Evidence map›Paper›PMID 31664079›Full record

ArticleScientific reports2019

Extreme sampling design in genetic association mapping of quantitative trait loci using balanced and unbalanced case-control samples.

Yi Li, Orna Levran, JongJoo Kim, Tiejun Zhang, Xingdong Chen, Chen Suo

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
–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

28 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  6. Mathematical bounds on rTheoretical population biology · 2025
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  8. Mathematical bounds onbioRxiv : the preprint server for biology · 2024
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  11. Article
  12. Article
  13. Genome-Wide Association Study of Fiber Diameter in Alpacas.Animals : an open access journal from MDPI · 2023
    Article
  14. Article
  15. Article
  16. Article
  17. Genetic susceptibility loci forFrontiers in immunology · 2022
    Article
  18. Review
  19. Article
  20. Review
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.

Yi LiSchool of Statistics, Shanxi University of Finance & Economics, Shanxi, China.
Orna LevranLaboratory of the Biology of Addictive Diseases, Rockefeller University, New York, US.
JongJoo KimDepartment of Biotechnology, Yeungnam University, Gyeongsan, Korea.
Tiejun ZhangDepartment of Epidemiology, School of Public Health, Fudan University, Shanghai, China.
Xingdong ChenState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China. xingdongchen@fudan.edu.cn.
Chen SuoDepartment of Epidemiology, School of Public Health, Fudan University, Shanghai, China. suochen@fudan.edu.cn.

Funding

Transforming Translational Science and Education to Benefit Human HealthUL1TR000043 · NCATS · ROCKEFELLER UNIVERSITY · PI COLLER, BARRY · 2012 to 2015
$26.8M
NCATS NIH HHS UL1 TR000043
6 · The paper itself

Abstract

It is extremely expensive to conduct large sample size array- or sequencing based genome scale association studies. For a quantitative trait, an extreme case-control study design may improve the power and reduce the cost of variant calling. We investigated the performance of extreme study design when various proportions of samples are selected from the tails of phenotype distribution. Using simulations, we show that when risk genotypes become rare in the population and effect size is relatively small, it is beneficial to carry out an extreme sampling study. In particular, the number of selected cases and controls can even be unbalanced such that power is further increased, compared with a balanced selection. Our application to two data sets: methadone dose data and yearling weight data, demonstrated that similar results for full data analysis can be obtained using extreme sampling with only a fraction of the data. Using power analysis with simulated data and an experimental data application, we conclude that when full data is unavailable due to restricted budget, it is rewarding to employ an extreme sampling design in the sense that there can be immense cost reductions and qualitatively similar power as in the full data analysis.

Indexed as

Genome-Wide Association StudyQuantitative Trait LociAnimalsBody WeightCase-Control StudiesCattleGenotypeHumansMethadonePhenotypeSampling StudiesMethadone

Identifiers

PMID31664079
PMCPMC6820758

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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.