Danilo Hottis Lyra
Papers
1
Total Citations
46
H-Index
1
About
Dr. Danilo Hottis Lyra is a quantitative geneticist and plant breeder whose research lies at the intersection of high-throughput phenotyping, statistical genetics, and crop improvement. His work is pivotal in developing and applying analytical frameworks to harness the massive datasets generated by automated field platforms. In his highly cited 2020 study, "Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform" (46 citations), Lyra tackled the complex challenge of analyzing longitudinal phenotypic data. He systematically compared multiple approaches for quantitative trait locus (QTL) mapping and genomic prediction, providing critical methodological guidance for the plant science community. This work demonstrates his core contribution: bridging the gap between cutting-edge phenotyping technology and robust genetic analysis to accelerate breeding for complex traits. By refining how researchers can extract meaningful genetic signals from time-series data, Lyra is enabling more efficient selection for key agronomic traits like plant height and growth dynamics, directly impacting the development of resilient, high-yielding wheat varieties.
Research Focus
Key Achievements
Top Papers
- 1