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

• Published in: PloS one Vol. 12; no. 7; p. e0181420
• Main Authors: Jeong, Seongmun, Kim, Jae-Yoon, Jeong, Soon-Chun, Kang, Sung-Taeg, Moon, Jung-Kyung, Kim, Namshin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.07.2017; Public Library of Science (PLoS)
• Subjects: Access to Information; Algorithms; Analysis; Bioinformatics; Biology and Life Sciences; Biotechnology; Breeding; Collection; Computer and Information Sciences; Computer programs; Cost analysis; Crop science; Crops; Databases, Genetic; Datasets; Datasets as Topic; Gene Frequency; Gene polymorphism; Genetic aspects; Genetic distance; Genetic diversity; Genetic markers; Genome-wide association studies; Genomes; Genomics; Internet; Markers; Methods; Oryza - genetics; Phenotype; Physical Sciences; Picking; Plant breeding; Polymorphism; Polymorphism, Single Nucleotide; Principal Component Analysis; Reproducibility of Results; Research and Analysis Methods; Rice; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Software; Software development; Soybeans; Studies; Triticum - genetics
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0181420

Selecting core subsets from plant genotype datasets is important for enhancing cost-effectiveness and to shorten the time required for analyses of genome-wide association studies (GWAS), and genomics-assisted breeding of crop species, etc. Recently, a large number of genetic markers (>100,000 single nucleotide polymorphisms) have been identified from high-density single nucleotide polymorphism (SNP) arrays and next-generation sequencing (NGS) data. However, there is no software available for picking out the efficient and consistent core subset from such a huge dataset. It is necessary to develop software that can extract genetically important samples in a population with coherence. We here present a new program, GenoCore, which can find quickly and efficiently the core subset representing the entire population. We introduce simple measures of coverage and diversity scores, which reflect genotype errors and genetic variations, and can help to select a sample rapidly and accurately for crop genotype dataset. Comparison of our method to other core collection software using example datasets are performed to validate the performance according to genetic distance, diversity, coverage, required system resources, and the number of selected samples. GenoCore selects the smallest, most consistent, and most representative core collection from all samples, using less memory with more efficient scores, and shows greater genetic coverage compared to the other software tested. GenoCore was written in R language, and can be accessed online with an example dataset and test results at https://github.com/lovemun/Genocore.