Gota Morota

Synopsis

Dr. Morota investigates the genome–phenome relationship using statistical and quantitative genetics.

Description

Dr. Morota's research interests center on statistical quantitative genetics and its application to animal and plant breeding. His work connects phenotypic variation with high-dimensional omic information, including single nucleotide polymorphisms, gene expression, metabolites, and DNA methylation. His primary research involves developing statistical methods for the prediction of complex traits and genome-wide association studies. In particular, the advent of high throughput phenotyping coupled with the wealth of genotypic data generated by next-generation sequencing technologies provides new and exciting resources to study complex traits. However, these new technologies in digital agriculture also bring about new challenges in quantitative genetics, namely developing robust frameworks that can accommodate spatial, temporal, heterogeneous, and high-dimensional data. To address this gap, Dr. Morota has been working on statistical genetic modeling of image-derived phenotypes, network analysis to elucidate the genetic interrelationships among classical phenotypes and high-throughput phenotyping data, and variance heterogeneity genome-wide association analysis. His most recent work includes computer vision and image analysis applied to high-throughput phenotyping data in animals to capture morphological characters systematically.