Combined analysis of transposable elements and structural variation in maize genomes reveals genome contraction outpaces expansion

Abstract

BackgroundStructural differences between genomes are a major source of genetic variation that contributes to phenotypic differences. Transposable elements, mobile genetic sequences capable of increasing their copy number and propagating themselves within genomes, can generate structural variation. However, their repetitive nature makes it difficult to characterize fine-scale differences in their presence at specific positions, limiting our understanding of their impact on genome variation. Domesticated maize is a particularly good system for exploring the impact of transposable element proliferation as over 70% of the genome is annotated as transposable elements. High-quality transposable element annotations were recently generated forde-novogenome assemblies of 26 diverse inbred maize lines.ResultsWe generated base-pair resolved pairwise alignments between the B73 maize reference genome and the remaining 25 inbred maize line assemblies. From this data, we classified transposable elements as either shared or polymorphic in a given pairwise comparison. Our analysis uncovered substantial structural variation between lines, representing both putative insertion and deletion events. Putative insertions in SNP depleted regions, which represent recently diverged identity by state blocks, suggest some TE families may still be active. However, our analysis reveals that, genome-wide, deletions of transposable elements account for more structural variation than insertions. These deletions are often large structural variants containing multiple transposable elements.ConclusionsCombined, our results highlight how transposable elements contribute to structural variation and demonstrate that deletion events are a major contributor to genomic differences.

Transposable element abundance subtly contributes to lower fitness in maize

“…The mutagenic impact of de novo TE insertion has been extensively documented through genetic analysis of spontaneous mutants of many model species, including those in maize that led to the discovery of transposition itself (McClintock, 1950). While maize has an abundance of TEs, making up over 85% of the genome (Schnable et al ., 2009; Hufford et al ., 2021), TE content is extremely variable between maize individuals (Brunner et al ., 2005; Morgante et al ., 2005; Anderson et al ., 2019; Munasinghe et al ., 2023). However, most TEs present in the maize genome have not transposed recently – the median age of a TE insertion is 150,000 years (Stitzer et al ., 2021).…”

“…While maize has an abundance of TEs, making up over 85% of the genome (Schnable et al, 2009;Hufford et al, 2021), TE content is extremely variable between maize individuals (Brunner et al, 2005;Morgante et al, 2005;Anderson et al, 2019;Munasinghe et al, 2023). However, most TEs present in the maize genome have not transposed recently -the median age of a TE insertion is 150,000 years (Stitzer et al, 2021).…”

“…These TEs come from over 27,000 families in all known plant TE superfamilies, and are found in variable genic, chromatin, methylation, and recombinational environments within the genome (Baucom et al ., 2009; Stitzer et al ., 2021). Maize TEs are extremely polymorphic, with only half of TE insertions shared at the same position between any two individuals (Brunner et al ., 2005; Morgante et al ., 2005; Anderson et al ., 2019; Munasinghe et al ., 2023). The high abundance and diversity of TEs in maize, as compared to previously investigated model taxa, suggests the fitness costs of TEs cannot possibly be as high as those previously measured in yeast and flies.…”

Structural variants contribute to phenotypic variation in maize

“…This pattern is consistent with the polymorphism being caused by a deletion and the longer 'insertion' allele being the ancestral type. The frequency spectrum was relatively consistent across SV types (Munasinghe et al, 2023).…”

“…The proportions of SVs for each category are consistent with those prior to filtering ("TE = SV" (32,414, 32.9%), followed by "multi TE SVs" (26,689, 27.1%), "incomplete TE SVs" (19,013, 19.3%), "no TE SVs" (12,385, 12.6%), and "TE within SVs" (7,921, 8.0%), indicating little to no biases in our filtering method for each SV category. For more information about how SVs are classified into TE groupings, see Munasinghe et al (2023).…”