Understanding the impact of domestication on deleterious mutations has fascinated evolutionary biologists and breeders alike. A “cost of domestication” has been reported for some organisms through accumulation of gene disruptions or radical amino acid changes. However, recent evidence paints a more complex picture of this phenomenon in different domesticated species. In this study, we used genomic sequences of 253 tomato accessions to investigate the evolution of deleterious mutations and genomic structural variants (SVs) through tomato domestication history. We apply phylogeny-based methods to identify deleterious mutations in the cultivated tomato as well as its semi-wild and wild relatives. Our results implicate a downward trend throughout domestication in the number of genetic variants, regardless of their functional impact. This suggests that demographic factors have reduced overall genetic diversity, leading to lower deleterious load and SVs as well as loss of some beneficial alleles during tomato domestication. However, we detected an increase in proportions of nonsynonymous and deleterious alleles (relative to synonymous and neutral nonsynonymous alleles, respectively) during the initial stage of tomato domestication in Ecuador. Additionally, deleterious alleles in fully cultivated tomato seem to be more frequent than expected under a neutral hypothesis of molecular evolution. Our analyses also revealed frequent deleterious alleles in several well-studied tomato genes, probably involved in response to biotic and abiotic stress as well as fruit development and flavor regulation. To provide a practical guide for breeding experiments, we created TomDel, a public searchable database of 21,162 deleterious alleles identified in this study (hosted on the Solanaceae Genomic Network; https://solgenomics.net/).

To accelerate plant breeding genetic gain, spatial heterogeneity must be considered. Previously, design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments. This study proposes a two-stage approach for improving agronomic trait genomic prediction (GP) using high-throughput phenotyping (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index (NDVI) is measured using a multi-spectral MicaSense camera and ImageBreed. The first stage separates additive genetic effects from local environmental effects (LEE) present in the NDVI throughout the growing season. Considered NDVI LEE (NLEE) are spatial effects from univariate/multivariate two-dimensional splines (2DSpl) and separable autoregressive (AR1) models, as well as permanent environment (PE) effects from random regression models (RR). The second stage leverages the NLEE within genomic best linear unbiased prediction (GBLUP) in two distinct implementations, either modelling an empirical plot-to-plot covariance (L) for random effects or modelling fixed effects (FE). Testing on Genomes-to-Fields (G2F) hybrid maize (Zea mays) field experiments in 2017, 2019, and 2020 for grain yield (GY), grain moisture (GM), and ear height (EH) improves heritability and model fit equally-or-greater than spatial corrections; however, genotypic effect estimation across replicates is not significantly improved. Electrical conductance (EC), elevation, and curvature from a 2019 soil survey significantly improve GP model fit, but less than NLEE. Soil EC and curvature are most correlated to univariate 2DSpl NLEE. Defining L significantly improves genomic heritability and model fit more than setting FE, and RR NLEE can most significantly improve GP for GY and GM.