical significance after adjustment for correlated tests (PACT = 0.10; PACT significance threshold was P < 0.1). SNPs in Wingless/Integrated (Wnt) and Transforming growth factor (TGF) beta-signaling genes (FRZB, SMAD3, SMAD7) from pathways affected by Se intake were also associated with CRC risk after multiple testing adjustments. Interactions with Se status (using existing serum Se and Selenoprotein P data) were tested at the SNP, gene, and pathway levels. Pathway analyses using the modified Adaptive Rank Truncated Product method suggested that genes and gene x Se status interactions in antioxidant, apoptosis, and TGF-beta signaling pathways may be associated with CRC risk. This study suggests that SNPs in the Se pathway alone or in combination with suboptimal Se status may contribute to CRC development.

Association of breast cancer risk with polymorphisms in genes involved in the metabolism of xenobiotics and interaction with tobacco smoking: A gene-set analysis

Single nucleotide polymorphisms (SNPs) in genes involved in xenobiotics metabolism (XM) are suspected to play a role in breast cancer risk. However, previous findings based on a SNP by SNP approach need to be replicated taking into account the combined effects of multiple SNPs. We used a gene-set analysis method to study the association between breast cancer risk and genetic variation in XM genes (seen as a set of SNPs) and in the XM pathway (seen as a set of genes). We also studied the interaction between variants in XM genes and tobacco smoking. The analysis was conducted in a case-control study of 1,125 cases and 1,172 controls. Using a dedicated chip, genotyping data of 585 SNPs in 68 XM genes were available. Genetic variation in the whole XM pathway was significantly associated with premenopausal breast cancer risk (p = 0.008). This association was mainly driven by genetic variation in NAT2, CYP2C18, CYP2C19, AKR1C2 and ALDH1A3. The association between the XM gene pathway and breast cancer was observed among current and previous smokers, but not among never smokers (p = 0.013 for interaction between XM genes and tobacco smoking status). The association with breast cancer risk indicates that XM genes variants may play a role in breast carcinogenesis through their detoxification function of environmental pollutants, such as those contained in tobacco smoke.

Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic

A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.

Circadian genes and risk of prostate cancer: Findings from the EPICAP study

Circadian rhythms regulate several physiological functions and genes controlling the circadian rhythm were found to regulate cell proliferation, cell cycle and apoptosis. Few studies have investigated the role of those circadian genes in prostate cancer occurrence. We aim to investigate the relationship between circadian genes polymorphisms and prostate cancer risk based on data from the EPICAP study, a population-based case-control study including 1,515 men (732 cases / 783 controls) with genotyped data. Odds Ratios (ORs) for association between prostate cancer and circadian gene variants were estimated for each of the 872 single nucleotide polymorphisms (SNPs) in 31 circadian clock genes. We also used a gene-based and pathway-based approach with a focus on the pathway including 9 core circadian genes. Separate analyses were conducted by prostate cancer aggressiveness. The core-circadian pathway (p = 0.0006) was significantly associated to prostate cancer, for either low (p = 0.002) or high (p = 0.01) grade tumor. At the gene level, we observed significant associations between all prostate cancer and NPAS2 and PER1 after correcting for multiple testing, while only RORA was significant for aggressive tumors. At the SNP-level, no significant association was observed. Our findings provide additional evidence of a potential link between genetic variants in circadian genes and prostate cancer risk. Further investigation is warranted to confirm these findings and to better understand the biological pathways involved.

rPCMP: robust p-value combination by multiple partitions with applications to ATAC-seq data

BACKGROUND

Evaluating the significance for a group of genes or proteins in a pathway or biological process for a disease could help researchers understand the mechanism of the disease. For example, identifying related pathways or gene functions for chromatin states of tumor-specific T cells will help determine whether T cells could reprogram or not, and further help design the cancer treatment strategy. Some existing p-value combination methods can be used in this scenario. However, these methods suffer from different disadvantages, and thus it is still challenging to design more powerful and robust statistical method.

RESULTS

The existing method of Group combined p-value (GCP) first partitions p-values to several groups using a set of several truncation points, but the method is often sensitive to these truncation points. Another method of adaptive rank truncated product method(ARTP) makes use of multiple truncation integers to adaptively combine the smallest p-values, but the method loses statistical power since it ignores the larger p-values. To tackle these problems, we propose a robust p-value combination method (rPCMP) by considering multiple partitions of p-values with different sets of truncation points. The proposed rPCMP statistic have a three-layer hierarchical structure. The inner-layer considers a statistic which combines p-values in a specified interval defined by two thresholds points, the intermediate-layer uses a GCP statistic which optimizes the statistic from the inner layer for a partition set of threshold points, and the outer-layer integrates the GCP statistic from multiple partitions of p-values. The empirical distribution of statistic under null distribution could be estimated by permutation procedure.

CONCLUSIONS

Our proposed rPCMP method has been shown to be more robust and have higher statistical power. Simulation study shows that our method can effectively control the type I error rates and have higher statistical power than the existing methods. We finally apply our rPCMP method to an ATAC-seq dataset for discovering the related gene functions with chromatin states in mouse tumors T cell.

Associations of clock genes polymorphisms with soft tissue sarcoma susceptibility and prognosis

Background

Dysfunction of the circadian clock and polymorphisms of some circadian genes have been linked to cancer development and progression. We investigated the relationship between circadian genes germline variation and susceptibility or prognosis of patients with soft tissue sarcoma.

Patients and methods

We considered the 14 single nucleotide polymorphisms (SNPs) of 6 core circadian genes that have a minor allele frequency > 5% and that are known to be associated with cancer risk or prognosis. Genotyping was performed by q-PCR. Peripheral blood and clinic-pathological data were available for 162 patients with liposarcoma or leiomyosarcoma and 610 healthy donors. Associations between the selected clock genes polymorphisms and sarcoma susceptibility or prognosis were tested assuming 3 models of inheritance: additive, recessive and dominant. Subgroup analysis based on sarcoma histotype was performed under the additive genetic model. Multivariate logistic regression and multivariate Cox proportional hazard regression analyses were utilized to assess the association between SNPs with patient susceptibility and survival, respectively. Pathway variation analysis was conducted employing the Adaptive Rank Truncated Product method.

Results

Six out of the 14 analyzed SNPs were statistically significantly associated with susceptibility or prognosis of soft tissue sarcoma (P < 0.05). The present analysis suggested that carriers of the minor allele of the CLOCK polymorphism rs1801260 (C) or of PER2 rs934945 (T) had a reduced predisposition to sarcoma (26% and 35% respectively with the additive model) and liposarcoma (33% and 41% respectively). The minor allele (A) of NPAS2 rs895520 was associated with an increased predisposition to sarcoma of 33% and leiomyosarcoma of 44%. RORA rs339972 C allele was associated with a decreased predisposition to develop sarcoma assuming an additive model (29%) and leiomyosarcoma (36%). PER1 rs3027178 was associated with a reduced predisposition only in liposarcoma subgroup (32%). rs7602358 located upstream PER2 was significantly associated with liposarcoma survival (HR: 1.98; 95% CI 1.02–3.85; P = 0.04). Germline genetic variation in the circadian pathway was associated with the risk of developing soft tissue sarcoma (P = 0.035).

Conclusions

Genetic variation of circadian genes appears to play a role in the determinism of patient susceptibility and prognosis. These findings prompt further studies to fully dissect the molecular mechanisms.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1715-0) contains supplementary material, which is available to authorized users.

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.

Methods and results from the genome-wide association group at GAW20

BACKGROUND

This paper summarizes the contributions from the Genome-wide Association Study group (GWAS group) of the GAW20. The GWAS group contributions focused on topics such as association tests, phenotype imputation, and application of empirical kinships. The goals of the GWAS group contributions were varied. A real or a simulated data set based on the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was employed by different methods. Different outcomes and covariates were considered, and quality control procedures varied throughout the contributions.

RESULTS

The consideration of heritability and family structure played a major role in some contributions. The inclusion of family information and adaptive weights based on data were found to improve power in genome-wide association studies. It was proven that gene-level approaches are more powerful than single-marker analysis. Other contributions focused on the comparison between pedigree-based kinship and empirical kinship matrices, and investigated similar results in heritability estimation, association mapping, and genomic prediction. A new approach for linkage mapping of triglyceride levels was able to identify a novel linkage signal.

CONCLUSIONS

This summary paper reports on promising statistical approaches and findings of the members of the GWAS group applied on real and simulated data which encompass the current topics of epigenetic and pharmacogenomics.

Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort

BACKGROUND

Common variants have explained less than the amount of heritability expected for complex diseases, which has led to interest in less-common variants and more powerful approaches to the analysis of whole-genome scans. Because of low frequency (low statistical power), less-common variants are best analyzed using SNP-set methods such as gene-set or pathway-based analyses. However, there is as yet no clear consensus regarding how to focus in on potential risk variants following set-based analyses. We used a stepwise, telescoping approach to analyze common- and rare-variant data from the Illumina Metabochip array to assess genomic association with colorectal cancer (CRC) in the Japanese sub-population of the Multiethnic Cohort (676 cases, 7180 controls). We started with pathway analysis of SNPs that are in genes and pathways having known mechanistic roles in colorectal cancer, then focused on genes within the pathways that evidenced association with CRC, and finally assessed individual SNPs within the genes that evidenced association. Pathway SNPs downloaded from the dbSNP database were cross-matched with Metabochip SNPs and analyzed using the logistic kernel machine regression approach (logistic SNP-set kernel-machine association test, or sequence kernel association test; SKAT) and related methods.

RESULTS

The TGF-β and WNT pathways were associated with all CRC, and the WNT pathway was associated with colon cancer. Individual genes demonstrating the strongest associations were TGFBR2 in the TGF-β pathway and SMAD7 (which is involved in both the TGF-β and WNT pathways). As partial validation of our approach, a known CRC risk variant in SMAD7 (in both the TGF-β and WNT pathways: rs11874392) was associated with CRC risk in our data. We also detected two novel candidate CRC risk variants (rs13075948 and rs17025857) in TGFBR2, a gene known to be associated with CRC risk.

CONCLUSIONS

A stepwise, telescoping approach identified some potentially novel risk variants associated with colorectal cancer, so it may be a useful method for following up on results of set-based SNP analyses. Further work is required to assess the statistical characteristics of the approach, and additional applications should aid in better clarifying its utility.

Adaptive Group-combined P-values Test for Two-sample Location Problem with Applications to Microarray Data

The purpose of this article is to propose a test for two-sample location problem in high-dimensional data. In general highdimensional case, the data dimension can be much larger than the sample size and the underlying distribution may be far from normal. Existing tests requiring explicit relationship between the data dimension and sample size or designed for multivariate normal distributions may lose power significantly and even yield type I error rates strayed from nominal levels. To overcome this issue, we propose an adaptive group p-values combination test which is robust against both high dimensionality and normality. Simulation studies show that the proposed test controls type I error rates correctly and outperforms some existing tests in most situations. An Ageing Human Brain Microarray data are used to further exemplify the method.

Associations between genetic polymorphisms of membrane transporter genes and prognosis after chemotherapy: meta-analysis and finding from Seoul Breast Cancer Study (SEBCS)

Membrane transporters can be major determinants of the pharmacokinetic profiles of anticancer drugs. The associations between genetic variations of ATP-binding cassette (ABC) and solute carrier (SLC) genes and cancer survival were investigated through a meta-analysis and an association study in the Seoul Breast Cancer Study (SEBCS). Including the SEBCS, the meta-analysis was conducted among 38 studies of genetic variations of transporters on various cancer survivors. The population of SEBCS consisted of 1338 breast cancer patients who had been treated with adjuvant chemotherapy. A total of 7750 SNPs were selected from 453 ABC and/or SLC genes typed by an Affymetrix 6.0 chip. ABCB1 rs1045642 was associated with poor progression-free survival in a meta-analysis (HR = 1.33, 95% CI: 1.07-1.64). ABCB1, SLC8A1, and SLC12A8 were associated with breast cancer survival in SEBCS (P_gene < 0.05). ABCB1 rs1202172 was differentially associated with survival depending on the chemotherapy (P_interaction = 0.035). Our finding provides suggestive associations of membrane transporters on cancer survival.

Association analysis of multiple traits by an approach of combining P values

Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants.

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.

Association study of genetic variation in the autophagy lysosome pathway genes and risk of eight kinds of cancers

The autophagy lysosome pathway is essential to maintain cell viability and homeostasis in response to many stressful environments, which is reported to play a vital role in cancer development and therapy. However, the association of genetic alterations of this pathway with risk of cancer remains unclear. Based on genome-wide association study data of eight kinds of cancers, we used an adaptive rank truncated product approach to perform a pathway-level and gene-level analysis, and used a logistic model to calculate SNP-level associations to examine whether an altered autophagy lysosome pathway contributes to cancer susceptibility. Among eight kinds of cancers, four of them showed significant statistics in the pathway-level analysis, including breast cancer (p = 0.00705), gastric cancer (p = 0.00880), lung cancer (p = 0.000100) and renal cell carcinoma (p = 0.00190). We also found that some autophagy lysosome genes had signals of association with cancer risk. Our results demonstrated that inherited genetic variants in the overall autophagy lysosome pathway and certain associated genes might contribute to cancer susceptibility, which warrant further evaluation in other independent datasets.

Genetic variants in the histone methylation and acetylation pathway and their risks in eight types of cancers

OBJECTIVES

The histone methylation and acetylation pathway genes regulate cell growth and survival. Aberrations in this pathway are implicated in a variety of cancers. This study aimed to identify germline genetic variants in histone methylation and acetylation pathway genes that may contribute to risk in eight types of cancers and to explore the relation between the whole pathway and their risks in these types of cancers.

METHODS

Germline genetic variants in 89 genes in the histone methylation and acetylation pathway were explored. Gene-based and pathway-based associations with eight types of cancers were analyzed using logistic regression models and the permutation-based adaptive rank-truncated product method, respectively.

RESULTS

Gene-level associations revealed that genetic variants in 45 genes were significantly associated with the risk of cancer. The total histone methylation and acetylation pathway was significantly associated with the risk of esophageal squamous cell carcinoma (P = 0.0492) and prostate (P = 0.0038), lung (P = 0.00015), and bladder cancer (P = 0.00135), but not with breast (P = 0.182), pancreatic (P = 0.336) and gastric cancer (P = 0.347) and renal cell carcinoma (P =0.828).

CONCLUSIONS

Our study suggested there is an association between germline genetic variation at the overall histone methylation and acetylation pathway level and some individual genes with cancer risk. Further studies are needed to validate these relations and to explore relative mechanisms.

A Review of Pathway-Based Analysis Tools That Visualize Genetic Variants

Pathway analysis is a powerful method for data analysis in genomics, most often applied to gene expression analysis. It is also promising for single-nucleotide polymorphism (SNP) data analysis, such as genome-wide association study data, because it allows the interpretation of variants with respect to the biological processes in which the affected genes and proteins are involved. Such analyses support an interactive evaluation of the possible effects of variations on function, regulation or interaction of gene products. Current pathway analysis software often does not support data visualization of variants in pathways as an alternate method to interpret genetic association results, and specific statistical methods for pathway analysis of SNP data are not combined with these visualization features. In this review, we first describe the visualization options of the tools that were identified by a literature review, in order to provide insight for improvements in this developing field. Tool evaluation was performed using a computational epistatic dataset of gene–gene interactions for obesity risk. Next, we report the necessity to include in these tools statistical methods designed for the pathway-based analysis with SNP data, expressly aiming to define features for more comprehensive pathway-based analysis tools. We conclude by recognizing that pathway analysis of genetic variations data requires a sophisticated combination of the most useful and informative visual aspects of the various tools evaluated.

Adaptive combination of Bayes factors as a powerful method for the joint analysis of rare and common variants

Multi-marker association tests can be more powerful than single-locus analyses because they aggregate the variant information within a gene/region. However, combining the association signals of multiple markers within a gene/region may cause noise due to the inclusion of neutral variants, which usually compromises the power of a test. To reduce noise, the “adaptive combination of P-values” (ADA) method removes variants with larger P-values. However, when both rare and common variants are considered, it is not optimal to truncate variants according to their P-values. An alternative summary measure, the Bayes factor (BF), is defined as the ratio of the probability of the data under the alternative hypothesis to that under the null hypothesis. The BF quantifies the “relative” evidence supporting the alternative hypothesis. Here, we propose an “adaptive combination of Bayes factors” (ADABF) method that can be directly applied to variants with a wide spectrum of minor allele frequencies. The simulations show that ADABF is more powerful than single-nucleotide polymorphism (SNP)-set kernel association tests and burden tests. We also analyzed 1,109 case-parent trios from the Schizophrenia Trio Genomic Research in Taiwan. Three genes on chromosome 19p13.2 were found to be associated with schizophrenia at the suggestive significance level of 5 × 10⁻⁵.

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.

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.