Evidence map›Paper›PMID 25971991›Full record

ArticleBMC bioinformatics2015

GENLIB: an R package for the analysis of genealogical data.

Héloïse Gauvin, Jean-François Lefebvre, Claudia Moreau, Eve-Marie Lavoie, Damian Labuda, Hélène Vézina, Marie-Hélène Roy-Gagnon

Abstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Héloïse GauvinDépartement de médecine sociale et préventive, Université de Montréal, Montréal, Québec, Canada. heloise.gauvin@umontreal.ca.
Jean-François LefebvreCentre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada. jflefebvre27@gmail.com.
Claudia MoreauCentre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada. claudiamoreau1@gmail.com.
Eve-Marie LavoieBALSAC Project, Université du Québec à Chicoutimi, Chicoutimi, Québec, Canada. eve-marie_lavoie@uqac.ca.
Damian LabudaCentre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada. damian.labuda@umontreal.ca.
Hélène VézinaBALSAC Project, Université du Québec à Chicoutimi, Chicoutimi, Québec, Canada. helene_vezina@uqac.ca.
Marie-Hélène Roy-GagnonCentre de recherche, Centre hospitalier universitaire Sainte-Justine, Montréal, Québec, Canada. mroygagn@uottawa.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Biological EvolutionGenealogy and HeraldrySoftwareAllelesDatabases, FactualDemographyGenetics, PopulationHumansMolecular EpidemiologyPedigreePopulation GroupsQuebec

Identifiers

PMID25971991
PMCPMC4431039

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.