Faculty Opinions recommendation of How does multiple testing correction work?

2016 ◽  
Author(s):  
Robert Powers
Keyword(s):  
Multiple Testing ◽  
Multiple Testing Correction

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.

scholarly journals 2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sangyoon Yi ◽  
Xianyang Zhang ◽  
Lu Yang ◽  
Jinyan Huang ◽  
Yuanhang Liu ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Statistical Power ◽  
Association Studies ◽  
Control Procedure ◽  
Multiple Testing Correction ◽  
New Approach ◽  
False Discovery ◽  
Traditional Procedure ◽  
Extensive Evaluation ◽  
Confounder Adjustment

AbstractOne challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

scholarly journals NRG1 Genetic Variant Influences the Efficacy of Androgen-Deprivation Therapy in Men with Prostate Cancer

Biomedicines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 528
Author(s):  
Shu-Pin Huang ◽  
Yei-Tsung Chen ◽  
Lih-Chyang Chen ◽  
Cheng-Hsueh Lee ◽  
Chao-Yuan Huang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Androgen Deprivation Therapy ◽  
Tyrosine Kinases ◽  
Multiple Testing ◽  
Cox Regression ◽  
Progression Free Survival ◽  
Androgen Deprivation ◽  
Nucleotide Polymorphisms ◽  
Multiple Testing Correction ◽  
Cancer Specific Survival

Neuregulins (NRGs) activate receptor tyrosine kinases of the ErbB family, and play essential roles in the proliferation, survival, and differentiation of normal and malignant tissue cells. We hypothesized that genetic variants of NRG signalling pathway genes may influence treatment outcomes in prostate cancer. To test this hypothesis, we performed a comprehensive analysis to evaluate the associations of 459 single-nucleotide polymorphisms in 19 NRG pathway genes with cancer-specific survival (CSS), overall survival (OS), and progression-free survival (PFS) in 630 patients with prostate cancer receiving androgen-deprivation therapy (ADT). After multivariate Cox regression and multiple testing correction, we found that NRG1 rs144160282 C > T is significantly associated with worsening CSS, OS, and PFS during ADT. Further analysis showed that low expression of NRG1 is closely related to prostate cancer, as indicated by a high Gleason score, an advanced stage, and a shorter PFS rate. Meta-analysis of 16 gene expression datasets of 1,081 prostate cancer samples and 294 adjacent normal samples indicate lower NRG1 expression in the former compared with the latter (p < 0.001). These results suggest that NRG1 rs144160282 might be a prognostic predictor of the efficacy of ADT. Further studies are required to confirm the significance of NRG1 as a biomarker and therapeutic target for prostate cancer.

scholarly journals Case-control study of adverse childhood experiences and multiple sclerosis risk and clinical outcomes

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262093
Author(s):  
Mary K. Horton ◽  
Shannon McCurdy ◽  
Xiaorong Shao ◽  
Kalliope Bellesis ◽  
Terrence Chinn ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Outcomes ◽  
Adverse Childhood Experiences ◽  
Multiple Testing ◽  
Multiple Testing Correction ◽  
Latent Factors ◽  
Childhood Experiences ◽  
Adverse Childhood ◽  
Rich Data ◽  
Control Study

Background Adverse childhood experiences (ACEs) are linked to numerous health conditions but understudied in multiple sclerosis (MS). This study’s objective was to test for the association between ACEs and MS risk and several clinical outcomes. Methods We used a sample of adult, non-Hispanic MS cases (n = 1422) and controls (n = 1185) from Northern California. Eighteen ACEs were assessed including parent divorce, parent death, and abuse. Outcomes included MS risk, age of MS onset, Multiple Sclerosis Severity Scale score, and use of a walking aid. Logistic and linear regression estimated odds ratios (ORs) (and beta coefficients) and 95% confidence intervals (CIs) for ACEs operationalized as any/none, counts, individual events, and latent factors/patterns. Results Overall, more MS cases experienced ≥1 ACE compared to controls (54.5% and 53.8%, respectively). After adjusting for sex, birthyear, and race, this small difference was attenuated (OR = 1.01, 95% CI: 0.87, 1.18). There were no trends of increasing or decreasing odds of MS across ACE count categories. Consistent associations between individual ACEs between ages 0–10 and 11–20 years and MS risk were not detected. Factor analysis identified five latent ACE factors, but their associations with MS risk were approximately null. Age of MS onset and other clinical outcomes were not associated with ACEs after multiple testing correction. Conclusion Despite rich data and multiple approaches to operationalizing ACEs, no consistent and statistically significant effects were observed between ACEs with MS. This highlights the challenges of studying sensitive, retrospective events among adults that occurred decades before data collection.

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.

scholarly journals P0360URINARY PEPTIDOMIC ANALYSIS IN PROLIFERATIVE VERSUS NON-PROLIFERATIVE LUPUS NEPHRITIS : RESULTS OF THE PEPTIDU-LUP STUDY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Maxence Tailliar ◽  
Joost Schanstra ◽  
Tim Dierckx ◽  
BREUIL Benjamin ◽  
Guillaume Hanouna ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Lupus Nephritis ◽  
Multiple Testing ◽  
Kidney Biopsy ◽  
Validation Cohort ◽  
Response To Therapy ◽  
Multiple Testing Correction ◽  
Discovery Cohort ◽  
Model Combining ◽  
Urinary Peptides

Abstract Background and Aims Lupus nephritis (LN) is a frequent manifestation of Systemic Lupus Erythematosus (SLE). The therapeutic strategy relies on the result of kidney biopsy, which differentiates proliferative LN (PLN) from non-proliferative LN (NPLN). The analysis of the urinary peptidome has led to the identification of prognostic biomarkers in chronic kidney disease (CKD, namely the CKD273 classifier), or, as a liquid biopsy, for the diagnosis of glomerulonephritides. We verified whether urinary peptidomics could predict the severity of renal pathological injury and damage in LN. Method Urine samples, collected before kidney biopsy, were analyzed by capillary-electrophoresis coupled to mass-spectrometry (CE-MS). Urinary peptide profiles were compared between PLN and NPLN. Predictive values for chronic pathological lesions (glomerulosclerosis and interstitial fibrosis/tubular atrophy (IF/TA)), response to therapy and development of CKD were also assessed. Clinical characteristics, routine laboratory parameters and immunological SLE markers were collected at inclusion and prospectively for at least 24 months. Results We collected 100 urinary samples from patients with LN, forming a discovery (n=67) and an independent validation (n=33) cohort. Overall there were 69 patients with PLN (class III or IV +/-V with active lesions) and 31 patients with NPLN (class II or V or chronic lesions). In the discovery cohort, the abundance of 36 urinary peptides differed between PLN and NPLN (Mann-Whitney test). However, these peptides did not resist multiple testing correction. Among them, 17 could be sequenced (fragments of collagen-I, -II, -III, apolipoprotein A-I, Fibrinogen alpha chain and Histone H2B). Of the 17 sequenced peptides, 7 were also part of the CKD273 classifier. A mathematical model combining the 36 peptides classified PLN and NPLN patients from the validation cohort with an AUC of 0.75 and sensitivity and specificity of 0.81 and 0.53, respectively. Positivity of anti-dsDNA antibodies had the best sensitivity (0.96) and sterile pyuria the best specificity (0.68) to differentiate PLN from NPLN, and including the CKD273 score did not improve the accuracy of the predictive models. No urinary peptidomics profile was identified to predict early response to therapy in patients with LN. Glomerulosclerosis was observed in 63/100 biopsies. In the discovery cohort, the abundance of 38 urinary peptides (including 22 sequenced) differed between patients with vs without glomerulosclerosis (Mann-Whitney test). Again, these peptides did not resist multiple testing correction. Of the 22 sequenced peptides, 7 (different from the peptides associated with PLN) belonged to the CKD273 classifier. A mathematical model combining the 38 peptides classified patients from the validation cohort with an AUC of 0.75 and sensitivity and specificity of 0.82 and 0.54, respectively, for the presence of glomerulosclerosis. Although the quantification of IF/TA was correlated to the pre-existing CKD273 classifier, including the CKD273 score did not improve the prediction of IF/TA when tested in combination with simple clinical parameters. Only 3 patients developed CKD after a mean follow-up of 4 years, therefore contribution of urinary peptidomics for the prediction of CKD in LN could not be evaluated. Conclusion Different urinary peptidomics signatures were identified among patients with LN, according to the presence of active proliferative lesions or chronic lesions. However, these panels did not resist multiple testing correction, and did not improve the diagnostic accuracy when combined with clinical or immunological markers. The contribution of urinary peptidomics to predict CKD in patients with LN, or as an early predictor of renal flares, could not be evaluated. Kidney biopsy remains central for the care of patients with LN.

scholarly journals Joint variable selection and network modeling for detecting eQTLs

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Xuan Cao ◽  
Lili Ding ◽  
Tesfaye B. Mersha
Keyword(s):  
Variable Selection ◽  
Multiple Testing ◽  
Bayes Factor ◽  
Graph Model ◽  
Real Data ◽  
Bayesian Regression ◽  
Joint Estimation ◽  
Eqtl Analysis ◽  
Multiple Testing Correction ◽  
Joint Variable

AbstractIn this study, we conduct a comparison of three most recent statistical methods for joint variable selection and covariance estimation with application of detecting expression quantitative trait loci (eQTL) and gene network estimation, and introduce a new hierarchical Bayesian method to be included in the comparison. Unlike the traditional univariate regression approach in eQTL, all four methods correlate phenotypes and genotypes by multivariate regression models that incorporate the dependence information among phenotypes, and use Bayesian multiplicity adjustment to avoid multiple testing burdens raised by traditional multiple testing correction methods. We presented the performance of three methods (MSSL – Multivariate Spike and Slab Lasso, SSUR – Sparse Seemingly Unrelated Bayesian Regression, and OBFBF – Objective Bayes Fractional Bayes Factor), along with the proposed, JDAG (Joint estimation via a Gaussian Directed Acyclic Graph model) method through simulation experiments, and publicly available HapMap real data, taking asthma as an example. Compared with existing methods, JDAG identified networks with higher sensitivity and specificity under row-wise sparse settings. JDAG requires less execution in small-to-moderate dimensions, but is not currently applicable to high dimensional data. The eQTL analysis in asthma data showed a number of known gene regulations such as STARD3, IKZF3 and PGAP3, all reported in asthma studies. The code of the proposed method is freely available at GitHub (https://github.com/xuan-cao/Joint-estimation-for-eQTL).

scholarly journals Longitudinal Changes of One-Carbon Metabolites and Amino Acid Concentrations during Pregnancy in the Women First Maternal Nutrition Trial

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Stephanie P Gilley ◽  
Nicholas E Weaver ◽  
Evan L Sticca ◽  
Purevsuren Jambal ◽  
Alexandra Palacios ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Amino Acid ◽  
Multiple Testing ◽  
Maternal Nutrition ◽  
Longitudinal Research ◽  
Maternal Blood ◽  
Replication Cohort ◽  
Multiple Testing Correction ◽  
Group A ◽  
Study Sites

ABSTRACT Background Maternal dietary restriction and supplementation of one-carbon (1C) metabolites can impact offspring growth and DNA methylation. However, longitudinal research of 1C metabolite and amino acid (AA) concentrations over the reproductive cycle of human pregnancy is limited. Objective To investigate longitudinal 1C metabolite and AA concentrations prior to and during pregnancy and the effects of a small-quantity lipid-based nutrition supplement (LNS) containing &gt;20 micronutrients and prepregnancy BMI (ppBMI). Methods This study was an ancillary study of the Women First Trial (NCT01883193, clinicaltrials.gov) focused on a subset of Guatemalan women (n = 134), 49% of whom entered pregnancy with a BMI ≥25 kg/m2. Ninety-five women received LNS during pregnancy (+LNS group), while the remainder did not (−LNS group). A subset of women from the Pakistan study site (n = 179) were used as a replication cohort, 124 of whom received LNS. Maternal blood was longitudinally collected on dried blood spot (DBS) cards at preconception, and at 12 and 34 wk gestation. A targeted metabolomics assay was performed on DBS samples at each time point using LC-MS/MS. Longitudinal analyses were performed using linear mixed modeling to investigate the influence of time, LNS, and ppBMI. Results Concentrations of 23 of 27 metabolites, including betaine, choline, and serine, changed from preconception across gestation after application of a Bonferroni multiple testing correction (P &lt; 0.00185). Sixteen of those metabolites showed similar changes in the replication cohort. Asymmetric and symmetric dimethylarginine were decreased by LNS in the participants from Guatemala. Only tyrosine was statistically associated with ppBMI at both study sites. Conclusions Time influenced most 1C metabolite and AA concentrations with a high degree of similarity between the 2 diverse study populations. These patterns were not significantly altered by LNS consumption or ppBMI. Future investigations will focus on 1C metabolite changes associated with infant outcomes, including DNA methylation. This trial was registered at clinicaltrials.gov as NCT01883193.

scholarly journals Continued misuse of multiple testing correction methods in population genetics‐A wake‐up call?

2019 ◽  
Vol 19 (1) ◽  
pp. 23-26 ◽  
Author(s):  
Arun Sethuraman ◽  
Nicolette M. Gonzalez ◽  
Christy E. Grenier ◽  
Khyati S. Kansagra ◽  
Ken K. Mey ◽  
...  
Keyword(s):  
Population Genetics ◽  
Multiple Testing ◽  
Multiple Testing Correction
Sign in / Sign up

Export Citation Format

Share Document