Evidence map›Paper›PMID 26345547›Full record

ArticleBioMed research international2015

Robust Association Tests for the Replication of Genome-Wide Association Studies.

Jungnam Joo, Ju-Hyun Park, Bora Lee, Boram Park, Sohee Kim, Kyong-Ah Yoon, Jin Soo Lee, Nancy L Geller

Abstract read
In one paragraph

Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Jungnam JooBiometric Research Branch, Research Institute and Hospital, National Cancer Center, Gyeonggi-do, Goyang-si 410-769, Republic of Korea.
Ju-Hyun ParkDepartment of Statistics, Dongguk University, Seoul 100-715, Republic of Korea.
Bora LeeBiometric Research Branch, Research Institute and Hospital, National Cancer Center, Gyeonggi-do, Goyang-si 410-769, Republic of Korea.
Boram ParkBiometric Research Branch, Research Institute and Hospital, National Cancer Center, Gyeonggi-do, Goyang-si 410-769, Republic of Korea.
Sohee KimBiometric Research Branch, Research Institute and Hospital, National Cancer Center, Gyeonggi-do, Goyang-si 410-769, Republic of Korea.
Kyong-Ah YoonLung Cancer Branch, Research Institute and Hospital, National Cancer Center, Gyeonggi-do, Goyang-si 410-769, Republic of Korea.
Jin Soo LeeLung Cancer Branch, Research Institute and Hospital, National Cancer Center, Gyeonggi-do, Goyang-si 410-769, Republic of Korea.
Nancy L GellerOffice of Biostatistics Research, National Heart, Lung and Blood Institute, Bethesda, MD 20892-7938, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In genome-wide association study (GWAS), robust genetic association tests such as maximum of three CATTs (MAX3), each corresponding to recessive, additive, and dominant genetic models, the minimum p value of Pearson's Chi-square test with 2 degrees of freedom, and CATT based on additive genetic model (MIN2), genetic model selection (GMS), and genetic model exclusion (GME) methods have been shown to provide better power performance under wide range of underlying genetic models. In this paper, we demonstrate how these robust tests can be applied to the replication study of GWAS and how the overall statistical significance can be evaluated using the combined test formed by p values of the discovery and replication studies.

Indexed as

Genome-Wide Association StudyModels, GeneticDNA ReplicationGenome

Identifiers

PMID26345547
PMCPMC4539975

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.