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Trait association and prediction through integrative k-mer analysis.

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  • 1Department of Plant Pathology, Kansas State University, Manhattan, Kansas, 66506, USA.

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Summary

K-mer genome-wide association studies (GWAS) effectively identify genetic elements linked to traits like maize color and oil content. This powerful approach aids in gene discovery and integrates diverse genomic data for trait analysis.

Keywords:
Zea maysGWASk‐merleaf angleoilpredictiontechnical advance

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Area of Science:

  • Genomics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) typically use single nucleotide polymorphisms (SNPs) to uncover genetic control of traits.
  • An alternative genotyping approach involves using k-mers, which are substrings of fixed length from sequencing reads.

Purpose of the Study:

  • To evaluate the efficacy of k-mer GWAS for identifying trait-associated genetic elements in maize.
  • To explore the utility of k-mers for gene discovery and data integration in complex trait analysis.

Main Methods:

  • K-mer GWAS was applied to maize traits including cob and kernel color, kernel oil, and leaf angle.
  • Co-expression analysis, functional validation of candidate genes (MADS transcription factor), and evolutionary selection analysis were performed.
  • Genomic prediction accuracy was compared between k-mer and SNP-based methods for multiple traits.

Main Results:

  • K-mer GWAS successfully identified k-mers associated with maize kernel color, including those from known causal genes.
  • Analysis of complex traits revealed k-mers linked to both known and candidate genes.
  • A MADS transcription factor gene was functionally validated for its role in leaf angle.
  • Evolutionary analysis indicated differential selection pressures on k-mers related to kernel oil and leaf angle.
  • K-mer based genomic prediction achieved accuracy comparable to SNP-based methods for kernel oil, leaf angle, and flowering time.

Conclusions:

  • K-mer GWAS is a robust method for identifying trait-associated genetic elements and discovering functional genes.
  • K-mers serve as a valuable tool for integrating diverse genomic data and advancing the understanding of complex traits.