Biology subjects

Baison, J.

Publications and source records attributed to Baison, J..

7 recordsLinked to original sources

bioRxiv2022

Teasing apart the joint effect of demography and natural selection in the birth of a contact zone

Vast population movements induced by recurrent climatic cycles have shaped the genetic structure of plant species. This is especially true in Scandinavia that was repeatedly glaciated. During glacial periods trees were confined to refugia, south and east of the ice sheet, from which they recolonized Scandinavia as the ice melted away. This multi-pronged recolonization led to large contact zones in most species. We leverage large genomic data from 5000 trees to reconstruct the demographic history of Norway spruce (Picea abies) and test for the presence of natural selection during the recolonization process and the establishment of the contact zone. Sweden is today made up of two large genetic clusters, a southern one originating from the Baltics and a Northern one originating from Northern Russia. The contact zone delineating these two clusters closely matches the limit between two major climatic regions. This suggests that natural selection contributed to the establishment and the maintenance of the contact zone. To test this hypothesis we first used Approximate Bayesian Computation; an Isolation-with migration model with genome-wide linked selection fits the data better than a purely neutral one. Secondly, we identified loci characterized by both extreme allele frequency differences between geographic regions and association to the variables defining the climatic zones. These loci, many of which are related to phenology, form clusters present on all linkage groups. Altogether, the current genetic structure reflects the joint effect of climatic cycles, recolonization and selection on the establishment of strong local adaptation and contact zones. Significance StatementUnderstanding how past climatic events, human actions and evolutionary forces contributed to the present distribution of genetic diversity is crucial to predict their reaction to the current climate crisis. Vast distribution shifts induced by past environmental changes, local ecological processes, natural selection and human transfers contributed to the current distribution of Norway spruce across Northern Europe. Genome-wide polymorphisms from thousands of individuals show that Scandinavia was recolonized after the Last Glacial from both south and north. This two-pronged recolonization established a contact zone between two genetic clusters that matches the limit between two major climate zones. The contact zone is shaped and maintained by natural selection on a large number of loci that form blocks of co-adapted loci spread genome-wide.

evolutionary biology↗

bioRxiv2019

Genetic control of tracheid properties in Norway spruce wood

[tpltrtarr] Through the use of genome-wide association (GWAS) mapping it is possible to establish the genetic basis of phenotypic trait variation. Our GWAS study presents the first such an effort in Norway spruce (Picea abies (L). Karst.) for the traits related to wood tracheid characteristics.\n[tpltrtarr]The study employed an exome capture genotyping approach that generated 178 101 high quality Single Nucleotide Polymorphisms (SNPs) from 40 018 probes within a population of 517 Norway spruce mother trees. We applied a LASSO based association mapping method using a functional multi-locus mapping approach that utilizes latent traits, with a stability selection probability method as the hypothesis testing approach to determine significant Quantitative Trait Loci (QTLs). Expression of the identified candidate genes was examined using publicly available spruce databases.\n[tpltrtarr]The analysis have provided 31 loci and 26 mostly novel candidate genes, majority of which showing specific expression in wood-forming tissues or high ubiquitous expression, potentially controlling tracheids dimensions, their cell wall thickness and microfibril angle. Among most promising candidates, the analysis identified Picea abies BIG GRAIN 2 (PabBG2) with predicted function in auxin transport and sensitivity, and MA_373300g0010 - similar to wall-associated receptor kinases (WAKs), both associated to cell wall thickness.\n[tpltrtarr]The results demonstrate feasibility of GWAS to identify novel candidate genes controlling industrially-relevant tracheid traits in Norway spruce. The presence of many traits with several significant QTLs supports the notion that the majority of these traits are polygenic in nature.

genetics↗

bioRxiv2019

Genomic prediction accuracies and abilities for growth and wood quality traits of Scots pine, using genotyping-by-sequencing (GBS) data

Higher genetic gains can be achieved through genomic selection (GS) by shortening time of progeny testing in tree breeding programs. Genotyping-by-sequencing (GBS), combined with two imputation methods, allowed us to perform the current genomic prediction study in Scots pine (Pinus sylvestris L.). 694 individuals representing 183 full-sib families were genotyped and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic prediction models. In addition, the impact on the predictive ability (PA) and prediction accuracy to estimate genomic breeding values was evaluated by assigning different ratios of training and validation sets, as well as different subsets of SNP markers. Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed higher PAs and prediction accuracies than Bayesian LASSO (BL). A subset of approximately 4000 markers was sufficient to provide the same PAs and accuracies as the full set of 8719 markers. Furthermore, PAs were similar for both pedigree- and genomic-based estimations, whereas accuracies and heritabilities were slightly higher for pedigree-based estimations. However, prediction accuracies of genomic models were sufficient to achieve a higher selection efficiency per year, varying between 50-87% compared to the traditional pedigree-based selection.

genetics↗

bioRxiv2018

Increased prediction ability in Norway spruce trials using a marker x environment interaction and non-additive genomic selection model

A genomic selection (GS) study of growth and wood quality traits is reported based on control-pollinated Norway spruce families established in two Northern Swedish trials at two locations using exome capture as a genotyping platform. Non-additive effects including dominance and first-order epistatic interactions (including additive by additive, dominance by dominance, and additive by dominance) and marker-by-environment interaction (MxE) effects were dissected in genomic and phenotypic selection models. GS models partitioned additive and non-additive genetic variances more precisely compared with pedigree-based models. In addition, predictive ability (PA) in GS was substantially increased by including dominance and slightly increased by including MxE effects when these effects are significant. For velocity, response to GS (RGS) per year increased 91.3/43.7%, 86.9/82.9%, and 78.9/80.8% compared with response to phenotypic selection (RPS) per year when GS was based on 1) main marker effects (M), 2) M + MxE effects (A), and 3) A + dominance effects (AD) for site 1/site 2, respectively. This indicates that including MxE and dominance effects not only improves genetic parameter estimates but also may improve the genetic gain when they are significant. For tree height, Pilodyn, and modulus of elasticity (MOE), RGS per year improved up to 84.2%, 91.3%, and 92.6% compared with RPS per year, respectively.

genetics↗

bioRxiv2018

Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as genotyping platform in Norway spruce

BackgroundGenomic selection (GS) can increase genetic gain by reducing the length of breeding cycle in forest trees. Here we genotyped 1370 control-pollinated progeny trees from 128 full-sib families in Norway spruce (Picea abies (L.) Karst.), using exome capture as a genotyping platform. We used 116,765 high quality SNPs to develop genomic prediction models for tree height and wood quality traits. We assessed the impact of different genomic prediction methods, genotype-by-environment interaction (GxE), genetic composition, size of the training and validation set, relatedness, and the number of SNPs on the accuracy and predictive ability (PA) of GS.\n\nResultsUsing G matrix slightly altered heritability estimates relative to pedigree-based method. GS accuracies were about 11-14% lower than those based on pedigree-based selection. The efficiency of GS per year varied from 1.71 to 1.78, compared to that of the pedigree-based model if breeding cycle length was halved using GS. Height GS accuracy decreased more than 30% using one site as training for GS prediction to the second site, indicating that GxE for tree height should be accommodated in model fitting. Using half-sib family structure instead of full-sib led a significant reduction in GS accuracy and PA. The full-sib family structure only needed 750 makers to reach similar accuracy and PA as 100,000 markers required for half-sib family, indicating that maintaining the high relatedness in the model improves accuracy and PA. Using 4000-8000 markers in full-sib family structure was sufficient to obtain GS model accuracy and PA for tree height and wood quality traits, almost equivalent to that obtained with all makers.\n\nConclusionsThe study indicates GS would be efficient in reducing generation time of a breeding cycle in conifer tree breeding program that requires a long-term progeny testing. Sufficient number of trees within-family (16 for growth and 12 for wood quality traits) and number of SNPs (8000) are required for GS with full-sib family relationship. GS methods had little impact on GS efficiency for growth and wood quality traits. GS model should incorporate G x E effect when a strong GxE is detected.

genetics↗

bioRxiv2018

Association mapping identified novel candidate loci affecting wood formation in Norway spruce

[tpltrtarr] Norway spruce (Picea abies) is an important boreal forest tree species of significant ecological and economic importance. Hence there is a strong imperative to dissect the genetics controlling important wood quality traits in the species.\n[tpltrtarr]We performed a functional genome-wide association mapping of 17 wood traits in Norway spruce using 178101 single-nucleotide polymorphisms (SNPs) generated from exome genotyping of 517 mother trees. The wood traits were defined using functional modelling of wood properties across annual growth rings.\n[tpltrtarr]Association mapping was performed using a multilocus LASSO penalized regression method and we detected a total of 51 significant SNPs from 39 candidate genes that are involved in wood formation.\n[tpltrtarr]Our study represents the first functional multi-locus genome-wide association mapping (AM) in Norway spruce. The results advance our understanding of the genetics influencing wood traits, identify novel candidate genes for further functional studies and support current Norway spruce breeding efforts.

genetics↗

bioRxiv2018

An ultra-dense haploid genetic map for evaluating the highly fragmented genome assembly of Norway spruce (Picea abies)

Norway spruce (Picea abies (L.) Karst.) is a conifer species of substanital economic and ecological importance. In common with most conifers, the P. abies genome is very large ([~]20 Gbp) and contains a high fraction of repetitive DNA. The current P. abies genome assembly (v1.0) covers approximately 60% of the total genome size but is highly fragmented, consisting of >10 million scaffolds. The genome annotation contains 66,632 gene models that are at least partially validated (www.congenie.org), however, the fragmented nature of the assembly means that there is currently little information available on how these genes are physically distributed over the 12 P. abies chromosomes. By creating an ultra-dense genetic linkage map, we anchored and ordered scaffolds into linkage groups, which complements the fine-scale information available in assembly contigs. Our ultra-dense haploid consensus genetic map consists of 21,056 markers derived from 14,336 scaffolds that contain 17,079 gene models (25.6% of the validated gene models) that we have anchored to the 12 linkage groups. We used data from three independent component maps, as well as comparisons with previously published Picea maps to evaluate the accuracy and marker ordering of the linkage groups. We demonstrate that approximately 3.8% of the anchored scaffolds and 1.6% of the gene models covered by the consensus map have likely assembly errors as they contain genetic markers that map to different regions within or between linkage groups. We further evaluate the utility of the genetic map for the conifer research community by using an independent data set of unrelated individuals to assess genome-wide variation in genetic diversity using the genomic regions anchored to linkage groups. The results show that our map is sufficiently dense to enable detailed evolutionary analyses across the P. abies genome.

genetics↗