Trait association and prediction through integrative k‐mer analysis

Abstract

SUMMARYGenome‐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.