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MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information

BACKGROUND: Identification of genes or even nucleotides that are responsible for quantitative and adaptive trait variation is a difficult task due to the complex interdependence between a large number of genetic and environmental factors. The polymorphism of the mitogenome is one of the factors that...

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Autores principales: Ristov, Strahil, Brajkovic, Vladimir, Cubric-Curik, Vlatka, Michieli, Ivan, Curik, Ino
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018160/
https://www.ncbi.nlm.nih.gov/pubmed/27613390
http://dx.doi.org/10.1186/s12711-016-0242-9
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author Ristov, Strahil
Brajkovic, Vladimir
Cubric-Curik, Vlatka
Michieli, Ivan
Curik, Ino
author_facet Ristov, Strahil
Brajkovic, Vladimir
Cubric-Curik, Vlatka
Michieli, Ivan
Curik, Ino
author_sort Ristov, Strahil
collection PubMed
description BACKGROUND: Identification of genes or even nucleotides that are responsible for quantitative and adaptive trait variation is a difficult task due to the complex interdependence between a large number of genetic and environmental factors. The polymorphism of the mitogenome is one of the factors that can contribute to quantitative trait variation. However, the effects of the mitogenome have not been comprehensively studied, since large numbers of mitogenome sequences and recorded phenotypes are required to reach the adequate power of analysis. Current research in our group focuses on acquiring the necessary mitochondria sequence information and analysing its influence on the phenotype of a quantitative trait. To facilitate these tasks we have produced software for processing pedigrees that is optimised for maternal lineage analysis. RESULTS: We present MaGelLAn 1.0 (maternal genealogy lineage analyser), a suite of four Python scripts (modules) that is designed to facilitate the analysis of the impact of mitogenome polymorphism on quantitative trait variation by combining molecular and pedigree information. MaGelLAn 1.0 is primarily used to: (1) optimise the sampling strategy for molecular analyses; (2) identify and correct pedigree inconsistencies; and (3) identify maternal lineages and assign the corresponding mitogenome sequences to all individuals in the pedigree, this information being used as input to any of the standard software for quantitative genetic (association) analysis. In addition, MaGelLAn 1.0 allows computing the mitogenome (maternal) effective population sizes and probability of mitogenome (maternal) identity that are useful for conservation management of small populations. CONCLUSIONS: MaGelLAn is the first tool for pedigree analysis that focuses on quantitative genetic analyses of mitogenome data. It is conceived with the purpose to significantly reduce the effort in handling and preparing large pedigrees for processing the information linked to maternal lines. The software source code, along with the manual and the example files can be downloaded at http://lissp.irb.hr/software/magellan-1-0/ and https://github.com/sristov/magellan. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-016-0242-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-50181602016-09-11 MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information Ristov, Strahil Brajkovic, Vladimir Cubric-Curik, Vlatka Michieli, Ivan Curik, Ino Genet Sel Evol Software BACKGROUND: Identification of genes or even nucleotides that are responsible for quantitative and adaptive trait variation is a difficult task due to the complex interdependence between a large number of genetic and environmental factors. The polymorphism of the mitogenome is one of the factors that can contribute to quantitative trait variation. However, the effects of the mitogenome have not been comprehensively studied, since large numbers of mitogenome sequences and recorded phenotypes are required to reach the adequate power of analysis. Current research in our group focuses on acquiring the necessary mitochondria sequence information and analysing its influence on the phenotype of a quantitative trait. To facilitate these tasks we have produced software for processing pedigrees that is optimised for maternal lineage analysis. RESULTS: We present MaGelLAn 1.0 (maternal genealogy lineage analyser), a suite of four Python scripts (modules) that is designed to facilitate the analysis of the impact of mitogenome polymorphism on quantitative trait variation by combining molecular and pedigree information. MaGelLAn 1.0 is primarily used to: (1) optimise the sampling strategy for molecular analyses; (2) identify and correct pedigree inconsistencies; and (3) identify maternal lineages and assign the corresponding mitogenome sequences to all individuals in the pedigree, this information being used as input to any of the standard software for quantitative genetic (association) analysis. In addition, MaGelLAn 1.0 allows computing the mitogenome (maternal) effective population sizes and probability of mitogenome (maternal) identity that are useful for conservation management of small populations. CONCLUSIONS: MaGelLAn is the first tool for pedigree analysis that focuses on quantitative genetic analyses of mitogenome data. It is conceived with the purpose to significantly reduce the effort in handling and preparing large pedigrees for processing the information linked to maternal lines. The software source code, along with the manual and the example files can be downloaded at http://lissp.irb.hr/software/magellan-1-0/ and https://github.com/sristov/magellan. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-016-0242-9) contains supplementary material, which is available to authorized users. BioMed Central 2016-09-10 /pmc/articles/PMC5018160/ /pubmed/27613390 http://dx.doi.org/10.1186/s12711-016-0242-9 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 Software
Ristov, Strahil
Brajkovic, Vladimir
Cubric-Curik, Vlatka
Michieli, Ivan
Curik, Ino
MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information
title MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information
title_full MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information
title_fullStr MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information
title_full_unstemmed MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information
title_short MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information
title_sort magellan 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018160/
https://www.ncbi.nlm.nih.gov/pubmed/27613390
http://dx.doi.org/10.1186/s12711-016-0242-9
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