scholarly journals OSCA: a tool for omic-data-based complex trait analysis

2018 ◽  
Author(s):  
Futao Zhang ◽  
Wenhan Chen ◽  
Zhihong Zhu ◽  
Qian Zhang ◽  
Marta F. Nabais ◽  
...  

AbstractThe rapid increase of omic data in the past decades has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we proposed a mixed-linear-model-based method (called MOMENT) that tests for association between a DNAm probe and trait with all other distal probes fitted in multiple random-effect components to account for the effects of unobserved confounders as well as the correlations between distal probes induced by the confounders. We demonstrated by simulations that MOMENT showed a lower false positive rate and more robustness than existing methods. MOMENT has been implemented in a versatile software package (called OSCA) together with a number of other implementations for omic-data-based analysis including the estimation of variance in a trait captured by all measures of multiple omic profiles, omic-data-based quantitative trait locus (xQTL) analysis, and meta-analysis of xQTL data.

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.


2018 ◽  
Author(s):  
Qianying Wang ◽  
Jing Liao ◽  
Kaitlyn Hair ◽  
Alexandra Bannach-Brown ◽  
Zsanett Bahor ◽  
...  

AbstractBackgroundMeta-analysis is increasingly used to summarise the findings identified in systematic reviews of animal studies modelling human disease. Such reviews typically identify a large number of individually small studies, testing efficacy under a variety of conditions. This leads to substantial heterogeneity, and identifying potential sources of this heterogeneity is an important function of such analyses. However, the statistical performance of different approaches (normalised compared with standardised mean difference estimates of effect size; stratified meta-analysis compared with meta-regression) is not known.MethodsUsing data from 3116 experiments in focal cerebral ischaemia to construct a linear model predicting observed improvement in outcome contingent on 25 independent variables. We used stochastic simulation to attribute these variables to simulated studies according to their prevalence. To ascertain the ability to detect an effect of a given variable we introduced in addition this “variable of interest” of given prevalence and effect. To establish any impact of a latent variable on the apparent influence of the variable of interest we also introduced a “latent confounding variable” with given prevalence and effect, and allowed the prevalence of the variable of interest to be different in the presence and absence of the latent variable.ResultsGenerally, the normalised mean difference (NMD) approach had higher statistical power than the standardised mean difference (SMD) approach. Even when the effect size and the number of studies contributing to the meta-analysis was small, there was good statistical power to detect the overall effect, with a low false positive rate. For detecting an effect of the variable of interest, stratified meta-analysis was associated with a substantial false positive rate with NMD estimates of effect size, while using an SMD estimate of effect size had very low statistical power. Univariate and multivariable meta-regression performed substantially better, with low false positive rate for both NMD and SMD approaches; power was higher for NMD than for SMD. The presence or absence of a latent confounding variables only introduced an apparent effect of the variable of interest when there was substantial asymmetry in the prevalence of the variable of interest in the presence or absence of the confounding variable.ConclusionsIn meta-analysis of data from animal studies, NMD estimates of effect size should be used in preference to SMD estimates, and meta-regression should, where possible, be chosen over stratified meta-analysis. The power to detect the influence of the variable of interest depends on the effect of the variable of interest and its prevalence, but unless effects are very large adequate power is only achieved once at least 100 experiments are included in the meta-analysis.


eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Ben P Williams ◽  
Iain G Johnston ◽  
Sarah Covshoff ◽  
Julian M Hibberd

C4 photosynthesis has independently evolved from the ancestral C3 pathway in at least 60 plant lineages, but, as with other complex traits, how it evolved is unclear. Here we show that the polyphyletic appearance of C4 photosynthesis is associated with diverse and flexible evolutionary paths that group into four major trajectories. We conducted a meta-analysis of 18 lineages containing species that use C3, C4, or intermediate C3–C4 forms of photosynthesis to parameterise a 16-dimensional phenotypic landscape. We then developed and experimentally verified a novel Bayesian approach based on a hidden Markov model that predicts how the C4 phenotype evolved. The alternative evolutionary histories underlying the appearance of C4 photosynthesis were determined by ancestral lineage and initial phenotypic alterations unrelated to photosynthesis. We conclude that the order of C4 trait acquisition is flexible and driven by non-photosynthetic drivers. This flexibility will have facilitated the convergent evolution of this complex trait.


2020 ◽  
Author(s):  
Haoran Jiang ◽  
Lihao Zhao ◽  
Han Yang ◽  
Mengjing Zhao ◽  
Yuxia Duan ◽  
...  

Abstract Background: MAGEA3 is a member of melanoma antigen family and has been recognized to express in many types of human cancers recently. In spite of the development of cancer vaccine, the prognostic value of MAGEA3 has not been well evaluated, due to the variability of clinical data and lack of clinical trials on the prognostic values.Method: Studies that evaluated MAGEA3 expression with a follow-up for at least 36 months were selected by searching in PubMed, WOS, Cochrane library, Embase. Published data was recorded and calculated into odds ratios (OR) for mortality in three or five years with Mantel-Haenszel random-effect model. Results: 11 studies were selected. The median positive rate was 45%. MAGEA3 always combines with worse survival on three or five years survival. The correlation between MAGEA3 and squamous carcinoma seemed stronger than adenocarcinoma on three-year OS while things got a reverse when it came to five-year OS. Most importantly, we found that all solid tumors originated from endoderm seemed to enjoy a strongest correlation among all the three germ layers.Conclusion: In this meta-analysis, we found that the expression of MAGEA3 can connect with worse outcome, and it probably can be a predictor for patients’ prognosis in clinical practice.


1985 ◽  
Vol 10 (2) ◽  
pp. 75-98 ◽  
Author(s):  
Stephen W. Raudenbush ◽  
Anthony S. Bryk

As interest in quantitative research synthesis grows, investigators increasingly seek to use information about study features—study contexts, designs, treatments, and subjects—to account for variation in study outcomes. To facilitate analysis of diverse study findings, a mixed linear model with fixed and random effects is presented and illustrated with data from teacher expectancy experiments. This strategy enables the analyst to (a) estimate the variance of the effect size parameters by means of maximum likelihood; (b) pose a series of linear models to explain the effect parameter variance; (c) use information about study characteristics to derive improved empirical Bayes estimates of individual study effect sizes; and (d) examine the sensitivity of all substantive inferences to likely errors in the estimation of variance components.


2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii11-ii12
Author(s):  
T C Booth ◽  
A Chelliah ◽  
A Roman ◽  
A Al Busaidi ◽  
H Shuaib ◽  
...  

Abstract BACKGROUND The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed through machine learning (ML). MATERIAL AND METHODS PRISMA methodology was followed. Articles published 09/2018-01/2021 (since previous reviews) were searched for using MEDLINE, EMBASE, and the Cochrane Register by two reviewers independently. Included study participants were adult patients with high grade glioma who had undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide) and subsequently underwent follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics - the target condition). Risk of bias and applicability was assessed with QUADAS 2. A third reviewer arbitrated any discrepancy. Contingency tables were created for hold-out test sets and recall, specificity, precision, F1-score, balanced accuracy calculated. A meta-analysis was performed using a bivariate model for recall, false positive rate and area-under the receiver operator characteristic curve (AUC). RESULTS Eighteen studies were included with 1335 patients in training sets and 384 in test sets. To determine whether there was progression or a mimic, the reference standard combination of follow-up imaging and histopathology at re-operation was applied in 67% (13/18) of studies. The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data. Ten studies (10/18, 56%) had internal or external hold-out test set data that could be included in a meta-analysis of monitoring biomarker studies. The pooled sensitivity was 0.77 (0.65–0.86). The pooled false positive rate (1-specificity) was 0.35 (0.25–0.47). The summary point estimate for the AUC was 0.77. CONCLUSION There is likely good diagnostic performance of machine learning models that use MRI features to distinguish between progression and mimics. The diagnostic performance of ML using implicit features did not appear to be superior to ML using explicit features. There are a range of ML-based solutions poised to become treatment response monitoring biomarkers for glioblastoma. To achieve this, the development and validation of ML models require large, well-annotated datasets where the potential for confounding in the study design has been carefully considered. Therefore, multidisciplinary efforts and multicentre collaborations are necessary.


2008 ◽  
Vol 15 (4) ◽  
pp. 204-206 ◽  
Author(s):  
Jonathan P Bestwick ◽  
Wayne J Huttly ◽  
Nicholas J Wald

Objectives To examine the effect of smoking on three first trimester screening markers for Down's syndrome that constitute the Combined test, namely nuchal translucency (NT), pregnancy-associated plasma protein-A (PAPP-A) and free β human chorionic gonadotophin (free β-hCG) and to use the results to determine which of these markers need to be adjusted for smoking and by how much. Methods The difference in the median multiple of the median (MoM) values in smokers compared to non-smokers was determined for NT, PAPP-A and free β-hCG in 12,517 unaffected pregnancies that had routine first trimester Combined test screening. These results were then included in a meta-analysis of published studies and the effect of adjusting for smoking on screening performance of the Combined test was estimated. Results The results using the routine screening data were similar to the summary estimates from the meta-analysis of all studies. The results from the meta-analysis were; median MoM in smokers compared to non-smokers: 1.06 NT (95% confidence interval 1.03 to 1.10), 0.81 PAPP-A (0.80 to 0.83) and 0.94 free β-hCG (0.89 to 0.99). The effect of adjusting for smoking on the Combined test is small, with an estimated less than half percentage point increase in the detection rate (the proportion of affected pregnancies with a positive result) for a 3% false-positive rate (the proportion of unaffected pregnancies with a positive result) and less than 0.2 percentage point decrease in the false-positive rate for an 85% detection rate. Conclusion Adjusting first trimester screening markers for smoking has a minimal favourable effect on screening performance, but it is simple to implement and this paper provides the adjustment factors needed if a decision is made to make such an adjustment.


2017 ◽  
Vol 52 (12) ◽  
pp. 1168-1170 ◽  
Author(s):  
Zachary K. Winkelmann ◽  
Ashley K. Crossway

Reference/Citation:  Harmon KG, Zigman M, Drezner JA. The effectiveness of screening history, physical exam, and ECG to detect potentially lethal cardiac disorders in athletes: a systematic review/meta-analysis. J Electrocardiol. 2015;48(3):329–338. Clinical Question:  Which screening method should be considered best practice to detect potentially lethal cardiac disorders during the preparticipation physical examination (PE) of athletes? Data Sources:  The authors completed a comprehensive literature search of MEDLINE, CINAHL, Cochrane Library, Embase, Physiotherapy Evidence Database (PEDro), and SPORTDiscus from January 1996 to November 2014. The following key words were used individually and in combination: ECG, athlete, screening, pre-participation, history, and physical. A manual review of reference lists and key journals was performed to identify additional studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed for this review. Study Selection:  Studies selected for this analysis involved (1) outcomes of cardiovascular screening in athletes using the history, PE, and electrocardiogram (ECG); (2) history questions and PE based on the American Heart Association recommendations and guidelines; and (3) ECGs interpreted following modern standards. The exclusion criteria were (1) articles not in English, (2) conference abstracts, and (3) clinical commentary articles. Study quality was assessed on a 7-point scale for risk of bias; a score of 7 indicated the highest quality. Articles with potential bias were excluded. Data Extraction:  Data included number and sex of participants, number of true- and false-positives and negatives, type of ECG criteria used, number of cardiac abnormalities, and specific cardiac conditions. The sensitivity, specificity, false-positive rate, and positive predictive value of each screening tool were calculated and summarized using a bivariate random-effects meta-analysis model. Main Results:  Fifteen articles reporting on 47 137 athletes were fully reviewed. The overall quality of the 15 articles ranged from 5 to 7 on the 7-item assessment scale (ie, participant selection criteria, representative sample, prospective data with at least 1 positive finding, modern ECG criteria used for screening, cardiovascular screening history and PE per American Heart Association guidelines, individual test outcomes reported, and abnormal screening findings evaluated by appropriate diagnostic testing). The athletes (66% males and 34% females) were ethnically and racially diverse, were from several countries, and ranged in age from 5 to 39 years. The sensitivity and specificity of the screening methods were, respectively, ECG, 94% and 93%; history, 20% and 94%; and PE, 9% and 97%. The overall false-positive rate for ECG (6%) was less than that for history (8%) or PE (10%). The positive likelihood ratios of each screening method were 14.8 for ECG, 3.22 for history, and 2.93 for PE. The negative likelihood ratios were 0.055 for ECG, 0.85 for history, and 0.93 for PE. A total of 160 potentially lethal cardiovascular conditions were detected, for a rate of 0.3%, or 1 in 294 patients. The most common conditions were Wolff-Parkinson-White syndrome (n = 67, 42%), long QT syndrome (n = 18, 11%), hypertrophic cardiomyopathy (n = 18, 11%), dilated cardiomyopathy (n = 11, 7%), coronary artery disease or myocardial ischemia (n = 9, 6%), and arrhythmogenic right ventricular cardiomyopathy (n = 4, 3%). Conclusions:  The most effective strategy to screen athletes for cardiovascular disease was ECG. This test was 5 times more sensitive than history and 10 times more sensitive than PE, and it had a higher positive likelihood ratio, lower negative likelihood ratio, and lower false-positive rate than history or PE. The 12-lead ECG interpreted using modern criteria should be considered the best practice in screening athletes for cardiovascular disease, and the use of history and PE alone as screening tools should be reevaluated.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Adrienne Tin ◽  
Pascal Schlosser ◽  
Pamela R. Matias-Garcia ◽  
Chris H. L. Thio ◽  
Roby Joehanes ◽  
...  

AbstractElevated serum urate levels, a complex trait and major risk factor for incident gout, are correlated with cardiometabolic traits via incompletely understood mechanisms. DNA methylation in whole blood captures genetic and environmental influences and is assessed in transethnic meta-analysis of epigenome-wide association studies (EWAS) of serum urate (discovery, n = 12,474, replication, n = 5522). The 100 replicated, epigenome-wide significant (p < 1.1E–7) CpGs explain 11.6% of the serum urate variance. At SLC2A9, the serum urate locus with the largest effect in genome-wide association studies (GWAS), five CpGs are associated with SLC2A9 gene expression. Four CpGs at SLC2A9 have significant causal effects on serum urate levels and/or gout, and two of these partly mediate the effects of urate-associated GWAS variants. In other genes, including SLC7A11 and PHGDH, 17 urate-associated CpGs are associated with conditions defining metabolic syndrome, suggesting that these CpGs may represent a blood DNA methylation signature of cardiometabolic risk factors. This study demonstrates that EWAS can provide new insights into GWAS loci and the correlation of serum urate with other complex traits.


2019 ◽  
Author(s):  
Yang Wu ◽  
Ting Qi ◽  
Huanwei Wang ◽  
Futao Zhang ◽  
Zhili Zheng ◽  
...  

AbstractPromoter-anchored chromatin interactions (PAIs) play a pivotal role in transcriptional regulation. Current high-throughput technologies for detecting PAIs, such as promoter capture Hi-C, are not scalable to large cohorts. Here, we present an analytical approach that uses summary-level data from cohort-based DNA methylation (DNAm) quantitative trait locus (mQTL) studies to predict PAIs. Using mQTL data from human peripheral blood (n=1,980), we predicted 34,797 PAIs which showed strong overlap with the chromatin contacts identified by previous experimental assays. The promoter-interacting DNAm sites were enriched in enhancers or near expression QTLs. Genes whose promoters were involved in PAIs were more actively expressed, and gene pairs with promoter-promoter interactions were enriched for co-expression. Integration of the predicted PAIs with GWAS data highlighted interactions among 601 DNAm sites associated with 15 complex traits. This study demonstrates the use of mQTL data to predict PAIs and provides insights into the role of PAIs in complex trait variation.


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