The natural genetic diversity of agricultural species is an essential genetic resource for breeding programmes aiming to improve their ecosystem and production services. A large natural ecotype diversity is usually available for most grassland species. This could be used to recombine natural climatic adaptations and agronomic value to create improved populations of grassland species adapted to future regional climates. However describing natural genetic resources can be long and costly. Molecular markers may provide useful information to help this task. This opportunity was investigated for Lolium perenne L., using a set of 385 accessions from the natural diversity of this species collected right across Europe and provided by genebanks of several countries. For each of these populations, genotyping provided the allele frequencies of 189,781 SNP markers. GWAS were implemented for over 30 agronomic and/or putatively adaptive traits recorded in three climatically contrasted locations (France, Belgium, Germany). Significant associations were detected for hundreds of markers despite a strong confounding effect of the genetic background; most of them pertained to phenology traits. It is likely that genetic variability in these traits has had an important contribution to environmental adaptation and ecotype differentiation. Genomic prediction models calibrated using natural diversity were found to be highly effective to describe natural populations for almost all traits as well as commercial synthetic populations for some important traits such as disease resistance, spring growth or phenological traits. These results will certainly be valuable information to help the use of natural genetic resources of other species.

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.

Partial least square regression (PLSR) is a reference statistical model in chemometrics. In agronomy, it is used to predict components (response variables y) of chemical composition of vegetal materials from spectral near infrared (NIR) data X collected from spectrometers. PLSR reduces the dimension of the spectral data X by defining vectors that are then used as latent variables (LVs) in a multiple linear model. One difficulty is to determine the relevant dimensionality (number of LVs) for the given data. This step can be very time consuming when many datasets have to be processed and/or the datasets are frequently updated. The paper focuses on an alternative, bypassing the determination of the PLSR dimensionality and allowing for automatizing the predictions. The strategy uses ensemble learning methods, such as averaging or stacking the predictions of a set of PLSR models with different dimensionalities. The paper presents various methods of PLSR averaging and stacking and compares their performances to the usual PLSR on six real datasets on different types of forages. The main finding of the study was the overall superiority of the averaging methods compared to the usual PLSR. We therefore believe that such methods can be recommended to analyze NIR data on forages.