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A selective genotyping approach identifies QTL in a simulated population

BACKGROUND: Identification of QTLs for important phenotypic traits, through the use of medium-density genome-wide SNP panels, is one of the most challenging areas in animal genetics, for preventing the time-consuming direct sequencing of putative candidate genes, when searching for the mutations tha...

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Autores principales: Moioli, Bianca, Napolitano, Francesco, Catillo, Gennaro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4195409/
https://www.ncbi.nlm.nih.gov/pubmed/25519519
http://dx.doi.org/10.1186/1753-6561-8-S5-S5
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author Moioli, Bianca
Napolitano, Francesco
Catillo, Gennaro
author_facet Moioli, Bianca
Napolitano, Francesco
Catillo, Gennaro
author_sort Moioli, Bianca
collection PubMed
description BACKGROUND: Identification of QTLs for important phenotypic traits, through the use of medium-density genome-wide SNP panels, is one of the most challenging areas in animal genetics, for preventing the time-consuming direct sequencing of putative candidate genes, when searching for the mutations that affect the trait. Appropriate statistical analyses allow the identification of genomic regions associated with the investigated trait in the genotyped population. METHODS: The selective genotyping technique was applied to 1000 genotyped animals with known phenotype. Sliding windows composed of five consecutive SNPs were created for each chromosome; we assumed that the QTLs were encoded by the windows showing the highest difference in the frequency of the same alleles between the most divergent productive groups (the two tails of the distribution). RESULTS: Ten windows affected at least one trait. For five of these windows, the highest and significant effect was given by one only SNP, which could therefore be taken as the QTL itself. CONCLUSIONS: In this study we proposed a simple method to identify genomic regions associated to the phenotype under study. The identification of the DNA region is the first step to search for the mutation which is really responsible for the trait variability, through the direct sequencing of the genome regions that encode the QTL.
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spelling pubmed-41954092014-11-05 A selective genotyping approach identifies QTL in a simulated population Moioli, Bianca Napolitano, Francesco Catillo, Gennaro BMC Proc Proceedings BACKGROUND: Identification of QTLs for important phenotypic traits, through the use of medium-density genome-wide SNP panels, is one of the most challenging areas in animal genetics, for preventing the time-consuming direct sequencing of putative candidate genes, when searching for the mutations that affect the trait. Appropriate statistical analyses allow the identification of genomic regions associated with the investigated trait in the genotyped population. METHODS: The selective genotyping technique was applied to 1000 genotyped animals with known phenotype. Sliding windows composed of five consecutive SNPs were created for each chromosome; we assumed that the QTLs were encoded by the windows showing the highest difference in the frequency of the same alleles between the most divergent productive groups (the two tails of the distribution). RESULTS: Ten windows affected at least one trait. For five of these windows, the highest and significant effect was given by one only SNP, which could therefore be taken as the QTL itself. CONCLUSIONS: In this study we proposed a simple method to identify genomic regions associated to the phenotype under study. The identification of the DNA region is the first step to search for the mutation which is really responsible for the trait variability, through the direct sequencing of the genome regions that encode the QTL. BioMed Central 2014-10-07 /pmc/articles/PMC4195409/ /pubmed/25519519 http://dx.doi.org/10.1186/1753-6561-8-S5-S5 Text en Copyright © 2014 Moioli et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Proceedings
Moioli, Bianca
Napolitano, Francesco
Catillo, Gennaro
A selective genotyping approach identifies QTL in a simulated population
title A selective genotyping approach identifies QTL in a simulated population
title_full A selective genotyping approach identifies QTL in a simulated population
title_fullStr A selective genotyping approach identifies QTL in a simulated population
title_full_unstemmed A selective genotyping approach identifies QTL in a simulated population
title_short A selective genotyping approach identifies QTL in a simulated population
title_sort selective genotyping approach identifies qtl in a simulated population
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4195409/
https://www.ncbi.nlm.nih.gov/pubmed/25519519
http://dx.doi.org/10.1186/1753-6561-8-S5-S5
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