RT Journal Article
SR Electronic
T1 Trait Association and Prediction Through Integrative K-mer Analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.17.468725
DO 10.1101/2021.11.17.468725
A1 Cheng He
A1 Jacob D. Washburn
A1 Yangfan Hao
A1 Zhiwu Zhang
A1 Jinliang Yang
A1 Sanzhen Liu
YR 2021
UL http://biorxiv.org/content/early/2021/11/19/2021.11.17.468725.abstract
AB Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Here we employed an GWAS approach using k-mers, short substrings from sequencing reads. 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 pathway genes directly found k-mers from causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. 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, phenotypic prediction of kernel oil, leaf angle, and flowering time using k-mer data showed at least a similarly high prediction accuracy to the standard SNP-based method. Collectively, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.Competing Interest StatementThe authors have declared no competing interest.