Biology subjects

Ramstein, G. P.

Publications and source records attributed to Ramstein, G. P..

12 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↗

bioRxiv2026

In vivo validation of predicted fitness effects at single-base resolution in a Brachypodium distachyon mutant population

Computational tools, including biological language models (LMs), show substantial promise in predicting the impact of genetic variants on plant fitness. However, validating variant effect predictions (VEP) requires experimental populations where genetic variation consists of discrete point mutations rather than segregating recombination blocks. In this study, we generated a novel population of Brachypodium distachyon mutant lines to evaluate the accuracy of VEP at single-base resolution. These lines were advanced through single-seed descent for five generations (M1 to M5), with whole-genome sequencing performed at M2 and M5 and phenotypic measurements recorded at M3 and M4. Using state-of-the-art VEP models, we predicted the functional impact of missense protein-coding variants and gene-proximal non-coding variants. We validated these predictions by estimating the effect of mutations on whole-plant measurements (burden tests) and their probability of fixation from M2 to M5 (purging tests). Among missense variants, the protein LM ESM showed superior predictive accuracy compared to the bioinformatic standard SIFT and the genomic LM PlantCAD. Notably, the relationship between VEP scores and allele fixation suggested a log-linear relationship between VEP scores and variant fitness. Among gene-proximal variants, PlantCAD appeared more accurate than supervised models of regulatory activity, such as chromatin accessibility (a2z) and RNA abundance (PhytoExpr). Collectively, our findings highlight the utility of state-of-the-art VEP tools as predictors of fitness and demonstrate the potential of mutant populations to evaluate computational tools for precision breeding applications.

genomics↗

bioRxiv2026

Genomic language models improve cross-species gene expression prediction and accurately capture regulatory variant effects in Brachypodium mutant lines

Predicting gene expression from cis-regulatory DNA sequences at the promoter and terminator regions is a central challenge in plant genomics. This capability is also a prerequisite for assessing the effects of regulatory mutations on gene expression. Here, we developed deep learning sequence-to-expression (S2E) models that leverage context-aware sequence embeddings from the PlantCaduceus genomic language model instead of one-hot encoding of sequences, to predict gene expression across 17 plant species. To further improve predictions, we integrated chromatin accessibility data as auxiliary regulatory features. First, we evaluated our models to predict gene expression on unseen gene families via cross-validation, demonstrating our models prediction accuracy across all species outperforms PhytoExpr, the current state-of-the-art (SOTA) S2E model in plants (Pearson R=0.82 vs. R=0.74). We then validated variant effect predictions using an experimental dataset across 796 Brachypodium mutant lines, specifically designed to test predictions at single-base resolution. Our models outperformed SOTA S2E models in predicting between-gene expression differences (regression coefficient {beta}=0.78 vs. {beta}=0.57). Remarkably, they also accurately predicted the effects of single-nucleotide mutations on within-gene expression, while SOTA S2E models showed only weak associations (regression coefficient {beta}=0.38 vs. {beta}=0.08). Our results demonstrated the value of context-aware DNA sequence embeddings for predicting regulatory variant effects in plants. They also reveal a persistent accuracy gap in S2E models when moving from between-gene to allelic variation, a challenge that needs to be addressed in future S2E studies.

bioinformatics↗

bioRxiv2023

Transposable element abundance subtly contributes to lower fitness in maize

Transposable elements (TEs) have long been shown to have deleterious effects on the survival and reproduction of their host organism. As TEs are mobile DNA that jump to new positions, this deleterious cost can occur directly, by inserting into genes and regulatory sequences. Classical population genetic theory suggests copy-number dependent selection against TEs is necessary to prevent TEs from expanding so much they take over a genome. Such models have been difficult to interpret when applied to large genomes like maize, where there are hundreds of thousands of TE insertions that collectively make up 85% of the genome. Here, we use nearly 5000 inbred lines from maize mapping populations and a pan-genomic imputation approach to measure TE content. Segregating TE content gives rise to 100 Mb differences between individuals, and populations often show transgressive segregation in TE content. We use replicated phenotypes measured in hybrids across numerous years and environments to empirically measure the fitness costs of TEs. For an annual plant like maize, grain yield is not only a key agronomic phenotype, but also a direct measure of reproductive output. We find weak negative effects of TE accumulation on grain yield, nearing the limit of the efficacy of natural selection in maize. This results in a loss of one kernel ({approx}0.1% of average per-plant yield) for every additional 14 Mb of TE content. This deleterious load is enriched in TEs within 1 kilobase of genes and young TE insertions. Together, we provide rare empirical measurements of the fitness costs of TEs, and suggest that the TEs we see today in the genome have been filtered by selection against their deleterious consequences on maize fitness.

evolutionary biology↗

bioRxiv2022

Elucidating the patterns of pleiotropy and its biological relevance in maize

Pleiotropy - when a single gene controls two or more seemingly unrelated traits - has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of true biological pleiotropy across all maize phenotypes is largely unknown. Here we investigate the extent to which biological pleiotropy impacts phenotypes within maize through GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assessed the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide- and large-effect variants, leaving the prevalence of detectable pleiotropy relatively low. Author SummaryThe genetic basis of complex traits has been thought to exhibit pleiotropy, which is the notion that a single locus can control two or more unrelated traits. Widespread reports in the human disease literature show genomic signatures of pleiotropic loci across many traits. However, little is known about the prevalence and behavior of pleiotropy in maize across a large number of phenotypes. Using association mapping of common alleles in over one hundred thousand traits, we determine how pleiotropic each region was and use these quantitative scores to functionally characterize each region of the genome. Our results show little evidence that pleiotropy is a common phenomenon in maize. We observed that maize does not exhibit the same pleiotropic characteristics as human diseases in terms of prevalence, gene expression, chromatin accessibility, or sequence conservation. Rather than pervasive pleiotropy, we hypothesize that strong selection on large and wide effect loci and the need for trait independence at the gene level keep the prevalence of pleiotropy low, thus, allowing for the adaptation of maize varieties to novel environments and conditions.

plant biology↗

bioRxiv2021

Integration of GWAS and TWAS to elucidate the genetic architecture of natural variation for leaf cuticular conductance in maize

The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed. Dissecting the genetic architecture of natural variation for maize leaf cuticular conductance (gc) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we performed an integrated genome- and transcriptome-wide association study (GWAS/TWAS) to identify candidate genes putatively regulating variation in leaf gc. Of the 22 plausible candidate genes identified, five were predicted to be involved in cuticle precursor biosynthesis and export, two in cell wall modification, nine in intracellular membrane trafficking, and seven in the regulation of cuticle development. A gene encoding an INCREASED SALT TOLERANCE1-LIKE1 (ISTL1) protein putatively involved in intracellular protein and membrane trafficking was identified in GWAS and TWAS as the strongest candidate causal gene. A set of maize nested near-isogenic lines that harbor the ISTL1 genomic region from eight donor parents were evaluated for gc, confirming the association between gc and ISTL1 in a haplotype-based association analysis. The findings of this study provide novel insights into the role of regulatory variants in the development of the maize leaf cuticle, and will ultimately assist breeders to develop drought-tolerant maize for target environments. Sentence summaryWe performed an integrated GWAS/TWAS and identified 22 candidate genes putatively regulating variation in maize leaf gc. The association between gc and the strongest candidate causal gene, ISTL1, was validated with maize nested near-isogenic lines.

genetics↗

bioRxiv2021

Prediction of evolutionary constraint by genomic annotations improves prioritization of causal variants in maize

Crop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at single-site resolution. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we used genomic annotations to accurately predict nucleotide conservation across Angiosperms, as a proxy for fitness effect of mutations. Using only sequence analysis, we annotated non-synonymous mutations in 25,824 maize gene models, with information from bioinformatics (SIFT scores, GC content, transposon insertion, k-mer frequency) and deep learning (predicted effects of polymorphisms on protein representations by UniRep). Our predictions were validated by experimental information: within-species conservation, chromatin accessibility, gene expression and gene ontology enrichment. Importantly, they also improved genomic prediction for fitness-related traits (grain yield) in elite maize panels (+5% and +38% prediction accuracy within and across panels, respectively), by stringent prioritization of [≤] 1% of single-site variants (e.g., 104 sites and approximately 15deleterious alleles per haploid genome). Our results suggest that predicting nucleotide conservation across Angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach - Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC) - could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing.

genomics↗

bioRxiv2021

Haplotype Associated RNA Expression (HARE) Improves Prediction of Complex Traits in Maize

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for prediction within populations. However, it usually cannot capture the complex effects due to combination of alleles in haplotypes. Therefore, accuracy across populations has usually been low. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE, RNA expression of genes by haplotype), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism, so it would be more transferrable across different tissues and populations. We showed that HARE estimates captured one-third of the variation in gene expression and were more transferable across diverse tissues than the measured transcript expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels - a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel) - for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues whereas accuracy using HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations. Author summaryThe increasing availability of genomic data has been widely used in the prediction of many traits. However, genome wide prediction has been mostly carried out within populations and without explicit modeling of RNA or protein expression. In this study, we explored the prediction of field traits within and across populations using estimated RNA expression attributable to only the DNA sequence around a gene. We showed that the estimated RNA expression was more transferable than overall measured RNA expression. We improved prediction of field traits up to 15% using estimated gene expression as compared to observed expression or gene sequence alone. Overall, these findings indicate that structural and functional information in the gene sequence are highly transferable.

genetics↗

bioRxiv2019

A sorghum Practical Haplotype Graph facilitates genome-wide imputation and cost-effective genomic prediction

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to increase genetic gain and accelerate cultivar development. To help with data management and storage, we developed a sorghum Practical Haplotype Graph (PHG) pangenome database that stores all identified haplotypes and variant information for a given set of individuals. We developed two PHGs in sorghum, one with 24 individuals and another with 398 individuals, that reflect the diversity across genic regions of the sorghum genome. 24 founders of the Chibas sorghum breeding program were sequenced at low coverage (0.01x) and processed through the PHG to identify genome-wide variants. The PHG called SNPs with only 5.9% error at 0.01x coverage - only 3% lower than its accuracy when calling SNPs from 8x coverage sequence. Additionally, 207 progeny from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes in the progeny were imputed from the parental haplotypes available in the PHG and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from 0.57-0.73 for different traits, and are similar to prediction accuracies obtained with genotyping-by-sequencing (GBS) or markers from sequencing targeted amplicons (rhAmpSeq). This study provides a proof of concept for using a sorghum PHG to call and impute SNPs from low-coverage sequence data and also shows that the PHG can unify genotype calls from different sequencing platforms. By reducing the amount of input sequence needed, the PHG has the potential to decrease the cost of genotyping for genomic selection, making GS more feasible and facilitating larger breeding populations that can capture maximum recombination. Our results demonstrate that the PHG is a useful research and breeding tool that can maintain variant information from a diverse group of taxa, store sequence data in a condensed but readily accessible format, unify genotypes from different genotyping methods, and provide a cost-effective option for genomic selection for any species.

genomics↗

bioRxiv2019

The relevance of dominance and functional annotations to predict agronomic traits in hybrid maize

Heterosis has been key to the development of maize breeding but describing its genetic basis has been challenging. Previous studies of heterosis have shown the contribution of within-locus complementation effects (dominance) and their differential importance across genomic regions. However, they have generally considered panels of limited genetic diversity and have shown little benefit to including dominance effects for predicting genotypic value in breeding populations. This study examined within-locus complementation and enrichment of genetic effects by functional classes in maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the United States (1,106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1,640 hybrids). We assayed three agronomic traits: days to silking (DTS), plant height (PH) and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results point to mechanisms by which heterosis arises through local complementation in proximal gene regions and suggest the relevance of dominance and gene annotations for genomic prediction in maize.

genomics↗

bioRxiv2017

Extensions Of BLUP Models For Genomic Prediction In Heterogeneous Populations: Application In A Diverse Switchgrass Sample

Genomic prediction is a useful tool to accelerate genetic gain in selection using DNA marker information. However, this technology usually relies on models that are not designed to accommodate population heterogeneity, which results from differences in marker effects across genetic backgrounds. Previous studies have proposed to cope with population heterogeneity using diverse approaches: (i) either ignoring it, therefore relying on the robustness of standard approaches; (ii) reducing it, by selecting homogenous subsets of individuals in the sample; or (iii) modelling it by using interactive models. In this study we assessed all three possible approaches, applying existing and novel procedures for each of them. All procedures developed are based on deterministic optimizations, can account for heteroscedasticity, and are applicable in contexts of admixed populations. In a case study on a diverse switchgrass sample, we compared the procedures to a control where predictions rely on homogeneous subsamples. Ignoring heterogeneity was often not detrimental, and sometimes beneficial, to prediction accuracy, compared to the control. Reducing heterogeneity did not result in further increases in accuracy. However, in scenarios of limited subsample sizes, a novel procedure, which accounted for redundancy within subsamples, outperformed the existing procedure, which only considered relationships to selection candidates. Modelling heterogeneity resulted in substantial increases in accuracy, in the cases where accounting for population heterogeneity yielded a highly significant improvement in fit. Our study exemplifies advantages and limits of the various approaches that are promising in various contexts of population heterogeneity, e.g. prediction based on historical datasets or dynamic breeding.

genetics↗