Lab Research Interests
These are the main areas of research in the Cotton Breeding and Genetics Lab at New Mexico State University:
Quantitative Genetics
What genetic and environmental factors shape complex traits such as yield and abiotic stress tolerance in crops? How can this knowledge be leveraged to develop high-yielding, resilient cultivars?
Our research integrates quantitative genetics, genomics, and computational approaches to dissect the genetic architecture of key agronomic traits in cotton. We identify genetic variants and candidate genes underlying yield, fiber quality, and drought tolerance using QTL mapping and genome-wide association studies (GWAS). In parallel, we apply genomic prediction to accelerate genetic gain and enable data-driven breeding strategies. Ultimately, our goal is to translate genetic insights into improved cultivars adapted to challenging environments.
Example publications in this area:
Our research integrates quantitative genetics, genomics, and computational approaches to dissect the genetic architecture of key agronomic traits in cotton. We identify genetic variants and candidate genes underlying yield, fiber quality, and drought tolerance using QTL mapping and genome-wide association studies (GWAS). In parallel, we apply genomic prediction to accelerate genetic gain and enable data-driven breeding strategies. Ultimately, our goal is to translate genetic insights into improved cultivars adapted to challenging environments.
Example publications in this area:
High-Throughput Phenotyping (HTP)
How can scalable, high-throughput measurements improve breeding decisions and optimize resource allocation?
Our research integrates high-throughput phenotyping platforms to scale the measurement of phenotypes across thousands of genotypes, enabling faster and more cost-effective data collection. We are particularly interested in integrating HTP data with genomic information to improve phenotype prediction models and increase the accuracy of genomic prediction. Ultimately, this approach enhances selection intensity and accelerates genetic gain per unit of time.
Example publications in this area:
Our research integrates high-throughput phenotyping platforms to scale the measurement of phenotypes across thousands of genotypes, enabling faster and more cost-effective data collection. We are particularly interested in integrating HTP data with genomic information to improve phenotype prediction models and increase the accuracy of genomic prediction. Ultimately, this approach enhances selection intensity and accelerates genetic gain per unit of time.
Example publications in this area:
Artificial Intelligence (AI)
With the growing volume of data generated by multi-omics approaches, data-driven methods are essential to extract meaningful biological insights. Our research explores the use of artificial intelligence in plant genetics to improve genomic prediction and uncover the genetic basis of complex traits. This includes identifying candidate genes, gene–gene interactions, and genotype-by-environment interactions that drive phenotypic variation. Our ultimate goal is to generate biologically interpretable insights from AI models that can be experimentally validated and translated into practical applications in the field.
Example publications in this area:
Example publications in this area: