Abstract

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0581-5) contains supplementary material, which is available to authorized users.

Highlights

  • Founder populations have an important role in the study of genetic diseases

  • We have extended and implemented GENLIB in R to provide a freely accessible version of the software within a user-friendly and flexible environment and to allow a more efficient and easier integration of different types of data and analytical methods, facilitating future developments

  • We started with the same C++ functions in the translation of GENLIB into an R package and we extended both the C++ and R translation of the S code

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Summary

Introduction

Founder populations have an important role in the study of genetic diseases. Access to detailed genealogical records is often one of their advantages. 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. Potential advantages of founder populations include greater genetic and environmental homogeneity and in some instances, the availability of extensive genealogical records [1]. Each lacks some functionality that we have found useful in our studies of the Quebec population and neither PEDSYS nor PedHunter can be used within a statistical computing environment such as R

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