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 >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 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 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 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.

scholarly journals Phenome-wide association study of TTR and RBP4 genes in 361,194 individuals reveals novel insights in the genetics of hereditary and senile systemic amyloidoses

2019 ◽  
Author(s):  
Antonella De Lillo ◽  
Flavio De Angelis ◽  
Marco Di Girolamo ◽  
Marco Luigetti ◽  
Sabrina Frusconi ◽  
...  
Keyword(s):  
Association Study ◽  
Multiple Testing ◽  
Large Scale ◽  
Phenotypic Traits ◽  
Multiple Testing Correction ◽  
Gene Variation ◽  
Senile Systemic Amyloidosis ◽  
Phenotypic Spectrum ◽  
Tunnel Syndrome ◽  
Coding Variants

ABSTRACTTransthyretin (TTR) gene has a causal role in a hereditary form of amyloidosis (ATTRm) and is potentially involved in the risk of senile systemic amyloidosis (SSA). To understand the genetics of ATTRm and SSA, we conducted a phenome-wide association study of TTR gene in 361,194 participants of European descent testing coding and non-coding variants. Among the 382 clinically-relevant phenotypes tested, TTR non-coding variants were associated with 26 phenotypic traits after multiple testing correction. These included signs related to both ATTRm and SSA such as chronic ischaemic heart disease (rs140226130, p=2.00×10−6), heart failure (rs73956431, p=2.74×10−6), atrial fibrillation (rs10163755, p=4.63×10−6), dysphagia (rs2949506, p=3.95×10−6), intestine diseases (rs970866, p=7.14×10−6) and anxiety (rs554521234, p=8.85×10−6). Consistent results were observed for TTR disease-causing mutation Val122Ile (rs76992529) with respect to carpal tunnel syndrome (p=6.41×10−6) and mononeuropathies of upper limbs (p=1.22×10−5). Sex differences were also observed in line with ATTRm and SSA epidemiology. Additionally, we explored possible modifier genes related to TTR function, observing convergent associations of RBP4 variants with the clinical phenotypes associated with TTR locus. In conclusion, we provide novel insights regarding the molecular basis of ATTRm and SSA based on large-scale cohort, expanding our understanding of the phenotypic spectrum associated with TTR gene variation.

scholarly journals Large-scale proteome analysis of CSF implicates altered glucose metabolism in Alzheimer's disease

2021 ◽  
Author(s):  
Daniel J. Panyard ◽  
Justin McKetney ◽  
Yuetiva K. Deming ◽  
Autumn R. Morrow ◽  
Gilda E. Ennis ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Multiple Testing ◽  
Large Scale ◽  
Csf Biomarkers ◽  
Multiple Testing Correction ◽  
Comparative Performance ◽  
Amyloid A ◽  
Altered Glucose ◽  
Associated Proteins

A major hallmark of Alzheimer's disease (AD) is the aggregation of misfolded proteins (β-amyloid (A) and hyperphosphorylated tau (T)) in the brain. As these proteins can be monitored by cerebrospinal fluid (CSF) measures, the AD proteome in CSF has been of particular interest. Here, we conducted a proteome-wide assessment of the CSF in an AD cohort among participants with and without AD pathology (n = 137 total participants: 56 A-T-, 39 A+T-, and 42 A+T+; 915 proteins analyzed), identifying a diverse set of proteins in the CSF enriched for extracellular and immune system processes. We then interrogated the proteome using the amyloid, tau, and neurodegeneration (ATN) framework of AD and a panel of 9 CSF biomarkers for neurodegeneration and neuroinflammation. After multiple testing correction, we identified a total of 61 proteins significantly associated with AT group (P < 5.46 x 10-5; strongest was SMOC1, P = 1.87 x 10-12) and 636 significant protein-biomarker associations (P < 6.07 x 10-6; strongest was a positive association between neurogranin and EPHA4, P = 2.42 x 10-25) across all measures except for interleukin-6, which had no significantly associated proteins. Community network and pathway enrichment analyses highlighted three biomarker-associated protein networks: one related to amyloid and tau measures, one to CSF neurogranin, and one to the remaining CSF biomarkers. Glucose metabolic pathways were enriched primarily among the amyloid- and tau-associated proteins, including malate dehydrogenase and aldolase A, both of which were replicated as strongly associated with AD (P = 1.07 x 10-19 and P = 7.43 x 10-14, respectively) in an independent CSF proteomics cohort (n = 717 participants). Comparative performance of the CSF proteome in predicting AT categorization was high (mean AUC range 0.891-0.924 with number of protein predictors ranging from 37-97) relative to other omic predictors from the genome, CSF metabolome, and demographics from the same cohort of individuals. Collectively, these results emphasize the importance of the CSF proteome relative to other omics and implicate glucose metabolic dysregulation as amyloid and tau pathology emerges in AD.

scholarly journals Fetal Growth, Gestation Length And Phosphoglucomutase-1 Phenotype

1993 ◽  
Vol 11 (5-6) ◽  
pp. 251-262
Author(s):  
Frank D. Johnstone ◽  
John D. Westt ◽  
Robin J. Prescott ◽  
Judith M. Steel ◽  
Jean A. Flockhartt ◽  
...  
Keyword(s):  
Birth Weight ◽  
Fetal Growth ◽  
Multiple Testing ◽  
Published Data ◽  
Gestation Length ◽  
Maternal Weight ◽  
Cellulose Acetate Electrophoresis ◽  
Fetal Sex ◽  
Explanatory Variables ◽  
Gel Isoelectric Focusing

This study investigates reports that phosphoglucomutase-1 (PGM1) phenotype is associated with fetal growth and gestation length. A total of 350 women were studied, 234 having uncomplicated pregnancies and 114 with a baby weighing greater than 90th centile, corrected for parity, gestation and fetal sex. All women had gestation confirmed by early ultrasound. Conventional cellulose acetate electrophoresis was used to distinguish the three common PGM1 phenotypes and polyacrylamide gel isoelectric focusing to distinguish the ten PGM1 SUbtypes. Neither PGM I phenotype nor SUbtype were found to be associated with gestation length or standardised birth weight. Logistic regression, where maternal age, parity, fetal sex, maternal weight, gestation and smoking were introduced as explanatory variables in addition to PGM1 phenotype testing against the dependent variables birth weight, standardised birth weight and gestation length, did not show differences related to PGM1 phenotype.Two possible reasons for the discrepancy with previously published data are discussed. We conclude that the study provides no support for the belief that PGM1 phenotype is related to fetal growth or gestation length and that the original observations could have arisen as a result of statistical artefact due to multiple testing.

scholarly journals Genetic biomarkers in the VEGF pathway predicting response to anti-VEGF therapy in age-related macular degeneration

2019 ◽  
Vol 4 (1) ◽  
pp. e000273
Author(s):  
Irina Balikova ◽  
Laurence Postelmans ◽  
Brigitte Pasteels ◽  
Pascale Coquelet ◽  
Janet Catherine ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Macular Degeneration ◽  
Treatment Response ◽  
Multiple Testing ◽  
Age Related Macular Degeneration ◽  
Patient Specific ◽  
Multiple Testing Correction ◽  
Anti Vegf ◽  
Age Related ◽  
Vegf Pathway

ObjectiveAge-related macular degeneration (ARMD) is a leading cause of visual impairment. Intravitreal injections of anti-vascular endothelial growth factor (VEGF) are the standard treatment for wet ARMD. There is however, variability in patient responses, suggesting patient-specific factors influencing drug efficacy. We tested whether single nucleotide polymorphisms (SNPs) in genes encoding VEGF pathway members contribute to therapy response.Methods and analysisA retrospective cohort of 281 European wet ARMD patients treated with anti-VEGF was genotyped for 138 tagging SNPs in the VEGF pathway. Per patient, we collected best corrected visual acuity at baseline, after three loading injections and at 12 months. We also registered the injection number and changes in retinal morphology after three loading injections (central foveal thickness (CFT), intraretinal cysts and serous neuroepithelium detachment). Changes in CFT after 3 months were our primary outcome measure. Association of SNPs to response was assessed by binomial logistic regression. Replication was attempted by associating visual acuity changes to genotypes in an independent Japanese cohort.ResultsAssociation with treatment response was detected for seven SNPs, including in FLT4 (rs55667289: OR=0.746, 95% CI 0.63 to 0.88, p=0.0005) and KDR (rs7691507: OR=1.056, 95% CI 1.02 to 1.10, p=0.005; and rs2305945: OR=0.963, 95% CI 0.93 to 1.00, p=0.0472). Only association with rs55667289 in FLT4 survived multiple testing correction. This SNP was unavailable for testing in the replication cohort. Of six SNPs tested for replication, one was significant although not after multiple testing correction.ConclusionIdentifying genetic variants that define treatment response can help to develop individualised therapeutic approaches for wet ARMD patients and may point towards new targets in non-responders.

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