New genetic markers for 100% arabica coffee demonstrate high discriminatory potential for InDel-HRM-based coffee authentication.

Food authenticity is crucial in today's society, given the heightened consumer awareness and attention to the products they consume. Reliable and efficient techniques are needed to quickly detect potential food adulterations that can negatively impact product quality and economic value. Coffee, a globally traded agricultural product, holds immense economic importance, with an estimated value of USD 83 billion. It is widely consumed and recognized as a functional food that provides minerals (K, Mg, Mn, Cr), niacin, and antioxidants. However, the preferred coffee species, Coffea arabica, known for its superior drink quality, is often adulterated with Coffea canephora (Robusta and Conilon) beans, even in 100% Arabica coffee. To distinguish between these two coffee species, a comprehensive study was conducted using a robust approach to identify differences in Single-Ortholog Copy (SOC) based on InDel regions in these gene pairs. These differences were validated using a meticulous methodology that considered variations in amplicon size: electrophoretic profile, and high-resolution melting (HRM). The innovative combination of InDels and HRM resulted in highly distinctive HRM profiles, outperforming SNP-based methods previously used. The targeted InDel approach utilized in this study facilitated precise quantification of Coffea species beans with a detection sensitivity of 0.5%. The study's findings establish the reliability and accuracy in distinguishing between the two coffee species, showcasing the valuable application of InDels for quality control and ensuring the authenticity of coffee beans. This pioneering research contributes to the advancement of authenticity verification methods for both imported and exported coffee beans, as well as in future studies that require significant genetic differences between these species, such as C. arabica and C. canephora.

Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.

Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions about the relationships between inputs and the output allowing great flexibility to handle different types of complex non-additive effects, such as dominance and epistasis. Despite this advantage, the biological interpretability of ANNs is still limited. The aim of this research was to estimate the heritability and markers effects for two traits in Coffea canephora using an additive-dominance architecture ANN and to compare it with genomic best linear unbiased prediction (GBLUP). The data used consists of 51 clones of C. canephora varietal Conilon, 32 of varietal group Robusta and 82 intervarietal hybrids. From this, 165 phenotyped individuals were genotyped for 14,387 SNPs. Due to the high computational cost of ANNs, we used Bagging decision tree to reduce the dimensionality of the data, selecting the markers that accumulated 70% of the total importance. An ANN with three hidden layers was run, each varying from 1 to 40 neurons summing 64,000 neural networks. The network architectures with the best predictive ability were selected. The best architectures were composed by 4, 15, and 33 neurons in the first, second and third hidden layers, respectively, for yield, and by 13, 20, and 24 neurons, respectively for rust resistance. The predictive ability was greater when using ANN with three hidden layers than using one hidden layer and GBLUP, with 0.72 and 0.88 for yield and coffee leaf rust resistance, respectively. The concordance rate (CR) of the 10% larger markers effects among the methods varied between 10% and 13.8%, for additive effects and between 5.4% and 11.9% for dominance effects. The narrow-sense ([Formula: see text]) and dominance-only ([Formula: see text]) heritability estimates were 0.25 and 0.06, respectively, for yield, and 0.67 and 0.03, respectively for rust resistance. The ANN was able to estimate the heritabilities from an additive-dominance genomic architectures and the ANN with three hidden layers obtained best predictive ability when compared with those obtained from GBLUP and ANN with one hidden layer.