ABSTRACT
Potato (Solanum tuberosum) is an essential crop for food security and is ranked as the third most important crop worldwide for human consumption. The Diacol Capiro cultivar holds the dominant position in Colombian cultivation, primarily catering to the food processing industry. This highly heterozygous, autotetraploid cultivar belongs to the Andigenum group and it stands out for its adaptation to a wide variety of environments spanning altitudes from 1,800 to 3,200 meters above sea level. Here, a chromosome-scale assembly, referred to as DC, is presented for this cultivar. The assembly was generated by combining circular consensus sequencing with proximity ligation Hi-C for the scaffolding and represents 2.369 Gb with 48 pseudochromosomes covering 2,091 Gb and an anchor rate of 88.26%. The reference genome metrics, including an N50 of 50.5 Mb, a BUSCO (Benchmarking Universal Single-Copy Orthologue) score of 99.38%, and an Long Terminal Repeat Assembly Index score of 13.53, collectively signal the achieved high assembly quality. A comprehensive annotation yielded a total of 154,114 genes, and the associated BUSCO score of 95.78% for the annotated sequences attests to their completeness. The number of predicted NLR (Nucleotide-Binding and Leucine-Rich-Repeat genes) was 2107 with a large representation of NBARC (for nucleotide binding domain shared by Apaf-1, certain R gene products, and CED-4) containing domains (99.85%). Further comparative analysis of the proposed annotation-based assembly with high-quality known potato genomes, showed a similar genome metrics with differences in total gene numbers related to the ploidy status. The genome assembly and annotation of DC presented in this study represent a valuable asset for comprehending potato genetics. This resource aids in targeted breeding initiatives and contributes to the creation of enhanced, resilient, and more productive potato varieties, particularly beneficial for countries in Latin America.
ABSTRACT
Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.