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

• Published in: PLoS computational biology 2020-02, Vol.16 (2), p.e1007663-e1007663
• Main Authors: Hamazaki, Kosuke, Iwata, Hiroyoshi
• Format: Article
• Language: English
• Subjects: Alleles; Analysis; Biology and Life Sciences; Computational Biology; Computer and Information Sciences; Computer Simulation; Datasets; Engineering and Technology; Gene polymorphism; Genes; Genes, Plant; Genetic aspects; Genetic Association Studies; Genetic polymorphisms; Genetic Variation; Genome-wide association studies; Genomes; Genomics; Genotype; Genotype & phenotype; Haplotypes; Life sciences; Linkage Disequilibrium; Methods; Models, Genetic; Normal distribution; Novels; Nucleotides; Oryza - genetics; Oryza sativa indica; Phenotype; Physical Sciences; Polymorphism; Polymorphism, Single Nucleotide; Power (Philosophy); Programming Languages; Rainbows; Research and Analysis Methods; Rice; Single nucleotide polymorphisms; Single-nucleotide polymorphism
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007663

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).