Biology subjects

Johansen, N. H.

Publications and source records attributed to Johansen, N. H..

3 recordsLinked to original sources

bioRxiv2026

Temporal changes in allele frequency facilitate detection of adaptive variants in winter wheat (Triticum aestivum L.) breeding programs

In quantitative genetics, candidate SNPs are identified through genotype-phenotype associations inferred with genome-wide association studies (GWAS). In this study, we explore an alternative approach to detect genetic variants with non-neutral effects by tracking temporal trends in allele frequency in a winter wheat (Triticum aestivum L.) breeding population over an eight-year period, from which signals of selection may be inferred. Selection signatures were inferred with a generalized linear model, where we modeled trends in allele frequency as a function of time (crossing year). These signatures of selection were used to prioritize variants. Associations between phenotypic performance and individual load of prioritized variants were then investigated. Furthermore, we assessed whether incorporating selection information into a genomic best linear unbiased prediction (GBLUP) model improves model performance in terms of quality of fit and prediction ability. Our findings indicate that the inferred signals of selection are effective in identifying non-neutral variants. Variants under strong negative selection were associated with a decrease in protein content adjusted for grain yield (p-value < 0.01), while genetic variants that had been under moderate to high levels of positive selection were associated with increased grain yield (p-value < 0.01). However, incorporating selection information did not improve prediction accuracy. In conclusion, temporal trends in allele frequency can be used to detect non-neutral variants. The proposed approach may hence complement traditional quantitative genetic methods for detecting non-neutral genetic variation. This approach may allow breeders to detect non-neutral variants earlier in the breeding cycle, without resorting to phenotypic data.

genetics↗

bioRxiv2026

Evolutionary-scale protein language models uncover beneficial variants in a Sorghum bicolor diversity panel

Quantitative genetic approaches such as genome-wide association studies and genomic prediction are widely used to identify favourable genetic variation, but they have limited resolution due to linkage disequilibrium. Comparative genomics approaches, especially Protein Language Models (PLMs), have emerged as powerful alternatives, by detecting phylogenetic residue conservation (PRC) across evolutionary time scales. However, the extent to which these tools can guide the detection of impactful variants for field agronomic traits is still unclear. In this study, we used the pre-trained PLM ESM2 to predict PRC scores of nonsynonymous mutations segregating within a diverse panel of 387 accessions in sorghum (SAP). The distribution of fitness effects (DFE) of the same set of nonsynonymous mutations was inferred using unfolded site frequency spectra to assess whether the DFE distribution covaried with PRC scores. Furthermore, we estimated the load of putatively nonneutral mutations of SAP accessions and evaluated associations between this mutation load and phenotypic performance across multiple agronomic traits. Our results show that ESM2 can detect mutations associated with fitness-enhancing effects in SAP, as indicated by enrichments in positive selection signatures among the variants with positive PRC scores. Significant associations were also detected between phenotypic performance and mutation load for several agronomic traits, indicating that PLMs can identify functionally important genetic variation. However, these signals were not consistent across all traits in the SAP population. Altogether, our findings suggest that large language models may support breeding efforts, as PLM predictions covaried with fitness effects and captured agronomic performance for some traits in plant populations.

genetics↗

bioRxiv2025

Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution

BackgroundIn breeding the aim is to identify and accumulate beneficial variants. However, detection of these variants may be challenging in the presence of extensive genotype x environment interactions (GxE), as variant effects will be conditional on environment. MethodsThe study assesses the performance of 264 diploid perennial ryegrass accessions in a multi-environment field trial. We investigate the extent of GxE, for yield (total dry matter) and persistence traits, i.e. winter kill and spring cover, under environmental conditions experienced in Nordic and Baltic regions at the limit of the species distribution. Two different approaches to modelling GxE were tested: reaction norm and envirotyping, and the models were validated under three different breeding scenarios. ResultsOur analysis documented the presence of significant GxE interaction for all traits investigated in the study. Validation showed improvements in prediction accuracy when accounting for GxE: up to 4% for yield when predicting in unobserved environments, and up to 22% and 9% for spring cover and winter kill, respectively when predicting unobserved germplasm. Genome-wide-association-studies (GWAS) were utilized to detect genetic variants with marginal effects (environment-independent effect) and conditional effects (environment-dependent effects). Results showed the presence of large-effect genetic variants with marginal effects, in addition to few QTL whose effects were adaptive under specific environmental conditions while neutral or deleterious under different environmental conditions. ConclusionThis study demonstrates the usefulness and limitations of genomic prediction models for predicting GxE in highly diverse samples, and describes the extent of GxE interactions at the limit of species distribution for perennial ryegrass. Finally, our study points toward adaptive variation, which may enhance persistence of perennial ryegrass populations in Nordic and Baltic growing conditions.

genetics↗