Risk SNP-Mediated Promoter-Enhancer Switching Drives Prostate Cancer through lncRNA PCAT19.

The prostate cancer (PCa) risk-associated SNP rs11672691 is positively associated with aggressive disease at diagnosis. We showed that rs11672691 maps to the promoter of a short isoform of long noncoding RNA PCAT19 (PCAT19-short), which is in the third intron of the long isoform (PCAT19-long). The risk variant is associated with decreased and increased levels of PCAT19-short and PCAT19-long, respectively. Mechanistically, the risk SNP region is bifunctional with both promoter and enhancer activity. The risk variants of rs11672691 and its LD SNP rs887391 decrease binding of transcription factors NKX3.1 and YY1 to the promoter of PCAT19-short, resulting in weaker promoter but stronger enhancer activity that subsequently activates PCAT19-long. PCAT19-long interacts with HNRNPAB to activate a subset of cell-cycle genes associated with PCa progression, thereby promoting PCa tumor growth and metastasis. Taken together, these findings reveal a risk SNP-mediated promoter-enhancer switching mechanism underlying both initiation and progression of aggressive PCa.

Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

Molecular Characteristics of Early-Onset Colorectal Cancer According to Detailed Anatomical Locations: Comparison With Later-Onset Cases.

INTRODUCTION: Early-onset colorectal cancer diagnosed before the age of 50 years has been increasing. Likely reflecting the pathogenic role of the intestinal microbiome, which gradually changes across the entire colorectal length, the prevalence of certain tumor molecular characteristics gradually changes along colorectal subsites. Understanding how colorectal tumor molecular features differ by age and tumor location is important in personalized patient management. METHODS: Using 14,004 cases with colorectal cancer including 3,089 early-onset cases, we examined microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and KRAS and BRAF mutations in carcinomas of the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum and compared early-onset cases with later-onset cases. RESULTS: The proportions of MSI-high, CIMP-high, and BRAF -mutated early-onset tumors were lowest in the rectum (8.8%, 3.4%, and 3.5%, respectively) and highest in the ascending colon (46% MSI-high; 15% CIMP-high) or transverse colon (8.6% BRAF -mutated) (all Ptrend <0.001 across the rectum to ascending colon). Compared with later-onset tumors, early-onset tumors showed a higher prevalence of MSI-high status and a lower prevalence of CIMP-high status and BRAF mutations in most subsites. KRAS mutation prevalence was higher in the cecum compared with that in the other subsites in both early-onset and later-onset tumors ( P < 0.001). Notably, later-onset MSI-high tumors showed a continuous decrease in KRAS mutation prevalence from the rectum (36%) to ascending colon (9%; Ptrend <0.001), followed by an increase in the cecum (14%), while early-onset MSI-high cancers showed no such trend. DISCUSSION: Our findings support biogeographical and pathogenic heterogeneity of colorectal carcinomas in different colorectal subsites and age groups.

Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in All of Us Research Program Participants, 2 January to 18 March 2020.

BACKGROUND: With limited severe acute respiratory syndrome coronavirus (SARS-CoV-2) testing capacity in the United States at the start of the epidemic (January-March 2020), testing was focused on symptomatic patients with a travel history throughout February, obscuring the picture of SARS-CoV-2 seeding and community transmission. We sought to identify individuals with SARS-CoV-2 antibodies in the early weeks of the US epidemic. METHODS: All of Us study participants in all 50 US states provided blood specimens during study visits from 2 January to 18 March 2020. Participants were considered seropositive if they tested positive for SARS-CoV-2 immunoglobulin G (IgG) antibodies with the Abbott Architect SARS-CoV-2 IgG enzyme-linked immunosorbent assay (ELISA) and the EUROIMMUN SARS-CoV-2 ELISA in a sequential testing algorithm. The sensitivity and specificity of these ELISAs and the net sensitivity and specificity of the sequential testing algorithm were estimated, along with 95% confidence intervals (CIs). RESULTS: The estimated sensitivities of the Abbott and EUROIMMUN assays were 100% (107 of 107 [95% CI: 96.6%-100%]) and 90.7% (97 of 107 [83.5%-95.4%]), respectively, and the estimated specificities were 99.5% (995 of 1000 [98.8%-99.8%]) and 99.7% (997 of 1000 [99.1%-99.9%]), respectively. The net sensitivity and specificity of our sequential testing algorithm were 90.7% (97 of 107 [95% CI: 83.5%-95.4%]) and 100.0% (1000 of 1000 [99.6%-100%]), respectively. Of the 24 079 study participants with blood specimens from 2 January to 18 March 2020, 9 were seropositive, 7 before the first confirmed case in the states of Illinois, Massachusetts, Wisconsin, Pennsylvania, and Mississippi. CONCLUSIONS: Our findings identified SARS-CoV-2 infections weeks before the first recognized cases in 5 US states.

Diabetes mellitus in relation to colorectal tumor molecular subtypes: A pooled analysis of more than 9000 cases.

Diabetes is an established risk factor for colorectal cancer. However, colorectal cancer is a heterogeneous disease and it is not well understood whether diabetes is more strongly associated with some tumor molecular subtypes than others. A better understanding of the association between diabetes and colorectal cancer according to molecular subtypes could provide important insights into the biology of this association. We used data on lifestyle and clinical characteristics from the Colorectal Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), including 9756 colorectal cancer cases (with tumor marker data) and 9985 controls, to evaluate associations between reported diabetes and risk of colorectal cancer according to molecular subtypes. Tumor markers included BRAF and KRAS mutations, microsatellite instability and CpG island methylator phenotype. In the multinomial logistic regression model, comparing colorectal cancer cases to cancer-free controls, diabetes was positively associated with colorectal cancer regardless of subtype. The highest OR estimate was found for BRAF-mutated colorectal cancer, n = 1086 (ORfully adj : 1.67, 95% confidence intervals [CI]: 1.36-2.05), with an attenuated association observed between diabetes and colorectal cancer without BRAF-mutations, n = 7959 (ORfully adj : 1.33, 95% CI: 1.19-1.48). In the case only analysis, BRAF-mutation was differentially associated with diabetes (Pdifference  = .03). For the other markers, associations with diabetes were similar across tumor subtypes. In conclusion, our study confirms the established association between diabetes and colorectal cancer risk, and suggests that it particularly increases the risk of BRAF-mutated tumors.

Familial recurrence risk with varying amount of family history.

The familial recurrence risk is the probability a person will have disease, given a reported family history. When family histories are obtained as simple counts of disease among family members, as often obtained in cancer registries or surveys, we propose methods to estimate recurrence risks based on truncated binomial distributions. By this approach, we are able to obtain unbiased estimates of risk for a person with at least k-affected relatives, where k can be specified to determine how risk varies with k. We also derive robust variances of the recurrence risk estimate, to account for correlations within families, such as those induced by shared genes or shared environment, without explicitly modeling the factors that cause familial correlations. Furthermore, we illustrate how mixture models can be used to account for a sample composed of low- and high-risk families. Using simulations, we illustrate the properties of the proposed methods. Application of our methods to a family history survey of prostate cancer shows that the recurrence risk for prostate cancer increased from 16%, when there was at least one affected relative, to 52%, when there was at least five affected relatives.

Potential impact of family history-based screening guidelines on the detection of early-onset colorectal cancer.

BACKGROUND: Initiating screening at an earlier age based on cancer family history is one of the primary recommended strategies for the prevention and detection of early-onset colorectal cancer (EOCRC), but data supporting the effectiveness of this approach are limited. The authors assessed the performance of family history-based guidelines for identifying individuals with EOCRC. METHODS: The authors conducted a population-based, case-control study of individuals aged 40 to 49 years with (2473 individuals) and without (772 individuals) incident CRC in the Colon Cancer Family Registry from 1998 through 2007. They estimated the sensitivity and specificity of family history-based criteria jointly recommended by the American Cancer Society, the US Multi-Society Task Force on CRC, and the American College of Radiology in 2008 for early screening, and the age at which each participant could have been recommended screening initiation if these criteria had been applied. RESULTS: Family history-based early screening criteria were met by approximately 25% of cases (614 of 2473 cases) and 10% of controls (74 of 772 controls), with a sensitivity of 25% and a specificity of 90% for identifying EOCRC cases aged 40 to 49 years. Among 614 individuals meeting early screening criteria, 98.4% could have been recommended screening initiation at an age younger than the observed age of diagnosis. CONCLUSIONS: Of CRC cases aged 40 to 49 years, 1 in 4 met family history-based early screening criteria, and nearly all cases who met these criteria could have had CRC diagnosed earlier (or possibly even prevented) if earlier screening had been implemented as per family history-based guidelines. Additional strategies are needed to improve the detection and prevention of EOCRC for individuals not meeting family history criteria for early screening.

Returning genomic results in a Federally Qualified Health Center: the intersection of precision medicine and social determinants of health.

PURPOSE: This report describes the return of sequencing results to low-income Latino participants recruited through a Federally Qualified Health Center (FQHC). We describe challenges in returning research results secondary to social determinants of health and present lessons learned to guide future genomic medicine implementation studies in low-resource settings. METHODS: Five hundred Latino adults (76% women) consented to research sequencing for a predetermined panel of actionable genes. Providers and staff from the FQHC were engaged to align processes with the practice and a community advisory board grounded the project in the local community. RESULTS: A pathogenic/likely pathogenic variant was present in 10 participants (2%). Challenges in return of results included the time lag (582 ± 53 days) between enrollment and returning actionable results, difficulty reaching participants, missed appointments, low health literacy, lack of health insurance, and reconciling results with limited information on family history. Return of one actionable result was deferred due to acute emotional distress secondary to recent traumatic life events. CONCLUSION: The social determinants of health influence the implementation of genomic medicine in low-income populations in low-resource settings. Considering nonbiological factors that contribute to disparities will be necessary to better appreciate how genomic medicine may fit within the context of health equity.

Type 2 diabetes mellitus, blood cholesterol, triglyceride and colorectal cancer risk in Lynch syndrome.

BACKGROUND: Type 2 diabetes mellitus and high total cholesterol and triglycerides are known to be associated with increased colorectal cancer risk for the general population. These associations are unknown for people with a germline DNA mismatch repair gene mutation (Lynch syndrome), who are at high risk of colorectal cancer. METHODS: This study included 2023 (56.4% female) carriers with a mismatch repair gene mutation (737 in MLH1, 928 in MSH2, 230 in MSH6, 106 in PMS2, 22 in EPCAM) recruited by the Colon Cancer Family Registry between 1998 and 2012. Weighted Cox regression was used to estimate the hazard ratios (HR) and 95% confidence intervals (CI) for the associations between self-reported type 2 diabetes, high cholesterol, triglyceride and colorectal cancer risk. RESULTS: Overall, 802 carriers were diagnosed with colorectal cancer at a median age of 42 years. A higher risk of colorectal cancer was observed in those with self-reported type-2 diabetes (HR 1.92; 95% CI, 1.03-3.58) and high cholesterol (HR 1.76; CI 1.23-2.52) compared with those without these conditions. There was no evidence of high triglyceride being associated with colorectal cancer risk. CONCLUSION: For people with Lynch syndrome, self-reported type-2 diabetes mellitus and high cholesterol were associated with increased colorectal cancer risk.

Biallelic BRCA2 Mutations Shape the Somatic Mutational Landscape of Aggressive Prostate Tumors.

To identify clinically important molecular subtypes of prostate cancer (PCa), we characterized the somatic landscape of aggressive tumors via deep, whole-genome sequencing. In our discovery set of ten tumor/normal subject pairs with Gleason scores of 8-10 at diagnosis, coordinated analysis of germline and somatic variants, including single-nucleotide variants, indels, and structural variants, revealed biallelic BRCA2 disruptions in a subset of samples. Compared to the other samples, the PCa BRCA2-deficient tumors exhibited a complex and highly specific mutation signature, featuring a 2.88-fold increased somatic mutation rate, depletion of context-specific C>T substitutions, and an enrichment for deletions, especially those longer than 10 bp. We next performed a BRCA2 deficiency-targeted reanalysis of 150 metastatic PCa tumors, and each of the 18 BRCA2-mutated samples recapitulated the BRCA2 deficiency-associated mutation signature, underscoring the potent influence of these lesions on somatic mutagenesis and tumor evolution. Among all 21 individuals with BRCA2-deficient tumors, only about half carried deleterious germline alleles. Importantly, the somatic mutation signature in tumors with one germline and one somatic risk allele was indistinguishable from those with purely somatic mutations. Our observations clearly demonstrate that BRCA2-disrupted tumors represent a unique and clinically relevant molecular subtype of aggressive PCa, highlighting both the promise and utility of this mutation signature as a prognostic and treatment-selection biomarker. Further, any test designed to leverage BRCA2 status as a biomarker for PCa must consider both germline and somatic mutations and all types of deleterious mutations.

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.

The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10-12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

Motivation: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. Results: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. Availability and implementation: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer.

Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10-8; permuted p-value 3.51x10-8) region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74-0.91]; P = 2.1×10-4) and TT genotypes (OR,0.62 [95% CI, 0.51-0.75]; P = 1.3×10-6) but not associated among those with the CC genotype (p = 0.059). No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk.

gsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels.

Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare-variant analysis using component gene-level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family-wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case-control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open-source R code for public use to facilitate easy application of our methods to existing rare-variant analysis results.

Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array.

This corrects the article DOI: 10.1038/bjc.2016.50.

Comprehensively evaluating cis-regulatory variation in the human prostate transcriptome by using gene-level allele-specific expression.

The identification of cis-acting regulatory variation in primary tissues has the potential to elucidate the genetic basis of complex traits and further our understanding of transcriptomic diversity across cell types. Expression quantitative trait locus (eQTL) association analysis using RNA sequencing (RNA-seq) data can improve upon the detection of cis-acting regulatory variation by leveraging allele-specific expression (ASE) patterns in association analysis. Here, we present a comprehensive evaluation of cis-acting eQTLs by analyzing RNA-seq gene-expression data and genome-wide high-density genotypes from 471 samples of normal primary prostate tissue. Using statistical models that integrate ASE information, we identified extensive cis-eQTLs across the prostate transcriptome and found that approximately 70% of expressed genes corresponded to a significant eQTL at a gene-level false-discovery rate of 0.05. Overall, cis-eQTLs were heavily concentrated near the transcription start and stop sites of affected genes, and effects were negatively correlated with distance. We identified multiple instances of cis-acting co-regulation by using phased genotype data and discovered 233 SNPs as the most strongly associated eQTLs for more than one gene. We also noted significant enrichment (25/50, p = 2E-5) of previously reported prostate cancer risk SNPs in prostate eQTLs. Our results illustrate the benefit of assessing ASE data in cis-eQTL analyses by showing better reproducibility of prior eQTL findings than of eQTL mapping based on total expression alone. Altogether, our analysis provides extensive functional context of thousands of SNPs in prostate tissue, and these results will be of critical value in guiding studies examining disease of the human prostate.

DNA mismatch repair protein deficient non-neoplastic colonic crypts: a novel indicator of Lynch syndrome.

Lynch syndrome is the most common form of hereditary colorectal carcinoma. However, establishing the diagnosis of Lynch syndrome is challenging, and ancillary studies that distinguish between sporadic DNA mismatch repair (MMR) protein deficiency and Lynch syndrome are needed, particularly when germline mutation studies are inconclusive. The aim of this study was to determine if MMR protein-deficient non-neoplastic intestinal crypts can help distinguish between patients with and without Lynch syndrome. We evaluated the expression of MMR proteins in non-neoplastic intestinal mucosa obtained from colorectal surgical resection specimens from patients with Lynch syndrome-associated colorectal carcinoma (n = 52) and patients with colorectal carcinoma without evidence of Lynch syndrome (n = 70), including sporadic MMR protein-deficient colorectal carcinoma (n = 30), MMR protein proficient colorectal carcinoma (n = 30), and "Lynch-like" syndrome (n = 10). MMR protein-deficient non-neoplastic colonic crypts were identified in 19 of 122 (16%) patients. MMR protein-deficient colonic crypts were identified in 18 of 52 (35%) patients with Lynch syndrome compared to only 1 of 70 (1%) patients without Lynch syndrome (p < 0.001). This one patient had "Lynch-like" syndrome and harbored two MSH2-deficient non-neoplastic colonic crypts. MMR protein-deficient non-neoplastic colonic crypts were not identified in patients with sporadic MMR protein-deficient or MMR protein proficient colorectal carcinoma. Our findings suggest that MMR protein-deficient colonic crypts are a novel indicator of Lynch syndrome, and evaluation for MMR protein-deficient crypts may be a helpful addition to Lynch syndrome diagnostics.

FIRE: functional inference of genetic variants that regulate gene expression.

MOTIVATION: Interpreting genetic variation in noncoding regions of the genome is an important challenge for personal genome analysis. One mechanism by which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the regulation of gene expression. Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid interpretation of variants of unknown significance identified in whole-genome sequencing studies. RESULTS: We developed FIRE (Functional Inference of Regulators of Expression), a tool to score both noncoding and coding SNVs based on their potential to regulate the expression levels of nearby genes. FIRE consists of 23 random forests trained to recognize SNVs in cis-expression quantitative trait loci (cis-eQTLs) using a set of 92 genomic annotations as predictive features. FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different ancestry from non-eQTL SNVs with an AUC of 0.939. FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types. AVAILABILITY AND IMPLEMENTATION: FIRE scores for genome-wide SNVs in hg19/GRCh37 are available for download at https://sites.google.com/site/fireregulatoryvariation/. CONTACT: nilah@stanford.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Motivation: Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped. Results: We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. Availability and Implementation: The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ . Contact: hlin1@lsuhsc.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Germline miRNA DNA variants and the risk of colorectal cancer by subtype.

MicroRNAs (miRNAs) regulate up to one-third of all protein-coding genes including genes relevant to cancer. Variants within miRNAs have been reported to be associated with prognosis, survival, response to chemotherapy across cancer types, in vitro parameters of cell growth, and altered risks for development of cancer. Five miRNA variants have been reported to be associated with risk for development of colorectal cancer (CRC). In this study, we evaluated germline genetic variation in 1,123 miRNAs in 899 individuals with CRCs categorized by clinical subtypes and in 204 controls. The role of common miRNA variation in CRC was investigated using single variant and miRNA-level association tests. Twenty-nine miRNAs and 30 variants exhibited some marginal association with CRC in at least one subtype of CRC. Previously reported associations were not confirmed (n = 4) or could not be evaluated (n = 1). The variants noted for the CRCs with deficient mismatch repair showed little overlap with the variants noted for CRCs with proficient mismatch repair, consistent with our evolving understanding of the distinct biology underlying these two groups. © 2016 The Authors Genes, Chromosomes & Cancer Published by Wiley Periodicals, Inc.