There and back again; historical perspective and future directions for <i>Vaccinium</i> breeding and research studies

Horticulture Research · 2022 · 77 citations

• Biology
• Agroforestry
• Biotechnology

berry crops on topics that span taxonomy to genetics and genomics to breeding. We highlight the accomplishments made thus far for each of these crops, along their journey from the wild, and propose research areas and questions that will require investments by the community over the coming decades to guide future crop improvement efforts. New tools and resources are needed to underpin the development of superior cultivars that are not only more resilient to various environmental stresses and higher yielding, but also produce fruit that continue to meet a variety of consumer preferences, including fruit quality and health related traits.

Metabolomic selection for enhanced fruit flavor

Proceedings of the National Academy of Sciences · 2022 · 201 citations

• Computer Science
• Machine Learning
• Food science

Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of genotypes, previously limited by the low throughput of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation, and the accuracies of 18 different models were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.

Haplotype reconstruction in connected tetraploid F1 populations

Genetics · 2021 · 48 citations

• Biology
• Genetics
• Evolutionary biology

In diploid species, many multiparental populations have been developed to increase genetic diversity and quantitative trait loci (QTL) mapping resolution. In these populations, haplotype reconstruction has been used as a standard practice to increase the power of QTL detection in comparison with the marker-based association analysis. However, such software tools for polyploid species are few and limited to a single biparental F1 population. In this study, a statistical framework for haplotype reconstruction has been developed and implemented in the software PolyOrigin for connected tetraploid F1 populations with shared parents, regardless of the number of parents or mating design. Given a genetic or physical map of markers, PolyOrigin first phases parental genotypes, then refines the input marker map, and finally reconstructs offspring haplotypes. PolyOrigin can utilize single nucleotide polymorphism (SNP) data coming from arrays or from sequence-based genotyping; in the latter case, bi-allelic read counts can be used (and are preferred) as input data to minimize the influence of genotype calling errors at low depth. With extensive simulation we show that PolyOrigin is robust to the errors in the input genotypic data and marker map. It works well for various population designs with ≥30 offspring per parent and for sequences with read depth as low as 10x. PolyOrigin was further evaluated using an autotetraploid potato dataset with a 3 × 3 half-diallel mating design. In conclusion, PolyOrigin opens up exciting new possibilities for haplotype analysis in tetraploid breeding populations.

Genome‐wide association of volatiles reveals candidate loci for blueberry flavor

New Phytologist · 2020 · 109 citations

• Biology
• Computational biology
• Biotechnology

Plants produce a range of volatile organic compounds (VOCs), some of which are perceived by the human olfactory system, contributing to a myriad flavors. Despite the importance of flavor for consumer preference, most plant breeding programs have neglected it, mainly because of the costs of phenotyping and the complexity of disentangling the role of VOCs in human perception. To develop molecular breeding tools aimed at improving fruit flavor, we carried out target genotyping of and VOC extraction from a blueberry population. Metabolite genome-wide association analysis was used to elucidate the genetic architecture, while predictive models were tested to prove that VOCs can be accurately predicted using genomic information. A historical sensory panel was considered to assess how the volatiles influenced consumers. By gathering genomics, metabolomics, and the sensory panel, we demonstrated that VOCs are controlled by a few major genomic regions, some of which harbor biosynthetic enzyme-coding genes; can be accurately predicted using molecular markers; and can enhance or decrease consumers' overall liking. Here we emphasized how the understanding of the genetic basis and the role of VOCs in consumer preference can assist breeders in developing more flavorful cultivars at a more inexpensive and accelerated pace.

Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species

Frontiers in Plant Science · 2020 · 180 citations

• Machine Learning
• Artificial Intelligence
• Computer Science

Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in two important small fruits or berries: strawberry and blueberry. The two datasets contained a total of 1,358 allopolyploid strawberry (2n=8x=112) and 1,802 autopolyploid blueberry (2n=4x=48) individuals, genotyped for 9,908 and 73,045 single nucleotide polymorphism (SNP) markers, respectively, and phenotyped for five agronomic traits each. DL depends on numerous parameters that influence performance and optimizing hyperparameter values can be a critical step. Here we show that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance. In terms of genomic prediction, we did not find an advantage of DL over linear model methods, except when the epistasis component was important. Linear Bayesian models were better than convolutional neural networks for the full additive architecture, whereas the opposite was observed under strong epistasis. However, by using a parameterization capable of taking into account these non-linear effects, Bayesian linear models can match or exceed the predictive accuracy of DL. A semiautomatic implementation of the DL pipeline is available at https://github.com/lauzingaretti/deepGP/.

A Review for Southern Highbush Blueberry Alternative Production Systems

Agronomy · 2020 · 76 citations

• Business
• Economics

Southern highbush blueberry cultivation has expanded into non-traditional growing areas worldwide due to elite cultivars and improved horticultural practices. This article presents a comprehensive review of current production systems—alternatives to traditional open field production—such as production in protected environments, high-density plantings, evergreen production, and container-based production. We discuss the advantages and disadvantages of each system and compare their differences to open field production. In addition, we provide potential solutions for some of the disadvantages. We also highlight some of the gaps existing between academic studies and production in industry, providing a guide for future academic research. All these alternative systems have shown the potential to produce high yields with high-quality berries. Alternative systems, compared to field production, require higher establishment investments and thus create an entry barrier for new producers. Nevertheless, with their advantages, alternative productions have the potential to be profitable.