Identification of single nucleotide polymorphisms and candidate genes associated with fiber content in sweetpotato (Ipomoea batatas (L.) Lam.) through a genome-wide association study.

BACKGROUND

Sweetpotato (Ipomoea batatas (L.) Lam.) is an essential root crop with several nutritional benefits, including high dietary fiber content. While fiber contributes positively to human health by reducing the risk of metabolic and gastrointestinal diseases, excessive fiber accumulation can negatively impact texture and consumer preference. Despite its importance, the genetic mechanisms underlying fiber content in sweetpotato remain largely unexplored. Therefore, this study aimed to identify the genomic regions and candidate genes associated with fiber content through a genome-wide association study (GWAS).

RESULTS

Significant phenotypic variation in fiber content were observed among 140 sweetpotato genotypes. The GWAS analysis identified seven significant single nucleotide polymorphisms (SNPs), with Iba_chr07a_20294133 and Iba_chr12a_38616338 consistently detected across the FarmCPU and BLINK models. Notably, three SNPs (Iba_chr01a_17621178, Iba_chr10a_773882, and Iba_chr12a_38616338) showed significant phenotypic differentiation between homozygous alleles, making them promising candidates for marker development. Candidate gene analysis identified four genes with significantly upregulated expression in high-fiber genotypes: IbANT1 (adenine nucleotide transporter BT1), IbCYP86B1 (cytochrome P450 86B1), IbSCR3 (scarecrow-like protein 3), and IbFER (FERONIA receptor-like kinase). These genes are involved in suberin biosynthesis, cell wall remodeling, and metabolic regulation, suggesting their crucial roles in fiber accumulation.

CONCLUSION

This study provides novel insights into the genetic regulation of fiber content in sweetpotato. The identification of significant SNPs and candidate genes offers valuable resources for breeding programs targeting fiber optimization. Further validation is essential for the effective application of these SNPs and genes into marker-assisted selection strategies.