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The Use of Genomic Information in the Improvement of Beef Cattle Production

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A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal Genetics and Genomics".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 12479

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Dr. El Hamidi Hay

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Guest Editor

Fort Keogh Livestock and Range Research Laboratory, USDA Agricultural Research Service, Miles, MT 59301, USA
Interests: genomic selection; population genetics; animal breeding
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Dr. Tiago Paim

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Guest Editor

Federal Institute of Education, Science and Technology Goiano, Campus Rio Verde, Rio Verde 75900-000, GO, Brazil
Interests: sheep genetics; cattle genetics; population genetics; genetic improvement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues，

With the advent of new technologies, it is now possible to efficiently generate genomic information for beef cattle animals. Genomic selection is one example of the use of genomic information in the form of single-nucleotide polymorphisms (SNPs) to predict expected progeny differences (EPDs). This technology has shown an increase in the accuracy of EPDs for some traits. Genomic information has also contributed to the identification of recessive alleles, the detection of genetic variants associated with economically important traits, and the understanding of the underlying genetic mechanisms. Despite these benefits, the use of genomic information is still limited and suffers from several limitations. For example, the genomic evaluation of crossbred and admixed populations is still suboptimal; there is poor prediction accuracy of complex traits such as disease resistance, feed efficiency, and fertility; we have a limited understanding of the genetic mechanisms of phenotypes; and there is a lack of genotype by environment integration in national genetic evaluation programs.

The aim of this Special Issue is to contribute to the use of genomic information to improve selection and mating decisions of beef cattle. Moreover, it will contribute to the identification of causative/functional variants and the understanding of the genetic mechanisms of economically important and complex traits in beef cattle. Contributions to the Special Issue may cover reports on genomic selection, variations linked to various phenotypes, the interaction of genetics and environment, and related topics.

Dr. El Hamidi Hay
Dr. Tiago Paim
Guest Editors

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Keywords

beef production
genomic information
EPD
SNP
genomic selection

Published Papers (3 papers)

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Research

10 pages, 781 KiB

Open AccessArticle

Inbreeding Calculated with Runs of Homozygosity Suggests Chromosome-Specific Inbreeding Depression Regions in Line 1 Hereford

by Bethany Pilon, Kelly Hinterneder, El Hamidi A. Hay and Breno Fragomeni

Animals 2021, 11(11), 3105; https://doi.org/10.3390/ani11113105 - 30 Oct 2021

Cited by 5 | Viewed by 2912

Abstract

The goal of this study was to evaluate inbreeding in a closed beef cattle population and assess phenotype prediction accuracy using inbreeding information. Effects of inbreeding on average daily gain phenotype in the Line 1 Hereford cattle population were assessed in this study. Genomic data were used to calculate inbreeding based on runs of homozygosity (ROH), and pedigree information was used to calculate the probability of an allele being identical by descent. Prediction ability of phenotypes using inbreeding coefficients calculated based on pedigree information and runs of homozygosity over the whole genome was close to 0, even in the case of significant inbreeding coefficient effects. On the other hand, inbreeding calculated per individual chromosomes’ ROH yielded higher accuracies of prediction. Additionally, including only ROH from chromosomes with higher predicting ability further increased prediction accuracy. Phenotype prediction accuracy, inbreeding depression, and the effects of chromosome-specific ROHs varied widely across the genome. The results of this study suggest that inbreeding should be evaluated per individual regions of the genome. Moreover, mating schemes to avoid inbreeding depression should focus more on specific ROH with negative effects. Finally, using ROH as added information may increase prediction of the genetic merit of animals in a genomic selection program. Full article

(This article belongs to the Special Issue The Use of Genomic Information in the Improvement of Beef Cattle Production)

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8 pages, 446 KiB

Open AccessArticle

Prediction of Hanwoo Cattle Phenotypes from Genotypes Using Machine Learning Methods

by Swati Srivastava, Bryan Irvine Lopez, Himansu Kumar, Myoungjin Jang, Han-Ha Chai, Woncheoul Park, Jong-Eun Park and Dajeong Lim

Animals 2021, 11(7), 2066; https://doi.org/10.3390/ani11072066 - 11 Jul 2021

Cited by 7 | Viewed by 4341

Abstract

Hanwoo was originally raised for draft purposes, but the increase in local demand for red meat turned that purpose into full-scale meat-type cattle rearing; it is now considered one of the most economically important species and a vital food source for Koreans. The application of genomic selection in Hanwoo breeding programs in recent years was expected to lead to higher genetic progress. However, better statistical methods that can improve the genomic prediction accuracy are required. Hence, this study aimed to compare the predictive performance of three machine learning methods, namely, random forest (RF), extreme gradient boosting method (XGB), and support vector machine (SVM), when predicting the carcass weight (CWT), marbling score (MS), backfat thickness (BFT) and eye muscle area (EMA). Phenotypic and genotypic data (53,866 SNPs) from 7324 commercial Hanwoo cattle that were slaughtered at the age of around 30 months were used. The results showed that the boosting method XGB showed the highest predictive correlation for CWT and MS, followed by GBLUP, SVM, and RF. Meanwhile, the best predictive correlation for BFT and EMA was delivered by GBLUP, followed by SVM, RF, and XGB. Although XGB presented the highest predictive correlations for some traits, we did not find an advantage of XGB or any machine learning methods over GBLUP according to the mean squared error of prediction. Thus, we still recommend the use of GBLUP in the prediction of genomic breeding values for carcass traits in Hanwoo cattle. Full article

(This article belongs to the Special Issue The Use of Genomic Information in the Improvement of Beef Cattle Production)

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20 pages, 2883 KiB

Open AccessArticle

Genetic Background and Inbreeding Depression in Romosinuano Cattle Breed in Mexico

by Jorge Hidalgo, Alberto Cesarani, Andre Garcia, Pattarapol Sumreddee, Neon Larios, Enrico Mancin, José Guadalupe García, Rafael Núñez and Rodolfo Ramírez

Animals 2021, 11(2), 321; https://doi.org/10.3390/ani11020321 - 28 Jan 2021

Cited by 16 | Viewed by 3539

Abstract

The ultimate goal of genetic selection is to improve genetic progress by increasing favorable alleles in the population. However, with selection, homozygosity, and potentially harmful recessive alleles can accumulate, deteriorating genetic variability and hampering continued genetic progress. Such potential adverse side effects of selection are of particular interest in populations with a small effective population size like the Romosinuano beef cattle in Mexico. The objective of this study was to evaluate the genetic background and inbreeding depression in Mexican Romosinuano cattle using pedigree and genomic information. Inbreeding was estimated using pedigree (

F_{P E D}

) and genomic information based on the genomic relationship matrix (

F_{G R M}

) and runs of homozygosity (

F_{R O H}

) of different length classes. Linkage disequilibrium (

L D

) was evaluated using the correlation between pairs of loci, and the effective population size (

N_{e}

) was calculated based on

L D

and pedigree information. The pedigree file consisted of 4875 animals born between 1950 and 2019, of which 71 had genotypes.

L D

decreased with the increase in distance between markers, and

N_{e}

estimated using genomic information decreased from 610 to 72 animals (from 109 to 1 generation ago), the

N_{e}

estimated using pedigree information was 86.44. The reduction in effective population size implies the existence of genetic bottlenecks and the decline of genetic diversity due to the intensive use of few individuals as parents of the next generations. The number of runs of homozygosity per animal ranged between 18 and 102 segments with an average of 55. The shortest and longest segments were 1.0 and 36.0 Mb long, respectively, reflecting ancient and recent inbreeding. The average inbreeding was 2.98 ± 2.81, 2.98 ± 4.01, and 7.28 ± 3.68% for

F_{P E D}

F_{G R M}

, and

F_{R O H}

, respectively. The correlation between

F_{P E D}

and

F_{G R M}

was −0.25, and the correlations among

F_{P E D}

and

F_{R O H}

of different length classes were low (from 0.16 to 0.31). The correlations between

F_{G R M}

and

F_{R O H}

of different length classes were moderate (from 0.44 to 0.58), indicating better agreement. A 1% increase in population inbreeding decreased birth weight by 0.103 kg and weaning weight by 0.685 kg. A strategy such as optimum genetic contributions to maximize selection response and manage the long-term genetic variability and inbreeding could lead to more sustainable breeding programs for the Mexican Romosinuano beef cattle breed. Full article

(This article belongs to the Special Issue The Use of Genomic Information in the Improvement of Beef Cattle Production)

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