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Aucamp J, van der Zwan H, Geldenhuys Z, Abera A, Louw R, van der Sluis R. Diagnostic applications and limitations for the use of cell-free fetal DNA (cffDNA) in animal husbandry and wildlife management. Res Vet Sci 2023; 158:106-116. [PMID: 36989830 DOI: 10.1016/j.rvsc.2023.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/02/2023] [Accepted: 03/15/2023] [Indexed: 03/18/2023]
Abstract
In animal breeding, a species sex can influence the value of the animal. For example, in the horse breeding industry, mares are preferred as polo horses, while in wildlife breeding males with larger horns are more valuable. Therefore, the economic advantages of knowing the unborn fetus' sex are important to successful animal management. Ultrasonography is used to determine the sex of unborn fetuses, but this method places additional stress on the animal and require specialized equipment and expertise. Conversely, molecular-based sexing techniques require less invasive sampling and can determine sex more reliably. Although in humans, various studies have evaluated the use of cell-free fetal DNA (cffDNA) for prenatal sexing, very few animal studies have been published in this field. Several factors can affect the sensitivity of cffDNA-based sex determination, for example the gestational age. These factors are often not optimized and validated when establishing a protocol for prenatal sexing. In this review, we summarize the current literature on cffDNA in animals. We discuss the diagnostic applications and limitations in the use thereof in animal husbandry and wildlife management. Lastly, the feasibility of implementing diagnostic tests is evaluated and solutions are given to the current drawbacks of the technology.
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Cruz A, Sedano J, Burgos A, Gutiérrez JP, Wurzinger M, Gutiérrez-Reynoso G. Genomic selection improves genetic gain for fiber traits in a breeding program for alpacas. Livest Sci 2023. [DOI: 10.1016/j.livsci.2023.105195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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Fathoni A, Boonkum W, Chankitisakul V, Duangjinda M. An Appropriate Genetic Approach for Improving Reproductive Traits in Crossbred Thai-Holstein Cattle under Heat Stress Conditions. Vet Sci 2022; 9:vetsci9040163. [PMID: 35448661 PMCID: PMC9031002 DOI: 10.3390/vetsci9040163] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/19/2022] [Accepted: 03/26/2022] [Indexed: 01/16/2023] Open
Abstract
Thailand is a tropical country affected by global climate change and has high temperatures and humidity that cause heat stress in livestock. A temperature−humidity index (THI) is required to assess and evaluate heat stress levels in livestock. One of the livestock types in Thailand experiencing heat stress due to extreme climate change is crossbred dairy cattle. Genetic evaluations of heat tolerance in dairy cattle have been carried out for reproductive traits. Heritability values for reproductive traits are generally low (<0.10) because environmental factors heavily influence them. Consequently, genetic improvement for these traits would be slow compared to production traits. Positive and negative genetic correlations were found between reproductive traits and reproductive traits and yield traits. Several selection methods for reproductive traits have been introduced, i.e., the traditional method, marker-assisted selection (MAS), and genomic selection (GS). GS is the most promising technique and provides accurate results with a high genetic gain. Single-step genomic BLUP (ssGBLUP) has higher accuracy than the multi-step equivalent for fertility traits or low-heritability traits.
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Affiliation(s)
- Akhmad Fathoni
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Department of Animal Breeding and Reproduction, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
| | - Wuttigrai Boonkum
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Network Center for Animal Breeding and OMICS Research, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Vibuntita Chankitisakul
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Network Center for Animal Breeding and OMICS Research, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Monchai Duangjinda
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Network Center for Animal Breeding and OMICS Research, Khon Kaen University, Khon Kaen 40002, Thailand
- Correspondence: ; Tel.: +66-81-872-4207
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Weller JI. Genomic Prediction of Complex Traits in Animal Breeding with Long Breeding History, the Dairy Cattle Case. Methods Mol Biol 2022; 2467:447-467. [PMID: 35451786 DOI: 10.1007/978-1-0716-2205-6_16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In accordance with the infinitesimal model for quantitative traits, a very large number of genes affect nearly all economic traits. In only two cases has the causative polymorphism been determined for genes affecting economic traits in dairy cattle. Most current methods for genomic evaluation are based on the "two-step" method. Genetic evaluations are computed by the individual animal model, and functions of the evaluations of progeny-tested sires are the dependent variable for estimation of marker effects. With the adoption of genomic evaluation in 2008, annual rates of genetic gain in the US increased from ∼50-100% for yield traits and from threefold to fourfold for lowly heritable traits, including female fertility, herd-life and somatic cell concentration. Gradual elimination of the progeny test scheme has led to a reduction in the number of sires with daughter records and less genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of "single-step" methodologies. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and "environmentally friendly" production. Genetic variance for economic traits is maintained by increase in frequency of rare alleles, new mutations, and changes in selection goals and management.
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Affiliation(s)
- Joel Ira Weller
- Agricultural Research Organization, The Volcani Center, Rishon LeZion, Israel.
- Israel Cattle Breeders' Association, Caesarea Industrial Park, Israel.
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Mancisidor B, Cruz A, Gutiérrez G, Burgos A, Morón JA, Wurzinger M, Gutiérrez JP. ssGBLUP Method Improves the Accuracy of Breeding Value Prediction in Huacaya Alpaca. Animals (Basel) 2021; 11:ani11113052. [PMID: 34827784 PMCID: PMC8614529 DOI: 10.3390/ani11113052] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/03/2021] [Accepted: 09/14/2021] [Indexed: 11/10/2022] Open
Abstract
Simple Summary Alpaca breeding takes place in the most entrenched areas of the Andes, where the conditions to implement genetic improvement programs are very difficult. Likewise, taking phenotypic records is limited in its ability to predict genetic merit accurately. For this reason, genomic information is shown as an alternative that helps to predict the genetic values of fiber traits more precisely. This study showed how genomic information increased precision by 2.623% for the fiber diameter, 6.442% for the standard deviation of the fiber diameter, and 1.471% for the percentage of medullation compared to traditional methods for predicting genetic merit, suggesting that adding genomic data in prediction models could be beneficial for alpaca breeding programs in the future. Abstract Improving textile characteristics is the main objective of alpaca breeding. A recently developed SNP chip for alpacas could potentially be used to implement genomic selection and accelerate genetic progress. Therefore, this study aimed to compare the increase in prediction accuracy of three important fiber traits: fiber diameter (FD), standard deviation of fiber diameter (SD), and percentage of medullation (PM) in Huacaya alpacas. The data contains a total pedigree of 12,431 animals, 24,169 records for FD and SD, and 8386 records for PM and 60,624 SNP markers for each of the 431 genotyped animals of the Pacomarca Genetic Center. Prediction accuracy of breeding values was compared between a classical BLUP and a single-step Genomic BLUP (ssGBLUP). Deregressed phenotypes were predicted. The accuracies of the genetic and genomic values were calculated using the correlation between the predicted breeding values and the deregressed values of 100 randomly selected animals from the genotyped ones. Fifty replicates were carried out. Accuracies with ssGBLUP improved by 2.623%, 6.442%, and 1.471% on average for FD, SD, and PM, respectively, compared to the BLUP method. The increase in accuracy was relevant, suggesting that adding genomic data could benefit alpaca breeding programs.
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Affiliation(s)
- Betsy Mancisidor
- Departamento de Producción Animal, Universidad Nacional Agraria La Molina, Lima 12056, Peru; (B.M.); (G.G.); (J.A.M.); (M.W.)
| | - Alan Cruz
- Departamento de Producción Animal, Universidad Nacional Agraria La Molina, Lima 12056, Peru; (B.M.); (G.G.); (J.A.M.); (M.W.)
- Correspondence: ; Tel.: +51-940-202-666
| | - Gustavo Gutiérrez
- Departamento de Producción Animal, Universidad Nacional Agraria La Molina, Lima 12056, Peru; (B.M.); (G.G.); (J.A.M.); (M.W.)
| | - Alonso Burgos
- Centro Genético de Pacomarca–Inca Tops S.A., Miguel Forga 348, Arequipa 04001, Peru;
| | - Jonathan Alejandro Morón
- Departamento de Producción Animal, Universidad Nacional Agraria La Molina, Lima 12056, Peru; (B.M.); (G.G.); (J.A.M.); (M.W.)
| | - Maria Wurzinger
- Departamento de Producción Animal, Universidad Nacional Agraria La Molina, Lima 12056, Peru; (B.M.); (G.G.); (J.A.M.); (M.W.)
| | - Juan Pablo Gutiérrez
- Departamento de Producción Animal, Universidad Complutense de Madrid, E-28040 Madrid, Spain;
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Shao B, Sun H, Ahmad MJ, Ghanem N, Abdel-Shafy H, Du C, Deng T, Mansoor S, Zhou Y, Yang Y, Zhang S, Yang L, Hua G. Genetic Features of Reproductive Traits in Bovine and Buffalo: Lessons From Bovine to Buffalo. Front Genet 2021; 12:617128. [PMID: 33833774 PMCID: PMC8021858 DOI: 10.3389/fgene.2021.617128] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 02/25/2021] [Indexed: 11/13/2022] Open
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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Affiliation(s)
- Baoshun Shao
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Hui Sun
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Muhammad Jamil Ahmad
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Nasser Ghanem
- Department of Animal Production, Faculty of Agriculture, Cairo University, Giza, Egypt
| | - Hamdy Abdel-Shafy
- Department of Animal Production, Faculty of Agriculture, Cairo University, Giza, Egypt
| | - Chao Du
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Tingxian Deng
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Shahid Mansoor
- National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad, Pakistan
| | - Yang Zhou
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- International Joint Research Centre for Animal Genetics, Breeding and Reproduction, Wuhan, China
- Hubei Province’s Engineering Research Center in Buffalo Breeding and Products, Wuhan, China
| | - Yifen Yang
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- Department of Animal Production, Faculty of Agriculture, Cairo University, Giza, Egypt
| | - Shujun Zhang
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- International Joint Research Centre for Animal Genetics, Breeding and Reproduction, Wuhan, China
- Hubei Province’s Engineering Research Center in Buffalo Breeding and Products, Wuhan, China
| | - Liguo Yang
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- International Joint Research Centre for Animal Genetics, Breeding and Reproduction, Wuhan, China
- Hubei Province’s Engineering Research Center in Buffalo Breeding and Products, Wuhan, China
| | - Guohua Hua
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- International Joint Research Centre for Animal Genetics, Breeding and Reproduction, Wuhan, China
- Hubei Province’s Engineering Research Center in Buffalo Breeding and Products, Wuhan, China
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Luna-Nevarez G, Kelly AC, Camacho LE, Limesand SW, Reyna-Granados JR, Luna-Nevarez P. Discovery and validation of candidate SNP markers associated to heat stress response in pregnant ewes managed inside a climate-controlled chamber. Trop Anim Health Prod 2020; 52:3457-3466. [PMID: 32939704 DOI: 10.1007/s11250-020-02379-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 09/10/2020] [Indexed: 11/28/2022]
Abstract
Sheep production in desert environments during summer is challenging due to heat stress which reduces feed intake, growth, and fertility. Despite warm conditions, some ewes are able to maintain a normal performance suggesting the existence of genetic bases underlying heat tolerance. Our objective was to discover and validate genetic markers associated with thermo-tolerance in pregnant ewes exposed to warm environmental conditions. Using a well-defined model laboratory of heat stress in sheep, pregnant Columbia-Rambouillet crossbred ewes (n = 100) were examined. Following acclimation to the laboratory at thermo-neutral conditions, heat stress was induced in ewes by increasing the temperature-humidity index in a control environmental chamber during mid-gestation. Feed intake, water consumption, and rectal temperature were recorded daily and used to establish the heat stress tolerance index (HSTI) for each ewe. Rectal temperature was a predictor (P < 0.05) of feed intake, and the regression coefficient was used to classify the HSTI. In a subset of 24 ewes, a genome-wide association study (GWAS) was performed using the Illumina OvineSNP50 BeadChip. Single-marker analysis detected 3 intragenic SNPs associated with HSTI (P value = 10-5). Bayesian multi-marker approach discovered 26 chromosomal regions across the genome which accounted for 9.8% of the variation associated with HSTI. In an independent sheep population (n = 42), the three discovered SNPs were validated as molecular markers associated with thermo-tolerance phenotypic traits. These SNPs were located within the genes F13A1, PAM, and PRELID2. In conclusion, three SNPs appear to be novel molecular markers associated with heat stress tolerance in pregnant ewes providing new knowledge about genetic foundations of thermo-tolerance.
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Affiliation(s)
- Guillermo Luna-Nevarez
- Departamento de Ciencias Agronómicas y Veterinarias, Instituto Tecnológico de Sonora, 85000, Ciudad Obregón, Sonora, México
| | - Amy C Kelly
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Leticia E Camacho
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Sean W Limesand
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Javier R Reyna-Granados
- Departamento de Ciencias Agronómicas y Veterinarias, Instituto Tecnológico de Sonora, 85000, Ciudad Obregón, Sonora, México
| | - Pablo Luna-Nevarez
- Departamento de Ciencias Agronómicas y Veterinarias, Instituto Tecnológico de Sonora, 85000, Ciudad Obregón, Sonora, México.
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Mello RRC, Sinedino LDP, Ferreira JE, de Sousa SLG, de Mello MRB. Principal component and cluster analyses of production and fertility traits in Red Sindhi dairy cattle breed in Brazil. Trop Anim Health Prod 2019; 52:273-281. [PMID: 31372883 PMCID: PMC6969864 DOI: 10.1007/s11250-019-02009-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 07/08/2019] [Indexed: 12/02/2022]
Abstract
The objective of this study was to investigate the relationship among functional traits (age at first calving (AFC), calving interval (CI), reproductive efficiency (RE), total milk yield (TMY), and lactation period (LP)) in Red Sindhi breed through multivariate techniques. For this goal, performance data provided by the Brazilian Association of Zebu Breeders related to 560 Red Sindhi dairy cattle from 28 different herds in Brazil, born in the period from 1987 to 2011, were used. Principal component analysis with correlation matrix was used to find the relationship among AFC, CI, RE, TMY, and LP. It was found that for all functional traits, first 3 principal components explained more than 90% of the total variation. Clustering analysis was performed based on Tocher method, and results showed physiological relationships among functional traits. By cluster analysis, twelve different groups were generated from the pool of Sindhi herds analyzed, with a great homogeneity among females for the traits evaluated and only few females generating separate groups. Four hundred and twenty-nine females were clustered in one group, representing 76.60% of the genotypes. Total milk yield (TMY) showed 71.92% of the total variation, and age at first calving (AFC) contributed with 23.06% of the variation, being the two most important traits for the variability of the data set. In conclusion, the multivariate procedures were effective in generating the correlations among the functional traits, showing that CI is correlated with RE and all these functional traits are related with total milk yield.
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Affiliation(s)
- Raquel Rodrigues Costa Mello
- Department of Animal Evaluation and Reproduction, Federal Rural University of Rio de Janeiro (UFRRJ), BR 465, Km 07, Seropedica, Rio de Janeiro, Brazil.
| | - Letícia Del-Penho Sinedino
- Department of Animal Sciences, University of Florida (UF), 2250 Shealy Drive, Gainesville, FL, 32911, USA
| | - Joaquim Esquerdo Ferreira
- Department of Animal Evaluation and Reproduction, Federal Rural University of Rio de Janeiro (UFRRJ), BR 465, Km 07, Seropedica, Rio de Janeiro, Brazil
| | - Sabrina Luzia Gregio de Sousa
- Department of Animal Production, Federal Rural University of Rio de Janeiro (UFRRJ), BR 465, Km 07, Seropedica, Rio de Janeiro, Brazil
| | - Marco Roberto Bourg de Mello
- Department of Animal Evaluation and Reproduction, Federal Rural University of Rio de Janeiro (UFRRJ), BR 465, Km 07, Seropedica, Rio de Janeiro, Brazil
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Shor T, Kalka I, Geiger D, Erlich Y, Weissbrod O. Estimating variance components in population scale family trees. PLoS Genet 2019; 15:e1008124. [PMID: 31071088 PMCID: PMC6529016 DOI: 10.1371/journal.pgen.1008124] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 05/21/2019] [Accepted: 04/03/2019] [Indexed: 12/14/2022] Open
Abstract
The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records.
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Affiliation(s)
- Tal Shor
- Computer Science Department, Technion—Israel Institute of Technology, Haifa, Israel
- MyHeritage Ltd., Or Yehuda, Israel
| | - Iris Kalka
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Dan Geiger
- Computer Science Department, Technion—Israel Institute of Technology, Haifa, Israel
| | - Yaniv Erlich
- MyHeritage Ltd., Or Yehuda, Israel
- The New York Genome Center, New York, NY, United States of America
- Department of Computer Science, Fu School of Engineering, Columbia University, NY, United States of America
| | - Omer Weissbrod
- Computer Science Department, Technion—Israel Institute of Technology, Haifa, Israel
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
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Ibeagha-Awemu EM, Peters SO, Bemji MN, Adeleke MA, Do DN. Leveraging Available Resources and Stakeholder Involvement for Improved Productivity of African Livestock in the Era of Genomic Breeding. Front Genet 2019; 10:357. [PMID: 31105739 PMCID: PMC6499167 DOI: 10.3389/fgene.2019.00357] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 04/03/2019] [Indexed: 01/13/2023] Open
Abstract
The African continent is home to diverse populations of livestock breeds adapted to harsh environmental conditions with more than 70% under traditional systems of management. Animal productivity is less than optimal in most cases and is faced with numerous challenges including limited access to adequate nutrition and disease management, poor institutional capacities and lack of adequate government policies and funding to develop the livestock sector. Africa is home to about 1.3 billion people and with increasing demand for animal proteins by an ever growing human population, the current state of livestock productivity creates a significant yield gap for animal products. Although a greater section of the population, especially those living in rural areas depend largely on livestock for their livelihoods; the potential of the sector remains underutilized and therefore unable to contribute significantly to economic development and social wellbeing of the people. With current advances in livestock management practices, breeding technologies and health management, and with inclusion of all stakeholders, African livestock populations can be sustainably developed to close the animal protein gap that exists in the continent. In particular, advances in gene technologies, and application of genomic breeding in many Western countries has resulted in tremendous gains in traits like milk production with the potential that, implementation of genomic selection and other improved practices (nutrition, healthcare, etc.) can lead to rapid improvement in traits of economic importance in African livestock populations. The African livestock populations in the context of this review are limited to cattle, goat, pig, poultry, and sheep, which are mainly exploited for meat, milk, and eggs. This review examines the current state of livestock productivity in Africa, the main challenges faced by the sector, the role of various stakeholders and discusses in-depth strategies that can enable the application of genomic technologies for rapid improvement of livestock traits of economic importance.
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Affiliation(s)
- Eveline M. Ibeagha-Awemu
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada
| | - Sunday O. Peters
- Department of Animal Science, Berry College, Mount Berry, GA, United States
| | - Martha N. Bemji
- Department of Animal Breeding and Genetics, Federal University of Agriculture, Abeokuta, Abeokuta, Nigeria
| | - Matthew A. Adeleke
- School of Life Sciences, University of Kwazulu-Natal, Durban, South Africa
| | - Duy N. Do
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada
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Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs. Animal 2018; 12:2235-2245. [PMID: 29706144 DOI: 10.1017/s175173111800085x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.
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Weller JI, Ezra E, Ron M. Invited review: A perspective on the future of genomic selection in dairy cattle. J Dairy Sci 2017; 100:8633-8644. [PMID: 28843692 DOI: 10.3168/jds.2017-12879] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 07/05/2017] [Indexed: 11/19/2022]
Abstract
Genomic evaluation has been successfully implemented in the United States, Canada, Great Britain, Ireland, New Zealand, Australia, France, the Netherlands, Germany, and the Scandinavian countries. Adoption of this technology in the major dairy producing countries has led to significant changes in the worldwide dairy industry. Gradual elimination of the progeny test system has led to a reduction in the number of sires with daughter records and fewer genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of "single-step" methodologies. Although genomic selection has been successful in increasing rates of genetic gain, we still know very little about the genetic architecture of quantitative variation. Apparently, a very large number of genes affect nearly all economic traits, in accordance with the infinitesimal model for quantitative traits. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and on environmentally friendly production with reduced waste and gas emission. Genetic variance for economic traits is maintained by the increase in frequency of rare alleles, new mutations, and changes in selection goals and management. Thus, it is unlikely that a selection plateau will be reached in the near future.
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Affiliation(s)
- J I Weller
- Institute of Animal Sciences, Agricultural Research Organization, The Volcani Center, Rishon LeZion 7505101, Israel.
| | - E Ezra
- Israeli Cattle Breeders Association, Caesarea Industrial Park 3088900, Israel
| | - M Ron
- Institute of Animal Sciences, Agricultural Research Organization, The Volcani Center, Rishon LeZion 7505101, Israel
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Genomic-polygenic and polygenic evaluations for milk yield and fat percentage using random regression models with Legendre polynomials in a Thai multibreed dairy population. Livest Sci 2016. [DOI: 10.1016/j.livsci.2016.04.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Zhang Z, Wang X, Li R, Ju Z, Qi C, Zhang Y, Guo F, Luo G, Li Q, Wang C, Zhong J, Xu Y, Huang J. Genetic mutations potentially cause two novel NCF1 splice variants up-regulated in the mammary gland, blood and neutrophil of cows infected by Escherichia coli. Microbiol Res 2015; 174:24-32. [PMID: 25946326 DOI: 10.1016/j.micres.2015.03.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Revised: 03/07/2015] [Accepted: 03/09/2015] [Indexed: 11/18/2022]
Abstract
Neutrophil cytosolic factor 1 (NCF1) plays a crucial role in host defense against microbial pathogens. In this study, we investigated the potential alternative splicing patterns, expression and splice-relevant single nucleotide polymorphisms (SNPs) of the bovine NCF1 gene to increase insights into its potential role against bovine mastitis caused by Escherichia coli infection. Using RT-PCR and clone sequencing methods, we found two novel splice variants designed as NCF1-TV1 (retained intron 6) and NCF1-TV2 (retained part of intron 8), respectively, encoding two putative truncated proteins (239AA and 283AA). Two splice variants were drastically up-regulated in the mastitis-infected cows' mammary tissues, blood and neutrophils compared with these of healthy cows using real-time RT-PCR. Genomic sequencing analysis identified four novel SNPs g.10112 G>A, g.10766 T>C, SNPs g.12085 G>A and g.12430 T>C at the ends of intron 6 and intron 8 of NCF1. ESE motif predicted that the SNP (g.10766 T>C) might affect the binding with splicing-related factors and subsequently caused the production of aberrant splice variant NCF1-TV1, which is one of the potential reasons that the functional SNP was associated with increased milk somatic cell score in cow. Our results would help in better understanding the NCF1 gene function in the process against pathogen infection, and the effect of splicing-related SNP on the production of aberrant splice variant and careful functional characterization in dairy cattle.
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Affiliation(s)
- Zijing Zhang
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
| | - Xiuge Wang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, No. 159 North of Industry Road, Jinan, Shandong 250131, People's Republic of China(1)
| | - Rongling Li
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, No. 159 North of Industry Road, Jinan, Shandong 250131, People's Republic of China(1)
| | - Zhihua Ju
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, No. 159 North of Industry Road, Jinan, Shandong 250131, People's Republic of China(1)
| | - Chao Qi
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, No. 159 North of Industry Road, Jinan, Shandong 250131, People's Republic of China(1)
| | - Yan Zhang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, No. 159 North of Industry Road, Jinan, Shandong 250131, People's Republic of China(1)
| | - Fang Guo
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
| | - Guojing Luo
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
| | - Qiuling Li
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, No. 159 North of Industry Road, Jinan, Shandong 250131, People's Republic of China(1)
| | - Changfa Wang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, No. 159 North of Industry Road, Jinan, Shandong 250131, People's Republic of China(1)
| | - Jifeng Zhong
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, No. 159 North of Industry Road, Jinan, Shandong 250131, People's Republic of China(1)
| | - Yinxue Xu
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, People's Republic of China.
| | - Jinming Huang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, No. 159 North of Industry Road, Jinan, Shandong 250131, People's Republic of China(1).
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