Genetic diversity of Norway spruce ecotypes assessed by GBS-derived SNPs

Abstract

We investigated the genetic structure of three phenotypically distinct ecotypic groups of Norway spruce (Picea abies) belonging to three elevational classes; namely, low- (acuminata), medium- (europaea), and high-elevation (obovata) form, each represented by 150 trees. After rigorous filtering, we used 1916 Genotyping-by-Sequencing generated SNPs for analysis. Outputs from three multivariate analysis methods (Bayesian clustering algorithm implemented in STRUCTURE, Principal Component Analysis, and the Discriminant Analysis of Principal Components) indicated the presence of a distinct genetic cluster representing the high-elevation ecotypic group. Our findings bring a vital message to forestry practice affirming that artificial transfer of forest reproductive material, especially for stands under harsh climate conditions, should be considered with caution.

Revealing the Complex Relationship Among Hyperspectral Reflectance, Photosynthetic Pigments, and Growth in Norway Spruce Ecotypes

“…Low-elevation ( acuminata ) has a wide crown with long branches, while high-elevation ( obovata ) has a narrow crown with short branches pointing down. Genetic differences among the putative ecotypes were recently confirmed by Korecký et al (2021) . There are emerging pieces of evidence that some physiological traits also differ among the ecotypes, Tomášková et al (2021) found that parameters describing structural indicators of PSII in the thylakoid membrane (calculated from the OJIP test) distinguished obovata ecotype.…”

Circadian rhythm and redox homeostasis candidate genes showed association with shallow elevation in Norway spruce

“…Car e et al (2018) used genetic analysis of microsatellites and revealed that autochthonous high-elevation-type stands from the Thuringian Forest, Ore Mountains, and Harz Mountains (Germany) were not differentiated from the neighbouring allochthonous lowelevation-type stands, although differentiation was detected when comparing these populations with an old-growth relict narrow-crowned population of "Schlossbergfichte" in Thuringia. In addition, an association of candidate genes related to circadian rhythms with crown morphotypes and altitudinal adaptation in these spruce populations from Germany was described in Car e et al (2020), and more recently a shallow population genetic structure and differentiation was described between low and medium elevation morphotypes, on the one hand, and high elevation morphotypes on the other (Koreck y et al 2021). But beyond crown morphology, other adaptive phenotypic traits related to elevation, such as phenology or budburst, have not been targeted in association studies of Norway spruce.…”

Genetic diversity and structure of the 4th cycle breeding population of Chinese fir (Cunninghamia lanceolata (lamb.) hook)

“…And it was found that the highest number of SNP markers but the lowest genetic diversity value was obtained under the first set of criteria (i.e., MAF > 0.01, Call rate > 0.9), thus indicating that setting of MAF filtering criteria had a greater effect on the number of SNP markers obtained. The filtering criteria for Chinese fir SNP selection in this study were more stringent than those implemented for Picea abies (Koreckýet al, 2021), Ulmus pumila (Lyu et al, 2020), and other Chinese firs (Zheng et al, 2019). Principal component analysis where PC1 and PC2 represent the first and second principal components, respectively.…”

“…The RAD-seq simplified sequencing technique is developed to generate a wider range of SNP markers. It is a cost-effective genotyping technique that detects variant information on a genome-wide scale, but the quality of the obtained SNPs is usually variable and the lack of stringent filtering can seriously affect subsequent analyses (Koreckýet al, 2021). The initial 27,283,139 SNP markers obtained after the reference genome alignment, and implementation of strict filtering criteria helped obtaining highquality SNP markers and finally only 1.26% of SNPs were retained as high-quality SNP markers.…”

“…Genetic diversity parameters obtained from analysis using SNP markers are generally lower than those calculated using traditional molecular markers, like SSR, ISSR, and SRAP (Chen et al, 2017;Duan et al, 2017;Li et al, 2017;Garcıá et al, 2018;Lin et al, 2020), which are also similar in other plants (Van Inghelandt et al, 2010;Avican and Bilgen, 2022). Molecular markers can also impact the results of the experiment, as different molecular markers introduce bias in the genetic diversity analysis results for the same or different populations (Bıńováet al, 2020;Koreckýet al, 2021).…”