Identification of supervised and sparse functional genomic pathways.

Functional pathways involve a series of biological alterations that may result in the occurrence of many diseases including cancer. With the availability of various "omics" technologies it becomes feasible to integrate information from a hierarchy of biological layers to provide a more comprehensive understanding to the disease. In many diseases, it is believed that only a small number of networks, each relatively small in size, drive the disease. Our goal in this study is to develop methods to discover these functional networks across biological layers correlated with the phenotype. We derive a novel Network Summary Matrix (NSM) that highlights potential pathways conforming to least squares regression relationships. An algorithm called Decomposition of Network Summary Matrix via Instability (DNSMI) involving decomposition of NSM using instability regularization is proposed. Simulations and real data analysis from The Cancer Genome Atlas (TCGA) program will be shown to demonstrate the performance of the algorithm.

The Be-Well Study: a prospective cohort study of lifestyle and genetic factors to reduce the risk of recurrence and progression of non-muscle-invasive bladder cancer.

PURPOSE: Bladder cancer is one of the top five cancers diagnosed in the U.S. with a high recurrence rate, and also one of the most expensive cancers to treat over the life-course. However, there are few observational, prospective studies of bladder cancer survivors. METHODS: The Bladder Cancer Epidemiology, Wellness, and Lifestyle Study (Be-Well Study) is a National Cancer Institute-funded, multi-center prospective cohort study of non-muscle-invasive bladder cancer (NMIBC) patients (Stage Ta, T1, Tis) enrolled from the Kaiser Permanente Northern California (KPNC) and Southern California (KPSC) health care systems, with genotyping and biomarker assays performed at Roswell Park Comprehensive Cancer Center. The goal is to investigate diet and lifestyle factors in recurrence and progression of NMIBC, with genetic profiles considered, and to build a resource for future NMIBC studies. RESULTS: Recruitment began in February 2015. As of 30 June 2018, 1,281 patients completed the baseline interview (774 KPNC, 511 KPSC) with a recruitment rate of 54%, of whom 77% were male and 23% female, and 80% White, 6% Black, 8% Hispanic, 5% Asian, and 2% other race/ethnicity. Most patients were diagnosed with Ta (69%) or T1 (27%) tumors. Urine and blood specimens were collected from 67% and 73% of consented patients at baseline, respectively. To date, 599 and 261 patients have completed the 12- and 24-month follow-up questionnaires, respectively, with additional urine and saliva collection. CONCLUSIONS: The Be-Well Study will be able to answer novel questions related to diet, other lifestyle, and genetic factors and their relationship to recurrence and progression among early-stage bladder cancer patients.

Replication and validation of genetic polymorphisms associated with survival after allogeneic blood or marrow transplant.

Multiple candidate gene-association studies of non-HLA single-nucleotide polymorphisms (SNPs) and outcomes after blood or marrow transplant (BMT) have been conducted. We identified 70 publications reporting 45 SNPs in 36 genes significantly associated with disease-related mortality, progression-free survival, transplant-related mortality, and/or overall survival after BMT. Replication and validation of these SNP associations were performed using DISCOVeRY-BMT (Determining the Influence of Susceptibility COnveying Variants Related to one-Year mortality after BMT), a well-powered genome-wide association study consisting of 2 cohorts, totaling 2888 BMT recipients with acute myeloid leukemia, acute lymphoblastic leukemia, or myelodysplastic syndrome, and their HLA-matched unrelated donors, reported to the Center for International Blood and Marrow Transplant Research. Gene-based tests were used to assess the aggregate effect of SNPs on outcome. None of the previously reported significant SNPs replicated at P < .05 in DISCOVeRY-BMT. Validation analyses showed association with one previously reported donor SNP at P < .05 and survival; more associations would be anticipated by chance alone. No gene-based tests were significant at P < .05. Functional annotation with publicly available data shows these candidate SNPs most likely do not have biochemical function; only 13% of candidate SNPs correlate with gene expression or are predicted to impact transcription factor binding. Of these, half do not impact the candidate gene of interest; the other half correlate with expression of multiple genes. These findings emphasize the peril of pursing candidate approaches and the importance of adequately powered tests of unbiased genome-wide associations with BMT clinical outcomes given the ultimate goal of improving patient outcomes.

GOG 8020/210: Risk stratification of lymph node metastasis, disease progression and survival using single nucleotide polymorphisms in endometrial cancer: An NRG oncology/gynecologic oncology group study.

OBJECTIVES: The ability to stratify a patient's risk of metastasis and survival permits more refined care. A proof of principle study was undertaken to investigate the relationship between single nucleotide polymorphisms (SNPs) in literature based candidate cancer genes and the risk of nodal metastasis and clinical outcome in endometrioid endometrial cancer (EEC) patients. METHODS: Surgically-staged EEC patients from the Gynecologic Oncology Group or Washington University School of Medicine with germline DNA available were eligible. Fifty-four genes represented by 384 SNPs, were evaluated by Illumina Custom GoldenGate array. Association with lymph node metastases was the primary outcome. Progression-free survival (PFS) and overall survival (OS) was also evaluated. RESULTS: 361 SNPs with high quality genotype data were evaluated in 337 patients with outcome data. Five SNPs in CXCR2 had an odds ratio (OR) between 0.68 and 0.70 (p-value ≤ 0.025). The A allele rs946486 in ABL had an OR of 1.5 (p-value = 0.01) for metastasis. The G allele in rs7795743 in EGFR had an OR for metastasis of 0.68 (p-value = 0.02) and hazard ratio (HR) for progression of 0.66 (p-value = 0.004). Importantly, no SNP met genome wide significance after adjusting for multiple test correcting and clinical covariates. The A allele in rs2159359 SNP in NME1 and the G allele in rs13222385 in EGFR were associated with worse OS. Both exhibited genome wide significance; rs13222385 remained significant after adjusting for prognostic clinical variables. CONCLUSION: SNPs in cancer genes including rs2159359 SNP in NME1 and rs13222385 in EGFR may stratify risk in EEC and are prioritized for further investigation.

An NRG Oncology/GOG study of molecular classification for risk prediction in endometrioid endometrial cancer.

OBJECTIVES: The purpose of this study was to assess the prognostic significance of a simplified, clinically accessible classification system for endometrioid endometrial cancers combining Lynch syndrome screening and molecular risk stratification. METHODS: Tumors from NRG/GOG GOG210 were evaluated for mismatch repair defects (MSI, MMR IHC, and MLH1 methylation), POLE mutations, and loss of heterozygosity. TP53 was evaluated in a subset of cases. Tumors were assigned to four molecular classes. Relationships between molecular classes and clinicopathologic variables were assessed using contingency tests and Cox proportional methods. RESULTS: Molecular classification was successful for 982 tumors. Based on the NCI consensus MSI panel assessing MSI and loss of heterozygosity combined with POLE testing, 49% of tumors were classified copy number stable (CNS), 39% MMR deficient, 8% copy number altered (CNA) and 4% POLE mutant. Cancer-specific mortality occurred in 5% of patients with CNS tumors; 2.6% with POLE tumors; 7.6% with MMR deficient tumors and 19% with CNA tumors. The CNA group had worse progression-free (HR 2.31, 95%CI 1.53-3.49) and cancer-specific survival (HR 3.95; 95%CI 2.10-7.44). The POLE group had improved outcomes, but the differences were not statistically significant. CNA class remained significant for cancer-specific survival (HR 2.11; 95%CI 1.04-4.26) in multivariable analysis. The CNA molecular class was associated with TP53 mutation and expression status. CONCLUSIONS: A simple molecular classification for endometrioid endometrial cancers that can be easily combined with Lynch syndrome screening provides important prognostic information. These findings support prospective clinical validation and further studies on the predictive value of a simplified molecular classification system.

Genome-wide association study evaluating single-nucleotide polymorphisms and outcomes in patients with advanced stage serous ovarian or primary peritoneal cancer: An NRG Oncology/Gynecologic Oncology Group study.

OBJECTIVE: This study evaluated single nucleotide polymorphisms (SNPs) associated with progression free (PFS) and overall survival (OS) in patients with advanced stage serous EOC. METHODS: Patients enrolled in GOG-172 and 182 who provided specimens for translational research and consent were included. Germline DNA was evaluated with the Illumina's HumanOMNI1-Quad beadchips and scanned using Illumina's iScan optical imaging system. SNPs with allele frequency>0.05 and genotyping rate>0.98 were included. Analysis of SNPs for PFS and OS was done using Cox regression. Statistical significance was determined using Bonferroni corrected p-values with genomic control adjustment. RESULTS: The initial GWAS analysis included 1,124,677 markers in 396 patients. To obtain the final data set, quality control checks were performed and limited to serous tumors and self-identified Caucasian race. In total 636,555 SNPs and 289 patients passed all the filters. The pre-specified statistical level of significance was 7.855e-08. No SNPs met this criteria for PFS or OS, however, two SNPs were close to significance (rs10899426 p-2.144e-08) (rs6256 p-9.774e-07) for PFS and 2 different SNPs were identified (rs295315 p-7.536e-07; rs17693104 p-7.734e-07) which were close to significance for OS. CONCLUSIONS: Using the pre-specified level of significance of 1×10-08, we did not identify any SNPs of statistical significance for OS or PFS, however several were close. The SNP's identified in this GWAS study will require validation and these preliminary findings may lead to identification of novel pathways and biomarkers.

Identification of consistent functional genetic modules.

It is often of scientific interest to find a set of genes that may represent an independent functional module or network, such as a functional gene expression module causing a biological response, a transcription regulatory network, or a constellation of mutations jointly causing a disease. In this paper we are specifically interested in identifying modules that control a particular outcome variable such as a disease biomarker. We discuss the statistical properties that functional networks should possess and introduce the concept of network consistency which should be satisfied by real functional networks of cooperating genes, and directly use the concept in the pathway discovery method we present. Our method gives superior performance for all but the simplest functional networks.

Establishment of Definitions and Review Process for Consistent Adjudication of Cause-specific Mortality after Allogeneic Unrelated-donor Hematopoietic Cell Transplantation.

Clinical trials commonly use adjudication committees to refine endpoints, but observational research or genome-wide association studies rarely do. Our goals were to establish definitions of cause-specific death after unrelated-donor allogeneic hematopoietic cell transplantation (URD-HCT), to estimate discordance between reported and adjudicated cause-specific death, and to identify factors contributing to inconsistency in cause-specific death determination. A consensus panel adjudicated cause-specific death in 1484 patients who died within 1 year after HCT, derived from 3532 acute leukemia or myelodysplasia patients after URD-HCT from 2000 to 2011 reported by 151 US transplant centers to the Center for International Blood and Marrow Transplant Research. Deaths were classified as disease-related or transplant-related. The panel agreed with >99% of deaths reported by centers as disease-related and 80% reported as transplant-related. Year of transplant (cohort effect) and disease status significantly influenced agreement between the panel and centers. Sensitivity analysis of deaths < 100 days post-transplant yielded the lowest agreement between the panel and centers for myelodysplastic syndrome patients. Standard predefined criteria for adjudicating cause-specific death led to consistent application to similar clinical scenarios and clearer delineation of cause-specific death categories. Other studies of competing events such as cancer-specific versus treatment-related mortality would benefit from our results. Our detailed algorithm should result in more consistent reporting of cause-specific death by centers.

Information metrics in genetic epidemiology.

Information-theoretic metrics have been proposed for studying gene-gene and gene-environment interactions in genetic epidemiology. Although these metrics have proven very promising, they are typically interpreted in the context of communications and information transmission, diminishing their tangibility for epidemiologists and statisticians. In this paper, we clarify the interpretation of information-theoretic metrics. In particular, we develop the methods so that their relation to the global properties of probability models is made clear and contrast them with log-linear models for multinomial data. Hopefully, a better understanding of their properties and probabilistic implications will promote their acceptance and correct usage in genetic epidemiology. Our novel development also suggests new approaches to model search and computation.

Recruitment and Retention of Healthy, Postmenopausal Women of African and European Ancestry: Results from a Dietary Intervention with Repeated Biospecimen Collections.

Recruitment of minority participants to clinical trials, especially studies without therapeutic intent, has been historically challenging. This study describes barriers to and successes of recruitment and retention strategies to dietary studies. A flaxseed study was conducted in healthy, postmenopausal women of African ancestry (AA) and European ancestry (EA) to assess associations between gut microbial community composition and host metabolism (NCT01698294). To ensure equitable participation by AA and EA women, multiple forms of recruitment were utilized, including advertisements, posters, e-mail, word of mouth, and community outreach. Successful recruitment and retention of AA women to the intervention depended upon the specific methods used. AA women compared with EA women were more likely to respond to direct recruitment and community-based methods, rather than general advertisements. However, once women expressed interest, similar rates of consent were observed for AA and EA women (AA and EA: 51.6% vs. 55.7%, respectively; P > 0.05), supporting the willingness of minority populations to participate in clinical research. Retention, however, was lower among AA compared with EA women (AA and EA: 57.6% vs. 80.9%, respectively; P < 0.01), which may be related to multiple factors, including health reasons, intolerance to flaxseed, noncompliance with study requirements, time constraints, and nonspecified personal reasons. This study confirms the utility of direct community-based strategies for recruitment of diverse populations into nontherapeutic dietary intervention studies. The methods used successfully identified eligible women who expressed willingness to consent to the trial and were able to achieve >70% of recruitment goals for AA women. Future efforts are warranted to improve retention to complex studies. This trial was registered at www.clinicaltrials.gov as NCT01698294.

Balanced Functional Module Detection in genomic data.

Motivation: High-dimensional genomic data can be analyzed to understand the effects of variables on a target variable such as a clinical outcome. For understanding the underlying biological mechanism affecting the target, it is important to discover the complete set of relevant variables. Thus variable selection is a primary goal, which differs from a prediction criterion. Of special interest are functional modules, cooperating sets of variables affecting the target which can be characterized by a graph. In applications such as social networks, the concept of balance in undirected signed graphs characterizes the consistency of associations within the network. This property requires that the module variables have a joint effect on the target outcome with no internal conflict, an efficiency that may be applied to biological networks. Results: In this paper, we model genomic variables in signed undirected graphs for applications where the set of predictor variables influences an outcome. Consequences of the balance property are exploited to implement a new module discovery algorithm, balanced Functional Module Detection (bFMD), which selects a subset of variables from high-dimensional data that compose a balanced functional module. Our bFMD algorithm performed favorably in simulations as compared to other module detection methods. Additionally, bFMD detected interpretable results in an application using RNA-seq data obtained from subjects with Uterine Corpus Endometrial Carcinoma using the percentage of tumor invasion as the outcome of interest. The variables selected by bFMD have improved interpretability due to the logical consistency afforded by the balance property. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.

Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.

Enterolignan Production in a Flaxseed Intervention Study in Postmenopausal US Women of African Ancestry and European Ancestry.

Lignans are phytochemicals studied extensively as dietary factors in chronic disease etiology. Our goal was to examine associations between the gut microbiota and lignan metabolism and whether these associations differ by ethnicity. We conducted a flaxseed (FS) dietary intervention in 252 healthy, postmenopausal women of African ancestry (AA) and European ancestry (EA). Participants consumed ~10 g/d ground flaxseed for 6 weeks and provided overnight urine collections and fecal samples before and after intervention. The gut microbiota was characterized using 16S rRNA gene sequencing and differences in microbial community composition compared by ethnicity and intervention status. We observed a significant difference in the composition of the microbiota measured as beta diversity (p < 0.05) between AA and EA at baseline that was attenuated with FS consumption. Genera that were significantly associated with ENL production (e.g., Klebsiella, Lactobacillus, Slackia, Senegalimassilia) were unique to each group. Bacteria (e.g., Fusobacteria, Pyramidobacter and Odoribacter) previously associated with colorectal cancer and cardiovascular disease, both diet-related chronic diseases, were unique to either AA or EA and were significantly reduced in the FS intervention. This study suggests that ethnic variation in ENL metabolism may be linked to gut microbiota composition, and its impact on disease risk deserves future investigation.

Gene- or region-based analysis of genome-wide association studies.

With rapid advances in genotyping technologies in recent years and the growing number of available markers, genome-wide association studies are emerging as promising approaches for the study of complex diseases and traits. However, there are several challenges with analysis and interpretation of such data. First, there is a massive multiple testing problem, due to the large number of markers that need to be analyzed, leading to an increased risk of false positives and decreased ability for association studies to detect truly associated markers. In particular, the ability to detect modest genetic effects can be severely compromised. Second, a genetic association of a given single-nucleotide polymorphism as determined by univariate statistical analyses does not typically explain biologically interesting features, and often requires subsequent interpretation using a higher unit, such as a gene or region, for example, as defined by haplotype blocks. Third, missing genotypes in the data set and other data quality issues can pose challenges when comparisons across platforms and replications are planned. Finally, depending on the type of univariate analysis, computational burden can arise as the number of markers continues to grow into the millions. One way to deal with these and related challenges is to consider higher units for the analysis, such as genes or regions. This article summarizes analytical methods and strategies that have been proposed and applied by Group 16 to two genome-wide association data sets made available through the Genetic Analysis Workshop 16.

Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity.

BACKGROUND: Multifactorial diseases such as cancer and cardiovascular diseases are caused by the complex interplay between genes and environment. The detection of these interactions remains challenging due to computational limitations. Information theoretic approaches use computationally efficient directed search strategies and thus provide a feasible solution to this problem. However, the power of information theoretic methods for interaction analysis has not been systematically evaluated. In this work, we compare power and Type I error of an information-theoretic approach to existing interaction analysis methods. METHODS: The k-way interaction information (KWII) metric for identifying variable combinations involved in gene-gene interactions (GGI) was assessed using several simulated data sets under models of genetic heterogeneity driven by susceptibility increasing loci with varying allele frequency, penetrance values and heritability. The power and proportion of false positives of the KWII was compared to multifactor dimensionality reduction (MDR), restricted partitioning method (RPM) and logistic regression. RESULTS: The power of the KWII was considerably greater than MDR on all six simulation models examined. For a given disease prevalence at high values of heritability, the power of both RPM and KWII was greater than 95%. For models with low heritability and/or genetic heterogeneity, the power of the KWII was consistently greater than RPM; the improvements in power for the KWII over RPM ranged from 4.7% to 14.2% at for α = 0.001 in the three models at the lowest heritability values examined. KWII performed similar to logistic regression. CONCLUSIONS: Information theoretic models are flexible and have excellent power to detect GGI under a variety of conditions that characterize complex diseases.

Sparse canonical correlation analysis with application to genomic data integration.

Large scale genomic studies with multiple phenotypic or genotypic measures may require the identification of complex multivariate relationships. In multivariate analysis a common way to inspect the relationship between two sets of variables based on their correlation is canonical correlation analysis, which determines linear combinations of all variables of each type with maximal correlation between the two linear combinations. However, in high dimensional data analysis, when the number of variables under consideration exceeds tens of thousands, linear combinations of the entire sets of features may lack biological plausibility and interpretability. In addition, insufficient sample size may lead to computational problems, inaccurate estimates of parameters and non-generalizable results. These problems may be solved by selecting sparse subsets of variables, i.e. obtaining sparse loadings in the linear combinations of variables of each type. In this paper we present Sparse Canonical Correlation Analysis (SCCA) which examines the relationships between two types of variables and provides sparse solutions that include only small subsets of variables of each type by maximizing the correlation between the subsets of variables of different types while performing variable selection. We also present an extension of SCCA--adaptive SCCA. We evaluate their properties using simulated data and illustrate practical use by applying both methods to the study of natural variation in human gene expression.

Predictive Blood-Based Biomarkers in Patients with Epithelial Ovarian Cancer Treated with Carboplatin and Paclitaxel with or without Bevacizumab: Results from GOG-0218.

PURPOSE: GOG-0218, a double-blind placebo-controlled phase III trial, compared carboplatin and paclitaxel with placebo, bevacizumab followed by placebo, or bevacizumab followed by bevacizumab in advanced epithelial ovarian cancer (EOC). Results demonstrated significantly improved progression-free survival (PFS), but no overall survival (OS) benefit with bevacizumab. Blood samples were collected for biomarker analyses. EXPERIMENTAL DESIGN: Plasma samples were analyzed via multiplex ELISA technology for seven prespecified biomarkers [IL6, Ang-2, osteopontin (OPN), stromal cell-derived factor-1 (SDF-1), VEGF-D, IL6 receptor (IL6R), and GP130]. The predictive value of each biomarker with respect to PFS and OS was assessed using a protein marker by treatment interaction term within the framework of a Cox proportional hazards model. Prognostic markers were identified using Cox models adjusted for baseline covariates. RESULTS: Baseline samples were available from 751 patients. According to our prespecified analysis plan, IL6 was predictive of a therapeutic advantage with bevacizumab for PFS (P = 0.007) and OS (P = 0.003). IL6 and OPN were found to be negative prognostic markers for both PFS and OS (P < 0.001). Patients with high median IL6 levels (dichotomized at the median) treated with bevacizumab had longer PFS (14.2 vs. 8.7 months) and OS (39.6 vs. 33.1 months) compared with placebo. CONCLUSIONS: The inflammatory cytokine IL6 may be predictive of therapeutic benefit from bevacizumab when combined with carboplatin and paclitaxel. Aligning with results observed in patients with renal cancer treated with antiangiogenic therapies, it appears plasma IL6 may also define those patients with EOC more or less likely to benefit from the addition of bevacizumab to standard chemotherapy.

Filtering genes for cluster and network analysis.

BACKGROUND: Prior to cluster analysis or genetic network analysis it is customary to filter, or remove genes considered to be irrelevant from the set of genes to be analyzed. Often genes whose variation across samples is less than an arbitrary threshold value are deleted. This can improve interpretability and reduce bias. RESULTS: This paper introduces modular models for representing network structure in order to study the relative effects of different filtering methods. We show that cluster analysis and principal components are strongly affected by filtering. Filtering methods intended specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. To study more realistic situations, we analyze simulated "real" data based on well-characterized E. coli and S. cerevisiae regulatory networks. CONCLUSION: The methods introduced apply very generally, to any similarity matrix describing gene expression. One of the proposed methods, SUMCOV, performed well for all models simulated.

Estimating number of clusters based on a general similarity matrix with application to microarray data.

Many clustering methods require that the number of clusters believed present in a given data set be specified a priori, and a number of methods for estimating the number of clusters have been developed. However, the selection of the number of clusters is well recognized as a difficult and open problem and there is a need for methods which can shed light on specific aspects of the data. This paper adopts a model for clustering based on a specific structure for a similarity matrix. Publicly available gene expression data sets are analyzed to illustrate the method and the performance of our method is assessed by simulation.

Genetic Variation in Steroid and Xenobiotic Metabolizing Pathways and Enterolactone Excretion Before and After Flaxseed Intervention in African American and European American Women.

BACKGROUND: Metabolism and excretion of the phytoestrogen enterolactone (ENL), which has been associated with breast cancer risk, may be affected by variation in steroid hormone and xenobiotic-metabolizing genes. METHODS: We conducted a randomized, crossover flaxseed intervention study in 252 healthy, postmenopausal women [137 European ancestry (EA) and 115 African ancestry (AA)] from western New York. Participants were randomly assigned to maintain usual diet or consume 10 g/day ground flaxseed for 6 weeks. After a 2-month washout period, participants crossed over to the other diet condition for an additional 6 weeks. Urinary ENL excretion was measured by gas chromatography-mass spectrometry and 70 polymorphisms in 29 genes related to steroid hormone and xenobiotic metabolism were genotyped. Mixed additive genetic models were constructed to examine association of genetic variation with urinary ENL excretion at baseline and after the flaxseed intervention. RESULTS: SNPs in several genes were nominally (P < 0.05) associated with ENL excretion at baseline and/or after intervention: ESR1, CYP1B1, COMT, CYP3A5, ARPC1A, BCL2L11, SHBG, SLCO1B1, and ZKSCAN5. A greater number of SNPs were associated among AA women than among EA women, and no SNPs were associated in both races. No SNP-ENL associations were statistically significant after correction for multiple comparisons. CONCLUSIONS: Variation in several genes related to steroid hormone metabolism was associated with lignan excretion at baseline and/or after flaxseed intervention among postmenopausal women. IMPACT: These findings may contribute to our understanding of the differences observed in urinary ENL excretion among AA and EA women and thus hormone-related breast cancer risk.