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A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding

Modern potato breeding methods following a genomic-led approach provide means for shortening breeding cycles and increasing breeding efficiency across selection cycles. Acquiring genetic data for large breeding populations remains expensive. We present a pipeline to reduce the number of single nucle...

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Autores principales: Selga, Catja, Koc, Alexander, Chawade, Aakash, Ortiz, Rodomiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7824009/
https://www.ncbi.nlm.nih.gov/pubmed/33374406
http://dx.doi.org/10.3390/plants10010030
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author Selga, Catja
Koc, Alexander
Chawade, Aakash
Ortiz, Rodomiro
author_facet Selga, Catja
Koc, Alexander
Chawade, Aakash
Ortiz, Rodomiro
author_sort Selga, Catja
collection PubMed
description Modern potato breeding methods following a genomic-led approach provide means for shortening breeding cycles and increasing breeding efficiency across selection cycles. Acquiring genetic data for large breeding populations remains expensive. We present a pipeline to reduce the number of single nucleotide polymorphisms (SNPs) to lower the cost of genotyping. First, we reduced the number of individuals to be genotyped with a high-throughput method according to the multi-trait variation as defined by principal component analysis of phenotypic characteristics. Next, we reduced the number of SNPs by pruning for linkage disequilibrium. By adjusting the square of the correlation coefficient between two adjacent loci, we obtained reduced subsets of SNPs. We subsequently tested these SNP subsets by two methods; (1) a genome-wide association study (GWAS) for marker identification, and (2) genomic selection (GS) to predict genomic estimated breeding values. The results indicate that both GWAS and GS can be done without loss of information after SNP reduction. The pipeline allows for creating custom SNP subsets to cover all variation found in any particular breeding population. Low-throughput genotyping will reduce the genotyping cost associated with large populations, thereby making genomic breeding methods applicable to large potato breeding populations by reducing genotyping costs.
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spelling pubmed-78240092021-01-24 A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding Selga, Catja Koc, Alexander Chawade, Aakash Ortiz, Rodomiro Plants (Basel) Article Modern potato breeding methods following a genomic-led approach provide means for shortening breeding cycles and increasing breeding efficiency across selection cycles. Acquiring genetic data for large breeding populations remains expensive. We present a pipeline to reduce the number of single nucleotide polymorphisms (SNPs) to lower the cost of genotyping. First, we reduced the number of individuals to be genotyped with a high-throughput method according to the multi-trait variation as defined by principal component analysis of phenotypic characteristics. Next, we reduced the number of SNPs by pruning for linkage disequilibrium. By adjusting the square of the correlation coefficient between two adjacent loci, we obtained reduced subsets of SNPs. We subsequently tested these SNP subsets by two methods; (1) a genome-wide association study (GWAS) for marker identification, and (2) genomic selection (GS) to predict genomic estimated breeding values. The results indicate that both GWAS and GS can be done without loss of information after SNP reduction. The pipeline allows for creating custom SNP subsets to cover all variation found in any particular breeding population. Low-throughput genotyping will reduce the genotyping cost associated with large populations, thereby making genomic breeding methods applicable to large potato breeding populations by reducing genotyping costs. MDPI 2020-12-24 /pmc/articles/PMC7824009/ /pubmed/33374406 http://dx.doi.org/10.3390/plants10010030 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Selga, Catja
Koc, Alexander
Chawade, Aakash
Ortiz, Rodomiro
A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding
title A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding
title_full A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding
title_fullStr A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding
title_full_unstemmed A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding
title_short A Bioinformatics Pipeline to Identify a Subset of SNPs for Genomics-Assisted Potato Breeding
title_sort bioinformatics pipeline to identify a subset of snps for genomics-assisted potato breeding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7824009/
https://www.ncbi.nlm.nih.gov/pubmed/33374406
http://dx.doi.org/10.3390/plants10010030
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