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αβ T cell receptor (TCR) V(D)J genes code for billions of TCR combinations. However, only some appear on peripheral T cells in any individual because, to mature, thymocytes must react with low affinity but not high affinity with thymus expressed major histocompatibility (MHC)/peptides. MHC proteins are very polymorphic. Different alleles bind different peptides. Therefore, any individual might express many different MHC alleles to ensure that some peptides from an invader are bound to MHC and activate T cells. However, most individuals express limited numbers of MHC alleles. To explore this, we compared the TCR repertoires of naïve CD4 T cells in mice expressing one or two MHC alleles. Unexpectedly, the TCRs in heterozygotes were less diverse that those in the sum of their MHC homozygous relatives. Our results suggest that thymus negative selection cancels out the advantages of increased thymic positive selection in the MHC heterozygotes. Editor's summary: T cells undergo positive and negative selection in the thymus through T cell receptor (TCR) recognition of peptides presented on major histocompatibility (MHC) proteins. Although the genes encoding MHC proteins are among the most polymorphic, most vertebrates individually express relatively few MHC alleles. How the number of MHC alleles expressed affects thymic selection and the TCR repertoire on an individual level remains unclear. Using mice expressing different MHC loci on the same background, Brown et al. compared the TCR repertoires of naïve CD4 T cells from MHC homozygous and heterozygous mice. MHC heterozygotes expressed a less diverse TCR repertoire than expected compared with their MHC homozygous relatives, likely because of increased rates of negative selection. These findings suggest that the number of MHC alleles expressed in an individual animal has been evolutionarily optimized to minimize "holes" in the TCR repertoire. —Claire Olingy [ABSTRACT FROM AUTHOR]

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 initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years. The competition attracted registrants from around the world with representation from academic, government, industry, and nonprofit institutions as well as unaffiliated. These participants came from diverse disciplines, including 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 2 models combining machine learning and traditional breeding tools: 1 model emphasized environment using features extracted by random forest, ridge regression, and least squares, and 1 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., Competing Interests: Conflicts of interest: The authors declare no conflicts of interest., (Published by Oxford University Press on behalf of The Genetics Society of America 2024.)

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, classical 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., Competing Interests: Conflict of Interest The authors declare no conflict of interest.