Structural variants contribute to phenotypic variation in maize.

Area of Science:

• Genetics
• Genomics
• Maize Biology

Background:

• Identifying genetic loci for trait variation is difficult due to limitations in detecting various genetic variants.
• Structural variants (SVs), particularly transposable elements (TEs), influence phenotypic variation but are challenging to detect with standard methods.
• Existing short-read sequencing data analysis methods often miss polymorphic SVs and TEs.

Purpose of the Study:

• To develop and apply a method for detecting polymorphic SVs and TEs in maize using short-read sequencing data.
• To genotype a large maize diversity panel for identified SVs.
• To associate SV polymorphisms with life history traits and genotype-by-environment (GxE) interactions.

Main Methods:

• Whole genome alignment between two maize genotypes to identify polymorphic SVs.
• Genotyping a large maize diversity panel using short-read sequencing data and alignment support.
• Association analysis of SVs with phenotypic traits and GxE interactions.

Main Results:

• A technique was developed to genotype SV polymorphisms across a large maize panel.
• SV polymorphisms were associated with life history traits and GxE interactions.
• Most trait-associated SVs contained TEs; some were likely deletions, while others indicated TE insertions. One TE insertion showed significant gene expression associations.

Conclusions:

• The developed method enables genotyping of SV presence/absence variation in large populations.
• SV variation, including TEs, contributes to trait variation and GxE interactions in maize.
• While SVs are associated with traits, these associations were largely in linkage disequilibrium with nearby SNPs.