Biology subjects

Barre, P.

Publications and source records attributed to Barre, P..

3 recordsLinked to original sources

bioRxiv2026

How robust are genomic offset predictions to methodological choices? Insights from perennial ryegrass

Genomic offsets are increasingly used to quantify the mismatch between a populations current genetic composition and the composition predicted under changed environmental conditions. While genomic offset is a promising tool for assessing climate maladaptation, the sensitivity of predictions to different methodological choices is not well understood. In this study, we compared two fundamentally different approaches to detect outliers before predicting genomic offsets: Gradient Forest (GF, non-linear, non-parametric) and Canonical Correlation Analysis (CANCOR, linear, parametric). To do so, we used 457 natural populations of perennial ryegrass (Lolium perenne L.), an important agricultural forage species throughout Europe. Using a data set of 189,968 SNPs and 75 climatic variables, we experimentally validated genomic offsets against 105 phenotypic traits measured across three common gardens during multiple years. We also assessed the sensitivity of outlier detection and genomic offset predictions to the number and spatial distribution of sampled populations. Both GF and CANCOR detected a substantial number of outlier loci associated with environmental gradients (2,113 and 653, respectively), with 429 loci identified by both approaches. When used to model spatial variation in genetic adaptation and estimate genomic offsets, the different outlier sets produce spatially congruent projections. We also found significant correlations between experienced genomic offset in the common garden predicted by both outlier sets and phenotypic traits, identifying traits that could serve as good fitness proxies for assessing climate risk. Analyses based on different population subsamples revealed that GF was less sensitive to sample size and geographic biases than CANCOR. Our findings provide practical guidance for designing genomic offset studies in both agricultural and natural systems and suggest that non-linear, non-paramateric methods like GF may be less sensitive to sampling design and therefore potentially more robust for predicting climate maladaptation.

ecology↗

bioRxiv2022

Stack Mapping Anchor Points (SMAP): a versatile suite of tools for read-backed haplotyping

Here we present SMAP, a software package that implements a suite of computational tools to extract multi-allelic haplotypes using read-backed haplotyping. SMAP tools first perform accurate read processing and analyze read mapping distributions across sample sets. Then, two complementary modules can be invoked for haplotype calling: SMAP haplotype-sites combines known Single Nucleotide Polymorphisms (SNPs) and/or read mapping position polymorphisms (SMAPs) to reconstruct compressed, read-reference-encoded haplotype strings. In contrast, SMAP haplotype-window works independent of prior knowledge of polymorphisms, groups reads by locus, defines a window enclosed between two custom border sequences, and retains the entire corresponding DNA sequence as haplotype. Haplotype-window is, among many applications, especially useful for high-throughput CRISPR/Cas mutation screens. Either way, SMAP creates a single integrated haplotype call table across all loci and samples. SMAP haplotyping is extremely versatile and can be applied to highly multiplex amplicon sequencing (HiPlex), Shotgun (e.g. whole genome shotgun (WGS) sequencing, probe capture and RNA-Seq), or Genotyping-by-Sequencing (GBS) data; and to Illumina short reads, PacBio and MinION long reads. SMAP creates discrete genotype calls for individuals of any ploidy or quantitative haplotype frequency spectra for Pool-Seq data, and can scale from tens to thousands of loci and/or samples. SMAP, including the source code written in Python is available at https://gitlab.com/truttink/smap, and a detailed user manual and guidelines for accurate read processing is available at https://ngs-smap.readthedocs.io/, under the GNU Affero General Public License v3.0.

genomics↗

bioRxiv2018

Pleistocene climate changes explain large-scale genetic variation in a dominant grassland species, Lolium perenne L.

AimGrasslands have been pivotal in the development of herbivore breeding since the Neolithic and are still nowadays the most widespread agricultural land-use across Europe. However, it remains unclear whether the current large-scale genetic variation of plant species found in natural grasslands of Europe is the result of human activities or natural processes.\n\nLocationEurope.\n\nTaxonLolium perenne L (perennial ryegrass).\n\nMethodsWe reconstructed the phylogeographic history of L. perenne, a dominant grassland species, using 481 natural populations including 11 populations from closely related taxa. We combined the Genotyping-by-Sequencing (GBS) and Pool-sequencing (Pool-seq) methods to obtain high-quality allele frequency calls of ~ 500 k SNP loci. We performed genetic structure analyses and demographic reconstructions based on the site frequency spectrum (SFS). We additionally used the same genotyping protocol to assess the genomic diversity of a set of 32 cultivars representative of the L. perenne cultivars widely used for forage purposes.\n\nResultsExpansion across Europe took place during the Wurm glaciation (12-110 kya), a cooling period that decreased the dominance of trees in favour of grasses. Splits and admixtures in L. perenne fit historical sea level changes in the Mediterranean basin. The development of agriculture in Europe (7-3.5 kya), that caused an increase in the abundance of grasslands, did not have an effect on the demographic patterns of L. perenne. We found little differentiation between modern cultivars and certain natural variants. However, modern cultivars do not represent the wide genetic variation found in natural populations.\n\nMain conclusionsDemographic events in L. perenne can be explained by the changing climatic conditions during the Pleistocene. Natural populations maintain a wide genomic variability at continental scale that has been underused by recent breeding activities. This variability constitutes valuable standing genetic variation for future adaptation of grasslands to climate change, safeguarding the agricultural services they provide.

evolutionary biology↗