Evidence map›Paper›PMID 38606632›Full record

ArticleGenetic epidemiology2024

Structured testing of genetic association with mixed clinical outcomes.

Meiling Liu, Yu-Ru Su, Yang Liu, Li Hsu, Qianchuan He

Abstract read
In one paragraph

Article in Genetic epidemiology, 2024. 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

5 authors.

Meiling LiuPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.ORCID http://orcid.org/0000-0001-7953-1506
Yu-Ru SuBiostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.
Yang LiuDepartment of Mathematics and Statistics, Wright State University, Dayton, Ohio, USA.
Li HsuPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.ORCID http://orcid.org/0000-0001-8168-4712
Qianchuan HePublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Funding

Statistical Methods for RNA-seq Data AnalysisR01GM105785 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Wei Sun · 2014 to 2026
$4.6M
Statistical Methods for Analysis of Tumor Heterogeneity in Genetic EpidemiologyR01CA189532 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HSU, LI · 2015 to 2023
$4.2M
Precompetitive Collaboration on Liquid Biopsy for Early Cancer Assessment: Data Management and Coordinating UnitU24CA288185 · NCI · FRED HUTCHINSON CANCER CENTER · PI Wei Sun, Yingye Zheng · 2023 to 2026
$3.1M
Integrative Genomics into Genetic Association Studies of Blood Pressure and Stroke in African AmericansR01HL152439 · NHLBI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HSU, LI, KOOPERBERG, CHARLES L · 2021 to 2024
$2.8M
Methods for Analyzing Cancer Somatic Mutation DataR01CA223498 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HE, QIANCHUAN · 2018 to 2022
$2.0M
NCI NIH HHS R01 CA189532NCI NIH HHS R01 CA223498NCI NIH HHS U24 CA288185NHLBI NIH HHS R01 HL152439NIGMS NIH HHS R01 GM105785NIH HHS R01CA189532NIH HHS R01CA223498NIH HHS R01GM105785NIH HHS R01HL152439
6 · The paper itself

Abstract

Genetic factors play a fundamental role in disease development. Studying the genetic association with clinical outcomes is critical for understanding disease biology and devising novel treatment targets. However, the frequencies of genetic variations are often low, making it difficult to examine the variants one-by-one. Moreover, the clinical outcomes are complex, including patients' survival time and other binary or continuous outcomes such as recurrences and lymph node count, and how to effectively analyze genetic association with these outcomes remains unclear. In this article, we proposed a structured test statistic for testing genetic association with mixed types of survival, binary, and continuous outcomes. The structured testing incorporates known biological information of variants while allowing for their heterogeneous effects and is a powerful strategy for analyzing infrequent genetic factors. Simulation studies show that the proposed test statistic has correct type I error and is highly effective in detecting significant genetic variants. We applied our approach to a uterine corpus endometrial carcinoma study and identified several genetic pathways associated with the clinical outcomes.

Indexed as

association analysisgenetic factorshierarchical modelingscore testsstructured testingsurvival outcome

Identifiers

PMID38606632
PMCPMC11470132

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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.