Plant breeding has undergone a revolution thanks to genomic selection, which has accelerated the process and increased crop yields.
g problems due to climate change, more development is needed.
To solve this, researchers are using pangenomes and modern machine learning methods in genomic selection.
The entire genomic material of a species, also known as the pangenome, allows a detailed understanding of genetic variation.
rop improvement and reduce the negative impacts of climate change on agriculture by looking at examples from crop breeding, understanding the limitations of machine learning, and highlighting the promise of the methods. that.
We can pave the way for
Traditionally, single-reference genome assemblies have been telephone number list the primary focus of genomic selection, but pangenomes are now becoming more common. Plant pangenomes, rather than individual genome assemblies, reflect the genetic material of a species or family.
Important gene variants, including those not included in the reference assembly, are published by them. For several crops, pangenomes have been created, elucidating the history of domestication and plant breeding.
Their combination with genetic selection is only partially effective.
Breeders can use a wider variety of genetic markers, improving the accuracy of predictions and capturing all possible connections, by combining pangenomes in genomic selection.
But to deal with the comin
Traditional genomic selection methods have difficulties in dealing with non-adaptive effects such as epistasis, genomic imprinting, and genotype interactions. By simulating these effects, machine learning techniques provide feasible answers.
Recent studies have used machine learning techniques in genomic selection, with results ranging between databases and crops.
capable of handling BRB Directory complex data representations, such as mixed phenotypes and interactions between phenotypes or genotypes.
For example, machine learning algorithms have been used to predict yield characteristics and yield quality in polyploid crops such as strawberries and blueberries.
Although these systems have great potential, understanding the interpretation and adjustment of hyperparameters is essential for their effective use.