1. Trait association and prediction through integrative k-mer analysis.
- Author
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He C, Washburn JD, Schleif N, Hao Y, Kaeppler H, Kaeppler SM, Zhang Z, Yang J, and Liu S
- Subjects
- Quantitative Trait Loci genetics, Plant Leaves genetics, Genotype, Genome, Plant genetics, Zea mays genetics, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Phenotype
- Abstract
Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Alternatively, GWAS can use counts of substrings of length k from longer sequencing reads, k-mers, as genotyping data. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and genes directly found k-mers from known causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. A gene encoding a MADS transcription factor was functionally validated by showing that ectopic expression of the gene led to less upright leaves. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, genomic prediction of kernel oil, leaf angle, and flowering time using k-mer data resulted in a similarly high prediction accuracy to the standard SNP-based method. Collectively, we showed k-mer GWAS is a powerful approach for identifying trait-associated genetic elements. Further, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery., (© 2024 Society for Experimental Biology and John Wiley & Sons Ltd.)
- Published
- 2024
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