Evidence map›Paper›PMID 32059004›Full record

ArticlePLoS computational biology2020

RAINBOW: Haplotype-based genome-wide association study using a novel SNP-set method.

Kosuke Hamazaki, Hiroyoshi Iwata

Abstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

50 citing papers in PubMed.

  1. Standardized microhaplotype databases and frameworks for assessing and mining crop genetic diversity.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  2. Article
  3. Candidate genes for stem rust resistance in Italian ryegrass revealed by nested association mapping.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  4. Article
  5. Integration of proxy intermediate omics traits into a nonlinear two-step model for accurate phenotypic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  6. Article
  7. Exploring standing genetic variation for barley leaf rust resistance in Australian breeding panel.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  8. Article
  9. Article
  10. Haplotype analysis and molecular marker development for the cold tolerance gene OsCTS11 at the seedling stage of rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Article
  11. Article
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  14. Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Review
  15. Article
  16. Article
  17. Review
  18. Phenotypic simulation for fruit-related traits in FMolecular genetics and genomics : MGG · 2025
    Article
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  20. 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

2 authors.

Kosuke HamazakiDepartment of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.ORCID 0000-0002-7486-7438
Hiroyoshi IwataDepartment of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.ORCID 0000-0002-6747-7036

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Difficulty in detecting rare variants is one of the problems in conventional genome-wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set method named "RAINBOW" and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes. We prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. Our proposed method was shown to be superior to these in three aspects: (1) controlling false positives; (2) in detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset; and (3) it showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, primarily when the causal variants were located very close to each other, and the directions of their effects were opposite. By using the SNP-set approach as in this study, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as an R package named "RAINBOWR" and is available from CRAN (https://cran.r-project.org/web/packages/RAINBOWR/index.html) and GitHub (https://github.com/KosukeHamazaki/RAINBOWR).

Indexed as

Genes, PlantGenetic Association StudiesHaplotypesPolymorphism, Single NucleotideComputational BiologyComputer SimulationGenetic VariationGenotypeLinkage DisequilibriumModels, GeneticOryzaPhenotypeProgramming Languages

Identifiers

PMID32059004
PMCPMC7046296

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.