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).
Highlights
With the decreasing cost and increasing throughput of next-generation sequencing, the number of accessions that can be used for genome-wide association study (GWAS) is increasing [1,2,3]
The datasets and scripts generated and analyzed during the current study are available from the “KosukeHamazaki/ HGRAINBOW‘repository in the GitHub,https:// github.com/KosukeHamazaki/HGRAINBOW
One problem caused by rare variants is that the noncausal markers that have a strong linkage disequilibrium (LD) with one causal rare variant indicate a higher detection power than the true causal rare variant, which may interfere with the detection of the true causal variant
Summary
With the decreasing cost and increasing throughput of next-generation sequencing, the number of accessions that can be used for genome-wide association study (GWAS) is increasing [1,2,3] Using such large sequencing data, GWAS is widely used in human and in plant and animal genetics and breeding, and has identified novel genes related to important agronomic traits [4,5,6]. One problem caused by rare variants is that the noncausal markers that have a strong linkage disequilibrium (LD) with one causal rare variant indicate a higher detection power than the true causal rare variant, which may interfere with the detection of the true causal variant This phenomenon is known as “synthetic association”, and often happens when the minor allele frequency (MAF) of the non-causal marker is higher than that of the true rare variant [13]. This problem is closely related to the complex gene compositions comprising multiple alleles such as haplotypes because genes related to important agronomic traits often consist of multiple rare alleles, and this is why haplotypes are hard to detect using GWAS [14]
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