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Severe drought conditions and extreme weather events are increasing worldwide with climate change, threatening the persistence of native plant communities and ecosystems. Many studies have investigated the genomic basis of plant responses to drought. However, the extent of this research throughout the plant kingdom is unclear, particularly among species critical for the sustainability of natural ecosystems. This study aimed to broaden our understanding of genome-to-phenome (G2P) connections in drought-stressed plants and identify focal taxa for future research. Bioinformatics pipelines were developed to mine and link information from databases and abstracts from 7730 publications. This approach identified 1634 genes involved in drought responses among 497 plant taxa. Most (83.30%) of these species have been classified for human use, and most G2P interactions have been described within model organisms or crop species. Our analysis identifies several gaps in G2P research literature and database connectivity, with 21% of abstracts being linked to gene and taxonomy data in NCBI. Abstract text mining was more successful at identifying potential G2P pathways, with 34% of abstracts containing gene, taxa, and phenotype information. Expanding G2P studies to include non-model plants, especially those that are adapted to drought stress, will help advance our understanding of drought responsive G2P pathways. 

There is a gap in the conceptual framework linking genes to phenotypes (G2P) for non-model organisms, as most non-model organisms do not yet have genomic resources readily available. To address this, researchers often perform literature reviews to understand G2P linkages by curating a list of likely gene candidates, hinging upon other studies already conducted in closely related systems. Sifting through hundreds to thousands of articles is a cumbersome task that slows down the scientific process and may introduce bias into a study. To fill this gap, we created G2PMineR, a free and open source literature mining tool developed specifically for G2P research. This R package uses automation to make the G2P review process efficient and unbiased, while also generating hypothesized associations between genes and phenotypes within a taxonomical framework. We applied the package to a literature review for drought-tolerance in plants. The analysis provides biologically meaningful results within the known framework of drought tolerance in plants. Overall, the package is useful for conducting literature reviews for genome to phenome projects, and also has broad appeal to scientists investigating a wide range of study systems as it can conduct analyses under the auspices of three different kingdoms (Plantae, Animalia, and Fungi). 

Researchers have long debated which estimator of relatedness best captures the degree of relationship between two individuals. In the genomics era, this debate continues, with relatedness estimates being sensitive to the methods used to generate markers, marker quality, and levels of diversity in sampled individuals. Here, we compare six commonly used genome‐based relatedness estimators (kinship genetic distance [KGD], Wang maximum likelihood [TrioML], Queller and Goodnight [R_xy], Kinship INference for Genome‐wide association studies [KING‐robust), and pairwise relatedness [R_AB], allele‐sharing coancestry [AS]) across five species bred in captivity–including three birds and two mammals–with varying degrees of reliable pedigree data, using reduced‐representation and whole genome resequencing data. Genome‐based relatedness estimates varied widely across estimators, sequencing methods, and species, yet the most consistent results for known first order relationships were found usingR_xy,R_AB, and AS. However, AS was found to be less consistently correlated with known pedigree relatedness than eitherR_xyorR_AB. Our combined results indicate there is not a single genome‐based estimator that is ideal across different species and data types. To determine the most appropriate genome‐based relatedness estimator for each new data set, we recommend assessing the relative: (1) correlation of candidate estimators with known relationships in the pedigree and (2) precision of candidate estimators with known first‐order relationships. These recommendations are broadly applicable to conservation breeding programmes, particularly where genome‐based estimates of relatedness can complement and complete poorly pedigreed populations. Given a growing interest in the application of wild pedigrees, our results are also applicable to in situ wildlife management.

 

Climate change presents distinct ecological and physiological challenges to plants as extreme climate events become more common. Understanding how species have adapted to drought, especially ecologically important nonmodel organisms, will be crucial to elucidate potential biological pathways for drought adaptation and inform conservation strategies. To aid in genome‐to‐phenome research, a draft genome was assembled for a diploid individual ofArtemisia tridentatasubsp.tridentata, a threatened keystone shrub in western North America. While this taxon has few genetic resources available and genetic/genomics work has proven difficult due to genetic heterozygosity in the past, a draft genome was successfully assembled. Aquaporin (AQP) genes and their promoter sequences were mined from the draft genome to predict mechanisms regulating gene expression and generate hypotheses on key genes underpinning drought response. Fifty‐one AQP genes were fully assembled within the draft genome. Promoter and phylogenetic analyses revealed putative duplicates ofA. tridentatasubsp.tridentataAQPs which have experienced differentiation in promoter elements, potentially supporting novel biological pathways. Comparison with nondrought‐tolerant congener supports enrichments of AQP genes in this taxon during adaptation to drought stress. Differentiation of promoter elements revealed that paralogues of some genes have evolved to function in different pathways, highlighting these genes as potential candidates for future research and providing critical hypotheses for future genome‐to‐phenome work.

 

Plant communities are composed of complex phenotypes that not only differ among taxonomic groups and habitats but also change over time within a species. Restoration projects (e.g. translocations and reseeding) can introduce new functional variation in plants, which further diversifies phenotypes and complicates our ability to identify locally adaptive phenotypes for future restoration. Near‐infrared spectroscopy (NIRS) offers one approach to detect the chemical phenotypes that differentiate plant species, populations, and phenological states of individual plants over time. We use sagebrush (Artemisiaspp.) as a case study to test the accuracy by which NIRS can classify variation within taxonomy and phenology of a plant that is extensively managed and restored. Our results demonstrated that NIRS can accurately classify species of sagebrush within a study site (75–96%), populations of sagebrush within a subspecies (99%), annual phenology within a population (>99%), and seasonal phenology within individual plants (>97%). Low classification accuracy by NIRS in some sites may reflect heterogeneity associated with natural hybridization, translocation of nonlocal seed sources from past restoration, or complex gene‐by‐environment interactions. Advances in our ability to detect and interpret spectral signals from plants may improve both the selection of seed sources for targeted conservation and the capacity to monitor long‐term changes in vegetation.