Bioinformatics and Statistics
The primary objective of statistical research in crop sciences [biometry] is to help biological researchers obtain objective answers through computational analysis of molecular data.
Bioinformatics plays an important role in managing and exploiting microbial, plant, and animal genomic resources. Biometry integrates agronomic, sociological, and statistical aspects to determine the best practices for effective breeding practices (e.g. optimal rate of fertilizer usage). The two fields unite in areas such as genomic breeding and population genetics.
- Information Technology
- Population Genetics
- Plant Breeding
- Mathematical Modeling
Genome Sequencing Research
Current research in genome sequencing utilizes computer analysis to “assemble” plant genomes from short pieces of DNA sequenced in laboratory machines. After researchers assemble sequences, they locate genes and collect information about their potential function. This process detects genes for important crop traits, such as disease resistance, grain composition, and nutrient and water utilization. Genome sequencing also influences crop improvement such as genomic breeding and genome modification using methods such as CRIPSR / Cas9.
Statistical Models Genome-wide Association Studies
Models that accurately approximate the intricate relationship between genotype and phenotype can potentially enable researchers to identify specific genomic regions harboring genes that control phenotypic variation in crops. We are exploring the incorporation of multiple epistatic loci into statistical models. This approach could ultimately highlight novel genomic targets for marker-assisted selection for agronomically important traits.
Statistical Models for Genomic Selection
Although genomic selection is a very promising approach that enables breeders to use genomic information to predict which seeds will have optimal phenotypic characteristics, the incorporation of genomic regions associated with the phenotype and/or –omic data could increase the prediction accuracy of the statistical models typically used. Ultimately, the incorporation of such information could expedite the development of crops that have sufficient yields to feed future generations and thrive under increasingly hostile environments brought forward by climate change.