Genetic diversity evolution in the Mexican Charolais cattle population

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Abstract

Objective

The aim was to characterize the genetic diversity evolution of the registered Mexican Charolais cattle population by pedigree analysis.

Methods

Data consisted of 331,390 pedigree records of animals born from 1934 to 2018. Average complete generation equivalent, generation interval, effective population size (N _e), and effective numbers of founders (f _e), ancestors (f _a), and founder genomes (N _g) were calculated for seven five-year periods. The inbreeding coefficient was calculated per year of birth, from 1984 to 2018, whereas the gene contribution of the most influential ancestors was calculated for the latter period.

Results

Average complete generation equivalent consistently increased across periods, from 4.76, for the first period (1984 through 1988), to 7.86, for the last period (2014 through 2018). The inbreeding coefficient showed a relative steadiness across the last seventeen years, oscillating from 0.0110 to 0.0145. During the last period, the average generation interval for the father-offspring pathways was nearly 1 yr. longer than that of the mother-offspring pathways. The effective population size increased steadily since 1984 (105.0) and until 2013 (237.1), but showed a minor decline from 2013 to 2018 (233.2). The population displayed an increase in the f _a since 1984 and until 2008; however, showed a small decrease during the last decade. The effective number of founder genomes increased from 1984 to 2003, but revealed loss of genetic variability during the last fifteen years (from 136.4 to 127.7). The f _a:f _e ratio suggests that the genetic diversity loss was partially caused by formation of genetic bottlenecks in the pedigree; in addition, the N _g:f _a ratio indicates loss of founder alleles due to genetic drift. The most influential ancestor explained 1.8% of the total genetic variability in the progeny born from 2014 to 2018.

Conclusion

Inbreeding, N _e, f _a, and N _g are rather beyond critical levels; therefore, the current genetic status of the population is not at risk.

Related collections

Most cited references 30

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Is Open Access

Methods to estimate effective population size using pedigree data: Examples in dog, sheep, cattle and horse

Grégoire Leroy, Tristan Mary-Huard, Etienne Verrier … (2013)

Background Effective population sizes of 140 populations (including 60 dog breeds, 40 sheep breeds, 20 cattle breeds and 20 horse breeds) were computed using pedigree information and six different computation methods. Simple demographical information (number of breeding males and females), variance of progeny size, or evolution of identity by descent probabilities based on coancestry or inbreeding were used as well as identity by descent rate between two successive generations or individual identity by descent rate. Results Depending on breed and method, effective population sizes ranged from 15 to 133 056, computation method and interaction between computation method and species showing a significant effect on effective population size (P < 0.0001). On average, methods based on number of breeding males and females and variance of progeny size produced larger values (4425 and 356, respectively), than those based on identity by descent probabilities (average values between 93 and 203). Since breeding practices and genetic substructure within dog breeds increased inbreeding, methods taking into account the evolution of inbreeding produced lower effective population sizes than those taking into account evolution of coancestry. The correlation level between the simplest method (number of breeding males and females, requiring no genealogical information) and the most sophisticated one ranged from 0.44 to 0.60 according to species. Conclusions When choosing a method to compute effective population size, particular attention should be paid to the species and the specific genetic structure of the population studied.

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A note on ENDOG: a computer program for analysing pedigree information.

P. Gutierrez, F Goyache (2005)

The aim of this note is to describe the program ENDOG (v.3.0). The program handles pedigree information to conduct several demographic and genetic analyses including: (a) the individual inbreeding and average relatedness coefficients; (b) effective population size; (c) parameters characterizing the concentration of both gene and individuals origin such as the effective number of founders and ancestors, the effective number of founder herds; (d) F statistics and paired genetic distances for each subpopulation under study; (e) descriptors of the genetic importance of the herds in a population and (f) generation intervals. The program will help breeders and researchers to monitor the changes in genetic variability and population structure with limited costs of preparing datasets. The program, user's guide and example file can be down-loaded free of charge from the World Wide Web at http://www.ucm.es/ info/prodanim/Endog30.zip.

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The value of using probabilities of gene origin to measure genetic variability in a population

D Boichard, L Maignel, E Verrier (1997)

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All references

Author and article information

Journal

Journal ID (nlm-ta): Anim Biosci

Journal ID (iso-abbrev): Anim Biosci

Title: Animal Bioscience

Publisher: Animal Bioscience

ISSN (Print): 2765-0189

ISSN (Electronic): 2765-0235

Publication date (Print): July 2021

Publication date (Electronic): 30 August 2020

Volume: 34

Issue: 7

Pages: 1116-1122

Affiliations

[1 ]Campo Experimental La Posta, Centro de Investigación Regional Golfo-Centro, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Medellín, Veracruz 94277, México

[2 ]CENIDFyMA, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Colón, Querétaro 76280, México

[3 ]Facultad de Medicina Veterinaria y Zootecnia, Universidad Veracruzana, Veracruz, Veracruz 91710, México

[4 ]Campo Experimental Santiago Ixcuintla, Centro de Investigación Regional Pacífico-Centro, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Santiago Ixcuintla, Nayarit 63570, México

[5 ]Campo Experimental Mocochá, Centro de Investigación Regional Pacífico-Sur, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Mocochá, Yucatán 97454, México

[6 ]Campo Experimental La Campana, Centro de Investigación Regional Norte-Centro, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Aldama, Chihuahua 32910, México

Author notes

[* ] Corresponding Authors: Moisés Montaño-Bermúdez, Tel: +52-5538718700 Ext. 80220, E-mail: mmontanob@ 123456yahoo.com . Guillermo Martínez-Velázquez, Tel: +52-5538718700 Ext. 84411, E-mail: gmv1us@ 123456yahoo.com

Author information

Ángel Ríos-Utrera https://orcid.org/0000-0003-3108-1133

Moisés Montaño-Bermúdez https://orcid.org/0000-0003-3162-6949

Vicente Eliezer Vega-Murillo https://orcid.org/0000-0002-0847-8944

Guillermo Martínez-Velázquez https://orcid.org/0000-0001-6101-1297

Juan José Baeza-Rodríguez https://orcid.org/0000-0001-9339-9695

Sergio Iván Román-Ponce https://orcid.org/0000-0002-6704-6578

Article

Publisher ID: ajas-20-0401

DOI: 10.5713/ajas.20.0401

PMC ID: 8255882

PubMed ID: 32898959

SO-VID: b9b00afa-f0a5-4fb7-a0bb-1d64683aeed3

License:

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.

History

Date received : 13 June 2020

Date revision received : 22 July 2020

Date accepted : 16 August 2020