Morphological, cariological, and phytochemical studies of diploid and autotetraploid Hippeastrum papilio plants.

MAIN CONCLUSION: The polyploidization of Hippeastrum papilio influences its primary and secondary metabolism including the biosynthesis of bioactive alkaloids. Hippeastrum papilio is an ornamental plant that has advantages in comparison to the currently used plants for the extraction of galanthamine, a natural compound used for the cognitive treatment of Alzheimer's disease. In the present study, an autotetraploid line of H. papilio was induced for the first time, after treatment with 0.05% colchicine for 48 h. The chromosome number in diploids was found to be 2n = 2x = 22 and for autotetraploids 2n = 4x = 44. The flow cytometric analyses detected a DNA C-value of 14.88 ± 0.03 pg (1C) in diploids and 26.57 ± 0.12 pg in autotetraploids. The morphological, cytological, and phytochemical studies showed significant differences between diploids and autotetraploids. The length and width of stomata in autotetraploids were 22.47% and 17.94%, respectively, larger than those observed in the diploid leaves. The biomass of one-year-old autotetraploid H. papilio plants was reduced by 53.99% for plants' fresh weight, 56.53% for leaves' fresh weight, and 21.70% for bulb diameter. The GC-MS analysis of methanol extracts from one-year-old diploid and autotetraploid H. papilio plants revealed over 60 primary and secondary metabolites including alkaloids, phenolic acids, sterols, saccharides, and alcohols, among others. Principal component analysis of the metabolite profiles indicates a divergence of the metabolism between diploid and autotetraploid plants. The content of galanthamine and haemanthamine was found to be 49.73% and 80.10%, respectively, higher in the leaves of autotetraploids, compared to the diploid ones. The biosynthesis of the saccharides shows a tendency to be upregulated in tetraploid plants, while that of phenolic acids was downregulated. Polyploidization of H. papilio creates possibilities for further crop improvement aimed at high-galanthamine-producing genotypes.

DECO: a framework for jointly analyzing de novo and rare case/control variants, and biological pathways.

MOTIVATION: Rare variant-based analyses are beginning to identify risk genes for neuropsychiatric disorders and other diseases. However, the identified genes only account for a fraction of predicted causal genes. Recent studies have shown that rare damaging variants are significantly enriched in specific gene-sets. Methods which are able to jointly model rare variants and gene-sets to identify enriched gene-sets and use these enriched gene-sets to prioritize additional risk genes could improve understanding of the genetic architecture of diseases. RESULTS: We propose DECO (Integrated analysis of de novo mutations, rare case/control variants and omics information via gene-sets), an integrated method for rare-variant and gene-set analysis. The method can (i) test the enrichment of gene-sets directly within the statistical model, and (ii) use enriched gene-sets to rank existing genes and prioritize additional risk genes for tested disorders. In simulations, DECO performs better than a homologous method that uses only variant data. To demonstrate the application of the proposed protocol, we have applied this approach to rare-variant datasets of schizophrenia. Compared with a method which only uses variant information, DECO is able to prioritize additional risk genes. AVAILABILITY: DECO can be used to analyze rare-variants and biological pathways or cell types for any disease. The package is available on Github https://github.com/hoangtn/DECO.

Afatinib alone and in combination with vinorelbine or paclitaxel, in patients with HER2-positive breast cancer who failed or progressed on prior trastuzumab and/or lapatinib (LUX-Breast 2): an open-label, multicenter, phase II trial.

PURPOSE: Resistance to HER2 (ErbB2)-targeted therapy may be mediated by other members of the ErbB family. We investigated the efficacy and safety of the irreversible ErbB family blocker, afatinib, alone as first-line therapy in the advanced setting and in combination with vinorelbine or paclitaxel for those who progressed on afatinib monotherapy, in female patients with metastatic breast cancer who had failed or progressed on prior HER2-targeted therapy in the early disease setting. METHODS: In this phase II, single-arm, two-part study (ClinicalTrials.gov: NCT01271725), patients in part A received afatinib 40 mg/day in 21-day cycles until disease progression or intolerable adverse events (AEs). Patients with progressive disease could then receive afatinib plus weekly vinorelbine 25 mg/m2 or paclitaxel 80 mg/m2 until disease progression or intolerable AEs (part B). The primary endpoint was confirmed objective response rate (RECIST v1.1). RESULTS: Eighty-seven patients were enrolled and 74 were treated in part A (median age: 51 years [range 27-76]; 31 [42%] estrogen receptor-positive, 26 [35%] progesterone receptor-positive). Of these, 39 (53%) patients went on to receive afatinib plus vinorelbine (13 patients) or paclitaxel (26 patients) in part B. Thirteen (18%) and 12 (31%) patients achieved an objective response in parts A and B, respectively. The most common treatment-related AEs with afatinib monotherapy (any/grade ≥ 3) were diarrhea (68%/8%) and rash (49%/4%). Combination therapy was generally well tolerated, with no additive toxicity observed. CONCLUSION: Afatinib treatment, alone or in combination with vinorelbine or paclitaxel, was associated with objective responses in ≥ 18% of patients with metastatic breast cancer for whom prior HER2-targeted therapy has failed. Treatment-related AEs were generally manageable, with few grade ≥ 3 AEs reported. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01271725, registered 1 July 2011.

Identifying a novel biological mechanism for alcohol addiction associated with circRNA networks acting as potential miRNA sponges.

Our lab and others have shown that chronic alcohol use leads to gene and miRNA expression changes across the mesocorticolimbic (MCL) system. Circular RNAs (circRNAs) are noncoding RNAs that form closed-loop structures and are reported to alter gene expression through miRNA sequestration, thus providing a potentially novel neurobiological mechanism for the development of alcohol dependence (AD). Genome-wide expression of circRNA was assessed in the nucleus accumbens (NAc) from 32 AD-matched cases/controls. Significant circRNAs (unadj. p ≤ 0.05) were identified via regression and clustered in circRNA networks via weighted gene co-expression network analysis (WGCNA). CircRNA interactions with previously generated mRNA and miRNA were detected via correlation and bioinformatic analyses. Significant circRNAs (N = 542) clustered in nine significant AD modules (FWER p ≤ 0.05), within which we identified 137 circRNA hubs. We detected 23 significant circRNA-miRNA-mRNA interactions (FDR ≤ 0.10). Among these, circRNA-406742 and miR-1200 significantly interact with the highest number of mRNA, including genes associated with neuronal functioning and alcohol addiction (HRAS, PRKCB, HOMER1, and PCLO). Finally, we integrate genotypic information that revealed 96 significant circRNA expression quantitative trait loci (eQTLs) (unadj. p ≤ 0.002) that showed significant enrichment within recent alcohol use disorder (AUD) and smoking genome-wide association study (GWAS). To our knowledge, this is the first study to examine the role of circRNA in the neuropathology of AD. We show that circRNAs impact mRNA expression by interacting with miRNA in the NAc of AD subjects. More importantly, we provide indirect evidence for the clinical importance of circRNA in the development of AUD by detecting a significant enrichment of our circRNA eQTLs among GWAS of substance abuse.

Increasing the resolution and precision of psychiatric genome-wide association studies by re-imputing summary statistics using a large, diverse reference panel.

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary-statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel for example, ~2,500-subject coming from the 1000 Genome-Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: (a) uses a much larger and more diverse reference panel compared to traditional reference panels, and (b) can estimate weights of ethnic-mixture based solely on Z-scores, when allele frequencies are not available. We applied DISTMIX2 to GWAS summary-statistics from the psychiatric genetic consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder GWAS found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most psychiatric GWAS-studies.

Assessing the Role of Long Noncoding RNA in Nucleus Accumbens in Subjects With Alcohol Dependence.

BACKGROUND: Long noncoding RNA (lncRNA) have been implicated in the etiology of alcohol use. Since lncRNA provide another layer of complexity to the transcriptome, assessing their expression in the brain is the first critical step toward understanding lncRNA functions in alcohol use and addiction. Thus, we sought to profile lncRNA expression in the nucleus accumbens (NAc) in a large postmortem alcohol brain sample. METHODS: LncRNA and protein-coding gene (PCG) expressions in the NAc from 41 subjects with alcohol dependence (AD) and 41 controls were assessed via a regression model. Weighted gene coexpression network analysis was used to identify lncRNA and PCG networks (i.e., modules) significantly correlated with AD. Within the significant modules, key network genes (i.e., hubs) were also identified. The lncRNA and PCG hubs were correlated via Pearson correlations to elucidate the potential biological functions of lncRNA. The lncRNA and PCG hubs were further integrated with GWAS data to identify expression quantitative trait loci (eQTL). RESULTS: At Bonferroni adj. p-value ≤ 0.05, we identified 19 lncRNA and 5 PCG significant modules, which were enriched for neuronal and immune-related processes. In these modules, we further identified 86 and 315 PCG and lncRNA hubs, respectively. At false discovery rate (FDR) of 10%, the correlation analyses between the lncRNA and PCG hubs revealed 3,125 positive and 1,860 negative correlations. Integration of hubs with genotype data identified 243 eQTLs affecting the expression of 39 and 204 PCG and lncRNA hubs, respectively. CONCLUSIONS: Our study identified lncRNA and gene networks significantly associated with AD in the NAc, coordinated lncRNA and mRNA coexpression changes, highlighting potentially regulatory functions for the lncRNA, and our genetic (cis-eQTL) analysis provides novel insights into the etiological mechanisms of AD.

TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders.

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), for example, across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred to as transcriptomic wide association studies (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effects of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular basis of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to the quadratic (as a function of the number of SNPs) computational burden for computing LD across large chromosomal regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that (a) has a linear computational burden, (b) uses a large and diverse reference panel (33 K subjects), (c) is competitive (adjusts for background enrichment in gene TWAS statistics), and (d) is applicable as-is to ethnically mixed-cohorts. To underline its potential for increasing the power to uncover genetic signals over the commonly used nontranscriptomics methods, for example, MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts.

Motivation: To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on (i) summary statistics from genome-wide association studies (GWAS) and (ii) linkage disequilibrium patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals. Results: We propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses (i) cis-eQTL SNPs from the latest expression studies and (ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also (i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and (ii) to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of P-values. Availability and implementation: https://github.com/Chatzinakos/JEPEGMIX2. Supplementary information: Supplementary data are available at Bioinformatics online.

HiCcompare: an R-package for joint normalization and comparison of HI-C datasets.

BACKGROUND: Changes in spatial chromatin interactions are now emerging as a unifying mechanism orchestrating the regulation of gene expression. Hi-C sequencing technology allows insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence- and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, e.g., disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering comparative analysis of chromatin interactions. RESULTS: We developed a simple and effective method, HiCcompare, for the joint normalization and differential analysis of multiple Hi-C datasets. The method introduces a distance-centric analysis and visualization of the differences between two Hi-C datasets on a single plot that allows for a data-driven normalization of biases using locally weighted linear regression (loess). HiCcompare outperforms methods for normalizing individual Hi-C datasets and methods for differential analysis (diffHiC, FIND) in detecting a priori known chromatin interaction differences while preserving the detection of genomic structures, such as A/B compartments. CONCLUSIONS: HiCcompare is able to remove between-dataset bias present in Hi-C matrices. It also provides a user-friendly tool to allow the scientific community to perform direct comparisons between the growing number of pre-processed Hi-C datasets available at online repositories. HiCcompare is freely available as a Bioconductor R package https://bioconductor.org/packages/HiCcompare/ .

JEPEGMIX: gene-level joint analysis of functional SNPs in cosmopolitan cohorts.

MOTIVATION: To increase detection power, gene level analysis methods are used to aggregate weak signals. To greatly increase computational efficiency, most methods use as input summary statistics from genome-wide association studies (GWAS). Subsequently, gene statistics are constructed using linkage disequilibrium (LD) patterns from a relevant reference panel. However, all methods, including our own Joint Effect on Phenotype of eQTL/functional single nucleotide polymorphisms (SNPs) associated with a Gene (JEPEG), assume homogeneous panels, e.g. European. However, this renders these tools unsuitable for the analysis of large cosmopolitan cohorts. RESULTS: We propose a JEPEG extension, JEPEGMIX, which similar to one of our software tools, Direct Imputation of summary STatistics of unmeasured SNPs from MIXed ethnicity cohorts, is capable of estimating accurate LD patterns for cosmopolitan cohorts. JEPEGMIX uses this accurate LD estimates to (i) impute the summary statistics at unmeasured functional variants and (ii) test for the joint effect of all measured and imputed functional variants which are associated with a gene. We illustrate the performance of our tool by analyzing the GWAS meta-analysis summary statistics from the multi-ethnic Psychiatric Genomics Consortium Schizophrenia stage 2 cohort. This practical application supports the immune system being one of the main drivers of the process leading to schizophrenia. AVAILABILITY AND IMPLEMENTATION: Software, annotation database and examples are available at http://dleelab.github.io/jepegmix/. CONTACT: donghyung.lee@vcuhealth.org SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans.

MOTIVATION: For genetic studies, statistically significant variants explain far less trait variance than 'sub-threshold' association signals. To dimension follow-up studies, researchers need to accurately estimate 'true' effect sizes at each SNP, e.g. the true mean of odds ratios (ORs)/regression coefficients (RRs) or Z-score noncentralities. Naïve estimates of effect sizes incur winner's curse biases, which are reduced only by laborious winner's curse adjustments (WCAs). Given that Z-scores estimates can be theoretically translated on other scales, we propose a simple method to compute WCA for Z-scores, i.e. their true means/noncentralities. RESULTS: WCA of Z-scores shrinks these towards zero while, on P-value scale, multiple testing adjustment (MTA) shrinks P-values toward one, which corresponds to the zero Z-score value. Thus, WCA on Z-scores scale is a proxy for MTA on P-value scale. Therefore, to estimate Z-score noncentralities for all SNPs in genome scans, we propose F: DR I: nverse Q: uantile T: ransformation (FIQT). It (i) performs the simpler MTA of P-values using FDR and (ii) obtains noncentralities by back-transforming MTA P-values on Z-score scale. When compared to competitors, realistic simulations suggest that FIQT is more (i) accurate and (ii) computationally efficient by orders of magnitude. Practical application of FIQT to Psychiatric Genetic Consortium schizophrenia cohort predicts a non-trivial fraction of sub-threshold signals which become significant in much larger supersamples. CONCLUSIONS: FIQT is a simple, yet accurate, WCA method for Z-scores (and ORs/RRs, via simple transformations). AVAILABILITY AND IMPLEMENTATION: A 10 lines R function implementation is available at https://github.com/bacanusa/FIQT CONTACT: sabacanu@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Rate of Change in Alcohol Misuse Across Adolescence is Heritable.

BACKGROUND: Alcohol use typically begins during adolescence and escalates into young adulthood. This represents an important period for the establishment of alcohol use and misuse patterns, which can have psychosocial and medical consequences. Although changes in alcohol use during this time have been phenotypically characterized, their genetic nature is poorly understood. METHODS: Participants of the Avon Longitudinal Study of Parents and Children completed the Alcohol Use Disorders Identification Test (AUDIT) 4 times from age 16 to 20. We used Mplus to construct a growth model characterizing changes in AUDIT scores across time (N = 4,545, where data were available for at least 2 time points). The slope of the model was used as the phenotype in a genomewide association study (N = 3,380), followed by secondary genetic analyses. RESULTS: No individual marker met genomewide significance criteria. Top markers mapped to biologically plausible candidate genes. The slope term was moderately heritable (h2SNP = 0.26, p = 0.009), and replication attempts using a meta-analysis of independent samples provided support for implicated variants at the aggregate level. Nominally significant (p < 0.00001) markers mapped to putatively active genomic regions in brain tissue more frequently than expected by chance. CONCLUSIONS: These results build on prior studies by demonstrating that common genetic variation impacts alcohol misuse trajectories. Influential loci map to genes that merit additional research, as well as to intergenic regions with regulatory functions in the central nervous system. These findings underscore the complex biological nature of alcohol misuse across development.

Genomewide Association Study of Alcohol Dependence Identifies Risk Loci Altering Ethanol-Response Behaviors in Model Organisms.

BACKGROUND: Alcohol dependence (AD) shows evidence for genetic liability, but genes influencing risk remain largely unidentified. METHODS: We conducted a genomewide association study in 706 related AD cases and 1,748 unscreened population controls from Ireland. We sought replication in 15,496 samples of European descent. We used model organisms (MOs) to assess the role of orthologous genes in ethanol (EtOH)-response behaviors. We tested 1 primate-specific gene for expression differences in case/control postmortem brain tissue. RESULTS: We detected significant association in COL6A3 and suggestive association in 2 previously implicated loci, KLF12 and RYR3. None of these signals are significant in replication. A suggestive signal in the long noncoding RNA LOC339975 is significant in case:control meta-analysis, but not in a population sample. Knockdown of a COL6A3 ortholog in Caenorhabditis elegans reduced EtOH sensitivity. Col6a3 expression correlated with handling-induced convulsions in mice. Loss of function of the KLF12 ortholog in C. elegans impaired development of acute functional tolerance (AFT). Klf12 expression correlated with locomotor activation following EtOH injection in mice. Loss of function of the RYR3 ortholog reduced EtOH sensitivity in C. elegans and rapid tolerance in Drosophila. The ryanodine receptor antagonist dantrolene reduced motivation to self-administer EtOH in rats. Expression of LOC339975 does not differ between cases and controls but is reduced in carriers of the associated rs11726136 allele in nucleus accumbens (NAc). CONCLUSIONS: We detect association between AD and COL6A3, KLF12, RYR3, and LOC339975. Despite nonreplication of COL6A3, KLF12, and RYR3 signals, orthologs of these genes influence behavioral response to EtOH in MOs, suggesting potential involvement in human EtOH response and AD liability. The associated LOC339975 allele may influence gene expression in human NAc. Although the functions of long noncoding RNAs are poorly understood, there is mounting evidence implicating these genes in multiple brain functions and disorders.

A randomized, placebo-controlled, phase 1/2 study of tivantinib (ARQ 197) in combination with irinotecan and cetuximab in patients with metastatic colorectal cancer with wild-type KRAS who have received first-line systemic therapy.

Cetuximab in combination with an irinotecan-containing regimen is a standard treatment in patients with KRAS wild-type (KRAS WT), metastatic colorectal cancer (mCRC). We investigated the addition of the oral MET inhibitor tivantinib to cetuximab + irinotecan (CETIRI) based on preclinical evidence that activation of the MET pathway may confer resistance to anti-EGFR therapy. Previously treated patients with KRAS WT advanced or mCRC were enrolled. The phase 1, open-label 3 + 3, dose-escalation study evaluated the safety and maximally tolerated dose of tivantinib plus CETIRI. The phase 2, randomized, double-blinded, placebo-controlled study of biweekly CETIRI plus tivantinib or placebo was restricted to patients who had received only one prior line of chemotherapy. The phase 2 primary endpoint was progression-free survival (PFS). The recommended phase 2 dose was tivantinib (360 mg/m(2) twice daily) with biweekly cetuximab (500 mg/m(2)) and irinotecan (180 mg/m(2)). Among 117 patients evaluable for phase 2 analysis, no statistically significant PFS difference was observed: 8.3 months on tivantinib vs. 7.3 months on placebo (HR, 0.85; 95% confidence interval, 0.55-1.33; P = 0.38). Subgroup analyses trended in favor of tivantinib in patients with MET-High tumors by immunohistochemistry, PTEN-Low tumors, or those pretreated with oxaliplatin, but subgroups were too small to draw conclusions. Neutropenia, diarrhea, nausea and rash were the most frequent severe adverse events in tivantinib-treated patients. The combination of tivantinib and CETIRI was well tolerated but did not significantly improve PFS in previously treated KRAS WT mCRC. Tivantinib may be more active in specific subgroups.

DISTMIX: direct imputation of summary statistics for unmeasured SNPs from mixed ethnicity cohorts.

MOTIVATION: To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels. However, the ever increasing size and ethnic diversity of both reference panels and cohorts makes genotype imputation computationally challenging for moderately sized computer clusters. Moreover, genotype imputation requires subject-level genetic data, which unlike summary statistics provided by virtually all studies, is not publicly available. While there are much less demanding methods which avoid the genotype imputation step by directly imputing SNP statistics, e.g. Directly Imputing summary STatistics (DIST) proposed by our group, their implicit assumptions make them applicable only to ethnically homogeneous cohorts. RESULTS: To decrease computational and access requirements for the analysis of cosmopolitan cohorts, we propose DISTMIX, which extends DIST capabilities to the analysis of mixed ethnicity cohorts. The method uses a relevant reference panel to directly impute unmeasured SNP statistics based only on statistics at measured SNPs and estimated/user-specified ethnic proportions. Simulations show that the proposed method adequately controls the Type I error rates. The 1000 Genomes panel imputation of summary statistics from the ethnically diverse Psychiatric Genetic Consortium Schizophrenia Phase 2 suggests that, when compared to genotype imputation methods, DISTMIX offers comparable imputation accuracy for only a fraction of computational resources. AVAILABILITY AND IMPLEMENTATION: DISTMIX software, its reference population data, and usage examples are publicly available at http://code.google.com/p/distmix. CONTACT: dlee4@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.

JEPEG: a summary statistics based tool for gene-level joint testing of functional variants.

MOTIVATION: Gene expression is influenced by variants commonly known as expression quantitative trait loci (eQTL). On the basis of this fact, researchers proposed to use eQTL/functional information univariately for prioritizing single nucleotide polymorphisms (SNPs) signals from genome-wide association studies (GWAS). However, most genes are influenced by multiple eQTLs which, thus, jointly affect any downstream phenotype. Therefore, when compared with the univariate prioritization approach, a joint modeling of eQTL action on phenotypes has the potential to substantially increase signal detection power. Nonetheless, a joint eQTL analysis is impeded by (i) not measuring all eQTLs in a gene and/or (ii) lack of access to individual genotypes. RESULTS: We propose joint effect on phenotype of eQTL/functional SNPs associated with a gene (JEPEG), a novel software tool which uses only GWAS summary statistics to (i) impute the summary statistics at unmeasured eQTLs and (ii) test for the joint effect of all measured and imputed eQTLs in a gene. We illustrate the behavior/performance of the developed tool by analysing the GWAS meta-analysis summary statistics from the Psychiatric Genomics Consortium Stage 1 and the Genetic Consortium for Anorexia Nervosa. CONCLUSIONS: Applied analyses results suggest that JEPEG complements commonly used univariate GWAS tools by: (i) increasing signal detection power via uncovering (a) novel genes or (b) known associated genes in smaller cohorts and (ii) assisting in fine-mapping of challenging regions, e.g. major histocompatibility complex for schizophrenia. AVAILABILITY AND IMPLEMENTATION: JEPEG, its associated database of eQTL SNPs and usage examples are publicly available at http://code.google.com/p/jepeg/. CONTACT: dlee4@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phase II, multicenter, open-label, randomized study of YM155 plus docetaxel as first-line treatment in patients with HER2-negative metastatic breast cancer.

The objective of this study was to assess the efficacy and tolerability of YM155, a survivin suppressor, in combination with docetaxel, compared with docetaxel alone in patients with HER2-negative metastatic breast cancer. This phase II, multicenter, open-label, 2-arm study randomized patients (≥18 years) with histologically or cytologically confirmed stage IV HER2-negative metastatic breast cancer and ≥1 measurable lesion, to receive docetaxel alone or docetaxel plus YM155. The primary endpoint was progression-free survival (PFS). Secondary endpoints included objective response rate (ORR), overall survival (OS), duration of response (DOR), clinical benefit rate (CBR), time to response (TTR), biomarker assessment, and analysis of circulating tumor cells. Patients were women diagnosed with HER2-negative breast cancer; most had received prior drug therapies. The median PFS was 8.4 months with YM155 plus docetaxel (n = 50) and 10.5 months with docetaxel alone (n = 51; HR 1.53; 95 % CI 0.83, 2.83; P = 0.176). No statistically significant differences were observed for secondary endpoints, although slightly greater OS (630 vs 601 days; P = 0.768), CBR (84.3 vs 82.0 %; P = 0.855), DOR, and TTR were observed with docetaxel alone compared with YM155 plus docetaxel, whereas ORR was similar (25.5 vs 26.0). The most common TEAEs observed with YM155 plus docetaxel compared with docetaxel alone were neutropenia (83.3 vs 84.3 %), alopecia (62.5 vs 52.9 %), fatigue (50 vs 41.2 %), and nausea (37.5 vs 41.2 %). Although YM155 is a novel drug that suppresses survivin, YM155 plus docetaxel exhibited no statistically significant differences in endpoints compared with docetaxel alone. The combination regimen was well tolerated.

Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation.

Deep sequencing has become a popular tool for novel miRNA detection but its data must be viewed carefully as the state of the field is still undeveloped. Using three different programs, miRDeep (v1, 2), miRanalyzer and DSAP, we have analyzed seven data sets (six biological and one simulated) to provide a critical evaluation of the programs performance. We selected these software based on their popularity and overall approach toward the detection of novel and known miRNAs using deep-sequencing data. The program comparisons suggest that, despite differing stringency levels they all identify a similar set of known and novel predictions. Comparisons between the first and second version of miRDeep suggest that the stringency level of each of these programs may, in fact, be a result of the algorithm used to map the reads to the target. Different stringency levels are likely to affect the number of possible novel candidates for functional verification, causing undue strain on resources and time. With that in mind, we propose that an intersection across multiple programs be taken, especially if considering novel candidates that will be targeted for additional analysis. Using this approach, we identify and performed initial validation of 12 novel predictions in our in-house data with real-time PCR, six of which have been previously unreported.

A novel multi-omics mendelian randomization method for gene set enrichment and its application to psychiatric disorders.

Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often do not implicate specific genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS) that integrate xQTL and GWAS information, can link GWAS signals to effects on specific genes. To further increase detection power, gene signals are aggregated within relevant gene sets (GS) by performing gene set enrichment (GSE) analyses. Often GSE methods test for enrichment of "signal" genes in curated GS while overlooking their linkage disequilibrium (LD) structure, allowing for the possibility of increased false positive rates. Moreover, no GSE tool uses xQTL information to perform mendelian randomization (MR) analysis. To make causal inference on association between PD and GS, we develop a novel MR GSE (MR-GSE) procedure. First, we generate a "synthetic" GWAS for each MSigDB GS by aggregating summary statistics for x-level (mRNA, protein or DNA methylation (DNAm) levels) from the largest xQTL studies available) of genes in a GS. Second, we use synthetic GS GWAS as exposure in a generalized summary-data-based-MR analysis of complex trait outcomes. We applied MR-GSE to GWAS of nine important PD. When applied to the underpowered opioid use disorder GWAS, none of the four analyses yielded any signals, which suggests a good control of false positive rates. For other PD, MR-GSE greatly increased the detection of GO terms signals (2,594) when compared to the commonly used (non-MR) GSE method (286). Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects for supplementation with certain vitamins and/or omega-3 for schizophrenia, bipolar and major depression disorder patients. Similar to other MR methods, when applying MR-GSE researchers should be mindful of the confounding effects of horizontal pleiotropy on statistical inference.

An asymptotic model in acoustics: acoustic drift equations.

A rigorous asymptotic procedure with the Mach number as a small parameter is used to derive the equations of mean flows which coexist and are affected by the background acoustic waves in the limit of very high Reynolds number.