Evidence map›Paper›PMID 31857674›Full record

ArticleJournal of human genetics2020

Evaluating the predictive value of genetic risk score in colorectal cancer among Chinese Han population.

Ding Ye, Danjie Jiang, Simeng Gu, Yingying Mao, Sangni Qian, Shujuan Lin, Qilong Li, Jinhua Yang, Kunhong Zhong, Mingjuan Jin and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of human genetics, 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
0.5field-weighted citation impact, top 33% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  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

11 authors at 3 institutions in 1 country.

Ding YeDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou, China.
Danjie JiangDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou, China.
Simeng GuDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou, China.
Yingying MaoDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou, China.
Sangni QianDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou, China.
Shujuan LinDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou, China.
Qilong LiJiashan Institute of Cancer Prevention and Treatment, Jiaxing, China.
Jinhua YangJiashan Institute of Cancer Prevention and Treatment, Jiaxing, China.
Kunhong ZhongDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou, China.
Mingjuan JinDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou, China. jinmj@zju.edu.cn.
Kun ChenDepartment of Epidemiology and Biostatistics, Zhejiang University School of Public Health, Hangzhou, China. ck@zju.edu.cn.
Zhejiang University · CNSecond Affiliated Hospital of Zhejiang University · CNZhejiang Chinese Medical University · CN

Funding

National Natural Science Foundation of China (National Science Foundation of China) 81602917National Natural Science Foundation of China (National Science Foundation of China) 81673262
6 · The paper itself

Abstract

Increasing single nucleotide polymorphisms (SNPs) have been identified to be associated with colorectal cancer (CRC). We aimed to investigate whether genetic risk scores (GRS) that aggregate information from multiple genetic variants can predict the risk of CRC in a Chinese population. Fifty candidate SNPs were selected to explore the associations with CRC in a discovery sample with 1002 CRC cases and 999 healthy controls. We modeled the significant SNPs identified by the case-control study as a multilocus weighted GRS and estimated the association of GRS with CRC. Furthermore, 300 pairs of cases and controls were included as a validation sample to confirm the finding. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the predictive power of GRS in CRC. A total of seven SNPs were found to increase the risk of CRC, and two SNPs were found to be negatively associated with CRC in the discovery sample. Relative to participants with the lowest quartile of GRS, those with the highest quartile had a 2.64-fold (95% CI: 1.99-3.51) higher risk for CRC. For every 0.1 point of GRS increase, the risk of CRC increase by 11% (95% CI: 8-14%). AUROC for GRS alone were 0.59 (95% CI: 0.57-0.62) and 0.52 (95% CI: 0.46-0.58) in the discovery and validation sample, respectively. AUROC increased to 0.62 (95% CI: 0.59-0.64) and 0.71 (95% CI: 0.65-0.76) by combining environmental risk factors. Our findings support an association between GRS and risk of CRC, which provides evidence of improved prediction model for CRC in China.

Indexed as

Genetic Predisposition to DiseaseAdultChinaColorectal NeoplasmsFemaleGenome-Wide Association StudyHumansMaleMiddle AgedPolymorphism, Single NucleotideRisk AssessmentRisk FactorsROC Curve

Identifiers

PMID31857674
OpenAlexW2996133496

What OpenQuestion holds

Textmetadata
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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.