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Trait Association and Prediction Through Integrative K-mer Analysis

View ORCID ProfileCheng He, View ORCID ProfileJacob D. Washburn, View ORCID ProfileYangfan Hao, View ORCID ProfileZhiwu Zhang, View ORCID ProfileJinliang Yang, View ORCID ProfileSanzhen Liu
doi: https://doi.org/10.1101/2021.11.17.468725
Cheng He
1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506-5502, USA
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Jacob D. Washburn
2Plant Genetics Research Unit, USDA-ARS, Columbia, MO 65211, USA
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Yangfan Hao
1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506-5502, USA
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Zhiwu Zhang
3Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
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Jinliang Yang
4Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583-0915, USA
5Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
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Sanzhen Liu
1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506-5502, USA
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  • For correspondence: liu3zhen@ksu.edu
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ABSTRACT

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 Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/PlantG3/ZmKmerGWAS

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 19, 2021.
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Trait Association and Prediction Through Integrative K-mer Analysis
Cheng He, Jacob D. Washburn, Yangfan Hao, Zhiwu Zhang, Jinliang Yang, Sanzhen Liu
bioRxiv 2021.11.17.468725; doi: https://doi.org/10.1101/2021.11.17.468725
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Trait Association and Prediction Through Integrative K-mer Analysis
Cheng He, Jacob D. Washburn, Yangfan Hao, Zhiwu Zhang, Jinliang Yang, Sanzhen Liu
bioRxiv 2021.11.17.468725; doi: https://doi.org/10.1101/2021.11.17.468725

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