Evidence map›Paper›PMID 32216117›Full record

ArticleGenetic epidemiology2020

Effective SNP ranking improves the performance of eQTL mapping.

X Jessie Jeng, Jacob Rhyne, Teng Zhang, Jung-Ying Tzeng

Abstract read
In one paragraph

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

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

4 authors.

X Jessie JengDepartment of Statistics, North Carolina State University, Raleigh, North Carolina.ORCID 0000-0002-2182-133X
Jacob RhyneDepartment of Statistics, North Carolina State University, Raleigh, North Carolina.
Teng ZhangDepartment of Statistics, North Carolina State University, Raleigh, North Carolina.
Jung-Ying TzengDepartment of Statistics, North Carolina State University, Raleigh, North Carolina.

Funding

Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
NCI NIH HHS P01 CA142538
6 · The paper itself

Abstract

Genome-wide expression quantitative trait loci (eQTLs) mapping explores the relationship between gene expression and DNA variants, such as single-nucleotide polymorphism (SNPs), to understand genetic basis of human diseases. Due to the large number of genes and SNPs that need to be assessed, current methods for eQTL mapping often suffer from low detection power, especially for identifying trans-eQTLs. In this paper, we propose the idea of performing SNP ranking based on the higher criticism statistic, a summary statistic developed in large-scale signal detection. We illustrate how the HC-based SNP ranking can effectively prioritize eQTL signals over noise, greatly reduce the burden of joint modeling, and improve the power for eQTL mapping. Numerical results in simulation studies demonstrate the superior performance of our method compared to existing methods. The proposed method is also evaluated in HapMap eQTL data analysis and the results are compared to a database of known eQTLs.

Indexed as

Computer SimulationData AnalysisGene Expression RegulationGenome-Wide Association StudyHumansModels, GeneticPolymorphism, Single NucleotideQuantitative Trait LociHC rankinghotspotmultivariate responsepenalized regressiontrans-eQTL

Identifiers

PMID32216117
PMCPMC7725394

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Registered trials

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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.