Use of the gamma method for self-contained gene-set analysis of SNP data

Uncovering Evidence for Endocrine-Disrupting Chemicals That Elicit Differential Susceptibility through Gene-Environment Interactions

Exposure to endocrine-disrupting chemicals (EDCs) is linked to myriad disorders, characterized by the disruption of the complex endocrine signaling pathways that govern development, physiology, and even behavior across the entire body. The mechanisms of endocrine disruption involve a complex system of pathways that communicate across the body to stimulate specific receptors that bind DNA and regulate the expression of a suite of genes. These mechanisms, including gene regulation, DNA binding, and protein binding, can be tied to differences in individual susceptibility across a genetically diverse population. In this review, we posit that EDCs causing such differential responses may be identified by looking for a signal of population variability after exposure. We begin by summarizing how the biology of EDCs has implications for genetically diverse populations. We then describe how gene-environment interactions (GxE) across the complex pathways of endocrine signaling could lead to differences in susceptibility. We survey examples in the literature of individual susceptibility differences to EDCs, pointing to a need for research in this area, especially regarding the exceedingly complex thyroid pathway. Following a discussion of experimental designs to better identify and study GxE across EDCs, we present a case study of a high-throughput screening signal of putative GxE within known endocrine disruptors. We conclude with a call for further, deeper analysis of the EDCs, particularly the thyroid disruptors, to identify if these chemicals participate in GxE leading to differences in susceptibility.

Ontogeny Related Changes in the Pediatric Liver Metabolome

Background: A major challenge in implementing personalized medicine in pediatrics is identifying appropriate drug dosages for children. The majority of drug dosing studies have been based on adult populations, often with modification of the dosing for children based on size and weight. However, the growth and development experienced by children between birth and adulthood represents a dynamically changing biological system, with implications for effective drug dosing, efficacy as well as potential drug toxicity. The purpose of this study was to apply a metabolomics approach to gain preliminary insights into the ontogeny of liver function from newborn to adolescent. Methods: Metabolites were measured in 98 post-mortem pediatric liver samples in two experiments 3 batches of samples, allowing for both technical and biological validation. After extensive quality control, imputation and normalization, non-parametric tests were used to determine which metabolite levels differ between the four age groups (AG) ranging in age from newborn to adolescent (AG1-children <1 year; AG2-children with age between 1 and 6 years; AG3-children with age between 6 and 12 years; AG4-children with age between 12 and 18 years). To identify which metabolites had different concentration levels among the age groups, Kruskal-Wallis and Spearman correlation tests were conducted. Pathway analysis utilized the Gamma Method. Correction for multiple testing was completed using Bonferroni correction. Results: We found 41 metabolites (out of 884) that were biologically validated, and of those 25 were technically replicated, of which 24 were known metabolites. For the majority of these 24 metabolites, concentration levels were significantly lower in newborns than in the other age groups, many of which were long chain fatty acids or involved in pyrimidine or purine metabolism. Additionally, we found two KEGG pathways enriched for association with age: betaine metabolism and alpha linolenic acid and linoleic acid metabolism. Conclusions: Understanding the role that ontogeny of childhood liver plays may aid in determining better drug dosing algorithms for children.

DOT: Gene-set analysis by combining decorrelated association statistics

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets, including cost of management, difficulties in consolidation of records across research groups, etc. These issues make methods based on SNP-level summary statistics particularly appealing. The most common form of combining statistics is a sum of SNP-level squared scores, possibly weighted, as in burden tests for rare variants. The overall significance of the resulting statistic is evaluated using its distribution under the null hypothesis. Here, we demonstrate that this basic approach can be substantially improved by decorrelating scores prior to their addition, resulting in remarkable power gains in situations that are most commonly encountered in practice; namely, under heterogeneity of effect sizes and diversity between pairwise LD. In these situations, the power of the traditional test, based on the added squared scores, quickly reaches a ceiling, as the number of variants increases. Thus, the traditional approach does not benefit from information potentially contained in any additional SNPs, while our decorrelation by orthogonal transformation (DOT) method yields steady gain in power. We present theoretical and computational analyses of both approaches, and reveal causes behind sometimes dramatic difference in their respective powers. We showcase DOT by analyzing breast cancer and cleft lip data, in which our method strengthened levels of previously reported associations and implied the possibility of multiple new alleles that jointly confer disease risk.

The potential role of clock genes in children attention-deficit/hyperactivity disorder

BACKGROUND

Attention deficit/ hyperactivity disorder (ADHD) is a chronic neurodevelopmental disorder and is thought to be associated with circadian system.

METHODS

We performed a pathway-based study to test individual single nucleotide polymorphisms (SNPs) and the overall evidence of genetic polymorphisms involved in the circadian pathway in association with children ADHD susceptibility among a Chinese population. A community-based case-control study was conducted among Chinese children, and 168 ADHD patients and 233 controls were recruited using a combination diagnosis based on the diagnostic and statistical manual of mental disorders iv (DSM-IV) ADHD rating scale, Swanson, Nolan, and Pelham rating scale (SNAP-IV) rating scale, and semi-structured clinical interview.

RESULTS

The results of single-loci analyses identified that PER1 rs2518023 and ARNTL2 rs2306074 were nominally association with ADHD susceptibility (P < 0.05). Next, we applied multifactor dimensionality reduction (MDR), and classification and regression tree (CART) analyses to explore high-order gene-gene interactions among the functional SNPs to ADHD risks. The results indicated that interactions among the PER1 rs2518023, ARNTL2 rs2306074 and NR1D1 rs939347 were associated with the risk of ADHD in children. Individuals carrying the combination genotypes of the PER1 rs2518023 GG or GT, ARNTL2 rs2306074 TC or TT and NR1D1 rs939347 GA or AA displayed a significantly higher risk for ADHD than who carry the PER1 rs2518023 TT and CRY2 rs2292910 CA/CC genotypes (adjusted OR = 4.37, 95% CI = 2.16-8.85, P < 0.001).

CONCLUSIONS

These findings revealed the importance of genetic variations related to the circadian clock system to the susceptibility of children ADHD.

Statistical genomics in rare cancer

Rare cancers make of more than 20% of cancer cases. Due to the rare nature, less research has been conducted on rare cancers resulting in worse outcomes for patients with rare cancers compared to common cancers. The ability to study rare cancers is impaired by the ability to collect a large enough set of patients to complete an adequately powered genomic study. In this manuscript we outline analytical approaches and public genomic datasets that have been used in genomic studies of rare cancers. These statistical analysis approaches and study designs include: gene set / pathway analyses, pedigree and consortium studies, meta-analysis or horizontal integration, and integration of multiple types of genomic information or vertical integration. We also discuss some of the publicly available resources that can be leveraged in rare cancer genomic studies.

Meta-Qtest: meta-analysis of quadratic test for rare variants

Background

In genome-wide association studies (GWASs), meta-analysis has been widely used to improve statistical power by combining the results of different studies. Meta-analysis can detect phenotype associated variants that are failed to be detected in single studies. Especially, in biomedical sciences, meta-analysis is often necessary not only for improving statistical power, but also for reducing unavoidable limitation in data collection. As next-generation sequencing (NGS) technology has been developed, meta-analysis of rare variants is proceeding briskly along with meta-analysis of common variants in GWASs. However, meta-analysis on a single variant that is commonly used in common variant association test is improper for rare variants. A sparse signal of rare variant undermines the association signal and its large number causes multiple testing problem. To over-come these problems, we propose a meta-analysis method at the gene-level rather than variant level.

Results

Among many methods that have been developed, we used the unified quadratic tests (Q-tests); Q-test is more powerful than or as powerful as other tests such as Sequence Kernel Association Tests (SKAT). Since there are three different versions of Q-test (QTest1, QTest2, QTest3), each assumes different relationships among multiple rare variants, we extended them into meta-study accordingly. For meta-analysis, we consider two types of approaches, the one is to combine regression coefficients and the other is to combine test statistics from each single study. We extend the Q-test for meta-analysis, proposing Meta Quadratic Test (Meta-Qtest). Meta Q-test avoids the limitations of MetaSKAT. It does not only consider genetic heterogeneity among studies as MetaSKAT but also reflects diverse real situations; since we extend three different Q-tests into meta-analysis respectively, flexible Meta Q-test suggests way to deal with gene-level rare variant meta-analysis efficiently From the results of real data analysis of blood pressure trait, our meta-analysis could successfully discovered genes, KCNA5 and CABIN1 that are already well known for relevance with hypertension disease and they are not detected in MetaSKAT.

Conclusion

As exemplified by an application to T2D Genes projects data set, Meta-Qtest more effectively identified genes associated with hypertension disease than MetaSKAT did.

Application of the parametric bootstrap for gene-set analysis of gene-environment interactions

Testing for gene-environment (GE) interactions in a gene-set defined by a biological pathway can help us understand the interplay between genes and environments and provide insight into disease etiology. A self-contained gene-set analysis can be performed by combining gene-level p-values using approaches such as the Gamma Method. In a gene-set analysis of genetic main effects, permutation approaches are commonly used to avoid inflated probability of a type 1 error caused by correlation of genes within the same pathway. However, when testing interaction effects, it is typically not possible to construct an exact permutation test. We therefore propose using a parametric bootstrap. For testing an interaction term, this approach requires fitting the null model, which only contains main effects; however, for a gene-set GE interaction model, the number of main effects can be large and therefore they may not be estimable. To estimate the main effects of SNPs in a gene-set, we propose modeling them as random effects. We then repetitively simulate null data from this model and analyze it to generate the null distribution of gene-set GE p-values, allowing for an empirical assessment of significance of the global GE effect in the gene-set of interest. Through simulation, we demonstrate that this approach maintains correct type I error, and is well powered to detect GE interactions. We apply our method to test whether the association of obesity with bipolar disorder (BD) is modified by genetic variation in the Wnt signaling pathway.

Pathway analysis in attention deficit hyperactivity disorder: An ensemble approach

Despite a wealth of evidence for the role of genetics in attention deficit hyperactivity disorder (ADHD), specific and definitive genetic mechanisms have not been identified. Pathway analyses, a subset of gene-set analyses, extend the knowledge gained from genome-wide association studies (GWAS) by providing functional context for genetic associations. However, there are numerous methods for association testing of gene sets and no real consensus regarding the best approach. The present study applied six pathway analysis methods to identify pathways associated with ADHD in two GWAS datasets from the Psychiatric Genomics Consortium. Methods that utilize genotypes to model pathway-level effects identified more replicable pathway associations than methods using summary statistics. In addition, pathways implicated by more than one method were significantly more likely to replicate. A number of brain-relevant pathways, such as RhoA signaling, glycosaminoglycan biosynthesis, fibroblast growth factor receptor activity, and pathways containing potassium channel genes, were nominally significant by multiple methods in both datasets. These results support previous hypotheses about the role of regulation of neurotransmitter release, neurite outgrowth and axon guidance in contributing to the ADHD phenotype and suggest the value of cross-method convergence in evaluating pathway analysis results. © 2016 Wiley Periodicals, Inc.

Genetic variation in the immunosuppression pathway genes and breast cancer susceptibility: a pooled analysis of 42,510 cases and 40,577 controls from the Breast Cancer Association Consortium

Immunosuppression plays a pivotal role in assisting tumors to evade immune destruction and promoting tumor development. We hypothesized that genetic variation in the immunosuppression pathway genes may be implicated in breast cancer tumorigenesis. We included 42,510 female breast cancer cases and 40,577 controls of European ancestry from 37 studies in the Breast Cancer Association Consortium (2015) with available genotype data for 3595 single nucleotide polymorphisms (SNPs) in 133 candidate genes. Associations between genotyped SNPs and overall breast cancer risk, and secondarily according to estrogen receptor (ER) status, were assessed using multiple logistic regression models. Gene-level associations were assessed based on principal component analysis. Gene expression analyses were conducted using RNA sequencing level 3 data from The Cancer Genome Atlas for 989 breast tumor samples and 113 matched normal tissue samples. SNP rs1905339 (A>G) in the STAT3 region was associated with an increased breast cancer risk (per allele odds ratio 1.05, 95 % confidence interval 1.03-1.08; p value = 1.4 × 10(-6)). The association did not differ significantly by ER status. On the gene level, in addition to TGFBR2 and CCND1, IL5 and GM-CSF showed the strongest associations with overall breast cancer risk (p value = 1.0 × 10(-3) and 7.0 × 10(-3), respectively). Furthermore, STAT3 and IL5 but not GM-CSF were differentially expressed between breast tumor tissue and normal tissue (p value = 2.5 × 10(-3), 4.5 × 10(-4) and 0.63, respectively). Our data provide evidence that the immunosuppression pathway genes STAT3, IL5, and GM-CSF may be novel susceptibility loci for breast cancer in women of European ancestry.

Adaptive Set-Based Methods for Association Testing

With a typical sample size of a few thousand subjects, a single genome-wide association study (GWAS) using traditional one single nucleotide polymorphism (SNP)-at-a-time methods can only detect genetic variants conferring a sizable effect on disease risk. Set-based methods, which analyze sets of SNPs jointly, can detect variants with smaller effects acting within a gene, a pathway, or other biologically relevant sets. Although self-contained set-based methods (those that test sets of variants without regard to variants not in the set) are generally more powerful than competitive set-based approaches (those that rely on comparison of variants in the set of interest with variants not in the set), there is no consensus as to which self-contained methods are best. In particular, several self-contained set tests have been proposed to directly or indirectly "adapt" to the a priori unknown proportion and distribution of effects of the truly associated SNPs in the set, which is a major determinant of their power. A popular adaptive set-based test is the adaptive rank truncated product (ARTP), which seeks the set of SNPs that yields the best-combined evidence of association. We compared the standard ARTP, several ARTP variations we introduced, and other adaptive methods in a comprehensive simulation study to evaluate their performance. We used permutations to assess significance for all the methods and thus provide a level playing field for comparison. We found the standard ARTP test to have the highest power across our simulations followed closely by the global model of random effects (GMRE) and a least absolute shrinkage and selection operator (LASSO)-based test.

Pharmacogenomics of estrogens on changes in carotid artery intima-medial thickness and coronary arterial calcification: Kronos Early Estrogen Prevention Study

Prior to the initiation of menopausal hormone treatment (MHT), genetic variations in the innate immunity pathway were found to be associated with carotid artery intima-medial thickness (CIMT) and coronary arterial calcification (CAC) in women (n = 606) enrolled in the Kronos Early Estrogen Prevention Study (KEEPS). Whether MHT might affect these associations is unknown. The association of treatment outcomes with variation in the same 764 candidate genes was evaluated in the same KEEPS participants 4 yr after randomization to either oral conjugated equine estrogens (0.45 mg/day), transdermal 17β-estradiol (50 μg/day), each with progesterone (200 mg/day) for 12 days each month, or placebo pills and patch. Twenty SNPs within the innate immunity pathway most related with CIMT after 4 yr were not among those associated with CIMT prior to MHT. In 403 women who completed the study in their assigned treatment group, single nucleotide polymorphisms (SNPs) within the innate immunity pathway were found to alter the treatment effect on 4 yr change in CIMT (i.e., significant interaction between treatment and genetic variation in the innate immunity pathway; P < 0.001). No SNPs by treatment effects were observed with changes of CAC >5 Agatston units after 4 yr. Results of this study suggest that hormonal status may interact with genetic variants to influence cardiovascular phenotypes, specifically, the pharmacogenomic effects within the innate immunity pathway for CIMT.

Gene set analysis: A step-by-step guide

To maximize the potential of genome-wide association studies, many researchers are performing secondary analyses to identify sets of genes jointly associated with the trait of interest. Although methods for gene-set analyses (GSA), also called pathway analyses, have been around for more than a decade, the field is still evolving. There are numerous algorithms available for testing the cumulative effect of multiple SNPs, yet no real consensus in the field about the best way to perform a GSA. This paper provides an overview of the factors that can affect the results of a GSA, the lessons learned from past studies, and suggestions for how to make analysis choices that are most appropriate for different types of data. © 2015 Wiley Periodicals, Inc.

The Roles of Genes in the Neuronal Migration and Neurite Outgrowth Network in Developmental Dyslexia: Single- and Multiple-Risk Genetic Variants

Abnormal regulation of neural migration and neurite growth is thought to be an important feature of developmental dyslexia (DD). We investigated 16 genetic variants, selected by bioinformatics analyses, in six key genes in the neuronal migration and neurite outgrowth network in a Chinese population. We first observed that KIAA0319L rs28366021, KIAA0319 rs4504469, and DOCK4 rs2074130 were significantly associated with DD risk after false discovery rate (FDR) adjustment for multiple comparisons (odds ratio (OR) = 0.672, 95 % confidence interval (CI) = 0.505-0.894, P = 0.006; OR = 1.608, 95 % CI = 1.174-2.203, P = 0.003; OR = 1.681, 95 % CI = 1.203-2.348, P = 0.002). The following classification and regression tree (CART) analysis revealed a prediction value of gene-gene interactions among DOCK4 rs2074130, KIAA0319 rs4504469, DCDC2 rs2274305, and KIAA0319L rs28366021 variants. Compared with the lowest risk carriers of the combination of rs2074130 CC, rs4504469 CC, and rs2274305 GG genotype, individuals carrying the combined genotypes of rs2074130 CC, rs4504469 CT or TT, and rs28366021 GG had a significantly increased risk for DD (OR = 2.492, 95 % CI = 1.447-4.290, P = 0.001); individuals with the combination of rs2074130 CT or TT and rs28366021 GG genotype exhibited the highest risk for DD (OR = 2.770, 95 % CI = 2.265-6.276, P = 0.000). A significant dose effect was observed among these four variants (P for trend = 0.000). In summary, this study supports the importance of single- and multiple-risk variants in this network in DD susceptibility in China.

Relative performance of gene- and pathway-level methods as secondary analyses for genome-wide association studies

BACKGROUND

Despite the success of genome-wide association studies (GWAS), there still remains "missing heritability" for many traits. One contributing factor may be the result of examining one marker at a time as opposed to a group of markers that are biologically meaningful in aggregate. To address this problem, a variety of gene- and pathway-level methods have been developed to identify putative biologically relevant associations. A simulation was conducted to systematically assess the performance of these methods. Using genetic data from 4,500 individuals in the Wellcome Trust Case Control Consortium (WTCCC), case-control status was simulated based on an additive polygenic model. We evaluated gene-level methods based on their sensitivity, specificity, and proportion of false positives. Pathway-level methods were evaluated on the relationship between proportion of causal genes within the pathway and the strength of association.

RESULTS

The gene-level methods had low sensitivity (20-63%), high specificity (89-100%), and low proportion of false positives (0.1-6%). The gene-level program VEGAS using only the top 10% of associated single nucleotide polymorphisms (SNPs) within the gene had the highest sensitivity (28.6%) with less than 1% false positives. The performance of the pathway-level methods depended on their reliance upon asymptotic distributions or if significance was estimated in a competitive manner. The pathway-level programs GenGen, GSA-SNP and MAGENTA had the best performance while accounting for potential confounders.

CONCLUSIONS

Novel genes and pathways can be identified using the gene and pathway-level methods. These methods may provide valuable insight into the "missing heritability" of traits and provide biological interpretations to GWAS findings.

Genetic variants in DNA repair genes as potential predictive markers for oxaliplatin chemotherapy in colorectal cancer

Oxaliplatin-based chemotherapy exerts its effects through generating DNA damage. Hence, genetic variants in DNA repair pathways could modulate treatment response. We used a prospective cohort of 623 colorectal cancer patients with stage II-IV disease treated with adjuvant/palliative chemotherapy to comprehensively investigate 1727 genetic variants in the DNA repair pathways as potential predictive markers for oxaliplatin treatment. Single nucleotide polymorphisms (SNP) associations with overall survival and recurrence-free survival were assessed using a Cox regression model. Pathway analysis was performed using the gamma method. Patients carrying variant alleles of rs3783819 (MNAT1) and rs1043953 (XPC) experienced a longer overall survival after treatment with oxaliplatin than patients who did not carry the variant allele, while the opposite association was found in patients who were not treated with oxaliplatin (false discovery rate-adjusted P-values for heterogeneity 0.0047 and 0.0237, respectively). The nucleotide excision repair (NER) pathway was found to be most likely associated with overall survival in patients who received oxaliplatin (P-value=0.002). Our data show that genetic variants in the NER pathway are potentially predictive of treatment response to oxaliplatin.

A set-based association test identifies sex-specific gene sets associated with type 2 diabetes

Single variant analysis in genome-wide association studies (GWAS) has been proven to be successful in identifying thousands of genetic variants associated with hundreds of complex diseases. However, these identified variants only explain a small fraction of inheritable variability in many diseases, suggesting that other resources, such as multilevel genetic variations, may contribute to disease susceptibility. In this work, we proposed to combine genetic variants that belong to a gene set, such as at gene- and pathway-level to form an integrated signal aimed to identify major players that function in a coordinated manner conferring disease risk. The integrated analysis provides novel insight into disease etiology while individual signals could be easily missed by single variant analysis. We applied our approach to a genome-wide association study of type 2 diabetes (T2D) with male and female data analyzed separately. Novel sex-specific genes and pathways were identified to increase the risk of T2D. We also demonstrated the performance of signal integration through simulation studies.

Functional and genomic context in pathway analysis of GWAS data

Gene set analysis (GSA) is a promising tool for uncovering the polygenic effects associated with complex diseases. However, the available techniques reflect a wide variety of hypotheses about how genetic effects interact to contribute to disease susceptibility. The lack of consensus about the best way to perform GSA has led to confusion in the field and has made it difficult to compare results across methods. A clear understanding of the various choices made during GSA - such as how gene sets are defined, how single-nucleotide polymorphisms (SNPs) are assigned to genes, and how individual SNP-level effects are aggregated to produce gene- or pathway-level effects - will improve the interpretability and comparability of results across methods and studies. In this review we provide an overview of the various data sources used to construct gene sets and the statistical methods used to test for gene set association, as well as provide guidelines for ensuring the comparability of results.

Integrative gene set analysis: application to platinum pharmacogenomics

Integrative genomics has the potential to uncover relevant loci, as clinical outcome and response to chemotherapies are most likely not due to a single gene (or data type) but rather a complex relationship involving genetic variation, mRNA, DNA methylation, and copy number variation. In addition to this complexity, many complex phenotypes are thought to be controlled by the interplay of multiple genes within the same molecular pathway or gene set (GS). To address these two challenges, we propose an integrative gene set analysis approach and apply this strategy to a cisplatin (CDDP) pharmacogenomics study involving lymphoblastoid cell lines for which genome-wide SNP and mRNA expression data was collected. Application of the integrative GS analysis implicated the role of the RNA binding and cytoskeletal part GSs. The genes LMNB1 and CENPF, within the cytoskeletal part GS, were functionally validated with siRNA knockdown experiments, where the knockdown of LMNB1 and CENPF resulted in CDDP resistance in multiple cancer cell lines. This study demonstrates the utility of an integrative GS analysis strategy for detecting novel genes associated with response to cancer therapies, moving closer to tailored therapy decisions for cancer patients.

Soft truncation thresholding for gene set analysis of RNA-seq data: application to a vaccine study

Gene set analysis (GSA) has been used for analysis of microarray data to aid the interpretation and to increase statistical power. With the advent of next-generation sequencing, the use of GSA is even more relevant, as studies are often conducted on a small number of samples. We propose the use of soft truncation thresholding and the Gamma Method (GM) to determine significant gene set (GS), where a generalized linear model is used to assess per-gene significance. The approach was compared to other methods using an extensive simulation study and RNA-seq data from smallpox vaccine study. The GM was found to outperform other proposed methods. Application of the GM to the smallpox vaccine study found the GSs to be moderately associated with response, including focal adhesion (p = 0.04) and extracellular matrix receptor interaction (p = 0.05). The application of GSA to RNA-seq data will provide new insights into the genomic basis of complex traits.

Gene-environment interactions in genome-wide association studies: current approaches and new directions

BACKGROUND

Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized gene-environment interactions are now fairly common in human genetic research, and with the shift toward genome-wide association studies, genome-wide gene-environment interaction studies are beginning to emerge.

METHODS

We summarize the basic ideas behind gene-environment interaction, and provide an overview of possible study designs and traditional analysis methods in the context of genome-wide analysis. We then discuss novel approaches beyond the traditional strategy of analyzing the interaction between the environmental factor and each polymorphism individually.

RESULTS

Two-step filtering approaches that reduce the number of polymorphisms tested for interactions can substantially increase the power of genome-wide gene-environment studies. New analytical methods including data-mining approaches, and gene-level and pathway-level analyses, also have the capacity to improve our understanding of how complex genetic and environmental factors interact to influence psychologic and psychiatric traits. Such methods, however, have not yet been utilized much in behavioral and mental health research.

CONCLUSIONS

Although methods to investigate gene-environment interactions are available, there is a need for further development and extension of these methods to identify gene-environment interactions in the context of genome-wide association studies. These novel approaches need to be applied in studies of psychology and psychiatry.

Genome-wide gene-set analysis for identification of pathways associated with alcohol dependence

It is believed that multiple genetic variants with small individual effects contribute to the risk of alcohol dependence. Such polygenic effects are difficult to detect in genome-wide association studies that test for association of the phenotype with each single nucleotide polymorphism (SNP) individually. To overcome this challenge, gene-set analysis (GSA) methods that jointly test for the effects of pre-defined groups of genes have been proposed. Rather than testing for association between the phenotype and individual SNPs, these analyses evaluate the global evidence of association with a set of related genes enabling the identification of cellular or molecular pathways or biological processes that play a role in development of the disease. It is hoped that by aggregating the evidence of association for all available SNPs in a group of related genes, these approaches will have enhanced power to detect genetic associations with complex traits. We performed GSA using data from a genome-wide study of 1165 alcohol-dependent cases and 1379 controls from the Study of Addiction: Genetics and Environment (SAGE), for all 200 pathways listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results demonstrated a potential role of the 'synthesis and degradation of ketone bodies' pathway. Our results also support the potential involvement of the 'neuroactive ligand-receptor interaction' pathway, which has previously been implicated in addictive disorders. These findings demonstrate the utility of GSA in the study of complex disease, and suggest specific directions for further research into the genetic architecture of alcohol dependence.

An exponential combination procedure for set-based association tests in sequencing studies

State-of-the-art next-generation-sequencing technologies can facilitate in-depth explorations of the human genome by investigating both common and rare variants. For the identification of genetic factors that are associated with disease risk or other complex phenotypes, methods have been proposed for jointly analyzing variants in a set (e.g., all coding SNPs in a gene). Variants in a properly defined set could be associated with risk or phenotype in a concerted fashion, and by accumulating information from them, one can improve power to detect genetic risk factors. Many set-based methods in the literature are based on statistics that can be written as the summation of variant statistics. Here, we propose taking the summation of the exponential of variant statistics as the set summary for association testing. From both Bayesian and frequentist perspectives, we provide theoretical justification for taking the sum of the exponential of variant statistics because it is particularly powerful for sparse alternatives-that is, compared with the large number of variants being tested in a set, only relatively few variants are associated with disease risk-a distinctive feature of genetic data. We applied the exponential combination gene-based test to a sequencing study in anticancer pharmacogenomics and uncovered mechanistic insights into genes and pathways related to chemotherapeutic susceptibility for an important class of oncologic drugs.

Gene set analysis of survival following ovarian cancer implicates macrolide binding and intracellular signaling genes

BACKGROUND

Genome-wide association studies (GWAS) for epithelial ovarian cancer (EOC), the most lethal gynecologic malignancy, have identified novel susceptibility loci. GWAS for survival after EOC have had more limited success. The association of each single-nucleotide polymorphism (SNP) individually may not be well suited to detect small effects of multiple SNPs, such as those operating within the same biologic pathway. Gene set analysis (GSA) overcomes this limitation by assessing overall evidence for association of a phenotype with all measured variation in a set of genes.

METHODS

To determine gene sets associated with EOC overall survival, we conducted GSA using data from two large GWAS (N cases = 2,813, N deaths = 1,116), with a novel Principal Component-Gamma GSA method. Analysis was completed for all cases and then separately for high-grade serous histologic subtype.

RESULTS

Analysis of the high-grade serous subjects resulted in 43 gene sets with P < 0.005 (1.7%); of these, 21 gene sets had P < 0.10 in both GWAS, including intracellular signaling pathway (P = 7.3 × 10(-5)) and macrolide binding (P = 6.2 × 10(-4)) gene sets. The top gene sets in analysis of all cases were meiotic mismatch repair (P = 6.3 × 10(-4)) and macrolide binding (P = 1.0 × 10(-3)). Of 18 gene sets with P < 0.005 (0.7%), eight had P < 0.10 in both GWAS.

CONCLUSION

This research detected novel gene sets associated with EOC survival.

IMPACT

Novel gene sets associated with EOC survival might lead to new insights and avenues for development of novel therapies for EOC and pharmacogenomic studies.