Building a better plant - Increasing nitrogen efficiency of tropical maize

Research Expertise & Core Skills

Applied Crop Breeding (experience in Potato, Maize, and Wheat)
Biometrics, Quantitative Genetics & Statistical Genetics
Experimental Design & Analysis of Breeding Trials (Mixed, Multivariate, Spatial, and G×E models)
Predictive Breeding (Genomic Selection Modelling & Hybrid Prediction)
High-throughput Plant Phenotyping (Image Data Integration in Genomic Models)
Envirotyping analytics (Environmental Covariables Integration for Improving Breeding Efficiency)
Marker-trait and gene-trait discovery (QTL mapping, GWAS, Gene-based models)

About me

Danilo is a crop quantitative geneticist, statistical geneticist, and biometrician. He holds an M.S. and Ph.D. degree in Genetics and Plant Breeding having an extensive background in potato, maize, and wheat applied breeding, genetics, and genomics. Danilo has a research background in classical field-based breeding activities, and experience in modern statistical genomics approaches where he focuses on understanding the genetic architecture of quantitative traits for accelerating the discovery and deployment of genes and alleles of high value for breeding. Briefly, his expertise is Statistics, Biometrics, Quantitative Genetics, Population Genomics, Association Studies, Genomic Selection, High-throughput Phenomics, and Applied Plant Breeding. He received professional training at Iowa State University (Jianming Yu) and CIMMYT-Mexico (Jose Crossa). Previously, he looked at several genomic selection approaches (i) including multi-trait models (Lyra et al. 2017), (ii) controlling population structure (Lyra et al. 2018), and (iii) incorporating dominance and copy number variation effects (Lyra et al. 2019) in maize hybrids. He has worked as a research scientist (2018-2021) at Rothamsted Research with extensive collaborations in the Designing Future Wheat programme/UK. He was leading many projects in the Quantitative Genetics group such as (i) combining high-throughput plant phenotyping and molecular genetics for trait dissection (Lyra et al. 2020 & Virlet et al. 2022), (ii) gene-based mapping of trehalose biosynthetic pathway genes for source- and sink-related traits in a Biomass Panel (HIBAP) – in collaboration with Paul/RRes and Reynolds/CIMMYT (Lyra et al. 2021), (iii) development of a quantitative genetics pipeline for the wheat TILLING population – in collaboration with Andy Phillips/RRes and Cristobal Uauy/JIC, (iv) integration of genomic and metagenomic data (e.g. rhizosphere microbiomes) for trait dissection (BBSCR pump-priming grant). Currently, he has been working on developing, implementing, and supporting data analysis tasks and tools at KWS SAAT SE & Co. KGaA for oilseed rape (OSR) breeding program in the BTA department.

Bachelor’s degree (2008-2011)

I have built my short career since the early days of my undergraduate course working with plant genetic resources and pre-breeding of medicinal plants in the Brazilian semi-arid region. I also worked with on-farm conservation and participatory plant breeding (PPB). My key achievement was publishing papers as author and in collaboration with colleagues (Lyra et al. 2011a; 2011b; 2012; 2014; Brasileiro et al. 2013; Castro et al. 2011). Briefly, we were pioneers in studying the phenology and reproductive biology (pollen and seed analysis) of endemic species, as well as the local agrobiodiversity.

Master’s degree (2012-2013)

In two years of training, my research goal was to design new strategies (i.e. conventional methods) of potato breeding for heat stress aiming at developing cultivars adapted to the Brazilian growing conditions (Lyra et al. 2015; Figueiredo et al. 2015). Also, I was engaged in many other projects, such as the marker-assisted selection for disease resistance (e.g. X, Y, and Leafroll viruses) (Carneiro et al. 2017; Guedes et al. 2016). Briefly, my main training activities were to conduct field testing (i.e. multi-environment trials), select and cross genotypes in cycles of recurrent selection, characterize the germplasm bank, and evaluate disease resistance in the glasshouse and field.

Doctoral degree (2014-2017)

Advances in genomics are providing breeders with new tools that will allow a great leap forward in plant breeding. Thus, I extensively applied in my Ph.D. a methodology called genomic selection, which uses all molecular markers to predict the breeding values of the selection candidates, increasing genetics gains and reducing selection time (Crossa et al. 2017). Thus, my research goals were to apply genomic prediction (i) including multi-trait models (Lyra et al. 2017), (ii) controlling population structure (Lyra et al. 2018), and (iii) incorporating dominance and copy number variation effects (Lyra et al. 2019) in maize hybrids evaluated under contrasting nitrogen conditions. My published studies showed that applying multiple-trait predictions using selection indices (i.e. consider the performance under optimal and stress conditions) is a promising strategy (see educational video, link). Moreover, factors such as dominance, structural variants, and population structure can influence the accuracy of estimates of genomic breeding values. Over my Ph.D. I received professional training in Jianming Yu’s (Iowa State University, United States) and Jose Crossa’s (CIMMYT, Mexico) research groups. In the first collaboration, I was engaged in evaluating sorghum and maize field trials while developing approaches for controlling population structure in prediction (Lyra et al. 2018). In the second collaboration, I received a short training at the Biometrics Group, studying the effects of copy variants in the phenotypic variation of complex traits in maize (Lyra et al. 2019). I also collaborated with several colleagues in the Allogamous Plant Breeding Laboratory. We studied the impact of missing phenotypic data (Galli et al. 2018), marker selection (Sousa et al. 2019), Bayesian approaches (Alves et al. 2019), and uses of public databases (Moraes et al. 2020) on genomic prediction in maize. In addition, I contributed in analysing the association mapping (GWAS) data for traits related to nitrogen use efficiency (Morosini et al. 2017), and the interaction between Azospirillum brasilense and tropical maize genotypes (Vidotti et al. 2019). **See Publications for more details.

Research Scientist ● Rothamsted Research, UK (2018-2020)

As a quantitative geneticist, I was engaged in many projects as leading investigator. I was part of the collaboration with the Designing Future Wheat (DFW) programme. Briefly, I had four main projects, including (i) developing robust statistical and prediction strategies using the data from the Field Scanalyzer phenotyping platform (in collaboration with Malcolm Hawkesford) (Lyra et al. 2020), (ii) implementing gene-based models using exome-capture data of trehalose biosynthetic pathway genes in collaboration with Paul/RRes and Reynolds/CIMMYT (Lyra et al. 2021), (iii) integrating genomic and metagenomic data for trait dissection (FAPESP-BBSRC Pump-Priming grant - in collaboration with Tim Mauchline), and iv) developing a quantitative genetics pipeline for the wheat Cadenza TILLING population (in collaboration with Andy Phillips and Cristobal Uauy-JIC). **See Research for more details about the projects. For more details, check my Talk at “Genetics and Plant Breeding Virtual Symposium (INTERGEN) - Corteva Agriscience Plant Sciences Symposia Series”

Biometrics Scientist ● Wheat Crop Lead ● Biometrics and Breeding Research ● BASF Agricultural Solutions, Gent, Belgium (2021-2023)

I have supported the hybrid winter wheat EMEA breeding programs on the experimental designs, multi-environment trial analysis, envirotyping analytics, deployment of predictive methodologies (genomic selection, marker-assisted selection), optimization of crossing blocks (male and female pools), and heterotic pool formation. I have implemented quantitative genetic approaches in the wheat breeding in collaboration with breeders to increase selection gain. Therefore, I supported wheat breeders in using breeding data analysis tools and in the interpretation and usage of the statistical data analysis pipelines. I have lead the implementation of a pipeline for Product Profile Tool.

Key responsibilities:

Supporting four hybrid wheat breeding programs in collaboration with breeders (hybrid, parental, associate, and agronomists)
Performing experiment and breeding program design, data analysis for field trials and genetic analyses
MET data analysis (hybrid and inbred trials; disease trials; Discovery Breeding trials).
Supporting the Predictive Breeding project (genomic selection and marker-assisted selection)
Delivering KPIs to measure the Genetic Gain of the breeding programs

Research Scientist ● Oilseed Rape ● RD-MAOC-BTA ● KWS SAAT SE, Einbeck, Germany (2021-2023)

I have been supporting the hybrid winter OSR breeding programs on the Biostatistics and Breeding Information.