Evidence map›Paper›PMID 29190701›Full record

ArticlePloS one2017

A non-threshold region-specific method for detecting rare variants in complex diseases.

Ai-Ru Hsieh, Dao-Peng Chen, Amrita Sengupta Chattopadhyay, Ying-Ju Li, Chien-Ching Chang, Cathy S J Fann

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 26% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

6 authors at 2 institutions in 1 country.

Ai-Ru HsiehGraduate Institute of Biostatistics, China Medical University, Taichung, Taiwan.ORCID http://orcid.org/0000-0001-8461-3573
Dao-Peng ChenInstitute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan.
Amrita Sengupta ChattopadhyayInstitute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan.
Ying-Ju LiInstitute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan.
Chien-Ching ChangInstitute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan.
Cathy S J FannInstitute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan.
Institute of Biomedical Sciences, Academia Sinica · TWChina Medical University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A region-specific method, NTR (non-threshold rare) variant detection method, was developed-it does not use the threshold for defining rare variants and accounts for directions of effects. NTR also considers linkage disequilibrium within the region and accommodates common and rare variants simultaneously. NTR weighs variants according to minor allele frequency and odds ratio to combine the effects of common and rare variants on disease occurrence into a single score and provides a test statistic to assess the significance of the score. In the simulations, under different effect sizes, the power of NTR increased as the effect size increased, and the type I error of our method was controlled well. Moreover, NTR was compared with several other existing methods, including the combined multivariate and collapsing method (CMC), weighted sum statistic method (WSS), sequence kernel association test (SKAT), and its modification, SKAT-O. NTR yields comparable or better power in simulations, especially when the effects of linkage disequilibrium between variants were at least moderate. In an analysis of diabetic nephropathy data, NTR detected more confirmed disease-related genes than the other aforementioned methods. NTR can thus be used as a complementary tool to help in dissecting the etiology of complex diseases.

Indexed as

Genetic Association StudiesGenetic Diseases, InbornHumansLinkage Disequilibrium

Identifiers

PMID29190701
PMCPMC5708778
OpenAlexW2773939690

What OpenQuestion holds

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LicenceCC BY
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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.