Article in Genetic epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 39% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
7 authors at 3 institutions in 1 country.
Dandi QiaoChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.ORCID 0000-0001-9450-205X
Corwin M ZiglerDepartment of Statistics and Data Sciences, Department of Women's Health, University of Texas at Austin, Austin, Texas.
Michael H ChoChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Edwin K SilvermanChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Xiaobo ZhouChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Peter J CastaldiChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Nan H LairdDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts.
Brigham and Women's Hospital · USHarvard University · USTexas Health and Science University · US
Funding
Genetic and Genomic Characterization of the Occurrence and Progression of Interstitial Lung AbnormalitiesR01HL135142 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MICHAEL H. CHO, GARY MATTHEW HUNNINGHAKE · 2017 to 2026
$7.7M
Using Integrative Genomics To Identify and Characterize Emphysema-Associated eQTLR01HL124233 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CASTALDI, PETER · 2014 to 2024
$7.1M
Identifying Genetic Determinants of Severe, Early-Onset COPDR01HL113264 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., SILVERMAN, EDWIN K · 2012 to 2016
$6.0M
Metabolic Control of Stemness in lung epithelial progenitors by FAM13AR01HL127200 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI ZHOU, ANNY XIAOBO · 2015 to 2024
$4.9M
Genetic and Functional Dissection of a Cluster of COPD GWAS Signals on Chromosome 4qR01HL147148 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., SILVERMAN, EDWIN K · 2019 to 2022
$3.5M
Identifying functional variants in COPD GWAS lociR01HL137927 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., SILVERMAN, EDWIN K · 2017 to 2020
$3.5M
Functional Genomic Approaches to Dissect COPD GWAS LociR33HL120794 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K, ZHOU, ANNY XIAOBO · 2016 to 2018
$1.7M
Genetics of Smoke-Altered LTA4H in COPDR01HL126596 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI BLALOCK, J EDWIN, CASTALDI, PETER · 2015 to 2018
$1.6M
Network-based Methods for Understanding Genetic Determinants and Epistasis in COPDK01HL129039 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI QIAO, DANDI · 2015 to 2019
Noncoding DNA contains gene regulatory elements that alter gene expression, and the function of these elements can be modified by genetic variation. Massively parallel reporter assays (MPRA) enable high-throughput identification and characterization of functional genetic variants, but the statistical methods to identify allelic effects in MPRA data have not been fully developed. In this study, we demonstrate how the baseline allelic imbalance in MPRA libraries can produce biased results, and we propose a novel, nonparametric, adaptive testing method that is robust to this bias. We compare the performance of this method with other commonly used methods, and we demonstrate that our novel adaptive method controls Type I error in a wide range of scenarios while maintaining excellent power. We have implemented these tests along with routines for simulating MPRA data in the Analysis Toolset for MPRA (@MPRA), an R package for the design and analyses of MPRA experiments. It is publicly available at http://github.com/redaq/atMPRA.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Statistical considerations for the analysis of massively parallel reporter assays data. · full record | OpenQuestion