Set-based Test Research Articles

MBAT-combo: A more powerful test to detect gene-trait associations from GWAS data

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ2 statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects,e.g.,when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.

SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene–phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene–phenotype associations.

Differences in set-based tests for sparse alternatives when testing sets of outcomes compared to sets of explanatory factors in genetic association studies.

Set-based association tests are widely popular in genetic association settings for their ability to aggregate weak signals and reduce multiple testing burdens. In particular, a class of set-based tests including the Higher Criticism, Berk-Jones, and other statistics have recently been popularized for reaching a so-called detection boundary when signals are rare and weak. Such tests have been applied in two subtly different settings: (a) associating a genetic variant set with a single phenotype and (b) associating a single genetic variant with a phenotype set. A significant issue in practice is the choice of test, especially when deciding between innovated and generalized type methods for detection boundary tests. Conflicting guidance is present in the literature. This work describes how correlation structures generate marked differences in relative operating characteristics for settings (a) and (b). The implications for study design are significant. We also develop novel power bounds that facilitate the aforementioned calculations and allow for analysis of individual testing settings. In more concrete terms, our investigation is motivated by translational expression quantitative trait loci (eQTL) studies in lung cancer. These studies involve both testing for groups of variants associated with a single gene expression (multiple explanatory factors) and testing whether a single variant is associated with a group of gene expressions (multiple outcomes). Results are supported by a collection of simulation studies and illustrated through lung cancer eQTL examples.

Dominating set-based test prioritization algorithms for regression testing

Dominating set-based test prioritization algorithms for regression testing

Efficient testing and effect size estimation for set-based genetic association inference via semiparametric multilevel mixture modeling.

In genetic association studies, rare variants with extremely low allele frequencies play a crucial role in complex traits. Therefore, set-based testing methods that jointly assess the effects of groups of single nucleotide polymorphisms (SNPs) were developed to increase the powers of the association tests. However, these powers are still insufficient, and precise estimations of the effect sizes of individual SNPs are largely impossible. In this article, we provide an efficient set-based statistical inference framework that addresses both of these important issues simultaneously using an empirical Bayes method with semiparametric multilevel mixture modeling. We propose to utilize the hierarchical model that incorporates variations in set-specific effects and to apply the optimal discovery procedure (ODP) that achieves the largest overall power in multiple significance testing. In addition, we provide an optimal "set-based" estimator of the empirical distribution of effect sizes. The efficiency of the proposed methods is demonstrated through application to a genome-wide association study of coronary artery disease and through simulation studies. The results demonstrated numerous rare variants with large effect sizes for coronary artery disease, and the number of significant sets detected by the ODP was much greater than those identified by existingmethods.

Multi-marker genetic association and interaction tests with interval-censored survival outcomes.

The development of set-based genetic-survival association tests has been focusing on right-censored survival outcomes. However, interval-censored failure time data arise widely from health science studies, especially those on the development of chronic diseases. In this paper, we proposed a suite of set-based genetic association and interaction tests for interval-censored survival outcomes under a unified weighted-V-statistic framework. Besides dealing with interval censoring, the new tests can account for genetic effect heterogeneity and accommodate left truncation of survival outcomes. Simulation studies showed that the new tests perform well in terms of size and power under various scenarios and that the new interaction test is more powerful than the standard likelihood ratio test for testing gene-gene/gene-environment interactions. The practical utility of the developed tests was illustrated by a genome-wide association study of age to early childhood caries.

Endocannabinoid Gene × Gene Interaction Association to Alcohol Use Disorder in Two Adolescent Cohorts.

Genetic markers of the endocannabinoid system have been linked to a variety of addiction-related behaviors that extend beyond cannabis use. In the current study we investigate the relationship between endocannabinoid (eCB) genetic markers and alcohol use disorder (AUD) in European adolescents (14–18 years old) followed in the IMAGEN study (n = 2,051) and explore replication in a cohort of North American adolescents from Canadian Saguenay Youth Study (SYS) (n = 772). Case-control status is represented by a score of more than 7 on the Alcohol Use Disorder Identification Test (AUDIT). First a set-based test method was used to examine if a relationship between the eCB system and AUDIT case/control status exists at the gene level. Using only SNPs that are both independent and significantly associated to case-control status, we perform Fisher's exact test to determine SNP level odds ratios in relation to case-control status and then perform logistic regressions as post-hoc analysis, while considering various covariates. Generalized multifactor dimensionality reduction (GMDR) was used to analyze the most robust SNP×SNP interaction of the five eCB genes with positive AUDIT screen. While no gene-sets were significantly associated to AUDIT scores after correction for multiple tests, in the case/control analysis, 7 SNPs were significantly associated with AUDIT scores of > 7 (p < 0.05; OR<1). Two SNPs remain significant after correction by false discovery rate (FDR): rs9343525 in CNR1 (pcorrected =0.042, OR = 0.73) and rs507961 in MGLL (pcorrected = 0.043, OR = 0.78). Logistic regression showed that both rs9353525 (CNR1) and rs507961 (MGLL) remained significantly associated with positive AUDIT screens (p < 0.01; OR < 1) after correction for multiple covariables and interaction of covariable × SNP. This result was not replicated in the SYS cohort. The GMDR model revealed a significant three-SNP interaction (p = 0.006) involving rs484061 (MGLL), rs4963307 (DAGLA), and rs7766029 (CNR1) predicted case-control status, after correcting for multiple covariables in the IMAGEN sample. A binomial logistic regression of the combination of these three SNPs by phenotype in the SYS cohort showed a result in the same direction as seen in the IMAGEN cohort (BETA = 0.501, p = 0.06). While preliminary, the present study suggests that the eCB system may play a role in the development of AUD in adolescents.

Set-based genetic association and interaction tests for survival outcomes based on weighted V statistics.

With advancements in high-throughout technologies, studies have been conducted to investigate the role of massive genetic variants in human diseases. While set-based tests have been developed for binary and continuous disease outcomes, there are few computationally efficient set-based tests available for time-to-event outcomes. To facilitate the genetic association and interaction analyses of time-to-event outcomes, We develop a suite of multivariant tests based on weighted V statistics with or without considering potential genetic heterogeneity. In addition to the computation efficiency and nice asymptotic properties, all the new tests can deal with left truncation and competing risks in the survival data, and adjust for covariates. Simulation studies show that the new tests run faster, are more accurate in small samples, and account for confounding effect better than the existing multivariant survival tests. When the genetic effect is heterogeneous across individuals/subpopulations, the association test considering genetic heterogeneity is more powerful than the existing tests that do not account for genetic heterogeneity. Using the new methods, we perform a genome-wide association analysis of the genotype and age-to-Alzheimer's data from the Rush Memory and Aging Project and the Religious Orders Study. The analysis identifies two genes, APOE and APOC1, associated with age to Alzheimer's disease onset.

Association Between Polymorphisms in DNA Damage Repair Genes and Radiation Therapy–Induced Early Adverse Skin Reactions in a Breast Cancer Population: A Polygenic Risk Score Approach

Genetic variations in DNA damage repair (DDR) genes may influence radiation therapy (RT)-induced acute normal tissue toxicity in patients with breast cancer. Identifying an individual or multiple single-nucleotide polymorphisms (SNPs) associated with RT-induced early adverse skin reactions (EASR) is critical for precision medicine in radiation oncology. At the completion of RT, EASR was assessed using the Oncology Nursing Society scale (0-6) in 416 patients with breast cancer, and Oncology Nursing Society score ≥4 was considered RT-induced EASR. PLINK set-based tests and subsequent individual SNP association analyses were conducted to identify genes and SNPs associated with EASR among the 53 DDR genes and 1968 SNPs. A weighted polygenic risk score (PRS) model was constructed to ascertain the association between the joint effect of risk alleles and EASR. The study population consisted of 264 Hispanic whites, 86 blacks or African Americans, 55 non-Hispanic whites, and 11 others. A total of 115 patients (27.6%) developed EASR. Five genes (ATM, CHEK1, ERCC2, RAD51C, and TGFB1) were significantly associated with RT-induced EASR. Nine SNPs within these 5 genes were further identified: ATM rs61915066, CHEK1 rs11220184, RAD51C rs302877, rs405684, TBFB1 rs4803455, rs2241714, and ERCC2 rs60152947, rs10404465, rs1799786. In a multivariable-adjusted PRS model, patients in a higher quartile of PRS were more likely to develop EASR compared with patients in the lowest quartile (ORq2 vs.q1 = 1.94, 95% CI, 0.86-4.39; ORq3 vs.q1 = 3.46, 95% CI, 1.57-7.63; ORq4 vs.q1 = 8.64, 95% CI, 3.92-19.02; and Ptrend < .0001). We newly identified the associations between 9 SNPs in ATM, CHEK1, RAD51C, TGFB1, and ERCC2 and RT-induced EASR. PRS modeling showed its potential in identifying populations at risk. Multiple SNPs in DDR genes may jointly contribute to interindividual variation in RT-induced EASR. Validation in an independent external cohort is required to determine the clinical significance of these predictive biomarkers.

Complete Sequence of the 22q11.2 Allele in 1,053 Subjects with 22q11.2 Deletion Syndrome Reveals Modifiers of Conotruncal Heart Defects

The 22q11.2 deletion syndrome (22q11.2DS) results from non-allelic homologous recombination between low-copy repeats termed LCR22. About 60%-70% of individuals with the typical 3 megabase (Mb) deletion from LCR22A-D have congenital heart disease, mostly of the conotruncal type (CTD), whereas others have normal cardiac anatomy. In this study, we tested whether variants in the hemizygous LCR22A-D region are associated with risk for CTDs on the basis of the sequence of the 22q11.2 region from 1,053 22q11.2DS individuals. We found a significant association (FDR p < 0.05) of the CTD subset with 62 common variants in a single linkage disequilibrium (LD) block in a 350 kb interval harboring CRKL. A total of 45 of the 62 variants were associated with increased risk for CTDs (odds ratio [OR) ranges: 1.64-4.75). Associations of four variants were replicated in a meta-analysis of three genome-wide association studies of CTDs in affected individuals without 22q11.2DS. One of the replicated variants, rs178252, is located in an open chromatin region and resides in the double-elite enhancer, GH22J020947, that is predicted to regulate CRKL (CRK-like proto-oncogene, cytoplasmic adaptor) expression. Approximately 23% of patients with nested LCR22C-D deletions have CTDs, and inactivation of Crkl in mice causes CTDs, thus implicating this gene as a modifier. Rs178252 and rs6004160 are expression quantitative trait loci (eQTLs) of CRKL. Furthermore, set-based tests identified an enhancer that is predicted to target CRKL and is significantly associated with CTD risk (GH22J020946, sequence kernal association test (SKAT) p = 7.21× 10-5) in the 22q11.2DS cohort. These findings suggest that variance in CTD penetrance in the 22q11.2DS population can be explained in part by variants affecting CRKL expression.

Genetic Variant Set-Based Tests Using the Generalized Berk–Jones Statistic With Application to a Genome-Wide Association Study of Breast Cancer

Studying the effects of groups of single nucleotide polymorphisms (SNPs), as in a gene, genetic pathway, or network, can provide novel insight into complex diseases such as breast cancer, uncovering new genetic associations and augmenting the information that can be gleaned from studying SNPs individually. Common challenges in set-based genetic association testing include weak effect sizes, correlation between SNPs in a SNP-set, and scarcity of signals, with individual SNP effects often ranging from extremely sparse to moderately sparse in number. Motivated by these challenges, we propose the Generalized Berk–Jones (GBJ) test for the association between a SNP-set and outcome. The GBJ extends the Berk–Jones statistic by accounting for correlation among SNPs, and it provides advantages over the Generalized Higher Criticism test when signals in a SNP-set are moderately sparse. We also provide an analytic p-value calculation for SNP-sets of any finite size, and we develop an omnibus statistic that is robust to the degree of signal sparsity. An additional advantage of our work is the ability to conduct inference using individual SNP summary statistics from a genome-wide association study (GWAS). We evaluate the finite sample performance of the GBJ through simulation and apply the method to identify breast cancer risk genes in a GWAS conducted by the Cancer Genetic Markers of Susceptibility Consortium. Our results suggest evidence of association between FGFR2 and breast cancer and also identify other potential susceptibility genes, complementing conventional SNP-level analysis. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Genome-wide enriched pathway analysis of acute post-radiotherapy pain in breast cancer patients: a prospective cohort study

BackgroundAdjuvant radiotherapy (RT) can increase the risk of developing pain; however, the molecular mechanisms of RT-related pain remain unclear. The current study aimed to identify susceptibility loci and enriched pathways for clinically relevant acute post-RT pain, defined as having moderate to severe pain (pain score ≥ 4) at the completion of RT.MethodsWe conducted a genome-wide association study (GWAS) with 1,344,832 single-nucleotide polymorphisms (SNPs), a gene-based analysis using PLINK set-based tests of 19,621 genes, and a functional enrichment analysis of a gene list of 875 genes with p < 0.05 using NIH DAVID functional annotation module with KEGG pathways and GO terms (n = 380) among 1112 breast cancer patients.ResultsAbout 29% of patients reported acute post-RT pain. None of SNPs nor genes reached genome-wide significant level. Four SNPs showed suggestive associations with post-RT pain; rs16970540 in RFFL or near the LIG3 gene (p = 1.7 × 10−6), rs4584690, and rs7335912 in ABCC4/MPR4 gene (p = 5.5 × 10−6 and p = 7.8 × 10−6, respectively), and rs73633565 in EGFL6 gene (p = 8.1 × 10−6). Gene-based analysis suggested the potential involvement of neurotransmitters, olfactory receptors, and cytochrome P450 in post-RT pain, whereas functional analysis showed glucuronidation (FDR-adjusted p value = 9.46 × 10−7) and olfactory receptor activities (FDR-adjusted p value = 0.032) as the most significantly enriched biological features.ConclusionsThis is the first GWAS suggesting that post-RT pain is a complex polygenic trait influenced by many biological processes and functions such as glucuronidation and olfactory receptor activities. If validated in larger populations, the results can provide biological targets for pain management to improve cancer patients’ quality of life. Additionally, these genes can be further tested as predictive biomarkers for personalized pain management.

Identification of differentially expressed gene sets using the Generalized Berk-Jones statistic.

Cancer genomics studies frequently aim to identify genes that are differentially expressed between clinically distinct patient subgroups, generally by testing single genes one at a time. However, the results of any individual transcriptomic study are often not fully reproducible. A particular challenge impeding statistical analysis is the difficulty of distinguishing between differential expression comprising part of the genomic disease etiology and that induced by downstream effects. More robust analytical approaches that are well-powered to detect potentially causative genes, are less prone to discovering spurious associations, and can deliver reproducible findings across different studies are needed. We propose a set-based procedure for testing of differential expression and show that this set-based approach can produce more robust results by aggregating information across multiple, correlated genomic markers. Specifically, we adapt the Generalized Berk-Jones statistic to test for the transcription factors that may contribute to the progression of estrogen receptor positive breast cancer. We demonstrate the ability of our method to produce reproducible findings by applying the same analysis to 21 publicly available datasets, producing a similar list of significant transcription factors across most studies. Our Generalized Berk-Jones approach produces results that show improved consistency over three set-based testing algorithms: Generalized Higher Criticism, Gene Set Analysis and Gene Set Enrichment Analysis. Data are in the MetaGxBreast R package. Code is available at github.com/ryanrsun/gaynor_sun_GBJ_breast_cancer. Supplementary data are available at Bioinformatics online.

Genome-wide association analysis reveals KCTD12 and miR-383-binding genes in the background of rumination

Ruminative response style is a passive and repetitive way of responding to stress, associated with several disorders. Although twin and candidate gene studies have proven the genetic underpinnings of rumination, no genome-wide association study (GWAS) has been conducted yet. We performed a GWAS on ruminative response style and its two subtypes, brooding and reflection, among 1758 European adults recruited in the general population of Budapest, Hungary, and Manchester, United Kingdom. We evaluated single-nucleotide polymorphism (SNP)-based, gene-based and gene set-based tests, together with inferences on genes regulated by our most significant SNPs. While no genome-wide significant hit emerged at the SNP level, the association of rumination survived correction for multiple testing with KCTD12 at the gene level, and with the set of genes binding miR-383 at the gene set level. SNP-level results were concordant between the Budapest and Manchester subsamples for all three rumination phenotypes. SNP-level results and their links to brain expression levels based on external databases supported the role of KCTD12, SRGAP3, and SETD5 in rumination, CDH12 in brooding, and DPYSL5, MAPRE3, KCNK3, ATXN7L3B, and TPH2 in reflection, among others. The relatively low sample size is a limitation of our study. Results of the first GWAS on rumination identified genes previously implicated in psychiatric disorders underscoring the transdiagnostic nature of rumination, and pointed to the possible role of the dorsolateral prefrontal cortex, hippocampus, and cerebellum in this cognitive process.

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.

ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies

Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes, and effect directions of the causal variants and the choices of weights. Here we propose an aggregated Cauchy association test (ACAT), a general, powerful, and computationally efficient p value combination method for boosting power in sequencing studies. First, by combining variant-level p values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of causal variants in a variant set. Second, by combining different variant-set-level p values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and the burden test, and that ACAT-O has a substantially more robust and higher power than those of the alternative tests.

Abstract 5268: Interactions between genetic predictors of gene expression and dietary factors associated with risk of colorectal cancer

Abstract Genome-wide association studies of colorectal cancer (CRC) have identified over 50 susceptibility loci. These variants represent only a small fraction of total heritability for CRC. Gene-environment (GxE) interaction studies may help identify novel loci and biological interactions that give insight to the pathogenesis of CRC. Previous genome-wide GxE studies with dietary factors have identified interactions between loci and processed meat consumption and alcohol; however, limited statistical power remains a primary concern. Set-based SNP testing has the potential to increase statistical power to detect GxE interactions by aggregating functionally relevant SNPs. In this large pooled analysis using 14 case-control and nested case-control studies, we incorporated functional information from the transcriptome prediction tool, PrediXcan, into a novel set-based approach for testing GxE interactions. We used variant weights from the PrediXcan models of tissue-specific gene expression in the transverse colon as a priori variant information for a set-based GxE approach. We restricted our analysis to variants in the PrediXcan transverse colon gene models (n = 4,842). This discovery phase included 10,360 CRC and advanced adenoma cases, and 11,183 controls of European ancestry from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colon Cancer Family Registry. All 14 studies were analyzed together in a pooled data set using the Mixed Effects Score Tests for interactions. We tested for gene interactions with sex- and study- specific quartiles of dietary intake of red meat (servings/day), processed meat (servings/day), vegetables (servings/day), fruits (servings/day), and fiber (g/day). We detected two genes with suggestive interactions (false discovery rate (FDR) &amp;lt; 0.2) with intake of red meat and risk of CRC: Superoxide Dismutase 2 (SOD2) and Ubiquitin Conjugating Enzyme E2 H (UBE2H). No interactions at FDR &amp;lt; 0.2 were observed for processed meat, vegetables, fruits, or fiber. The SOD2 gene, which encodes an enzyme important in apoptotic signaling and clearing of reactive oxygen species, may regulate response to colonic exposure to heme iron or increased bile acid from high-fat content in red meat. UBE2H is part of the ubiquitin-proteasome system that has a role in Wnt signaling, which can be mediated by heme iron in red meat and is commonly found dysregulated in cancer. These findings highlight the efficacy of integrating functional information and set-based testing for novel discovery of genes interacting with known dietary risk factors of CRC. We plan to replicate these findings in additional studies. Citation Format: Paneen S. Petersen, Yu-Ru Su, Sonja I. Berndt, Stephanie A. Bien, Hermann Brenner, Graham Casey, Andrew T. Chan, Jenny Chang-Claude, Jane C. Figueiredo, Steven J. Gallinger, Robert W. Haile, Tabitha A. Harrison, Michael Hoffmeister, Mark A. Jenkins, Amit D. Joshi, Sébastien Küry, Loic Le Marchand, Yi Lin, Noralane M. Lindor, Polly A. Newcomb, John D. Potter, Robert Schoen, Martha L. Slattery, Stephen N. Thibodeau, Emily White, Li Hsu, Ulrike Peters, CCFR, GECCO. Interactions between genetic predictors of gene expression and dietary factors associated with risk of colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5268.

Abstract 231: Identification of susceptible loci and enriched pathways for post-radiotherapy pain in breast cancer patients

Abstract Pain is one of the most prevalent symptoms among breast cancer survivors, which negatively impact on their quality of life (QOL). Adjuvant radiotherapy (RT) can increase the risk of development or persistence of pain; however, little is known about the mechanisms of RT-related pain or genetic susceptibility. The current study aimed to identify susceptible loci and enriched pathways for clinically relevant pain, which is defined as pain score ≥ 4, at the completion of RT (post-RT pain). We conducted a genome-wide association study (GWAS) of 1,112 breast cancer patients with 1,344,832 tagging SNPs, gene-based analysis using PLINK set-based tests for 19,621 genes, and functional enrichment analysis of a gene list (875 genes having p &amp;lt; 0.05) using NIH DAVID functional annotation module with KEGG pathways and GO terms (n=380). About 29% of patients reported post-RT pain, and 4 SNPs showed suggestive associations; rs16970540 in RFFL or near to LIG3 gene (p=1.7x10-6), rs4584690 and rs7335912 in ABCC4/MPR4 gene (p=5.5x10-6 and p=7.8x10-6, respectively), and rs73633565 in EGFL6 gene (p=8.1x10-6). Gene-based analysis suggested the importance (involvement) of neurotransmitter, olfactory receptor, and cytochrome P450 in post-RT pain, and functional analysis suggested glucuronidation and olfactory receptor activity as the most enriched biological processes/features. With a limited size of sample, this is the first GWA study showing (confirming) that post-RT pain is a complex polygenic trait influenced by many biological processes and functions. If validated in a larger population, the results can provide biological targets for pain management to improve cancer survivor's QOL. Additionally, these findings may be used as predictive biomarkers of personalized pain management. Citation Format: Eunkyung Lee, Christina Takita, Jean L. Wright, Eden Martin, Susan Slifer, Jennifer Hu. Identification of susceptible loci and enriched pathways for post-radiotherapy pain in breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 231.

Analysis of the Influence of microRNAs in Lithium Response in Bipolar Disorder.

Bipolar disorder (BD) is a common, highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. Lithium is the best-established long-term treatment for BD, even though individual response is highly variable. Evidence suggests that some of this variability has a genetic basis. This is supported by the largest genome-wide association study (GWAS) of lithium response to date conducted by the International Consortium on Lithium Genetics (ConLiGen). Recently, we performed the first genome-wide analysis of the involvement of miRNAs in BD and identified nine BD-associated miRNAs. However, it is unknown whether these miRNAs are also associated with lithium response in BD. In the present study, we therefore tested whether common variants at these nine candidate miRNAs contribute to the variance in lithium response in BD. Furthermore, we systematically analyzed whether any other miRNA in the genome is implicated in the response to lithium. For this purpose, we performed gene-based tests for all known miRNA coding genes in the ConLiGen GWAS dataset (n = 2,563 patients) using a set-based testing approach adapted from the versatile gene-based test for GWAS (VEGAS2). In the candidate approach, miR-499a showed a nominally significant association with lithium response, providing some evidence for involvement in both development and treatment of BD. In the genome-wide miRNA analysis, 71 miRNAs showed nominally significant associations with the dichotomous phenotype and 106 with the continuous trait for treatment response. A total of 15 miRNAs revealed nominal significance in both phenotypes with miR-633 showing the strongest association with the continuous trait (p = 9.80E-04) and miR-607 with the dichotomous phenotype (p = 5.79E-04). No association between miRNAs and treatment response to lithium in BD in either of the tested conditions withstood multiple testing correction. Given the limited power of our study, the investigation of miRNAs in larger GWAS samples of BD and lithium response is warranted.

Genetic correlates of insight in schizophrenia

Insight in schizophrenia is clinically important as it is associated with several adverse outcomes. Genetic contributions to insight are unknown. We examined genetic contributions to insight by investigating if polygenic risk scores (PRS) and candidate regions were associated with insight. MethodSchizophrenia case-only analysis of the Clinical Antipsychotics Trials of Intervention Effectiveness trial. Schizophrenia PRS was constructed using Psychiatric Genomics Consortium (PGC) leave-one out GWAS as discovery data set. For candidate regions, we selected 105 schizophrenia-associated autosomal loci and 11 schizophrenia-related oligodendrocyte genes. We used regressions to examine PRS associations and set-based testing for candidate analysis. ResultsWe examined data from 730 subjects. Best-fit PRS at p-threshold of 1e−07 was associated with total insight (R2=0.005, P=0.05, empirical P=0.054) and treatment insight (R2=0.005, P=0.048, empirical P=0.048). For models that controlled for neurocognition, PRS significantly predicted treatment insight but at higher p-thresholds (0.1 to 0.5) but did not survive correction.Patients with highest polygenic burden had 5.9 times increased risk for poor insight compared to patients with lowest burden. PRS explained 3.2% (P=0.002, empirical P=0.011) of variance in poor insight. Set-based analyses identified two variants associated with poor insight- rs320703, an intergenic variant (within-set P=6e−04, FDR P=0.046) and rs1479165 in SOX2-OT (within-set P=9e−04, FDR P=0.046). ConclusionTo the best of our knowledge, this is the first study examining genetic basis of insight. We provide evidence for genetic contributions to impaired insight. Relevance of findings and necessity for replication are discussed.