Biology subjects

Degen, B.

Publications and source records attributed to Degen, B..

4 recordsLinked to original sources

bioRxiv2026

From long-term progeny trials to genomic selection: empirical prediction and simulation-guided redesign of Scots pine breeding in Germany

Long breeding cycles constrain genetic gain in Scots pine, while mature progeny trials can provide reference populations for genomic selection. We combined phenotypic records from two German trials established in 1990 with dense SNP data. We complemented these data with an offspring-level parentage audit, duplicate filtering, and simulations of breeding strategies. Of 1,986 phenotype-matched genotyped trees, 1,668 formed the parentage-green set and 1,651 remained after exclusion of 17 near-duplicate samples. The final population comprised 1,521 supported controlled-cross offspring and 130 mother-known, father-unknown offspring from 87 progeny labels. Across 14 trait-by-age measurements, GBLUP gave the clearest improvements for diameter and volume from age 20 onwards, whereas height was more mixed. At age 35, ABLUP versus GBLUP heritability was 0.155 versus 0.235 for diameter, 0.265 versus 0.263 for height, and 0.172 versus 0.236 for volume. Leave-progeny-out predictive ability increased from 0.205 to 0.238, 0.249 to 0.265, and 0.203 to 0.239, respectively. SNPscan_breeder simulations compared phenotypic selection, progeny testing, cross-generation genomic selection, and genomic selection with phenotypic thinning under five diversity variants. Progeny testing produced the greatest cumulative gain, but its 45-year cycle reduced annual response. Under the base assumptions, genomic selection with phenotypic thinning gave the highest annual gains for height and fungal resistance, whereas pure genomic selection gave the highest annual diameter gain. Genomic strategies accumulated more kinship than conventional strategies, although a {lambda} = 0.10 kinship penalty improved founder retention with little loss of gain; no mitigation option simultaneously maximised gain, prediction accuracy, and diversity. These results support genomic shortlisting within a field-tested programme with explicit reference-population updating and diversity management.

genetics↗

bioRxiv2023

Genomic variation of a keystone forest tree species reveals patterns of local adaptation and future maladaptation

Local adaptation is key for ecotypic differentiation and species evolution. Understanding the underlying genomic patterns can allow the prediction of future maladaptation and ecosystem stability. Here, we report the whole-genome resequencing of 874 individuals from 100 range-wide populations of European beech (Fagus sylvatica L.), one of the most important forest tree species in Europe. We show that genetic variation closely mirrors geography with a clear pattern of isolation-by-distance. Genome-wide analyses for genotype-environment associations (GEAs) identified relatively few potentially adaptive variants after correcting for an overwhelming signal of statistically significant but non-causal GEAs. We characterized the single high confidence genomic region and pinpoint a candidate gene possibly involved in winter temperature adaptation acting by modulating spring phenology. Surprisingly, however, allelic variation at this locus did not result in any apparent fitness differences in a common garden. More generally, reciprocal transplant experiments across large climate distances demonstrated extensive phenotypic plasticity. Nevertheless, we find indications of polygenic adaptation which may be essential in natural ecosystems. This polygenic signal exhibits broad- and fine-scale variation across the landscape highlighting the relevance of spatial resolution. In summary, our results emphasize the importance but also exemplify the complexity of employing natural genetic variation for forest conservation under climate change.

plant biology↗

bioRxiv2023

Advanced marker-assisted selection versus genomic selection in breeding programs

Advances in DNA sequencing technologies allow the sequencing of whole genomes of thousands of individuals and provide several million single nucleotide polymorphisms (SNPs) per individual. These data combined with precise and high-throughput phenotyping enable genome-wide association studies (GWAS) and the identification of SNPs underlying traits with complex genetic architectures. The identified causal SNPs and estimated allelic effects could then be used for advanced marker-assisted selection (MAS) in breeding programs. But could such MAS compete with the broadly used genomic selection? This question is of particular interest for the lengthy tree breeding strategies. Here, with our new software SNPscan we simulated a simple tree breeding program and compared the impact of different selection criteria on genetic gain and inbreeding. Further, we assessed different genetic architectures and different levels of kinship among individuals of the breeding population. Interestingly, apart from progeny-testing, genomic selection (GS) using gBLUP performed best under almost all simulated scenarios. MAS based on GWAS results outperformed GS only if the allelic effects were estimated in large populations (ca. 10,000 individuals) of unrelated individuals. Notably, GWAS using 3,000 extreme phenotypes performed as good as the use of 10,000 phenotypes. Genomic selection increased inbreeding and thus reduced genetic diversity more strongly compared to progeny testing and GWAS-based selection. In conclusion, our analyses further support the potential of genomic selection for forest tree breeding and improvement although MAS may gain relevance with decreasing sequencing costs in the future.

genomics↗

bioRxiv2016

A nearest neighbour approach by genic distance to the assignment of individuals to geographic origin

During the past decade, the use of DNA for forensic applications has been extensively implemented for plant and animal species, as well as in humans. Tracing back the geographical origin of an individual usually requires genetic assignment analysis. These approaches are based on reference samples that are grouped into populations or other aggregates and intend to identify the most likely group of origin. Often this grouping does not have a biological but rather a historical or political justification, such as \"country of origin\".\n\nIn this paper, we present a new nearest neighbour approach to individual assignment or classification within a given but potentially imperfect grouping of reference samples. This method, which is based on the genic distance between individuals, functions better in many cases than commonly used methods. We demonstrate the operation of our assignment method using two data sets. One set is simulated for a large number of trees distributed in a 120 km by 120 km landscape with individual genotypes at 150 SNPs, and the other set comprises experimental data of 1221 individuals of the African tropical tree species Entandrophragma cylindricum (Sapelli) genotyped at 61 SNPs. Judging by the level of correct self-assignment, our approach outperformed the commonly used frequency and Bayesian approaches by 15% for the simulated data set and by 5 to 7% for the Sapelli data set.\n\nOur new approach is less sensitive to overlapping sources of genetic differentiation, such as genic differences among closely-related species, phylogeographic lineages and isolation by distance, and thus operates better even for suboptimal grouping of individuals.

genetics↗