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

Cited 33 time in scopus
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Title
GenoCore : a simple and fast algorithm for core subset selection from large genotype datasets
Author(s)
Seongmun Jeong; Jae-Yoon Kim; Soon-Chun Jeong; S T Kang; J K Moon; Namshin Kim
Bibliographic Citation
PLoS One, vol. 12, no. 7, pp. e0181420-e0181420
Publication Year
2017
Abstract
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.
ISSN
1932-6203
Publisher
Public Library of Science
DOI
http://dx.doi.org/10.1371/journal.pone.0181420
Type
Article
Appears in Collections:
Division of Biomedical Research > Personalized Genomic Medicine Research Center > 1. Journal Articles
Ochang Branch Institute > 1. Journal Articles
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