Genetic Features of Reproductive Traits in Bovine and Buffalo: Lessons From Bovine to Buffalo

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Abstract

Bovine and buffalo are important livestock species that have contributed to human lives for more than 1000 years. Improving fertility is very important to reduce the cost of production. In the current review, we classified reproductive traits into three categories: ovulation, breeding, and calving related traits. We systematically summarized the heritability estimates, molecular markers, and genomic selection (GS) for reproductive traits of bovine and buffalo. This review aimed to compile the heritability and genome-wide association studies (GWASs) related to reproductive traits in both bovine and buffalos and tried to highlight the possible disciplines which should benefit buffalo breeding. The estimates of heritability of reproductive traits ranged were from 0 to 0.57 and there were wide differences between the populations. For some specific traits, such as age of puberty (AOP) and calving difficulty (CD), the majority beef population presents relatively higher heritability than dairy cattle. Compared to bovine, genetic studies for buffalo reproductive traits are limited for age at first calving and calving interval traits. Several quantitative trait loci (QTLs), candidate genes, and SNPs associated with bovine reproductive traits were screened and identified by candidate gene methods and/or GWASs. The IGF1 and LEP pathways in addition to non-coding RNAs are highlighted due to their crucial relevance with reproductive traits. The distribution of QTLs related to various traits showed a great differences. Few GWAS have been performed so far on buffalo age at first calving, calving interval, and days open traits. In addition, we summarized the GS studies on bovine and buffalo reproductive traits and compared the accuracy between different reports. Taken together, GWAS and candidate gene approaches can help to understand the molecular genetic mechanisms of complex traits. Recently, GS has been used extensively and can be performed on multiple traits to improve the accuracy of prediction even for traits with low heritability, and can be combined with multi-omics for further analysis.

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Most cited references 145

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Prediction of total genetic value using genome-wide dense marker maps.

T Meuwissen, B. Hayes, M. E. Goddard (2001)

Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of approximately 50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size N(e) = 100, the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.

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Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection.

George R. Wiggans, Felipe Ruíz-López, Curtis Van Tassell … (2016)

Seven years after the introduction of genomic selection in the United States, it is now possible to evaluate the impact of this technology on the population. Selection differential(s) (SD) and generation interval(s) (GI) were characterized in a four-path selection model that included sire(s) of bulls (SB), sire(s) of cows (SC), dam(s) of bulls (DB), and dam(s) of cows (DC). Changes in SD over time were estimated for milk, fat, and protein yield; somatic cell score (SCS); productive life (PL); and daughter pregnancy rate (DPR) for the Holstein breed. In the period following implementation of genomic selection, dramatic reductions were seen in GI, especially the SB and SC paths. The SB GI reduced from ∼7 y to less than 2.5 y, and the DB GI fell from about 4 y to nearly 2.5 y. SD were relatively stable for yield traits, although modest gains were noted in recent years. The most dramatic response to genomic selection was observed for the lowly heritable traits DPR, PL, and SCS. Genetic trends changed from close to zero to large and favorable, resulting in rapid genetic improvement in fertility, lifespan, and health in a breed where these traits eroded over time. These results clearly demonstrate the positive impact of genomic selection in US dairy cattle, even though this technology has only been in use for a short time. Based on the four-path selection model, rates of genetic gain per year increased from ∼50-100% for yield traits and from threefold to fourfold for lowly heritable traits.

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Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB

Zhi-Liang Hu, Carissa Park, James Reecy (2018)

Abstract Successful development of biological databases requires accommodation of the burgeoning amounts of data from high-throughput genomics pipelines. As the volume of curated data in Animal QTLdb (https://www.animalgenome.org/QTLdb) increases exponentially, the resulting challenges must be met with rapid infrastructure development to effectively accommodate abundant data curation and make metadata analysis more powerful. The development of Animal QTLdb and CorrDB for the past 15 years has provided valuable tools for researchers to utilize a wealth of phenotype/genotype data to study the genetic architecture of livestock traits. We have focused our efforts on data curation, improved data quality maintenance, new tool developments, and database co-developments, in order to provide convenient platforms for users to query and analyze data. The database currently has 158 499 QTL/associations, 10 482 correlations and 1977 heritability data as a result of an average 32% data increase per year. In addition, we have made >14 functional improvements or new tool implementations since our last report. Our ultimate goals of database development are to provide infrastructure for data collection, curation, and annotation, and more importantly, to support innovated data structure for new types of data mining, data reanalysis, and networked genetic analysis that lead to the generation of new knowledge.

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Author and article information

Contributors

Hui Sun: URI : http://loop.frontiersin.org/people/1167340/overview

Muhammad Jamil Ahmad: URI : http://loop.frontiersin.org/people/962112/overview

Nasser Ghanem: URI : http://loop.frontiersin.org/people/354555/overview

Hamdy Abdel-Shafy: URI : http://loop.frontiersin.org/people/993873/overview

Tingxian Deng: URI : http://loop.frontiersin.org/people/560030/overview

Shahid Mansoor: URI : http://loop.frontiersin.org/people/287349/overview

Shujun Zhang: URI : http://loop.frontiersin.org/people/792899/overview

Liguo Yang: URI : http://loop.frontiersin.org/people/582139/overview

Guohua Hua: URI : http://loop.frontiersin.org/people/741177/overview

Journal

Journal ID (nlm-ta): Front Genet

Journal ID (iso-abbrev): Front Genet

Journal ID (publisher-id): Front. Genet.

Title: Frontiers in Genetics

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-8021

Publication date (Electronic): 23 March 2021

Publication date Collection: 2021

Volume: 12

Electronic Location Identifier: 617128

Affiliations

[1] ¹Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University , Wuhan, China

[2] ²Department of Animal Production, Faculty of Agriculture, Cairo University , Giza, Egypt

[3] ³Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences , Nanning, China

[4] ⁴National Institute for Biotechnology and Genetic Engineering (NIBGE) , Faisalabad, Pakistan

[5] ⁵International Joint Research Centre for Animal Genetics, Breeding and Reproduction , Wuhan, China

[6] ⁶Hubei Province’s Engineering Research Center in Buffalo Breeding and Products , Wuhan, China

Author notes

Edited by: Fabyano Fonseca Silva, Universidade Federal de Viçosa, Brazil

Reviewed by: Sirlene Fernandes Lazaro, Purdue University, United States; Mahdi Mokhber, Urmia University, Iran

*Correspondence: Guohua Hua, huaguohua09@ 123456gmail.com

^†These authors have contributed equally to this work

This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

Article

DOI: 10.3389/fgene.2021.617128

PMC ID: 8021858

PubMed ID: 33833774

SO-VID: 4e6f6047-9e48-4e5d-95fa-bbfe67f01592

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 14 October 2020

Date accepted : 25 February 2021

Page count

Figures: 0, Tables: 5, Equations: 0, References: 145, Pages: 16, Words: 0

Funding

Funded by: National Natural Science Foundation of China 10.13039/501100001809

Award ID: 31872352

Funded by: Fundamental Research Funds for the Central Universities 10.13039/501100012226

Funded by: Earmarked Fund for Modern Agro-industry Technology Research System 10.13039/501100009997

Genetic Features of Reproductive Traits in Bovine and Buffalo: Lessons From Bovine to Buffalo

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Genomic Prediction

Most cited references 145

Prediction of total genetic value using genome-wide dense marker maps.

Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection.

Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB

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