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Sharif-Islam M, van der Werf JHJ, Wood BJ, Hermesch S. The predicted benefits of genomic selection on pig breeding objectives. J Anim Breed Genet 2024; 141:685-701. [PMID: 38779724 DOI: 10.1111/jbg.12873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/30/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024]
Abstract
The premise was tested that the additional genetic gain was achieved in the overall breeding objective in a pig breeding program using genomic selection (GS) compared to a conventional breeding program, however, some traits achieved larger gain than other traits. GS scenarios based on different reference population sizes were evaluated. The scenarios were compared using a deterministic simulation model to predict genetic gain in scenarios with and without using genomic information as an additional information source. All scenarios were compared based on selection accuracy and predicted genetic gain per round of selection for objective traits in both sire and dam lines. The results showed that GS scenarios increased overall response in the breeding objectives by 9% to 56% and 3.5% to 27% in the dam and sire lines, respectively. The difference in response resulted from differences in the size of the reference population. Although all traits achieved higher selection accuracy in GS, traits with limited phenotypic information at the time of selection or with low heritability, such as sow longevity, number of piglets born alive, pre- and post-weaning survival, as well as meat and carcass quality traits achieved the largest additional response. This additional response came at the expense of smaller responses for traits that are easy to measure, such as back fat and average daily gain in GS compared to the conventional breeding program. Sow longevity and drip loss percentage did not change in a favourable direction in GS with a reference population of 500 pigs. With a reference population of 1000 pigs or onwards, sow longevity and drip loss percentage began to change in a favourable direction. Despite the smaller responses for average daily gain and back fat thickness in GS, the overall breeding objective achieved additional gain in GS.
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Affiliation(s)
- Md Sharif-Islam
- AGBU, a Joint Venture of NSW Department of Primary Industries, University of New England, Armidale, New South Wales, Australia
| | - Julius H J van der Werf
- School of Environmental and Rural Science, University of New England, Armidale, New South Wales, Australia
| | - Benjamin J Wood
- School of Veterinary Science, The University of Queensland, Lawes, Queensland, Australia
| | - Susanne Hermesch
- AGBU, a Joint Venture of NSW Department of Primary Industries, University of New England, Armidale, New South Wales, Australia
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2
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Ghavi Hossein-Zadeh N. An overview of recent technological developments in bovine genomics. Vet Anim Sci 2024; 25:100382. [PMID: 39166173 PMCID: PMC11334705 DOI: 10.1016/j.vas.2024.100382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2024] Open
Abstract
Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.
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Affiliation(s)
- Navid Ghavi Hossein-Zadeh
- Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, 41635-1314, Iran
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3
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Fratto A, Torricelli M, Sebastiani C, Ciullo M, Felici A, Biagetti M. Survey on resistance occurrence for F4 + and F18 + enterotoxigenic Escherichia coli (ETEC) among pigs reared in Central Italy regions. Vet Res Commun 2024; 48:1279-1284. [PMID: 38175328 DOI: 10.1007/s11259-023-10287-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024]
Abstract
Porcine Post Weaning Diarrhoea (PWD) is one of the most important swine disease worldwide, caused by Enterotoxigenic Escherichia coli (ETEC) strains able to provoke management, welfare and sanitary issues. ETEC is determined by proteinaceous surface appendages. Numerous studies conducted by now in pigs have demonstrated, at the enterocytes level, that, the genes mucin 4 (MUC4) and fucosyltransferase (FUT1), coding for ETEC F4 and F18 receptors respectively, can be carriers of single nucleotide polymorphisms (SNPs) associated with natural resistance/susceptibility to PWD. The latter aspect was investigated in this study, evaluating the SNPs of the MUC4 and FUT1 genes in slaughtered pigs reared for the most in Central Italy. Genomic DNA was extracted from 362 swine diaphragmatic samples and then was subjected to the detection of known polymorphisms on MUC4 and FUT1candidate target genes by PCR-RFLP. Some of the identified SNPs were confirmed by sequencing analysis. Animals carrying the SNPs associated with resistance were 11% and 86% for the FUT1 and MUC4 genes respectively. Therefore, it can be assumed that the investigated animals may be an important resource and reservoir of favorable genetic traits for the breeding of pigs resistant to enterotoxigenic E.coli F4 variant.
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Affiliation(s)
- Anna Fratto
- Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche - Togo Rosati, Via G. Salvemini 1, Perugia, 06126, Italy
| | - Martina Torricelli
- Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche - Togo Rosati, Via G. Salvemini 1, Perugia, 06126, Italy.
| | - Carla Sebastiani
- Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche - Togo Rosati, Via G. Salvemini 1, Perugia, 06126, Italy
| | - Marcella Ciullo
- Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche - Togo Rosati, Via G. Salvemini 1, Perugia, 06126, Italy
| | - Andrea Felici
- Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche - Togo Rosati, Via G. Salvemini 1, Perugia, 06126, Italy
| | - Massimo Biagetti
- Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche - Togo Rosati, Via G. Salvemini 1, Perugia, 06126, Italy
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Grohmann CJ, Shull CM, Crum TE, Schwab C, Safranski TJ, Decker JE. Analysis of polygenic selection in purebred and crossbred pig genomes using generation proxy selection mapping. Genet Sel Evol 2023; 55:62. [PMID: 37710159 PMCID: PMC10500877 DOI: 10.1186/s12711-023-00836-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 08/25/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Artificial selection on quantitative traits using breeding values and selection indices in commercial livestock breeding populations causes changes in allele frequency over time at hundreds or thousands of causal loci and the surrounding genomic regions. In population genetics, this type of selection is called polygenic selection. Researchers and managers of pig breeding programs are motivated to understand the genetic basis of phenotypic diversity across genetic lines, breeds, and populations using selection mapping analyses. Here, we applied generation proxy selection mapping (GPSM), a genome-wide association analysis of single nucleotide polymorphism (SNP) genotypes (38,294-46,458 markers) of birth date, in four pig populations (15,457, 15,772, 16,595 and 8447 pigs per population) to identify loci responding to artificial selection over a period of five to ten years. Gene-drop simulation analyses were conducted to provide context for the GPSM results. Selected loci within and across each population of pigs were compared in the context of swine breeding objectives. RESULTS The GPSM identified 49 to 854 loci as under selection (Q-values less than 0.10) across 15 subsets of pigs based on combinations of populations. The number of significant associations increased when data were pooled across populations. In addition, several significant associations were identified in more than one population. These results indicate concurrent selection objectives, similar genetic architectures, and shared causal variants responding to selection across these pig populations. Negligible error rates (less than or equal to 0.02%) of false-positive associations were found when testing GPSM on gene-drop simulated genotypes, suggesting that GPSM distinguishes selection from random genetic drift in actual pig populations. CONCLUSIONS This work confirms the efficacy and the negligible error rates of the GPSM method in detecting selected loci in commercial pig populations. Our results suggest shared selection objectives and genetic architectures across swine populations. The identified polygenic selection highlights loci that are important to swine production.
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Ahmad SF, Singh A, Gangwar M, Kumar S, Dutt T, Kumar A. Haplotype-based association study of production and reproduction traits in multigenerational Vrindavani population. Gene 2023; 867:147365. [PMID: 36918047 DOI: 10.1016/j.gene.2023.147365] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/23/2023] [Accepted: 03/08/2023] [Indexed: 03/14/2023]
Abstract
Haplotype-based association analysis promises to reveal important information regarding the effect of genetic variants on economic traits of interest. The present study aimed to evaluate the haplotype structure of Vrindavani cattle and explore the association of haplotypes with (re)production traits of economic interest. Genotyping array data of medium density (Bovine50KSNP BeadChip) on 96 randomly selected Vrindavani cows was used in the present study. Genotypes were called in GenomeStudio program while quality control was undertaken in PLINK using standard thresholds. The phenotypic traits used in the present study included age at first calving, dry days, lactation length, peak yield, total lactation milk yield, inter-calving period and service period. The haplotype structure of Vrindavani population was assessed, using a sliding window of 20 SNP with a shift of 5 SNPs at a time, in terms of the size of haplotype blocks regarding their length (in Kb) and frequency in chromosome-wise fashion. Haplotype blocks were assessed for possible association with important production and reproduction traits across three lactation cycles in Vrindavani cattle population. The first ten principal components were included in the model for haplotype-based association analysis to correct for stratification effects of assessed individuals. Multiple haplotypes were found to be associated with age at first calving, total lactation milk yield, peak yield, dry days, inter-calving period and service period. Various candidate genes were found to overlap haplotypes that were significantly associated with age at first calving (CDH18, MARCHF11, MYO10, FBXL7), total lactation milk yield (TGF, PDE1A, and COL8A1), peak yield (PPARGC1A, RCAN1, KCNE1, SMIM34 and MRPS6), dry days (CPNE4, ACAD11 and MRAS), inter-calving period (ABCG5, ABCG8 and COX7A2L) and service period (FOXL2 and PIK3CB). The putative candidate genes overlapping the significantly associated haplotypes revealed important pathways affecting the production and reproduction performance of animals. The identified genes and pathways may serve as good candidate markers to select animals for improved production and reproduction performance in future generations.
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Affiliation(s)
- Sheikh Firdous Ahmad
- ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Akansha Singh
- ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Munish Gangwar
- ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Subodh Kumar
- ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Amit Kumar
- ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
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Camargo LSA, Saraiva NZ, Oliveira CS, Carmickle A, Lemos DR, Siqueira LGB, Denicol AC. Perspectives of gene editing for cattle farming in tropical and subtropical regions. Anim Reprod 2023; 19:e20220108. [PMID: 36819485 PMCID: PMC9924776 DOI: 10.1590/1984-3143-ar2022-0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/23/2023] [Indexed: 02/19/2023] Open
Abstract
Cattle productivity in tropical and subtropical regions can be severely affected by the environment. Reproductive performance, milk and meat production are compromised by the heat stress imposed by the elevated temperature and humidity. The resulting low productivity contributes to reduce the farmer's income and to increase the methane emissions per unit of animal protein produced and the pressure on land usage. The introduction of highly productive European cattle breeds as well as crossbreeding with local breeds have been adopted as strategies to increase productivity but the positive effects have been limited by the low adaptation of European animals to hot climates and by the reduction of the heterosis effect in the following generations. Gene editing tools allow precise modifications in the animal genome and can be an ally to the cattle industry in tropical and subtropical regions. Alleles associated with production or heat tolerance can be shifted between breeds without the need of crossbreeding. Alongside assisted reproductive biotechnologies and genome selection, gene editing can accelerate the genetic gain of indigenous breeds such as zebu cattle. This review focuses on some of the potential applications of gene editing for cattle farming in tropical and subtropical regions, bringing aspects related to heat stress, milk yield, bull reproduction and methane emissions.
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Affiliation(s)
| | | | | | - Allie Carmickle
- Department of Animal Science, University of California Davis, Davis, CA, USA
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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; 9:163. [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] [Grants] [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
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8
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Kushanov FN, Turaev OS, Ernazarova DK, Gapparov BM, Oripova BB, Kudratova MK, Rafieva FU, Khalikov KK, Erjigitov DS, Khidirov MT, Kholova MD, Khusenov NN, Amanboyeva RS, Saha S, Yu JZ, Abdurakhmonov IY. Genetic Diversity, QTL Mapping, and Marker-Assisted Selection Technology in Cotton ( Gossypium spp.). FRONTIERS IN PLANT SCIENCE 2021; 12:779386. [PMID: 34975965 PMCID: PMC8716771 DOI: 10.3389/fpls.2021.779386] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 11/23/2021] [Indexed: 02/05/2023]
Abstract
Cotton genetic resources contain diverse economically important traits that can be used widely in breeding approaches to create of high-yielding elite cultivars with superior fiber quality and adapted to biotic and abiotic stresses. Nevertheless, the creation of new cultivars using conventional breeding methods is limited by the cost and proved to be time consuming process, also requires a space to make field observations and measurements. Decoding genomes of cotton species greatly facilitated generating large-scale high-throughput DNA markers and identification of QTLs that allows confirmation of candidate genes, and use them in marker-assisted selection (MAS)-based breeding programs. With the advances of quantitative trait loci (QTL) mapping and genome-wide-association study approaches, DNA markers associated with valuable traits significantly accelerate breeding processes by replacing the selection with a phenotype to the selection at the DNA or gene level. In this review, we discuss the evolution and genetic diversity of cotton Gossypium genus, molecular markers and their types, genetic mapping and QTL analysis, application, and perspectives of MAS-based approaches in cotton breeding.
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Affiliation(s)
- Fakhriddin N. Kushanov
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
- Department of Biology, National University of Uzbekistan, Tashkent, Uzbekistan
| | - Ozod S. Turaev
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Dilrabo K. Ernazarova
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
- Department of Biology, National University of Uzbekistan, Tashkent, Uzbekistan
| | - Bunyod M. Gapparov
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Barno B. Oripova
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
- Department of Biology, National University of Uzbekistan, Tashkent, Uzbekistan
| | - Mukhlisa K. Kudratova
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Feruza U. Rafieva
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Kuvandik K. Khalikov
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Doston Sh. Erjigitov
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Mukhammad T. Khidirov
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Madina D. Kholova
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Naim N. Khusenov
- Center of Genomics and Bioinformatics, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Roza S. Amanboyeva
- Department of Biology, National University of Uzbekistan, Tashkent, Uzbekistan
| | - Sukumar Saha
- Crop Science Research Laboratory, USDA-ARS, Washington, DC, United States
| | - John Z. Yu
- Southern Plains Agricultural Research Center, USDA-ARS, Washington, DC, United States
| | - Ibrokhim Y. Abdurakhmonov
- Center of Genomics and Bioinformatics, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
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Liao Y, Wang Z, Glória LS, Zhang K, Zhang C, Yang R, Luo X, Jia X, Lai SJ, Chen SY. Genome-Wide Association Studies for Growth Curves in Meat Rabbits Through the Single-Step Nonlinear Mixed Model. Front Genet 2021; 12:750939. [PMID: 34691158 PMCID: PMC8531506 DOI: 10.3389/fgene.2021.750939] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 09/15/2021] [Indexed: 11/13/2022] Open
Abstract
Growth is a complex trait with moderate to high heritability in livestock and must be described by the longitudinal data measured over multiple time points. Therefore, the used phenotype in genome-wide association studies (GWAS) of growth traits could be either the measures at the preselected time point or the fitted parameters of whole growth trajectory. A promising alternative approach was recently proposed that combined the fitting of growth curves and estimation of single-nucleotide polymorphism (SNP) effects into single-step nonlinear mixed model (NMM). In this study, we collected the body weights at 35, 42, 49, 56, 63, 70, and 84 days of age for 401 animals in a crossbred population of meat rabbits and compared five fitting models of growth curves (Logistic, Gompertz, Brody, Von Bertalanffy, and Richards). The logistic model was preferably selected and subjected to GWAS using the approach of single-step NMM, which was based on 87,704 genome-wide SNPs. A total of 45 significant SNPs distributed on five chromosomes were found to simultaneously affect the two growth parameters of mature weight (A) and maturity rate (K). However, no SNP was found to be independently associated with either A or K. Seven positional genes, including KCNIP4, GBA3, PPARGC1A, LDB2, SHISA3, GNA13, and FGF10, were suggested to be candidates affecting growth performances in meat rabbits. To the best of our knowledge, this is the first report of GWAS based on single-step NMM for longitudinal traits in rabbits, which also revealed the genetic architecture of growth traits that are helpful in implementing genome selection.
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Affiliation(s)
- Yonglan Liao
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Zhicheng Wang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Leonardo S Glória
- Laboratory of Animal Science, State University of Northern of Rio de Janeiro, Campos dos Goytacazes, Brazil
| | - Kai Zhang
- Sichuan Academy of Grassland Sciences, Chengdu, China
| | - Cuixia Zhang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, China
| | - Rui Yang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, China
| | - Xinmao Luo
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Xianbo Jia
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Song-Jia Lai
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Shi-Yi Chen
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
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10
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Söllner JH, Mettenleiter TC, Petersen B. Genome Editing Strategies to Protect Livestock from Viral Infections. Viruses 2021; 13:1996. [PMID: 34696426 PMCID: PMC8539128 DOI: 10.3390/v13101996] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 09/27/2021] [Accepted: 09/29/2021] [Indexed: 12/26/2022] Open
Abstract
The livestock industry is constantly threatened by viral disease outbreaks, including infections with zoonotic potential. While preventive vaccination is frequently applied, disease control and eradication also depend on strict biosecurity measures. Clustered regularly interspaced palindromic repeats (CRISPR) and associated proteins (Cas) have been repurposed as genome editors to induce targeted double-strand breaks at almost any location in the genome. Thus, CRISPR/Cas genome editors can also be utilized to generate disease-resistant or resilient livestock, develop vaccines, and further understand virus-host interactions. Genes of interest in animals and viruses can be targeted to understand their functions during infection. Furthermore, transgenic animals expressing CRISPR/Cas can be generated to target the viral genome upon infection. Genetically modified livestock can thereby reduce disease outbreaks and decrease zoonotic threats.
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Affiliation(s)
- Jenny-Helena Söllner
- Institute of Farm Animal Genetics, Friedrich-Loeffler-Institut, 31535 Neustadt am Rübenberge, Germany;
| | | | - Björn Petersen
- Institute of Farm Animal Genetics, Friedrich-Loeffler-Institut, 31535 Neustadt am Rübenberge, Germany;
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11
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Zhang J, Khazalwa EM, Abkallo HM, Zhou Y, Nie X, Ruan J, Zhao C, Wang J, Xu J, Li X, Zhao S, Zuo E, Steinaa L, Xie S. The advancements, challenges, and future implications of the CRISPR/Cas9 system in swine research. J Genet Genomics 2021; 48:347-360. [PMID: 34144928 DOI: 10.1016/j.jgg.2021.03.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/10/2021] [Accepted: 03/13/2021] [Indexed: 12/11/2022]
Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) genome editing technology has dramatically influenced swine research by enabling the production of high-quality disease-resistant pig breeds, thus improving yields. In addition, CRISPR/Cas9 has been used extensively in pigs as one of the tools in biomedical research. In this review, we present the advancements of the CRISPR/Cas9 system in swine research, such as animal breeding, vaccine development, xenotransplantation, and disease modeling. We also highlight the current challenges and some potential applications of the CRISPR/Cas9 technologies.
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Affiliation(s)
- Jinfu Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Emmanuel M Khazalwa
- Animal and Human Health Program, Biosciences, International Livestock Research Institute (ILRI), P.O. Box 30709, Nairobi 00100, Kenya
| | - Hussein M Abkallo
- Animal and Human Health Program, Biosciences, International Livestock Research Institute (ILRI), P.O. Box 30709, Nairobi 00100, Kenya
| | - Yuan Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Xiongwei Nie
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Jinxue Ruan
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Changzhi Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Jieru Wang
- Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Livestock and Poultry Epidemic Diseases Research Center of Anhui Province, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei 230031, PR China
| | - Jing Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, PR China; The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, PR China; The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Erwei Zuo
- Lingnan Guangdong Laboratory of Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, PR China.
| | - Lucilla Steinaa
- Animal and Human Health Program, Biosciences, International Livestock Research Institute (ILRI), P.O. Box 30709, Nairobi 00100, Kenya.
| | - Shengsong Xie
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, PR China; Animal and Human Health Program, Biosciences, International Livestock Research Institute (ILRI), P.O. Box 30709, Nairobi 00100, Kenya; The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, PR China.
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12
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Orbán L, Shen X, Phua N, Varga L. Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals? Front Genet 2021; 12:506754. [PMID: 33968125 PMCID: PMC8097054 DOI: 10.3389/fgene.2021.506754] [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: 10/22/2019] [Accepted: 03/15/2021] [Indexed: 01/08/2023] Open
Abstract
Due to the steadily increasing need for seafood and the plateauing output of fisheries, more fish need to be produced by aquaculture production. In parallel with the improvement of farming methods, elite food fish lines with superior traits for production must be generated by selection programs that utilize cutting-edge tools of genomics. The purpose of this review is to provide a historical overview and status report of a selection program performed on a catadromous predator, the Asian seabass (Lates calcarifer, Bloch 1790) that can change its sex during its lifetime. We describe the practices of wet lab, farm and lab in detail by focusing onto the foundations and achievements of the program. In addition to the approaches used for selection, our review also provides an inventory of genetic/genomic platforms and technologies developed to (i) provide current and future support for the selection process; and (ii) improve our understanding of the biology of the species. Approaches used for the improvement of terrestrial farm animals are used as examples and references, as those processes are far ahead of the ones used in aquaculture and thus they might help those working on fish to select the best possible options and avoid potential pitfalls.
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Affiliation(s)
- László Orbán
- Reproductive Genomics Group, Temasek Life Sciences Laboratory, Singapore, Singapore.,Frontline Fish Genomics Research Group, Department of Applied Fish Biology, Institute of Aquaculture and Environmental Safety, Hungarian University of Agriculture and Life Sciences, Keszthely, Hungary
| | - Xueyan Shen
- Reproductive Genomics Group, Temasek Life Sciences Laboratory, Singapore, Singapore.,Tropical Futures Institute, James Cook University, Singapore, Singapore
| | - Norman Phua
- Reproductive Genomics Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - László Varga
- Institute of Genetics and Biotechnology, Hungarian University of Agriculture and Life Sciences, Gödöllõ, Hungary.,Institute for Farm Animal Gene Conservation, National Centre for Biodiversity and Gene Conservation, Gödöllõ, Hungary
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13
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Krishnappa G, Savadi S, Tyagi BS, Singh SK, Mamrutha HM, Kumar S, Mishra CN, Khan H, Gangadhara K, Uday G, Singh G, Singh GP. Integrated genomic selection for rapid improvement of crops. Genomics 2021; 113:1070-1086. [PMID: 33610797 DOI: 10.1016/j.ygeno.2021.02.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/08/2020] [Accepted: 02/15/2021] [Indexed: 11/15/2022]
Abstract
An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.
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Affiliation(s)
| | | | | | | | | | - Satish Kumar
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Hanif Khan
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | | | - Gyanendra Singh
- Indian Institute of Wheat and Barley Research, Karnal, India
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14
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Sakar ÇM, Zülkadir U. Determination of the relationship between Anatolian black cattle growth properties and myostatin, GHR and Pit-1 gene. Anim Biotechnol 2021; 33:536-545. [PMID: 33587679 DOI: 10.1080/10495398.2021.1884566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
The aim of this study is to determine the relationship between some growth and development characteristics in Anatolian Black cattle from birth to twelve months of age with the Pit-1, GHR and Myostatin genes. PCR-RFLP method was used to detect the polymorphism. Genotype and allele frequencies were found to be AA/AB/BB: 0.096/0.519/0.385 and A/B: 0.356/0.644; AA/AG/GG: 0.346/0.385/0.269 and A/G: 0.538/0.462 in the Pit-1 and GHR genes respectively. Myostatin gene was found to be also monomorphic in all genotypes. Although the chi-square χ2 test in the Pit-1 gene showed an agreement to Hardy-Weinberg equilibrium (p > 0.05), in the GHR gene did not showed an agreement (p < 0.05). The results of the statistical analysis indicated an association between Pit-1 and GHR genes polymorphism and growth traits at different stage ages in Anatolian Black cattle. But Pit-1/HinfI gene and GHR/Alul polymorphisms were not found statistically significant in the specified periods, at all characters. On the other hand, since the MSTN/BstF5I gene was found to be monomorphic, no association analysis was performed between the measured values and this gene. In conclusion, mutation of these genes is difficult to suggest as a potential marker in a herd selection regarding the growth and development characteristics.
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Affiliation(s)
| | - Uğur Zülkadir
- Department of Animal Science, Faculty of Agriculture, Selçuk University, Konya, Turkey
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15
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de Los Ríos-Pérez L, Nguinkal JA, Verleih M, Rebl A, Brunner RM, Klosa J, Schäfer N, Stüeken M, Goldammer T, Wittenburg D. An ultra-high density SNP-based linkage map for enhancing the pikeperch (Sander lucioperca) genome assembly to chromosome-scale. Sci Rep 2020; 10:22335. [PMID: 33339898 PMCID: PMC7749136 DOI: 10.1038/s41598-020-79358-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/04/2020] [Indexed: 02/08/2023] Open
Abstract
Pikeperch (Sander lucioperca) is a fish species with growing economic significance in the aquaculture industry. However, successful positioning of pikeperch in large-scale aquaculture requires advances in our understanding of its genome organization. In this study, an ultra-high density linkage map for pikeperch comprising 24 linkage groups and 1,023,625 single nucleotide polymorphisms markers was constructed after genotyping whole-genome sequencing data from 11 broodstock and 363 progeny, belonging to 6 full-sib families. The sex-specific linkage maps spanned a total of 2985.16 cM in females and 2540.47 cM in males with an average inter-marker distance of 0.0030 and 0.0026 cM, respectively. The sex-averaged map spanned a total of 2725.53 cM with an average inter-marker distance of 0.0028 cM. Furthermore, the sex-averaged map was used for improving the contiguity and accuracy of the current pikeperch genome assembly. Based on 723,360 markers, 706 contigs were anchored and oriented into 24 pseudomolecules, covering a total of 896.48 Mb and accounting for 99.47% of the assembled genome size. The overall contiguity of the assembly improved with a scaffold N50 length of 41.06 Mb. Finally, an updated annotation of protein-coding genes and repetitive elements of the enhanced genome assembly is provided at NCBI.
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Affiliation(s)
- Lidia de Los Ríos-Pérez
- Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Julien A Nguinkal
- Institute of Genome Biology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Marieke Verleih
- Institute of Genome Biology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Alexander Rebl
- Institute of Genome Biology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Ronald M Brunner
- Institute of Genome Biology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Jan Klosa
- Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Nadine Schäfer
- Institute of Genome Biology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Marcus Stüeken
- Mecklenburg-Vorpommern Research Centre for Agriculture and Fisheries, Malchower Chaussee 1, 17194, Hohen Wangelin, Germany
| | - Tom Goldammer
- Institute of Genome Biology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany. .,Molecular Biology and Fish Genetics, Faculty of Agriculture and Environmental Sciences, University of Rostock, 18059, Rostock, Germany.
| | - Dörte Wittenburg
- Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany.
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16
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Zhao W, Lai X, Liu D, Zhang Z, Ma P, Wang Q, Zhang Z, Pan Y. Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations. Front Genet 2020; 11:598318. [PMID: 33343636 PMCID: PMC7744740 DOI: 10.3389/fgene.2020.598318] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 11/11/2020] [Indexed: 01/01/2023] Open
Abstract
Genomic prediction (GP) has revolutionized animal and plant breeding. However, better statistical models that can improve the accuracy of GP are required. For this reason, in this study, we explored the genomic-based prediction performance of a popular machine learning method, the Support Vector Machine (SVM) model. We selected the most suitable kernel function and hyperparameters for the SVM model in eight published genomic data sets on pigs and maize. Next, we compared the SVM model with RBF and the linear kernel functions to the two most commonly used genome-enabled prediction models (GBLUP and BayesR) in terms of prediction accuracy, time, and the memory used. The results showed that the SVM model had the best prediction performance in two of the eight data sets, but in general, the predictions of both models were similar. In terms of time, the SVM model was better than BayesR but worse than GBLUP. In terms of memory, the SVM model was better than GBLUP and worse than BayesR in pig data but the same with BayesR in maize data. According to the results, SVM is a competitive method in animal and plant breeding, and there is no universal prediction model.
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Affiliation(s)
- Wei Zhao
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Xueshuang Lai
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Dengying Liu
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Zhenyang Zhang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Peipei Ma
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Qishan Wang
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
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17
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Granado-Tajada I, Legarra A, Ugarte E. Exploring the inclusion of genomic information and metafounders in Latxa dairy sheep genetic evaluations. J Dairy Sci 2020; 103:6346-6353. [DOI: 10.3168/jds.2019-18033] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 02/25/2020] [Indexed: 11/19/2022]
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18
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Pozovnikova MV, Rotar LN, Kudinov AA, Dementieva NV. The Linkage of Polymorphic Variants of Genes Gh, Prl, and Pit-1 and Milk Productivity of Cows with Morphology of Cumulus-Oocyte Complex Sampled Post Mortem. CYTOL GENET+ 2020. [DOI: 10.3103/s0095452720030111] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Naval-Sánchez M, Porto-Neto LR, Cardoso DF, Hayes BJ, Daetwyler HD, Kijas J, Reverter A. Selection signatures in tropical cattle are enriched for promoter and coding regions and reveal missense mutations in the damage response gene HELB. Genet Sel Evol 2020; 52:27. [PMID: 32460767 PMCID: PMC7251699 DOI: 10.1186/s12711-020-00546-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 05/11/2020] [Indexed: 01/14/2023] Open
Abstract
Background Distinct domestication events, adaptation to different climatic zones, and divergent selection in productive traits have shaped the genomic differences between taurine and indicine cattle. In this study, we assessed the impact of artificial selection and environmental adaptation by comparing whole-genome sequences from European taurine and Asian indicine breeds and from African cattle. Next, we studied the impact of divergent selection by exploiting predicted and experimental functional annotation of the bovine genome. Results We identified selective sweeps in beef cattle taurine and indicine populations, including a 430-kb selective sweep on indicine cattle chromosome 5 that is located between 47,670,001 and 48,100,000 bp and spans five genes, i.e. HELB, IRAK3, ENSBTAG00000026993, GRIP1 and part of HMGA2. Regions under selection in indicine cattle display significant enrichment for promoters and coding genes. At the nucleotide level, sites that show a strong divergence in allele frequency between European taurine and Asian indicine are enriched for the same functional categories. We identified nine single nucleotide polymorphisms (SNPs) in coding regions that are fixed for different alleles between subspecies, eight of which were located within the DNA helicase B (HELB) gene. By mining information from the 1000 Bull Genomes Project, we found that HELB carries mutations that are specific to indicine cattle but also found in taurine cattle, which are known to have been subject to indicine introgression from breeds, such as N’Dama, Anatolian Red, Marchigiana, Chianina, and Piedmontese. Based on in-house genome sequences, we proved that mutations in HELB segregate independently of the copy number variation HMGA2-CNV, which is located in the same region. Conclusions Major genomic sequence differences between Bos taurus and Bos indicus are enriched for promoter and coding regions. We identified a 430-kb selective sweep in Asian indicine cattle located on chromosome 5, which carries SNPs that are fixed in indicine populations and located in the coding sequences of the HELB gene. HELB is involved in the response to DNA damage including exposure to ultra-violet light and is associated with reproductive traits and yearling weight in tropical cattle. Thus, HELB likely contributed to the adaptation of tropical cattle to their harsh environment.
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Affiliation(s)
- Marina Naval-Sánchez
- CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD, 4067, Australia. .,Institute of Molecular Biosciences, The University of Queensland, 306 Carmody Road, St. Lucia, Brisbane, QLD, 4067, Australia.
| | - Laercio R Porto-Neto
- CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD, 4067, Australia
| | - Diercles F Cardoso
- CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD, 4067, Australia.,Department of Animal Science, School of Agricultural and Veterinarian Sciences, Sao Paulo State University (UNESP), Jaboticabal, SP, Brazil.,Centre for Genetic Improvement of Livestock, University of Guelph, 50 Stone Road East, Guelph, ON, N1G2W1, Canada
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia
| | - Hans D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
| | - James Kijas
- CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD, 4067, Australia
| | - Antonio Reverter
- CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD, 4067, Australia
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20
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Wang JJ, Zhang T, Chen QM, Zhang RQ, Li L, Cheng SF, Shen W, Lei CZ. Genomic Signatures of Selection Associated With Litter Size Trait in Jining Gray Goat. Front Genet 2020; 11:286. [PMID: 32273886 PMCID: PMC7113370 DOI: 10.3389/fgene.2020.00286] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 03/09/2020] [Indexed: 12/28/2022] Open
Abstract
Litter size (LS), an important economic trait in livestock, is so complicate that involves many aspects of reproduction, the underlying mechanism of which particularly in goat has always been scanty. To uncover the genetic basis of LS, the genomic sequence of Jining Gray goat groups (one famous breed for high prolificacy in China) with LS 1, 2, and 3 for firstborn was analyzed, obtaining 563.67 Gb sequence data and a total of 31,864,651 high-quality single nucleotide polymorphisms loci were identified. Particularly, the increased heterozygosity in higher LS groups, and large continuous homozygous segments associated with lower LS group had been uncovered. Through an integrated analysis of three popular methods for detecting selective sweeps (Fst, nucleotide diversity, and Tajima’s D statistic), 111 selected regions and 42 genes associated with LS were scanned genome wide. The candidate genes with highest selective signatures included KIT, KCNH7, and KMT2E in LS2 and PAK1, PRKAA1, and SMAD9 in LS3 group, respectively. Meanwhile, functional terms of programmed cell death involved in cell development and regulation of insulin receptor signaling pathway were mostly enriched with 42 candidate genes, which also included reproduction related terms of steroid metabolic process and cellular response to hormone stimulus. In conclusion, our study identified novel candidate genes involving in regulation of LS in goat, which expand our understanding of genetic fundament of reproductive ability, and the novel insights regarding to LS would be potentially applied to improve reproductive performance.
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Affiliation(s)
- Jun-Jie Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Teng Zhang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Qiu-Ming Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Rui-Qian Zhang
- Key Laboratory of Animal Reproduction and Germplasm Enhancement in Universities of Shandong, College of Life Sciences, Qingdao Agricultural University, Qingdao, China
| | - Lan Li
- Key Laboratory of Animal Reproduction and Germplasm Enhancement in Universities of Shandong, College of Life Sciences, Qingdao Agricultural University, Qingdao, China
| | - Shun-Feng Cheng
- Key Laboratory of Animal Reproduction and Germplasm Enhancement in Universities of Shandong, College of Life Sciences, Qingdao Agricultural University, Qingdao, China
| | - Wei Shen
- Key Laboratory of Animal Reproduction and Germplasm Enhancement in Universities of Shandong, College of Life Sciences, Qingdao Agricultural University, Qingdao, China
| | - Chu-Zhao Lei
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
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21
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Paim TDP, Hay EHA, Wilson C, Thomas MG, Kuehn LA, Paiva SR, McManus C, Blackburn HD. Dynamics of genomic architecture during composite breed development in cattle. Anim Genet 2020; 51:224-234. [PMID: 31961956 PMCID: PMC7065137 DOI: 10.1111/age.12907] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 12/16/2019] [Accepted: 12/19/2019] [Indexed: 12/31/2022]
Abstract
Some livestock breeds face the challenge of reduced genetic variation, increased inbreeding depression owing to genetic drift and selection. Hybridization can reverse these processes and increase levels of productivity and adaptation to various environmental stressors. Samples from American Brangus were used to evaluate the indicine/taurine composition through nine generations (~45 years) after the hybridization process was completed. The purpose was to determine how hybridization alters allelic combinations of a breed over time when genetic factors such as selection and drift are operating. Furthermore, we explored genomic regions with deviations from the expected composition from the progenitor breeds and related these regions to traits under selection. The Brangus composition deviated from the theoretical expectation, defined by the breed association, of 62.5% taurine, showing taurine composition to be 70.4 ± 0.6%. Taurine and indicine proportion were not consistent across chromosomes. Furthermore, these non‐uniform areas were found to be associated with traits that were probably under selection such as intermuscular fat and average daily gain. Interestingly, the sex chromosomes were predominantly taurine, which could be due to the composite being formed particularly in the final cross that resulted in progeny designated as purebred Brangus. This work demonstrated the process of new breed formation on a genomic level. It suggests that factors like genetic drift, selection and complementarity shift the genetic architecture into a uniquely different population. These findings are important to better understand how hybridization and crossbreeding systems shape the genetic architecture of composite populations.
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Affiliation(s)
- T do P Paim
- Instituto Federal de Educação, Ciência e Tecnologia Goiano, Avenida Oeste n. 350, Iporá, 76.200-000, Brazil.,Universidade de Brasília, Asa Norte, Campus Darcy Ribeiro, ICC Sul, Brasília, 70.910-900, Brazil
| | - E H A Hay
- US Department of Agriculture, Fort Keogh Livestock and Range Research Laboratory, Agricultural Research Service, 243 Fort Keogh Road, Miles City, 59301, USA
| | - C Wilson
- US Department of Agriculture, National Laboratory for Genetic Resources Preservation, Agricultural Research Service, National Animal Germplasm Program, 1111 S Mason St., Fort Collins, 80521, USA
| | - M G Thomas
- Department of Animal Sciences, Colorado State University, 350 W. Pitkin St., Fort Collins, 80523-1171, USA
| | - L A Kuehn
- US Department of Agriculture, Agricultural Research Service, US Meat Animal Research Center, 844 Rd 313, Clay Center, 68933, USA
| | - S R Paiva
- Embrapa Recursos Genéticos e Biotecnologia, Parque Estação Biológica, PqEB, Av. W5 Norte (final) Caixa Postal 02372, Brasília, 70.770-917, Brazil
| | - C McManus
- Universidade de Brasília, Asa Norte, Campus Darcy Ribeiro, ICC Sul, Brasília, 70.910-900, Brazil
| | - H D Blackburn
- US Department of Agriculture, National Laboratory for Genetic Resources Preservation, Agricultural Research Service, National Animal Germplasm Program, 1111 S Mason St., Fort Collins, 80521, USA
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22
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An 11-bp Indel Polymorphism within the CSN1S1 Gene Is Associated with Milk Performance and Body Measurement Traits in Chinese Goats. Animals (Basel) 2019; 9:ani9121114. [PMID: 31835668 PMCID: PMC6940862 DOI: 10.3390/ani9121114] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 12/08/2019] [Accepted: 12/09/2019] [Indexed: 02/06/2023] Open
Abstract
The casein alpha s1 (CSN1S1) gene encodes α-s1 casein, one of the proteins constituting milk, which affects milk performance, as well as improving the absorption of calcium and bone development in mammals. A previous study found that an 11-bp insertion/deletion (indel) of this gene strongly affected litter size in goats. However, to our knowledge, the relationships between this polymorphism and the milk performance and body measurement traits of goats have not been reported. In this paper, the previously identified indel has been recognized in three Chinese goat breeds, namely the Guanzhong dairy goat (GZDG; n = 235), Shaanbei white cashmere goat (SBWC; n = 1092), and Hainan black goat (HNBG; n = 278), and the following three genotypes have been studied for all of the breeds: insertion/insertion (II), deletion/deletion (DD), and insertion/deletion (ID). The allele frequencies analyzed signified that the frequencies of the "D" allele were higher (47.8%-65.5%), similar to the previous report, which indicates that this polymorphism is genetically stable in different goat breeds. Further analysis showed that this indel was markedly associated with milk fat content, total solids content, solids-not-fat content, freezing point depression, and acidity in GZDG (p < 0.05), and also affected different body measurement traits in all three breeds (p < 0.05). The goats with II genotypes had superior milk performance, compared with the others; however, goats with DD genotypes had better body measurement sizes. Hence, it may be necessary to select goats with an II or DD genotype, based on the desired traits, while breeding. Our study provides information on the potential impact of the 11-bp indel polymorphism of the CSN1S1 gene for improving the milk performance and body measurement traits in goats.
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23
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Silva ÉF, Lopes MS, Lopes PS, Gasparino E. A genome-wide association study for feed efficiency-related traits in a crossbred pig population. Animal 2019; 13:2447-2456. [PMID: 31133085 DOI: 10.1017/s1751731119000910] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Feed efficiency (FE) is one of the most important traits in pig production. However, it is difficult and costly to measure it, limiting the collection of large amount of data for an accurate selection for better FE. Therefore, the identification of single-nucleotide polymorphisms (SNPs) associated with FE-related traits to be used in the genetic evaluation is of great interest of pig breeding programs for increasing the prediction accuracy and the genetic progress of these traits. The objective of this study was to identify SNPs significantly associated with FE-related traits: average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR). We also aimed to identify potential candidate genes for these traits. Phenotypic information recorded on a population of 2386 three-way crossbreed pigs that were genotyped for 51 468 SNPs was used. We identified three loci of quantitative trait (QTL) regions associated with ADG and three QTL regions associated with ADFI; however, no significant association was found for FCR. A false discovery rate (FDR) ≤ 0.005 was used as the threshold for declaring an association as significant. The QTL regions associated with ADG on Sus scrofa chromosome (SSC) 1 were located between 177.01 and 185.47 Mb, which overlaps with the QTL regions for ADFI on SSC1 (173.26 and 185.47 Mb). The other QTL region for ADG was located on SSC12 (2.87 and 3.22 Mb). The most significant SNPs in these QTL regions explained up to 3.26% of the phenotypic variance of these traits. The non-identification of genomic regions associated with FCR can be explained by the complexity of this trait, which is a ratio between ADG and ADFI. Finally, the genes CDH19, CDH7, RNF152, MC4R, PMAIP1, FEM1B and GAA were the candidate genes found in the 1 Mb window around the QTL regions identified in this study. Among them, the MC4R gene (SSC1) has a well-known function related to ADG and ADFI. In this study, we identified three QTL regions for ADG (SSC1 and SSC12) and three for ADFI (SSC1). These regions were previously described in purebred pig populations; however, to our knowledge, this is the first study to confirm the relevance of these QTL regions in a crossbred pig population. The potential use of the SNPs and genes identified in this study in prediction models that combine genomic selection and marker-assisted selection should be evaluated for increasing the prediction accuracy of these traits in this population.
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Affiliation(s)
- É F Silva
- Departamento de Zootecnia, UEM - Universidade Estadual de Maringá, Av. Colombo, 5790, 87.020-900, Maringá, PR, Brazil
- Topigs Norsvin, Rua Visconde do Rio Branco, 1310 - Sala 52, 80.420-210, Curitiba, PR, Brazil
| | - M S Lopes
- Topigs Norsvin, Rua Visconde do Rio Branco, 1310 - Sala 52, 80.420-210, Curitiba, PR, Brazil
- Topigs Norsvin Research Center, Schoenaker 6, 6641 SZ, Beuningen, the Netherlands
| | - P S Lopes
- Departamento de Zootecnia, UFV - Universidade Federal de Viçosa, Campus Universitário, 36.570-000, Viçosa, MG, Brazil
| | - E Gasparino
- Departamento de Zootecnia, UEM - Universidade Estadual de Maringá, Av. Colombo, 5790, 87.020-900, Maringá, PR, Brazil
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Zuidema D, Sutovsky P. The domestic pig as a model for the study of mitochondrial inheritance. Cell Tissue Res 2019; 380:263-271. [DOI: 10.1007/s00441-019-03100-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 08/22/2019] [Indexed: 02/06/2023]
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Zhang H, Yin L, Wang M, Yuan X, Liu X. Factors Affecting the Accuracy of Genomic Selection for Agricultural Economic Traits in Maize, Cattle, and Pig Populations. Front Genet 2019; 10:189. [PMID: 30923535 PMCID: PMC6426750 DOI: 10.3389/fgene.2019.00189] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 02/21/2019] [Indexed: 11/20/2022] Open
Abstract
Genomic Selection (GS) has been proved to be a powerful tool for estimating genetic values in plant and livestock breeding. Newly developed sequencing technologies have dramatically reduced the cost of genotyping and significantly increased the scale of genotype data that used for GS. Meanwhile, state-of-the-art statistical methods were developed to make the best use of high marker density genotype data. In this study, 14 traits from four data sets of three species (maize, cattle, and pig) and five influential factors that affect the prediction accuracy were evaluated, including marker density (from 1 to ~600 k), statistical method (GBLUP-A, GBLUP-AD, and BayesR), minor allele frequency (MAF), heritability, and genetic architecture. Results indicate that in the GBLUP method, higher marker density leads to a higher prediction accuracy. In contrast, BayesR method needs more Monte Carlo Markov Chain (MCMC) iterations to reach the convergence and get reliable prediction values. BayesR outperforms GBLUP in predicting high or medium heritability trait that affected by one or several genes with large effects, while GBLUP performs similarly or slightly better than BayesR in predicting low heritability trait that controlled by a large amount of genes with minor effects. Prediction accuracy of trait with complex genetic architecture can be improved by increasing the marker density. Interestingly, for simple traits that controlled by one or several genes with large effects, higher marker density can cause a lower prediction accuracy if the QTN is included, but leads to a higher prediction accuracy if the QTN is excluded. The quantity of genetic markers with low MAF would not significantly affect the prediction accuracy of GBLUP, but results in a bad prediction accuracy performance of BayesR method. Compared with GBLUP-A, GBLUP-AD didn't show any advantages in capturing the non-additive variance for the traits with high heritability. The factors that affected prediction accuracy are discussed in this study and indicate that a combination of either GBLUP or BayesR method with moderate marker density and favorable polymorphism single nucleotide polymorphisms (SNPs) (~25 k SNPs) would always produce a good and stable prediction accuracy with acceptable breeding and computational costs.
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Affiliation(s)
- Haohao Zhang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Lilin Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, College of Animal Science and Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Meiyue Wang
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Xiaohui Yuan
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education and Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, College of Animal Science and Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
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Montesinos-López A, Montesinos-López OA, Gianola D, Crossa J, Hernández-Suárez CM. Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture. G3 (BETHESDA, MD.) 2018; 8:3813-3828. [PMID: 30291107 PMCID: PMC6288841 DOI: 10.1534/g3.118.200740] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 09/26/2018] [Indexed: 12/22/2022]
Abstract
Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of genomic selection. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. We compared DL methods with densely connected network architecture to one of the most often used genome-enabled prediction models: Genomic Best Linear Unbiased Prediction (GBLUP). We used nine published real genomic data sets to compare a fraction of all possible deep learning models to obtain a "meta picture" of the performance of DL methods with densely connected network architecture. In general, the best predictions were obtained with the GBLUP model when genotype×environment interaction (G×E) was taken into account (8 out of 9 data sets); when the interactions were ignored, the DL method was better than the GBLUP in terms of prediction accuracy in 6 out of the 9 data sets. For this reason, we believe that DL should be added to the data science toolkit of scientists working on animal and plant breeding. This study corroborates the view that there are no universally best prediction machines.
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Affiliation(s)
- Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, 44430, Guadalajara, Jalisco, México
| | | | - Daniel Gianola
- Departments of Animal Sciences, Dairy Science, and Biostatistics and Medical Informatics, University of Wisconsin-Madison, 53706, Madison, Wisconsin
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Ciudad de México, México
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St John JC, Tsai TS. The association of mitochondrial DNA haplotypes and phenotypic traits in pigs. BMC Genet 2018; 19:41. [PMID: 29980191 PMCID: PMC6035439 DOI: 10.1186/s12863-018-0629-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Accepted: 06/19/2018] [Indexed: 11/29/2022] Open
Abstract
Background The mitochondrial genome (mtDNA) is an emerging determiner of phenotypic traits and disease. mtDNA is inherited in a strict maternal fashion from the population of mitochondria present in the egg at fertilisation. Individuals are assigned to mtDNA haplotypes and those with sequences that cluster closely have common origins and their migration patterns can be mapped. Previously, we identified five mtDNA haplotypes in the commercial breeding lines of Australian pigs, which defined their common origins, and showed how these mtDNA haplotypes influenced litter size and reproductive function in terms of egg and embryo quality and fertilisation efficiency. Results We have determined whether mtDNA haplotypes influence other phenotypic traits. These include fat density; muscle depth; fat to leanness ratios; lifetime daily gain; teat quality; muscle score; front and rear leg assessments; percentage offspring weaned; weaning to oestrus intervals; gilt age at selection; and gestational length. In all, we assessed 5687 pigs of which 2762 were females and 2925 were males. We assessed all animals together and then by gender. We further assessed by gender based on whether a sire had joined with females from only one haplotype or from more than one haplotype. We determined that fat density, muscle depth, fat to leanness ratios, lifetime daily gain and teat quality were influenced by mtDNA haplotype and that there were gender specific effects on teat quality. Conclusions Our data illustrate that mtDNA haplotypes are associated with a number of important phenotypic traits indicative of economic breeding values in breeding pigs with gender-specific differences. Interestingly, there are ‘trade offs’ whereby some mtDNA haplotypes perform better for one selection criterion, such as muscle depth, but less so for another, for example teat quality, indicating that pig mtDNA haplotypes are afforded an advantage in one respect but a disadvantage in another. Electronic supplementary material The online version of this article (10.1186/s12863-018-0629-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Justin C St John
- Centre for Genetic Diseases, Hudson Institute of Medical Research and Department of Molecular and Translational Science, Monash University, 27-31 Wright Street, Clayton, Vic 3168, Australia.
| | - Te-Sha Tsai
- Centre for Genetic Diseases, Hudson Institute of Medical Research and Department of Molecular and Translational Science, Monash University, 27-31 Wright Street, Clayton, Vic 3168, Australia
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Eriksson S, Jonas E, Rydhmer L, Röcklinsberg H. Invited review: Breeding and ethical perspectives on genetically modified and genome edited cattle. J Dairy Sci 2017; 101:1-17. [PMID: 29102147 DOI: 10.3168/jds.2017-12962] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 08/29/2017] [Indexed: 12/12/2022]
Abstract
The hot topic of genetic modification and genome editing is sometimes presented as a rapid solution to various problems in the field of animal breeding and genetics. These technologies hold potential for future use in agriculture but we need to be aware of difficulties in large-scale application and integration in breeding schemes. In this review, we discuss applications of both classical genetic modifications (GM) using vectors and genome editing in dairy cattle breeding. We use an interdisciplinary approach considering both ethical and animal breeding perspectives. Decisions on how to make use of these techniques need to be made based not only on what is possible, but on what is reasonable to do. Principles of animal integrity, naturalness, risk perception, and animal welfare issues are examples of ethically relevant factors to consider. These factors also influence public perception and decisions about regulations by authorities. We need to acknowledge that we lack complete understanding of the genetic background of complex traits. It may be difficult, therefore, to predict the full effect of certain modifications in large-scale breeding programs. We present 2 potential applications: genome editing to dispense with dehorning, and insertion of human genes in bovine genomes to improve udder health as an example of classical GM. Both of these cases could be seen as beneficial for animal welfare but they differ in other aspects. In the former case, a genetic variant already present within the species is introduced, whereas in the latter case, transgenic animals are generated-this difference may influence how society regards the applications. We underline that the use of GM, as well as genome editing, of farm animals such as cattle is not independent of the context, and should be considered as part of an entire process, including, for example, the assisted reproduction technology that needs to be used. We propose that breeding organizations and breeding companies should take an active role in ethical discussions about the use of these techniques and thereby signal to society that these questions are being responsibly addressed.
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Affiliation(s)
- S Eriksson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden.
| | - E Jonas
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden
| | - L Rydhmer
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden
| | - H Röcklinsberg
- Department of Animal Environment and Health, 75007 Uppsala, Sweden
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29
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Genome editing in livestock: Are we ready for a revolution in animal breeding industry? Transgenic Res 2017; 26:715-726. [DOI: 10.1007/s11248-017-0049-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2017] [Accepted: 10/24/2017] [Indexed: 12/25/2022]
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Sell-Kubiak E, Wimmers K, Reyer H, Szwaczkowski T. Genetic aspects of feed efficiency and reduction of environmental footprint in broilers: a review. J Appl Genet 2017; 58:487-498. [PMID: 28342159 PMCID: PMC5655602 DOI: 10.1007/s13353-017-0392-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 01/18/2017] [Accepted: 03/08/2017] [Indexed: 11/28/2022]
Abstract
Currently, optimization of feed efficiency is one of the main challenges in improvement programs of livestock and poultry genetics. The objective of this review is to present the genetic aspects of feed efficiency related traits in meat-type chicken and possible ways to reduce the environmental impact of poultry meat production with effective breeding. Basic measures of feed efficiency are defined and the genetic background of these traits, including a review of heritabilities is described. Moreover, a number of genomic regions and candidate genes determining feed efficiency traits of broilers that were detected over the past decades are described. Classical and genomic selection strategies for feed efficiency in the context of its relationships with other performance traits are discussed as well. Finally, future strategies to improve feed digestibility are described as it is expected that they will decrease wastes and greenhouse gas emission. Further genetic improvement of feed efficiency, should be examined jointly with appropriate feeding strategies in broilers.
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Affiliation(s)
- Ewa Sell-Kubiak
- Departament of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska st. 33, 60-637, Poznan, Poland
| | - Klaus Wimmers
- Institute of Genome Biology, Leibniz Institute of Farm Animal Biology, Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Henry Reyer
- Institute of Genome Biology, Leibniz Institute of Farm Animal Biology, Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Tomasz Szwaczkowski
- Departament of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska st. 33, 60-637, Poznan, Poland.
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Abernathy J, Brezas A, Snekvik KR, Hardy RW, Overturf K. Integrative functional analyses using rainbow trout selected for tolerance to plant diets reveal nutrigenomic signatures for soy utilization without the concurrence of enteritis. PLoS One 2017; 12:e0180972. [PMID: 28723948 PMCID: PMC5517010 DOI: 10.1371/journal.pone.0180972] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 06/24/2017] [Indexed: 12/13/2022] Open
Abstract
Finding suitable alternative protein sources for diets of carnivorous fish species remains a major concern for sustainable aquaculture. Through genetic selection, we created a strain of rainbow trout that outperforms parental lines in utilizing an all-plant protein diet and does not develop enteritis in the distal intestine, as is typical with salmonids on long-term plant protein-based feeds. By incorporating this strain into functional analyses, we set out to determine which genes are critical to plant protein utilization in the absence of gut inflammation. After a 12-week feeding trial with our selected strain and a control trout strain fed either a fishmeal-based diet or an all-plant protein diet, high-throughput RNA sequencing was completed on both liver and muscle tissues. Differential gene expression analyses, weighted correlation network analyses and further functional characterization were performed. A strain-by-diet design revealed differential expression ranging from a few dozen to over one thousand genes among the various comparisons and tissues. Major gene ontology groups identified between comparisons included those encompassing central, intermediary and foreign molecule metabolism, associated biosynthetic pathways as well as immunity. A systems approach indicated that genes involved in purine metabolism were highly perturbed. Systems analysis among the tissues tested further suggests the interplay between selection for growth, dietary utilization and protein tolerance may also have implications for nonspecific immunity. By combining data from differential gene expression and co-expression networks using selected trout, along with ontology and pathway analyses, a set of 63 candidate genes for plant diet tolerance was found. Risk loci in human inflammatory bowel diseases were also found in our datasets, indicating rainbow trout selected for plant-diet tolerance may have added utility as a potential biomedical model.
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Affiliation(s)
- Jason Abernathy
- Hagerman Fish Culture Experiment Station, USDA-ARS, Hagerman, Idaho, United States of America
| | - Andreas Brezas
- Aquaculture Research Institute, University of Idaho, Hagerman, Idaho, United States of America
| | - Kevin R. Snekvik
- Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, Washington, United States of America
| | - Ronald W. Hardy
- Aquaculture Research Institute, University of Idaho, Hagerman, Idaho, United States of America
| | - Ken Overturf
- Hagerman Fish Culture Experiment Station, USDA-ARS, Hagerman, Idaho, United States of America
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Howard JT, Tiezzi F, Pryce JE, Maltecca C. Geno-Diver: A combined coalescence and forward-in-time simulator for populations undergoing selection for complex traits. J Anim Breed Genet 2017; 134:553-563. [PMID: 28464287 DOI: 10.1111/jbg.12277] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Accepted: 03/25/2017] [Indexed: 01/30/2023]
Abstract
Geno-Diver is a combined coalescence and forward-in-time simulator designed to simulate complex traits with a quantitative and/or fitness component and implement multiple selection and mating strategies utilizing pedigree or genomic information. The simulation is carried out in two steps. The first step generates whole-genome sequence data for founder individuals. A variety of trait architectures can be generated for quantitative and fitness traits along with their covariance. The second step generates new individuals forward-in-time based on a variety of selection and mating scenarios. Genetic values are predicted for individuals utilizing pedigree or genomic information. Relationship matrices and their associated inverses are generated using computationally efficient routines. We benchmarked Geno-Diver with a previous simulation program and described how to simulate a traditional quantitative trait along with a quantitative and fitness trait. A user manual with examples, source code in C++11 and executable versions of Geno-Diver for Linux are freely available at https://github.com/jeremyhoward/Geno-Diver.
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Affiliation(s)
- J T Howard
- Department of Animal Science, North Carolina State University, Raleigh, NC, USA
| | - F Tiezzi
- Department of Animal Science, North Carolina State University, Raleigh, NC, USA
| | - J E Pryce
- Department of Economic Development, Jobs, Transport and Resources and Dairy Futures Cooperative Research Centre, Bundoora, Vic., Australia.,La Trobe University, Bundoora, Vic., Australia
| | - C Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, NC, USA
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Lopes MS, Bovenhuis H, van Son M, Nordbø Ø, Grindflek EH, Knol EF, Bastiaansen JWM. Using markers with large effect in genetic and genomic predictions. J Anim Sci 2017; 95:59-71. [PMID: 28177367 DOI: 10.2527/jas.2016.0754] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The first attempts of applying marker-assisted selection (MAS) in animal breeding were not very successful because the identification of markers closely linked to QTL using low-density microsatellite panels was difficult. More recently, the use of high-density SNP panels in genome-wide association studies (GWAS) have increased the power and precision of identifying markers linked to QTL, which offer new possibilities for MAS. However, when GWAS started to be performed, the focus of many breeders had already shifted from the use of MAS to the application of genomic selection (using all available markers without any preselection of markers linked to QTL). In this study, we aimed to evaluate the prediction accuracy of a MAS approach that accounts for GWAS findings in the prediction models by including the most significant SNP from GWAS as a fixed effect in the marker-assisted BLUP (MA-BLUP) and marker-assisted genomic BLUP (MA-GBLUP) prediction models. A second aim was to compare the prediction accuracies from the marker-assisted models with those obtained from a Bayesian variable selection (BVS) model. To compare the prediction accuracies of traditional BLUP, MA-BLUP, genomic BLUP (GBLUP), MA-GBLUP, and BVS, we applied these models to the trait "number of teats" in 4 distinct pig populations, for validation of the results. The most significant SNP in each population was located at approximately 103.50 Mb on chromosome 7. Applying MAS by accounting for the most significant SNP in the prediction models resulted in improved prediction accuracy for number of teats in all evaluated populations compared with BLUP and GBLUP. Using MA-BLUP instead of BLUP, the increase in prediction accuracy ranged from 0.021 to 0.124, whereas using MA-GBLUP instead of GBLUP, the increase in prediction accuracy ranged from 0.003 to 0.043. The BVS model resulted in similar or higher prediction accuracies than MA-GBLUP. For the trait number of teats, BLUP resulted in the lowest prediction accuracies whereas the highest were observed when applying MA-GBLUP or BVS. In the same data set, MA-BLUP can yield similar or superior accuracies compared with GBLUP. The superiority of MA-GBLUP over traditional GBLUP is more pronounced when training populations are smaller and when relationships between training and validation populations are smaller. Marker-assisted GBLUP did not outperform BVS but does have implementation advantages in large-scale evaluations.
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Howard JT, Tiezzi F, Huang Y, Gray KA, Maltecca C. Characterization and management of long runs of homozygosity in parental nucleus lines and their associated crossbred progeny. Genet Sel Evol 2016; 48:91. [PMID: 27884108 PMCID: PMC5123398 DOI: 10.1186/s12711-016-0269-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 11/10/2016] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND In nucleus populations, regions of the genome that have a high frequency of runs of homozygosity (ROH) occur and are associated with a reduction in genetic diversity, as well as adverse effects on fitness. It is currently unclear whether, and to what extent, ROH stretches persist in the crossbred genome and how genomic management in the nucleus population might impact low diversity regions and its implications on the crossbred genome. METHODS We calculated a ROH statistic based on lengths of 5 (ROH5) or 10 (ROH10) Mb across the genome for genotyped Landrace (LA), Large White (LW) and Duroc (DU) dams. We simulated crossbred dam (LA × LW) and market [DU × (LA × LW)] animal genotypes based on observed parental genotypes and the ROH frequency was tabulated. We conducted a simulation using observed genotypes to determine the impact of minimizing parental relationships on multiple diversity metrics within nucleus herds, i.e. pedigree-(A), SNP-by-SNP relationship matrix or ROH relationship matrix. Genome-wide metrics included, pedigree inbreeding, heterozygosity and proportion of the genome in ROH of at least 5 Mb. Lastly, the genome was split into bins of increasing ROH5 frequency and, within each bin, heterozygosity, ROH5 and length (Mb) of ROH were evaluated. RESULTS We detected regions showing high frequencies of either ROH5 and/or ROH10 across both LW and LA on SSC1, SSC4, and SSC14, and across all breeds on SSC9. Long haplotypes were shared across parental breeds and thus, regions of ROH persisted in crossbred animals. Averaged across replicates and breeds, progeny had higher levels of heterozygosity (0.0056 ± 0.002%) and lower proportion of the genome in a ROH of at least 5 Mb (-0.015 ± 0.003%) than their parental genomes when genomic relationships were constrained, while pedigree relationships resulted in negligible differences at the genomic level. Across all breeds, only genomic data was able to target low diversity regions. CONCLUSIONS We show that long stretches of ROH present in the parents persist in crossbred animals. Furthermore, compared to using pedigree relationships, using genomic information to constrain parental relationships resulted in maintaining more genetic diversity and more effectively targeted low diversity regions.
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Affiliation(s)
- Jeremy T Howard
- Department of Animal Science, North Carolina State University, Raleigh, NC, 27695-7627, USA.
| | - Francesco Tiezzi
- Department of Animal Science, North Carolina State University, Raleigh, NC, 27695-7627, USA
| | - Yijian Huang
- Smithfield Premium Genetics, Rose Hill, NC, 28458, USA
| | - Kent A Gray
- Smithfield Premium Genetics, Rose Hill, NC, 28458, USA
| | - Christian Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, NC, 27695-7627, USA.,Genetics Program, North Carolina State University, Raleigh, NC, 27695-7627, USA
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Jonas E, de Koning DJ. Goals and hurdles for a successful implementation of genomic selection in breeding programme for selected annual and perennial crops. Biotechnol Genet Eng Rev 2016; 32:18-42. [DOI: 10.1080/02648725.2016.1177377] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Elisabeth Jonas
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Ulls väg 26, 75007 Uppsala, Sweden
| | - Dirk Jan de Koning
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Ulls väg 26, 75007 Uppsala, Sweden
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Tsai HY, Hamilton A, Tinch AE, Guy DR, Gharbi K, Stear MJ, Matika O, Bishop SC, Houston RD. Genome wide association and genomic prediction for growth traits in juvenile farmed Atlantic salmon using a high density SNP array. BMC Genomics 2015; 16:969. [PMID: 26582102 PMCID: PMC4652364 DOI: 10.1186/s12864-015-2117-9] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Accepted: 10/20/2015] [Indexed: 01/01/2023] Open
Abstract
Background The genetic architecture of complex traits in farmed animal populations is of interest from a scientific and practical perspective. The use of genetic markers to predict the genetic merit (breeding values) of individuals is commonplace in modern farm animal breeding schemes. Recently, high density SNP arrays have become available for Atlantic salmon, which facilitates genomic prediction and association studies using genome-wide markers and economically important traits. The aims of this study were (i) to use a high density SNP array to investigate the genetic architecture of weight and length in juvenile Atlantic salmon; (ii) to assess the utility of genomic prediction for these traits, including testing different marker densities; (iii) to identify potential candidate genes underpinning variation in early growth. Results A pedigreed population of farmed Atlantic salmon (n = 622) were measured for weight and length traits at one year of age, and genotyped for 111,908 segregating SNP markers using a high density SNP array. The heritability of both traits was estimated using pedigree and genomic relationship matrices, and was comparable at around 0.5 and 0.6 respectively. The results of the GWA analysis pointed to a polygenic genetic architecture, with no SNPs surpassing the genome-wide significance threshold, and one SNP associated with length at the chromosome-wide level. SNPs surpassing an arbitrary threshold of significance (P < 0.005, ~ top 0.5 % of markers) were aligned to an Atlantic salmon reference transcriptome, identifying 109 SNPs in transcribed regions that were annotated by alignment to human, mouse and zebrafish protein databases. Prediction of breeding values was more accurate when applying genomic (GBLUP) than pedigree (PBLUP) relationship matrices (accuracy ~ 0.7 and 0.58 respectively) and 5,000 SNPs were sufficient for obtaining this accuracy increase over PBLUP in this specific population. Conclusions The high density SNP array can effectively capture the additive genetic variation in complex traits. However, the traits of weight and length both appear to be very polygenic with only one SNP surpassing the chromosome-wide threshold. Genomic prediction using the array is effective, leading to an improvement in accuracy compared to pedigree methods, and this improvement can be achieved with only a small subset of the markers in this population. The results have practical relevance for genomic selection in salmon and may also provide insight into variation in the identified genes underpinning body growth and development in salmonid species. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2117-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hsin-Yuan Tsai
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, Edinburgh, UK.
| | - Alastair Hamilton
- Landcatch Natural Selection Ltd., 15 Beta Centre, Stirling University Innovation Park, Stirling, FK9 4NF, UK.
| | - Alan E Tinch
- Landcatch Natural Selection Ltd., 15 Beta Centre, Stirling University Innovation Park, Stirling, FK9 4NF, UK.
| | - Derrick R Guy
- Landcatch Natural Selection Ltd., 15 Beta Centre, Stirling University Innovation Park, Stirling, FK9 4NF, UK.
| | - Karim Gharbi
- Edinburgh Genomics, Ashworth Laboratories, King's Buildings, The University of Edinburgh, Edinburgh, EH9 3JT, UK.
| | - Michael J Stear
- Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Bearsden Road, Glasgow, G61 1QH, UK.
| | - Oswald Matika
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, Edinburgh, UK.
| | - Steve C Bishop
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, Edinburgh, UK.
| | - Ross D Houston
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, Edinburgh, UK.
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