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Abstract Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner’s strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams’ methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition. 

Abstract Host-specific interactions can maintain genetic and phenotypic diversity in parasites that attack multiple host species. Host diversity, in turn, may promote parasite diversity by selection for genetic divergence or plastic responses to host type. The parasitic weed purple witchweed [ Striga hermonthica (Delile) Benth.] causes devastating crop losses in sub-Saharan Africa and is capable of infesting a wide range of grass hosts. Despite some evidence for host adaptation and host-by- Striga genotype interactions, little is known about intraspecific Striga genomic diversity. Here we present a study of transcriptomic diversity in populations of S. hermonthica growing on different hosts (maize [ Zea mays L.] vs. grain sorghum [ Sorghum bicolor (L.) Moench]). We examined gene expression variation and differences in allelic frequency in expressed genes of aboveground tissues from populations in western Nigeria parasitizing each host. Despite low levels of host-based genome-wide differentiation, we identified a set of parasite transcripts specifically associated with each host. Parasite genes in several different functional categories implicated as important in host–parasite interactions differed in expression level and allele on different hosts, including genes involved in nutrient transport, defense and pathogenesis, and plant hormone response. Overall, we provide a set of candidate transcripts that demonstrate host-specific interactions in vegetative tissues of the emerged parasite S. hermonthica . Our study shows how signals of host-specific processes can be detected aboveground, expanding the focus of host–parasite interactions beyond the haustorial connection. 

Abstract A long‐standing question in biology is how organisms change through time and space in response to their environment. This knowledge is of particular relevance to predicting how organisms might respond to future environmental changes caused by human‐induced global change. Usually researchers make inferences about past events based on an understanding of current static genetic patterns, but these are limited in their capacity to inform on underlying past processes. Natural history collections (NHCs) represent a unique and critical source of information to provide temporally deep and spatially broad time‐series of samples. By using NHC samples, researchers can directly observe genetic changes over time and space and link those changes with specific ecological/evolutionary events. Until recently, such genetic studies were hindered by the intrinsic challenges of NHC samples (i.e. low yield of highly fragmented DNA). However, recent methodological and technological developments have revolutionized the possibilities in the novel field of NHC genomics. In this Special Feature, we compile a range of studies spanning from methodological aspects to particular case studies which demonstrate the enormous potential of NHC samples for accessing large genomic data sets from the past to advance our knowledge on how populations and species respond to global change at multiple spatial–temporal scales. We also highlight possible limitations, recommendations and a few opportunities for future researchers aiming to study NHC genomics.