GenoCore: A simple and fast algorithm for core subset selection from large genotype datasets.

Area of Science:

• Plant genetics
• Bioinformatics
• Computational biology

Background:

• Selecting core subsets from plant genotype data is crucial for cost-effective and timely analyses in genome-wide association studies (GWAS) and crop breeding.
• The increasing volume of genetic markers from high-density SNP arrays and NGS data necessitates efficient software for core subset extraction.

Purpose of the Study:

• To develop a novel program, GenoCore, for the rapid and efficient identification of genetically coherent and representative core subsets from large plant genotype datasets.
• To introduce simple coverage and diversity scores for accurate sample selection reflecting genotype errors and genetic variation.

Main Methods:

• GenoCore utilizes simple measures of coverage and diversity scores to assess genotype errors and genetic variation.
• The program was compared against existing core collection software using various datasets, evaluating genetic distance, diversity, coverage, system resource usage, and sample size.

Main Results:

• GenoCore efficiently identifies the smallest, most consistent, and representative core collection.
• The software demonstrates greater genetic coverage compared to other tested methods.
• GenoCore operates with less memory and more efficient scoring.

Conclusions:

• GenoCore provides an efficient solution for selecting core subsets from large plant genotype datasets.
• The software aids in accelerating genomic analyses and improving crop breeding strategies.
• GenoCore is implemented in R and publicly accessible for use.