GENLIB: an R package for the analysis of genealogical data

BMC Bioinformatics

Software

Hélène Vézina¹  Eve-Marie Lavoie¹  Jean-François Lefebvre²  Claudia Moreau²  Damian Labuda³  Marie-Hélène Roy-Gagnon⁴  Héloïse Gauvin⁵ 

[1] BALSAC Project, Université du Québec à Chicoutimi, Chicoutimi, Québec, Canada;Centre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada;Centre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada;Département de pédiatrie, Université de Montréal, Montréal, Québec, Canada;Centre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada;School of Epidemiology, Public Health and Preventive Medicine, Faculty of Medicine, University of Ottawa, 600 Peter Morand Cres, Room 101E, K1G 5Z3, Ottawa, ON, Canada;Département de médecine sociale et préventive, Université de Montréal, Montréal, Québec, Canada;Centre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada;

关键词: Genealogical data;    Founder populations;    Software;    Historical demography;    Kinship;    Inbreeding;    Genetics;    Gene-dropping simulations;   

DOI  :  10.1186/s12859-015-0581-5

 received in 2014-12-19, accepted in 2015-04-22,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundFounder populations have an important role in the study of genetic diseases. Access to detailed genealogical records is often one of their advantages. These genealogical data provide unique information for researchers in evolutionary and population genetics, demography and genetic epidemiology. However, analyzing large genealogical datasets requires specialized methods and software. The GENLIB software was developed to study the large genealogies of the French Canadian population of Quebec, Canada. These genealogies are accessible through the BALSAC database, which contains over 3 million records covering the whole province of Quebec over four centuries. Using this resource, extended pedigrees of up to 17 generations can be constructed from a sample of present-day individuals.ResultsWe have extended and implemented GENLIB as a package in the R environment for statistical computing and graphics, thus allowing optimal flexibility for users. The GENLIB package includes basic functions to manage genealogical data allowing, for example, extraction of a part of a genealogy or selection of specific individuals. There are also many functions providing information to describe the size and complexity of genealogies as well as functions to compute standard measures such as kinship, inbreeding and genetic contribution. GENLIB also includes functions for gene-dropping simulations.The goal of this paper is to present the full functionalities of GENLIB. We used a sample of 140 individuals from the province of Quebec (Canada) to demonstrate GENLIB’s functions. Ascending genealogies for these individuals were reconstructed using BALSAC, yielding a large pedigree of 41,523 individuals. Using GENLIB’s functions, we provide a detailed description of these genealogical data in terms of completeness, genetic contribution of founders, relatedness, inbreeding and the overall complexity of the genealogical tree. We also present gene-dropping simulations based on the whole genealogy to investigate identical-by-descent sharing of alleles and chromosomal segments of different lengths and estimate probabilities of identical-by-descent sharing.ConclusionsThe R package GENLIB provides a user friendly and flexible environment to analyze extensive genealogical data, allowing an efficient and easy integration of different types of data, analytical methods and additional developments and making this tool ideal for genealogical analysis.

【 授权许可】

Unknown   
© Gauvin et al.; licensee BioMed Central. 2015. 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 credited. 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.