Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding

Assortative Mating between European Corn Borer Pheromone Races: Beyond Assortative Meeting

BACKGROUND: Sex pheromone communication systems may be a major force driving moth speciation by causing behavioral reproductive isolation via assortative meeting of conspecific individuals. The 'E' and 'Z' pheromone races of the European corn borer (ECB) are a textbook example in this respect. 'Z' females produce and 'Z' males preferentially respond to a 'Z' pheromone blend, while the 'E' race communicates via an 'E' blend. Both races do not freely hybridize in nature and their populations are genetically differentiated. A straightforward explanation would be that their reproductive isolation is a mere consequence of "assortative meeting" resulting from their different pheromones specifically attracting males towards same-race females at long range. However, previous laboratory experiments and those performed here show that even when moths are paired in a small box - i.e., when the meeting between sexual partners is forced - inter-race couples still have a lower mating success than intra-race ones. Hence, either the difference in attractivity of E vs. Z pheromones for males of either race still holds at short distance or the reproductive isolation between E and Z moths may not only be favoured by assortative meeting, but must also result from an additional mechanism ensuring significant assortative mating at close range. Here, we test whether this close-range mechanism is linked to the E/Z female sex pheromone communication system. METHODOLOGY/PRINCIPAL FINDINGS: Using crosses and backcrosses of E and Z strains, we found no difference in mating success between full-sisters emitting different sex pheromones. Conversely, the mating success of females with identical pheromone types but different coefficients of relatedness to the two parental strains was significantly different, and was higher when their genetic background was closer to that of their male partner's pheromone race. CONCLUSIONS/SIGNIFICANCE: We conclude that the close-range mechanism ensuring assortative mating between the E and Z ECB pheromone races is unrelated to the difference in female sex pheromone. Although the nature of this mechanism remains elusive, our results show that it is expressed in females, acts at close range, segregates independently of the autosome carrying Pher and of both sex chromosomes, and is widely distributed since it occurs both in France and in the US

Metagenomics - a guide from sampling to data analysis

Metagenomics applies a suite of genomic technologies and bioinformatics tools to directly access the genetic content of entire communities of organisms. The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now. With the growing numbers of activities also comes a plethora of methodological knowledge and expertise that should guide future developments in the field. This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing. As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared