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  1. VITTE, Clémentine (Ed.)

    Structural 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. We 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 simple and complex connections between TEs and structural variants. 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 within these recently diverged genomic regions, deletions of transposable elements likely account for more structural variation events and base pairs than insertions. These deletions are often large structural variants containing multiple transposable elements. Combined, our results highlight how transposable elements contribute to structural variation and demonstrate that deletion events are a major contributor to genomic differences.

     
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    Free, publicly-accessible full text available December 22, 2024
  2. Free, publicly-accessible full text available September 1, 2024
  3. The origins of maize were the topic of vigorous debate for nearly a century, but neither the current genetic model nor earlier archaeological models account for the totality of available data, and recent work has highlighted the potential contribution of a wild relative,Zea maysssp.mexicana. Our population genetic analysis reveals that the origin of modern maize can be traced to an admixture between ancient maize andZea maysssp.mexicanain the highlands of Mexico some 4000 years after domestication began. We show that variation in admixture is a key component of maize diversity, both at individual loci and for additive genetic variation underlying agronomic traits. Our results clarify the origin of modern maize and raise new questions about the anthropogenic mechanisms underlying dispersal throughout the Americas.

     
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    Free, publicly-accessible full text available December 1, 2024
  4. Millions of species are currently being sequenced, and their genomes are being compared. Many of them have more complex genomes than model systems and raise novel challenges for genome alignment. Widely used local alignment strategies often produce limited or incongruous results when applied to genomes with dispersed repeats, long indels, and highly diverse sequences. Moreover, alignment using many-to-many or reciprocal best hit approaches conflicts with well-studied patterns between species with different rounds of whole-genome duplication. Here, we introduce Anchored Wavefront alignment (AnchorWave), which performs whole-genome duplication–informed collinear anchor identification between genomes and performs base pair–resolved global alignment for collinear blocks using a two-piece affine gap cost strategy. This strategy enables AnchorWave to precisely identify multikilobase indels generated by transposable element (TE) presence/absence variants (PAVs). When aligning two maize genomes, AnchorWave successfully recalled 87% of previously reported TE PAVs. By contrast, other genome alignment tools showed low power for TE PAV recall. AnchorWave precisely aligns up to three times more of the genome as position matches or indels than the closest competitive approach when comparing diverse genomes. Moreover, AnchorWave recalls transcription factor–binding sites at a rate of 1.05- to 74.85-fold higher than other tools with significantly lower false-positive alignments. AnchorWave complements available genome alignment tools by showing obvious improvement when applied to genomes with dispersed repeats, active TEs, high sequence diversity, and whole-genome duplication variation. 
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  5. The seasonal timing of seed germination determines a plant’s realized environmental niche, and is important for adaptation to climate. The timing of seasonal germination depends on patterns of seed dormancy release or induction by cold and interacts with flowering-time variation to construct different seasonal life histories. To characterize the genetic basis and climatic associations of natural variation in seed chilling responses and associated life-history syndromes, we selected 559 fully sequenced accessions of the model annual species Arabidopsis thaliana from across a wide climate range and scored each for seed germination across a range of 13 cold stratification treatments, as well as the timing of flowering and senescence. Germination strategies varied continuously along 2 major axes: 1) Overall germination fraction and 2) induction vs. release of dormancy by cold. Natural variation in seed responses to chilling was correlated with flowering time and senescence to create a range of seasonal life-history syndromes. Genome-wide association identified several loci associated with natural variation in seed chilling responses, including a known functional polymorphism in the self-binding domain of the candidate gene DOG1. A phylogeny of DOG1 haplotypes revealed ancient divergence of these functional variants associated with periods of Pleistocene climate change, and Gradient Forest analysis showed that allele turnover of candidate SNPs was significantly associated with climate gradients. These results provide evidence that A. thaliana ’s germination niche and correlated life-history syndromes are shaped by past climate cycles, as well as local adaptation to contemporary climate. 
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  6. Summary

    The relevance of flowering time variation and plasticity to climate adaptation requires a comprehensive empirical assessment. We investigated natural selection and the genetic architecture of flowering time in Arabidopsis through field experiments in Europe across multiple sites and seasons.

    We estimated selection for flowering time, plasticity and canalization. Loci associated with flowering time, plasticity and canalization by genome‐wide association studies were tested for a geographic signature of climate adaptation.

    Selection favored early flowering and increased canalization, except at the northernmost site, but was rarely detected for plasticity. Genome‐wide association studies revealed significant associations with flowering traits and supported a substantial polygenic inheritance. Alleles associated with late flowering, including functionalFRIGIDAvariants, were more common in regions experiencing high annual temperature variation. Flowering time plasticity to fall vs spring and summer environments was associated withGIGANTEA SUPPRESSOR 5, which promotes early flowering under decreasing day length and temperature.

    The finding that late flowering genotypes and alleles are associated with climate is evidence for past adaptation. Real‐time phenotypic selection analysis, however, reveals pervasive contemporary selection for rapid flowering in agricultural settings across most of the species range. The response to this selection may involve genetic shifts in environmental cuing compared to the ancestral state.

     
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  7. Summary Open Research Badges

    This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available athttps://github.com/SNAnderson/maizeTE_variation;https://mcstitzer.github.io/maize_TEs.

     
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