scholarly journals POEAS: Automated Plant Phenomic Analysis Using Plant Ontology

2014 ◽  
Vol 8 ◽  
pp. BBI.S19057 ◽  
Author(s):  
Khader Shameer ◽  
Mahantesha Bn Naika ◽  
Oommen K. Mathew ◽  
Ramanathan Sowdhamini
Keyword(s):  
Multiple Testing ◽  
Large Scale ◽  
Statistical Significance ◽  
Enrichment Analysis ◽  
Multiple Testing Correction ◽  
Background Population ◽  
Complementary Resource ◽  
Plant Ontology ◽  
Plant Genes ◽  
Cellular Components

Biological enrichment analysis using gene ontology (GO) provides a global overview of the functional role of genes or proteins identified from large-scale genomic or proteomic experiments. Phenomic enrichment analysis of gene lists can provide an important layer of information as well as cellular components, molecular functions, and biological processes associated with gene lists. Plant phenomic enrichment analysis will be useful for performing new experiments to better understand plant systems and for the interpretation of gene or proteins identified from high-throughput experiments. Plant ontology (PO) is a compendium of terms to define the diverse phenotypic characteristics of plant species, including plant anatomy, morphology, and development stages. Adoption of this highly useful ontology is limited, when compared to GO, because of the lack of user-friendly tools that enable the use of PO for statistical enrichment analysis. To address this challenge, we introduce Plant Ontology Enrichment Analysis Server (POEAS) in the public domain. POEAS uses a simple list of genes as input data and performs enrichment analysis using Ontologizer 2.0 to provide results in two levels, enrichment results and visualization utilities, to generate ontological graphs that are of publication quality. POEAS also offers interactive options to identify user-defined background population sets, various multiple-testing correction methods, different enrichment calculation methods, and resampling tests to improve statistical significance. The availability of such a tool to perform phenomic enrichment analyses using plant genes as a complementary resource will permit the adoption of PO-based phenomic analysis as part of analytical workflows. POEAS can be accessed using the URL http://caps.ncbs.res.in/poeas .

scholarly journals Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

2018 ◽  
Author(s):  
David M. Howard ◽  
Mark J. Adams ◽  
Toni-Kim Clarke ◽  
Jonathan D. Hafferty ◽  
Jude Gibson ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Drug Repositioning ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Synaptic Structure ◽  
Genome Wide

AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

Next-generation RNA sequencing reveals transcriptomic changes after brief exposure to preoperative nab-paclitaxel, bevacizumab, and trastuzumab.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10508-10508
Author(s):  
Vinay Varadan ◽  
Sitharthan Kamalakaran ◽  
Angel Janevski ◽  
Nila Banerjee ◽  
Kimberly Lezon-Geyda ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Day Treatment ◽  
Transcriptional Response ◽  
Enrichment Analysis ◽  
Transcript Abundance ◽  
Differentially Expressed ◽  
Read Length ◽  
Breast Cancers ◽  
Rna Seq ◽  
Multiple Testing Correction

10508 Background: Identification of differentially expressed transcripts after brief exposure to preoperative therapy can help determine likely response markers. We quantify and compare differential gene and isoform expression using RNA-seq on patient samples with 10 day exposure to one dose of trastuzumab, bevacizumab or nab-paclitaxel. Methods: We sequenced transcriptomes of 23 pairs of core biopsy RNA from breast cancers pre/post 10 day exposure to therapy. Paired-end sequencing was done on the Illumina GAII platform using amplified total RNA with 74bp read length, yielding data on transcript abundance for a total of 22,160 genes and 34,449 transcripts. Differential expression of transcripts between pre/post samples was estimated assuming Poisson-distributed read-counts, followed by multiple testing correction and enrichment analysis of 185 KEGG pathways. Results: PAM50-based clustering showed individual samples cluster together, demonstrating that tumor subtypes do not change over the 10-day treatment (SABCS 2011). We identified genes that were significantly differentially expressed (p<0.05; FDR<0.1) in at least 60% of samples within each therapy arm: 780 genes in trastuzumab, 302 in bevacizumab, and 176 in nab-paclitaxel. Surprisingly, only THAP11 and TINF2 were common amongst them. THAP11 is involved in stem cell maintenance and TINF2 is important for regulation of telomere length. Immune system and metabolism-related pathways were commonly affected (p<0.05) across all arms. The bevacizumab arm showed significant down-regulation of angiogenesis-associated genes: ESM1 and VEGFR2 in > 80% of samples. The nab-paclitaxel arm exhibited changes in TGF-beta signaling, Nod-like receptor and Wnt signaling. The trastuzumab arm exhibited consistent alteration of ErbB2 and mTOR pathways, with SOX11 and TOP2B downregulated in every sample. Conclusions: This is the first study to compare gene expression with brief exposure across therapies using RNA-seq technology. The unique aspects of transcriptional response to each treatment underscore the need for specific markers of therapeutic response to nab-paclitaxel, bevacizumab and trastuzumab.

scholarly journals RNA Sequencing Reveals Key Metabolic Pathways Are Modified by Short-Term Whole Egg Consumption

2021 ◽  
Vol 8 ◽  
Author(s):  
Amanda E. Bries ◽  
Joe L. Webb ◽  
Brooke Vogel ◽  
Claudia Carrillo ◽  
Timothy A. Day ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Multiple Testing ◽  
Cholesterol Biosynthesis ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Dietary Treatment ◽  
Glutathione Metabolism ◽  
Multiple Testing Correction ◽  
Egg Consumption ◽  
Whole Egg

Eggs are protein-rich, nutrient-dense, and contain bioactive ingredients that have been shown to modify gene expression and impact health. To understand the effects of egg consumption on tissue-specific mRNA and microRNA expression, we examined the role of whole egg consumption (20% protein, w/w) on differentially expressed genes (DEGs) between rat (n = 12) transcriptomes in the prefrontal cortex (PFC), liver, kidney, and visceral adipose tissue (VAT). Principal component analysis with hierarchical clustering was used to examine transcriptome profiles between dietary treatment groups. We performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis as well as genetic network and disease enrichment analysis to examine which metabolic pathways were the most predominantly altered in each tissue. Overall, our data demonstrates that whole egg consumption for 2 weeks modified the expression of 52 genes in the PFC, 22 genes in VAT, and two genes in the liver (adj p &lt; 0.05). Additionally, 16 miRNAs were found to be differentially regulated in the PFC, VAT, and liver, but none survived multiple testing correction. The main pathways influenced by WE consumption were glutathione metabolism in VAT and cholesterol biosynthesis in the PFC. These data highlight key pathways that may be involved in diseases and are impacted by acute consumption of a diet containing whole eggs.

scholarly journals Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Sean M. Burnard ◽  
Rodney A. Lea ◽  
Miles Benton ◽  
David Eccles ◽  
Daniel W. Kennedy ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Complex Traits ◽  
Multiple Testing ◽  
Large Scale ◽  
Disease Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Elastic Net ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction

Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.

scholarly journals PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS summary statistics

2017 ◽  
Author(s):  
Jie Zheng ◽  
Tom G. Richardson ◽  
Louise A. C. Millard ◽  
Gibran Hemani ◽  
Christopher Raistrick ◽  
...  
Keyword(s):  
Complex Traits ◽  
Multiple Testing ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Phenotypic Correlation ◽  
Summary Statistics ◽  
Multiple Testing Correction ◽  
Individual Level ◽  
Phenotypic Correlations ◽  
Complex Human Traits

AbstractBackgroundIdentifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. State-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using genome-wide association study (GWAS) summary statistics.ResultsHere, we present an integrated R toolkit, PhenoSpD, to 1) apply metaCCA (or LD score regression) to estimate phenotypic correlations using GWAS summary statistics; and 2) to utilize the estimated phenotypic correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest it is possible to estimate phenotypic correlation using samples with only a partial overlap, but as overlap decreases correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples. In a case study, PhenoSpD using GWAS results suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true phenotypic correlation. We further applied PhenoSpD to estimated 7,503 pair-wise phenotypic correlations among 123 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested 44.9 number of independent tests for theses metabolites.ConclusionPhenoSpD integrates existing methods and provides a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics, which is particularly valuable for post-GWAS analysis of complex molecular traits.AvailabilityR code and documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).

scholarly journals Do Candidate Genes Affect the Brain’s White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans

2017 ◽  
Author(s):  
Neda Jahanshad ◽  
Habib Ganjgahi ◽  
Janita Bralten ◽  
Anouk den Braber ◽  
Joshua Faskowitz ◽  
...  
Keyword(s):  
White Matter ◽  
Multiple Testing ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Candidate Snps ◽  
Nucleotide Polymorphisms ◽  
Multiple Testing Correction ◽  
Healthy Human ◽  
Multiple Granularity ◽  
Single Cohort

Abstract:Susceptibility genes for psychiatric and neurological disorders - including APOE, BDNF, CLU,CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, and ZNF804A - have been reported to affect white matter (WM) microstructure in the healthy human brain, as assessed through diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain only a small fraction of the overall variance and are challenging to detect reliably in single cohort studies. To date, few studies have evaluated the reproducibility of these results. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects (CEU ancestry N=4,458) from 11 cohorts worldwide to evaluate effects of 15 candidate SNPs by examining their associations with WM microstructure. Additive association tests were conducted for each SNP. We used several meta-analytic and mega-analytic designs, and we evaluated regions of interest at multiple granularity levels. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Even so, in this very large sample, no significant associations remained after multiple-testing correction for the 15 SNPs investigated. Suggestive associations (1.3×10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta-and mega-analyses revealed similar findings. Regardless of the approach, the previously reported candidate SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure.

scholarly journals How to determine the statistical significance of trends in seasonal records: application to Antarctic temperatures

2021 ◽  
Author(s):  
Armin Bunde ◽  
Josef Ludescher ◽  
Hans Joachim Schellnhuber
Keyword(s):  
Multiple Testing ◽  
Statistical Significance ◽  
Climate Science ◽  
Seasonal Temperature ◽  
Multiple Testing Correction ◽  
Significance Level ◽  
Persistent Data ◽  
Short And Long Term ◽  
False Discoveries

AbstractWe consider trends in the m seasonal subrecords of a record. To determine the statistical significance of the m trends, one usually determines the p value of each season either numerically or analytically and compares it with a significance level $${{\tilde{\alpha }}}$$ α ~ . We show in great detail for short- and long-term persistent records that this procedure, which is standard in climate science, is inadequate since it produces too many false positives (false discoveries). We specify, on the basis of the family wise error rate and by adapting ideas from multiple testing correction approaches, how the procedure must be changed to obtain more suitable significance criteria for the m trends. Our analysis is valid for data with all kinds of persistence. Specifically for long-term persistent data, we derive simple analytical expressions for the quantities of interest, which allow to determine easily the statistical significance of a trend in a seasonal record. As an application, we focus on 17 Antarctic station data. We show that only four trends in the seasonal temperature data are outside the bounds of natural variability, in marked contrast to earlier conclusions.

scholarly journals Association between the pregnancy exposome and fetal growth

2020 ◽  
Vol 49 (2) ◽  
pp. 572-586 ◽  
Author(s):  
Lydiane Agier ◽  
Xavier Basagaña ◽  
Carles Hernandez-Ferrer ◽  
Léa Maitre ◽  
Ibon Tamayo Uria ◽  
...  
Keyword(s):  
Birth Weight ◽  
Fetal Growth ◽  
Multiple Testing ◽  
Large Scale ◽  
Fine Particulate Matter ◽  
Health Concern ◽  
Selective Reporting ◽  
Multiple Testing Correction ◽  
Single Exposure ◽  
Environmental Models

Abstract Background Several environmental contaminants were shown to possibly influence fetal growth, generally from single exposure family studies, which are prone to publication bias and confounding by co-exposures. The exposome paradigm offers perspectives to avoid selective reporting of findings and to control for confounding by co-exposures. We aimed to characterize associations of fetal growth with the pregnancy chemical and external exposomes. Methods Within the Human Early-Life Exposome project, 131 prenatal exposures were assessed using biomarkers and environmental models in 1287 mother–child pairs from six European cohorts. We investigated their associations with fetal growth using a deletion-substitution-addition (DSA) algorithm considering all exposures simultaneously, and an exposome-wide association study (ExWAS) considering each exposure independently. We corrected for exposure measurement error and tested for exposure–exposure and sex–exposure interactions. Results The DSA model identified lead blood level, which was associated with a 97 g birth weight decrease for each doubling in lead concentration. No exposure passed the multiple testing-corrected significance threshold of ExWAS; without multiple testing correction, this model was in favour of negative associations of lead, fine particulate matter concentration and absorbance with birth weight, and of a positive sex-specific association of parabens with birth weight in boys. No two-way interaction between exposure variables was identified. Conclusions This first large-scale exposome study of fetal growth simultaneously considered &gt;100 environmental exposures. Compared with single exposure studies, our approach allowed making all tests (usually reported in successive publications) explicit. Lead exposure is still a health concern in Europe and parabens health effects warrant further investigation.

scholarly journals IL7RA rs6897932 Polymorphism is Associated with Better CD4+ T-Cell Recovery in HIV Infected Patients Starting Combination Antiretroviral Therapy

Biomolecules ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 233 ◽  
Author(s):  
Resino ◽  
Navarrete-Muñoz ◽  
Blanco ◽  
Pacheco ◽  
Castro ◽  
...  
Keyword(s):  
T Cells ◽  
Antiretroviral Therapy ◽  
Cd4 T Cells ◽  
Multiple Testing ◽  
Statistical Significance ◽  
Combination Antiretroviral Therapy ◽  
Cd4 Cells ◽  
Cell Recovery ◽  
Multiple Testing Correction

Interleukin-7 receptor subunit alpha (IL7RA) rs6897932 polymorphism is related to CD4+ recovery after combination antiretroviral therapy (cART), but no studies so far have analyzed its potential impact in patients with very low CD4+ T-cells count. We aimed to analyze the association between IL7RA rs6897932 polymorphism and CD4+ T-cells count restoration in HIV-infected patients starting combination antiretroviral therapy (cART) with CD4+ T-cells count <200 cells/mm3. We performed a retrospective study in 411 patients followed for 24 months with a DNA sample available for genotyping. The change in CD4+ T-cells count during the follow-up was considered as the primary outcome. The rs6897932 polymorphism had a minimum allele frequency (MAF) >20% and was in Hardy–Weinberg equilibrium (p = 0.550). Of 411 patients, 256 carried the CC genotype, while 155 had the CT/TT genotype. The CT/TT genotype was associated with a higher slope of CD4+ T-cells recovery (arithmetic mean ratio; AMR = 1.16; p = 0.016), higher CD4+ T-cells increase (AMR = 1.19; p = 0.004), and higher CD4+ T-cells count at the end of follow-up (AMR = 1.13; p = 0.006). Besides, rs6897932 CT/TT was related to a higher odds of having a value of CD4+ T-cells at the end of follow-up ≥500 CD4+ cells/mm3 (OR = 2.44; p = 0.006). After multiple testing correction (Benjamini–Hochberg), only the increase of ≥ 400 CD4+ cells/mm3 lost statistical significance (p = 0.052). IL7RA rs6897932 CT/TT genotype was related to a better CD4+ T-cells recovery and it could be used to improve the management of HIV-infected patients starting cART with CD4+ T-cells count <200 cells/mm3.

scholarly journals Associations between untargeted plasma metabolomic signatures and gut microbiota composition in the Milieu Intérieur population of healthy adults

2020 ◽  
pp. 1-11
Author(s):  
Valentin Partula ◽  
Mélanie Deschasaux-Tanguy ◽  
Stanislas Mondot ◽  
Agnès Victor-Bala ◽  
Nadia Bouchemal ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Multiple Testing ◽  
Large Scale ◽  
Linear Models ◽  
Healthy Adults ◽  
Rrna Gene ◽  
Microbiota Composition ◽  
Multiple Testing Correction ◽  
Negatively Associated ◽  
Gut Microbiota Composition

Abstract Host–microbial co-metabolism products are being increasingly recognised to play important roles in physiological processes. However, studies undertaking a comprehensive approach to consider host–microbial metabolic relationships remain scarce. Metabolomic analysis yielding detailed information regarding metabolites found in a given biological compartment holds promise for such an approach. This work aimed to explore the associations between host plasma metabolomic signatures and gut microbiota composition in healthy adults of the Milieu Intérieur study. For 846 subjects, gut microbiota composition was profiled through sequencing of the 16S rRNA gene in stools. Metabolomic signatures were generated through proton NMR analysis of plasma. The associations between metabolomic variables and α- and β-diversity indexes and relative taxa abundances were tested using multi-adjusted partial Spearman correlations, permutational ANOVA and multivariate associations with linear models, respectively. A multiple testing correction was applied (Benjamini–Hochberg, 10 % false discovery rate). Microbial richness was negatively associated with lipid-related signals and positively associated with amino acids, choline, creatinine, glucose and citrate (−0·133 ≤ Spearman’s ρ ≤ 0·126). Specific associations between metabolomic signals and abundances of taxa were detected (twenty-five at the genus level and nineteen at the species level): notably, numerous associations were observed for creatinine (positively associated with eleven species and negatively associated with Faecalibacterium prausnitzii). This large-scale population-based study highlights metabolites associated with gut microbial features and provides new insights into the understanding of complex host–gut microbiota metabolic relationships. In particular, our results support the implication of a ‘gut–kidney axis’. More studies providing a detailed exploration of these complex interactions and their implications for host health are needed.

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