Decroocq, V. | bioRxiv Science

bioRxiv2025

Building pangenomes for domesticated and wild tree species: genomic complexity and strategies

Long-read sequencing and pangenomics are revolutionizing crop research by providing more complete genome information and revealing crucial structural variations linked to important agricultural traits. Building on recent advances in intraspecific pangenome construction, this study addresses the challenge of creating broader, cross-taxon pangenomes, using the Armeniaca taxonomic section as a model. Leveraging a diverse panel of genome assemblies, we constructed a pangenome graph and cataloged associated single nucleotide polymorphisms (SNPs) and structural variants. We characterized the diversity of these variants and assessed the extent to which different taxa contribute to overall pangenome expansion. Additionally, we evaluated the performance of low-depth sample mapping to the graph-based reference, highlighting key technical limitations that may affect the quality of downstream analyses. We further identified specific subsets of SVs that exhibit associations with particular classes of transposable elements. As a case study illustrating the potential functional and phenotypic relevance of graph-derived SVs, we examined the genomic configuration of the DAM locus within the Armeniaca pangenome.

bioinformatics↗

bioRxiv2023

Estimation of contemporary effective population size in plant populations: limitations of genomic datasets

Effective population size (Ne) is a pivotal evolutionary parameter with crucial implications in conservation practice and policy. Genetic methods to estimate Ne have been preferred over demographic methods because they rely on genetic data rather than time-consuming ecological monitoring. Methods based on linkage disequilibrium, in particular, have become popular in conservation as they require a single sampling and provide estimates that refer to recent generations. A software programme based on the linkage disequilibrium method, GONE, looks particularly promising to estimate contemporary and recent-historical Ne (up to 200 generations in the past). Genomic datasets from non-model species, especially plants, may present some constraints to the use of GONE, as linkage maps and reference genomes are seldom available, and SNP genotyping is usually based on reduced-representation methods. In this study, we use empirical datasets from four plant species to explore the limitations of plant genomic datasets when estimating Ne using the algorithm implemented in GONE, in addition to exploring some typical biological limitations that may affect Ne estimation using the linkage disequilibrium method, such as the occurrence of population structure. We show how accuracy and precision of Ne estimates potentially change with the following factors: occurrence of missing data, limited number of SNPs/individuals sampled, and lack of information about the location of SNPs on chromosomes, with the latter producing a significant bias, previously unexplored with empirical data. We finally compare the Ne estimates obtained in GONE for the last generations with the contemporary Ne estimates obtained in the programmes currentNe and NeEstimator.

evolutionary biology↗