Biology subjects

Neyhart, J.

Publications and source records attributed to Neyhart, J..

4 recordsLinked to original sources

bioRxiv2025

Dissecting Genotype-Environment interactions with functional implications for parental selection in Cannabis Breeding

As climate variability continues to impact agricultural systems, identifying genetic factors that contribute to environmental adaptation will be essential for optimizing breeding strategies for the development of climate resilient varieties. Through human cultivation and naturalization, Cannabis sativa has dispersed globally, adapting to a range of environmental conditions across various climates and latitudes. We combined raw data from multiple public sources to conduct an Environmental Genomic Selection (EGS) analysis on 149 Cannabis sativa samples, to assess how different populations of Cannabis relate to their environmental conditions. Exploring Genomic Estimated Adaptive Values (GEAVs) across bioclimatic variables can facilitate the selection of parental material adapted for a specific condition. We further explore potential mechanisms of local adaptation by characterizing the individual marker effects which underlie these GEAV scores. To facilitate interpretation, we used previously described genetic groupings (Basal, Hemp-type, Drug-type feral, Drug-type). Distinct patterns emerged across population groups with the drug-type (Type I) group showing consistently narrow GEAV ranges, whereas the drug-type feral group showed a broader distribution, often having high GEAVs for precipitation variables. A key climate variable difference was seen in monthly average values, revealing a seasonal response to precipitation in drug-type feral samples. Exploring marker-effect differences between seasonal GEAVs indicated a response to seasonal precipitation in drug-type feral samples. As this samples are sourced from geographic regions that have seasonal monsoons, they may have traits conferring flood tolerance (water logging) that could be introgressed into other backgrounds. The basal group also exhibited broad GEAV ranges across several bioclimatic traits, indicating they may be a valuable genetic resource for introgression to enhance environmental resilience. These findings underscore the importance of incorporating diverse germplasm into breeding programs to improve Cannabis resilience to changing environmental conditions. EGS provides a fast method to enable climate-conscious parental selection while gaining mechanistic information. Ultimately, we hope that such a strategy could support the development of climate-resilient Cannabis varieties tailored to both current and future environmental challenges.

plant biology↗

bioRxiv2024

Back to the Future: Environmental genomic selection to take advantage of polygenic local adaptation

The genetic basis of adaptation is a fundamental question in evolutionary genetics. Environmental association analysis (EAA) and various allele frequency comparisons in genomic environmental association (GEA) have become standard approaches for investigating the genetic basis of adaptation to natural environments. While these analyses provide insight into local adaptation, they have not been widely adopted in breeding or conservation programs. This may be attributable to the difficulty in identifying the best individuals for transplantation/relocation in conservation efforts or identification of the best parents in breeding programs. To explore the use of EAA and GEA for future breeding programs, we used a cereal crop - barley (Hordeum vulgare L.) as our case-study species due to its wide adaptability to different environments and agro-ecologies, ranging from marginal and low input fields to high-productive farms. Here, we use publicly available data to conduct environmental genomic selection (EGS) on 753 landrace barley accessions using a mini-core of 31 landrace accessions and a de-novo core of 100 as the training populations. Environmental genomic selection is to environmental association analysis (EAA) what genomic selection is to genome-wide association studies (GWAS). Since local adaptation to the environment is polygenic, a whole-genome approach is likely to be more accurate for selecting for environmental adaptation. Here we show distinct genetic background and population differences and how an integrative approach coupling environmental genomic selection and species distribution modelling can help identify key parents for breeding for adaptation to specific environmental variables and geographies to minimize linkage drag.

plant biology↗

bioRxiv2024

Climate resilience conserved in global germplasm repositories: Picking the most promising parents for agile plant breeding

Crop diversity is an essential resource for national and international breeding programs aimed at preparing global agriculture for a changing climate to ensure global food security. To do this there are related risks that need to be evaluated (1) does the genetic diversity needed for climate adaptation exist somewhere? And (2) is such genetic diversity accessible? To evaluate these risks, we consider the test case of publicly available genotyped and georeferenced sorghum landraces (n = 1,937) to ask if diversity is sufficient to support breeding for climate change adaptation. Answering these questions allows for characterization of the best potential parents and the geographies that harbor the most potentially promising genetypes for crop improvement. We subset this data into national, regional, and global geographic regions, and complete/mini core collections to understand the potential for climate adaptation in regional germplasm. Study accessions were given a future climate resilience score based on future climatic projections and a genomic adaptive capacity score using genomic estimated adaptive values (GEAVs) generated from environmental genomic selection - EGS) to ask whether this accessible diversity stored in germplasm repositories is potentially sufficient to meet forecasted changes in growing environments under climate change. We find that genomic resilience capacity is highly variable among countries and regions. High geographical variability was also found for climate resilience. To equitably adapt agriculture to future climate conditions, increased accessibility to plant genetic resources is essential.

genomics↗

bioRxiv2019

Multi-Trait Improvement by Predicting Genetic Correlations in Breeding Crosses

The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations - an important breeding step - that will deliver more favorable genetic correlations (rG). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e. pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11-27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.

genetics↗