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Kilusungu ZH, Kassam D, Kimera ZI, Mgaya FX, Nandolo W, Kunambi PP, Ulomi W, Matee MIN. Tetracycline and sulphonamide residues in farmed fish in Dar es Salaam, Tanzania and human health risk implications. Food Addit Contam Part B Surveill 2024:1-10. [PMID: 38516743 DOI: 10.1080/19393210.2024.2331106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/12/2024] [Indexed: 03/23/2024]
Abstract
In Africa, antibiotic residue investigations in animal food have primarily been focused on meat, neglecting farmed fish. This cross-sectional study conducted in Dar es Salaam, Tanzania, aimed to assess sulphonamide and tetracycline residues in farmed fish, comparing levels with Codex Alimentarius Commission's acceptable daily intake (ADI) and maximum residue limits (MRLs). A total of 84 farmed fish were sampled and analysed in the presence of tetracycline and sulphonamide residues. All samples were positive for sulphonamide residues (100%; n = 84), and 2.4% (n = 2) were positive for tetracycline and consequently also positive for both compounds. Tetracycline levels were below ADI and MRL, 28.5% (n = 24) surpassed the ADI, and 6% (n = 5) of the samples exceeded the MRL for sulphonamide. Regular monitoring of antibiotic residues in aquaculture products is crucial to mitigate health risks and expanding assessments to include other commonly used compounds is warranted.
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Affiliation(s)
- Zainabu H Kilusungu
- Department of Aquaculture and Fisheries Science, Africa Centre of Excellence in Aquaculture and Fisheries (AquaFish), Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Daud Kassam
- Department of Aquaculture and Fisheries Science, Africa Centre of Excellence in Aquaculture and Fisheries (AquaFish), Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Zuhura Idd Kimera
- Department of Environmental and Occupational Health, School of Public Health and Social Sciences, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Fauster X Mgaya
- Department of Microbiology and Immunology, School of Diagnostic Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Wilson Nandolo
- Department of Animal Science, Lilongwe University of Agriculture and Natural Resources(LUANAR), Lilongwe, Malawi
| | - Peter P Kunambi
- Department of Microbiology and Immunology, School of Diagnostic Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Winstone Ulomi
- Testing Department, Directorate of Testing and Calibration, Tanzania Bureau of Standards, Dar es Salaam, Tanzania
| | - Mecky I N Matee
- Department of Microbiology and Immunology, School of Diagnostic Medicine, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
- SACIDS Foundation for One Health (SACIDS), Sokoine University of Agriculture (SUA), Morogoro, Tanzania
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Woodward-Greene MJ, Kinser JM, Huson HJ, Sonstegard TS, Soelkner J, Vaisman II, Boettcher P, Masiga CW, Mukasa C, Abegaz S, Agaba M, Ahmed SS, Maminiaina OF, Getachew T, Gondwe TN, Haile A, Hassan Y, Kihara A, Kouriba A, Mruttu HA, Mujibi D, Nandolo W, Rischkowsky BA, Rosen BD, Sayre B, Taela M, Van Tassell CP. Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network-Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes. Front Genet 2023; 14:1200770. [PMID: 37745840 PMCID: PMC10512022 DOI: 10.3389/fgene.2023.1200770] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/25/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction: The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) is an accessible, easy to use, low-cost procedure to collect phenotypic data via digital images. The AGIN-ICP collects images to extract several phenotype measures including health status indicators (anemia status, age, and weight), body measurements, shapes, and coat color and pattern, from digital images taken with standard digital cameras or mobile devices. This strategy is to quickly survey, record, assess, analyze, and store these data for use in a wide variety of production and sampling conditions. Methods: The work was accomplished as part of the multinational African Goat Improvement Network (AGIN) collaborative and is presented here as a case study in the AGIN collaboration model and working directly with community-based breeding programs (CBBP). It was iteratively developed and tested over 3 years, in 12 countries with over 12,000 images taken. Results and discussion: The AGIN-ICP development is described, and field implementation and the quality of the resulting images for use in image analysis and phenotypic data extraction are iteratively assessed. Digital body measures were validated using the PreciseEdge Image Segmentation Algorithm (PE-ISA) and software showing strong manual to digital body measure Pearson correlation coefficients of height, length, and girth measures (0.931, 0.943, 0.893) respectively. It is critical to note that while none of the very detailed tasks in the AGIN-ICP described here is difficult, every single one of them is even easier to accidentally omit, and the impact of such a mistake could render a sample image, a sampling day's images, or even an entire sampling trip's images difficult or unusable for extracting digital phenotypes. Coupled with tissue sampling and genomic testing, it may be useful in the effort to identify and conserve important animal genetic resources and in CBBP genetic improvement programs by providing reliably measured phenotypes with modest cost. Potential users include farmers, animal husbandry officials, veterinarians, regional government or other public health officials, researchers, and others. Based on these results, a final AGIN-ICP is presented, optimizing the costs, ease, and speed of field implementation of the collection method without compromising the quality of the image data collection.
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Affiliation(s)
- M. Jennifer Woodward-Greene
- National Agricultural Library, USDA Agricultural Research Service, Beltsville, MD, United States
- Animal Genomics Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States
- Bioinformatics and Computational Biology Program, School of Systems Biology, College of Science, George Mason University, Manassas, VA, United States
| | - Jason M. Kinser
- School of Physics, Astronomy, and Computational Sciences, College of Science, George Mason University, Fairfax, VA, United States
| | - Heather J. Huson
- Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
| | | | - Johann Soelkner
- Department of Sustainable Agricultural Systems, Division of Livestock Sciences, BOKU—University of Natural Resources and Life Sciences, Vienna, Austria
| | - Iosif I. Vaisman
- Bioinformatics and Computational Biology Program, School of Systems Biology, College of Science, George Mason University, Manassas, VA, United States
| | - Paul Boettcher
- Food and Agriculture Organization of the United Nations, Animal Production and Health Division, Rome, Italy
| | - Clet W. Masiga
- Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA), Entebbe, Uganda
| | | | - Solomon Abegaz
- Ethiopian Institute of Agricultural Research, Addis Ababa, Ethiopia
| | - Morris Agaba
- Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania
| | - Sahar S. Ahmed
- Cell Biology Department, Biotechnology Research Institute, National Research Centre, Giza, Egypt
| | - Oliver F. Maminiaina
- Department of Zootechnical, Veterinary and Piscicultural Research (DRZVP), National Center for Applied Research in Rural Development (CENRADERU), Antananarivo, Madagascar
| | - Tesfaye Getachew
- International Center for Agricultural Research in the Dry Areas (ICARDA), Addis Ababa, Ethiopia
| | - Timothy N. Gondwe
- Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Aynalem Haile
- International Center for Agricultural Research in the Dry Areas (ICARDA), Addis Ababa, Ethiopia
| | - Yassir Hassan
- Department of Animal Genetic Resources Development, Animal Production Research Center, Ministry of Animal Resources, Khartoum North, Sudan
| | | | | | | | - Denis Mujibi
- International Livestock Research Institute, Nairobi, Kenya
| | - Wilson Nandolo
- Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Barbara A. Rischkowsky
- International Center for Agricultural Research in the Dry Areas (ICARDA), Addis Ababa, Ethiopia
| | - Benjamin D. Rosen
- Animal Genomics Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States
| | - Brian Sayre
- Department of Biology, Virginia State University, Petersburg, VA, United States
| | - Maria Taela
- Agrarian Research Institute of Mozambique, Directorate of Animal Science, Maputo, Mozambique
| | - Curtis P. Van Tassell
- Animal Genomics Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States
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3
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Van Tassell CP, Rosen BD, Woodward-Greene MJ, Silverstein JT, Huson HJ, Sölkner J, Boettcher P, Rothschild MF, Mészáros G, Nakimbugwe HN, Gondwe TN, Muchadeyi FC, Nandolo W, Mulindwa HA, Banda LJ, Kaumbata W, Getachew T, Haile A, Soudre A, Ouédraogo D, Rischkowsky BA, Mwai AO, Dzomba EF, Nash O, Abegaz S, Masiga CW, Wurzinger M, Sayre BL, Stella A, Tosser-Klopp G, Sonstegard TS. The African Goat Improvement Network: a scientific group empowering smallholder farmers. Front Genet 2023; 14:1183240. [PMID: 37712066 PMCID: PMC10497955 DOI: 10.3389/fgene.2023.1183240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/27/2023] [Indexed: 09/16/2023] Open
Abstract
The African Goat Improvement Network (AGIN) is a collaborative group of scientists focused on genetic improvement of goats in small holder communities across the African continent. The group emerged from a series of workshops focused on enhancing goat productivity and sustainability. Discussions began in 2011 at the inaugural workshop held in Nairobi, Kenya. The goals of this diverse group were to: improve indigenous goat production in Africa; characterize existing goat populations and to facilitate germplasm preservation where appropriate; and to genomic approaches to better understand adaptation. The long-term goal was to develop cost-effective strategies to apply genomics to improve productivity of small holder farmers without sacrificing adaptation. Genome-wide information on genetic variation enabled genetic diversity studies, facilitated improved germplasm preservation decisions, and provided information necessary to initiate large scale genetic improvement programs. These improvements were partially implemented through a series of community-based breeding programs that engaged and empowered local small farmers, especially women, to promote sustainability of the production system. As with many international collaborative efforts, the AGIN work serves as a platform for human capacity development. This paper chronicles the evolution of the collaborative approach leading to the current AGIN organization and describes how it builds capacity for sustained research and development long after the initial program funds are gone. It is unique in its effectiveness for simultaneous, multi-level capacity building for researchers, students, farmers and communities, and local and regional government officials. The positive impact of AGIN capacity building has been felt by participants from developing, as well as developed country partners.
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Affiliation(s)
- Curtis P. Van Tassell
- Animal Genomics and Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States
| | - Benjamin D. Rosen
- Animal Genomics and Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States
| | - M. Jennifer Woodward-Greene
- Animal Genomics and Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD, United States
- National Agricultural Library, USDA Agricultural Research Service, Beltsville, MD, United States
| | - Jeffrey T. Silverstein
- Office of National Programs, USDA Agricultural Research Service, Beltsville, MD, United States
| | - Heather J. Huson
- Department of Animal Science, Cornell University, Ithaca, NY, United States
| | - Johann Sölkner
- Division of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Paul Boettcher
- Animal Production and Health Division, Food and Agriculture Organization of the United Nations, Rome, Italy
| | - Max F. Rothschild
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Gábor Mészáros
- Division of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria
| | | | - Timothy N. Gondwe
- Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Farai C. Muchadeyi
- Biotechnology Platform, Agricultural Research Council, Pretoria, South Africa
| | - Wilson Nandolo
- Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | | | - Liveness J. Banda
- Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Wilson Kaumbata
- Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Tesfaye Getachew
- International Center for Agricultural Research in the Dry Areas, Addis Ababa, Ethiopia
| | - Aynalem Haile
- International Center for Agricultural Research in the Dry Areas, Addis Ababa, Ethiopia
| | - Albert Soudre
- Unité de Formation et de Recherches - Sciences et Technologies, Université Norbert ZONGO, Koudougou, Burkina Faso
| | | | | | | | - Edgar Farai Dzomba
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Oyekanmi Nash
- National Biotechnology Development Agency, Abuja, Nigeria
| | - Solomon Abegaz
- Ethiopian Institute of Agricultural Research, Addis Ababa, Ethiopia
| | | | - Maria Wurzinger
- Division of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Brian L. Sayre
- Department of Biology, Virginia State University, Petersburg, VA, United States
| | - Alessandra Stella
- Institute of Agricultural Biology and Biotechnology (IBBA), Milano, Italy
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4
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Nandolo W, Mészáros G, Wurzinger M, Banda LJ, Gondwe TN, Mulindwa HA, Nakimbugwe HN, Clark EL, Woodward-Greene MJ, Liu M, Liu GE, Van Tassell CP, Rosen BD, Sölkner J. Detection of copy number variants in African goats using whole genome sequence data. BMC Genomics 2021; 22:398. [PMID: 34051743 PMCID: PMC8164248 DOI: 10.1186/s12864-021-07703-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 05/11/2021] [Indexed: 12/21/2022] Open
Abstract
Background Copy number variations (CNV) are a significant source of variation in the genome and are therefore essential to the understanding of genetic characterization. The aim of this study was to develop a fine-scaled copy number variation map for African goats. We used sequence data from multiple breeds and from multiple African countries. Results A total of 253,553 CNV (244,876 deletions and 8677 duplications) were identified, corresponding to an overall average of 1393 CNV per animal. The mean CNV length was 3.3 kb, with a median of 1.3 kb. There was substantial differentiation between the populations for some CNV, suggestive of the effect of population-specific selective pressures. A total of 6231 global CNV regions (CNVR) were found across all animals, representing 59.2 Mb (2.4%) of the goat genome. About 1.6% of the CNVR were present in all 34 breeds and 28.7% were present in all 5 geographical areas across Africa, where animals had been sampled. The CNVR had genes that were highly enriched in important biological functions, molecular functions, and cellular components including retrograde endocannabinoid signaling, glutamatergic synapse and circadian entrainment. Conclusions This study presents the first fine CNV map of African goat based on WGS data and adds to the growing body of knowledge on the genetic characterization of goats. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07703-1.
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Affiliation(s)
- Wilson Nandolo
- University of Natural Resources and Life Sciences, Vienna, Austria.,Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Gábor Mészáros
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Maria Wurzinger
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Liveness J Banda
- Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Timothy N Gondwe
- Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | | | | | - Emily L Clark
- The Roslin Institute, University of Edinburgh, Edinburgh, Scotland, UK
| | - M Jennifer Woodward-Greene
- Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, USA.,National Agricultural Library, USDA-ARS, Beltsville, MD, USA
| | - Mei Liu
- Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, USA
| | | | - George E Liu
- Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, USA
| | | | - Benjamin D Rosen
- Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, USA.
| | - Johann Sölkner
- University of Natural Resources and Life Sciences, Vienna, Austria
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5
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Rosen BD, Bickhart DM, Schnabel RD, Koren S, Elsik CG, Tseng E, Rowan TN, Low WY, Zimin A, Couldrey C, Hall R, Li W, Rhie A, Ghurye J, McKay SD, Thibaud-Nissen F, Hoffman J, Murdoch BM, Snelling WM, McDaneld TG, Hammond JA, Schwartz JC, Nandolo W, Hagen DE, Dreischer C, Schultheiss SJ, Schroeder SG, Phillippy AM, Cole JB, Van Tassell CP, Liu G, Smith TPL, Medrano JF. De novo assembly of the cattle reference genome with single-molecule sequencing. Gigascience 2020; 9:5810242. [PMID: 32191811 PMCID: PMC7081964 DOI: 10.1093/gigascience/giaa021] [Citation(s) in RCA: 299] [Impact Index Per Article: 74.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 01/31/2020] [Accepted: 02/14/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Major advances in selection progress for cattle have been made following the introduction of genomic tools over the past 10-12 years. These tools depend upon the Bos taurus reference genome (UMD3.1.1), which was created using now-outdated technologies and is hindered by a variety of deficiencies and inaccuracies. RESULTS We present the new reference genome for cattle, ARS-UCD1.2, based on the same animal as the original to facilitate transfer and interpretation of results obtained from the earlier version, but applying a combination of modern technologies in a de novo assembly to increase continuity, accuracy, and completeness. The assembly includes 2.7 Gb and is >250× more continuous than the original assembly, with contig N50 >25 Mb and L50 of 32. We also greatly expanded supporting RNA-based data for annotation that identifies 30,396 total genes (21,039 protein coding). The new reference assembly is accessible in annotated form for public use. CONCLUSIONS We demonstrate that improved continuity of assembled sequence warrants the adoption of ARS-UCD1.2 as the new cattle reference genome and that increased assembly accuracy will benefit future research on this species.
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Affiliation(s)
- Benjamin D Rosen
- USDA-ARS, Beltsville, MD, 20705-2350 , Animal Genomics and Improvement Laboratory, USDA-ARS, 10300 Baltimore Ave, Beltsville, MD 20705-2350, USA
| | - Derek M Bickhart
- Dairy Forage Research Center, USDA-ARS, 1925 Linden Drive, Madison, WI, 53706, USA
| | - Robert D Schnabel
- Division of Animal Sciences, University of Missouri, 162 Animal Science Research Center, Columbia, MO 65211, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Christine G Elsik
- Division of Animal Sciences, University of Missouri, 162 Animal Science Research Center, Columbia, MO 65211, USA
| | - Elizabeth Tseng
- Pacific Biosciences, 1305 O'Brien Drive, Menlo Park, CA 94025, USA
| | - Troy N Rowan
- Division of Animal Sciences, University of Missouri, 162 Animal Science Research Center, Columbia, MO 65211, USA
| | - Wai Y Low
- The Davies Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA 5371, Australia
| | - Aleksey Zimin
- Johns Hopkins University, Welch Library of Medicine, Ste 105, 1900 E. Monument St., Baltimore, MD 21205, USA
| | - Christine Couldrey
- Livestock Improvement Corporation, Private Bag 3016, Hamilton 3240, New Zealand
| | - Richard Hall
- Pacific Biosciences, 1305 O'Brien Drive, Menlo Park, CA 94025, USA
| | - Wenli Li
- Dairy Forage Research Center, USDA-ARS, 1925 Linden Drive, Madison, WI, 53706, USA
| | - Arang Rhie
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Jay Ghurye
- Department of Computer Science, University of Maryland, 8125 Paint Branch Drive, College Park, MD 20742 USA
| | - Stephanie D McKay
- Department of Animal and Veterinary Sciences, University of Vermont, Burlington, VT 05405, USA
| | - Françoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Jinna Hoffman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Brenda M Murdoch
- Department of Animal and Veterinary Science, University of Idaho, 875 Perimeter Drive MS 2330, Moscow, ID 83844-2330, USA
| | - Warren M Snelling
- U.S. Meat Animal Research Center, USDA-ARS, 844 Road 313, Clay Center, NE 68933, USA
| | - Tara G McDaneld
- U.S. Meat Animal Research Center, USDA-ARS, 844 Road 313, Clay Center, NE 68933, USA
| | | | | | - Wilson Nandolo
- Division of Livestock Sciences, University of Natural Resources and Life Sciences, Gregor Mendel str. 33, A-1180, Vienna, Austria.,Animal Science Department, Lilongwe University of Agriculture and Natural Resources, P.O. Box 219, Lilongwe, Malawi
| | - Darren E Hagen
- Department of Animal and Food Sciences, Oklahoma State University, 101 Animal Science Building, Stillwater, OK 74078, USA
| | | | | | - Steven G Schroeder
- USDA-ARS, Beltsville, MD, 20705-2350 , Animal Genomics and Improvement Laboratory, USDA-ARS, 10300 Baltimore Ave, Beltsville, MD 20705-2350, USA
| | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - John B Cole
- USDA-ARS, Beltsville, MD, 20705-2350 , Animal Genomics and Improvement Laboratory, USDA-ARS, 10300 Baltimore Ave, Beltsville, MD 20705-2350, USA
| | - Curtis P Van Tassell
- USDA-ARS, Beltsville, MD, 20705-2350 , Animal Genomics and Improvement Laboratory, USDA-ARS, 10300 Baltimore Ave, Beltsville, MD 20705-2350, USA
| | - George Liu
- USDA-ARS, Beltsville, MD, 20705-2350 , Animal Genomics and Improvement Laboratory, USDA-ARS, 10300 Baltimore Ave, Beltsville, MD 20705-2350, USA
| | - Timothy P L Smith
- U.S. Meat Animal Research Center, USDA-ARS, 844 Road 313, Clay Center, NE 68933, USA
| | - Juan F Medrano
- Department of Animal Science, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
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6
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Nandolo W, Mészáros G, Banda LJ, Gondwe TN, Lamuno D, Mulindwa HA, Nakimbugwe HN, Wurzinger M, Utsunomiya YT, Woodward-Greene MJ, Liu M, Liu G, Van Tassell CP, Curik I, Rosen BD, Sölkner J. Timing and Extent of Inbreeding in African Goats. Front Genet 2019; 10:537. [PMID: 31214253 PMCID: PMC6558083 DOI: 10.3389/fgene.2019.00537] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 05/17/2019] [Indexed: 11/13/2022] Open
Abstract
Genetic characterization of African goats is one of the current priorities in the improvement of goats in the continent. This study contributes to the characterization effort by determining the levels and number of generations to common ancestors ("age") associated with inbreeding in African goat breeds and identifies regions that contain copy number variation mistyped as being homozygous. Illumina 50k single nucleotide polymorphism genotype data for 608 goats from 31 breeds were used to compute the level and age of inbreeding at both local (marker) and global levels (FG) using a model-based approach based on a hidden Markov model. Runs of homozygosity (ROH) segments detected using the Viterbi algorithm led to ROH-based inbreeding coefficients for all ROH (FROH) and for ROH longer than 2 Mb (FROH > 2Mb). Some of the genomic regions identified as having ROH are likely to be hemizygous regions (copy number deletions) mistyped as homozygous regions. Although the proportion of these miscalled ROH is small and does not substantially affect estimates of levels of inbreeding for individual animals, the inbreeding metrics were adjusted by removing these regions from the ROH. All the inbreeding metrics varied widely across breeds, with overall means of 0.0408, 0.0370, and 0.0691 and medians of 0.0125, 0.0098, and 0.0366 for FROH, FROH > 2Mb, and FG, respectively. Several breeds (including Menabe and Sofia from Madagascar) had high proportions of recent inbreeding, while Small East African, Ethiopian, and most of the West African breeds (including West African Dwarf) had more ancient inbreeding.
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Affiliation(s)
- Wilson Nandolo
- Division of Livestock Sciences, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria.,Department of Animal Science, Faculty of Agriculture, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi.,Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agriculture Research Service, Beltsville, MD, United States
| | - Gábor Mészáros
- Division of Livestock Sciences, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria
| | - Liveness Jessica Banda
- Department of Animal Science, Faculty of Agriculture, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Timothy N Gondwe
- Department of Animal Science, Faculty of Agriculture, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi
| | - Doreen Lamuno
- Division of Livestock Sciences, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria
| | | | | | - Maria Wurzinger
- Division of Livestock Sciences, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria
| | - Yuri T Utsunomiya
- School of Agricultural and Veterinarian Sciences, Jaboticabal, São Paulo State University (UNESP), São Paulo, Brazil
| | - M Jennifer Woodward-Greene
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agriculture Research Service, Beltsville, MD, United States
| | - Mei Liu
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agriculture Research Service, Beltsville, MD, United States
| | - George Liu
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agriculture Research Service, Beltsville, MD, United States
| | - Curtis P Van Tassell
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agriculture Research Service, Beltsville, MD, United States
| | - Ino Curik
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Zagreb, Croatia
| | - Benjamin D Rosen
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agriculture Research Service, Beltsville, MD, United States
| | - Johann Sölkner
- Division of Livestock Sciences, Department of Sustainable Agricultural Systems, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria
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Nandolo W, Utsunomiya YT, Mészáros G, Wurzinger M, Khayadzadeh N, Torrecilha RBP, Mulindwa HA, Gondwe TN, Waldmann P, Ferenčaković M, Garcia JF, Rosen BD, Bickhart D, van Tassell CP, Curik I, Sölkner J. Misidentification of runs of homozygosity islands in cattle caused by interference with copy number variation or large intermarker distances. Genet Sel Evol 2018; 50:43. [PMID: 30134820 PMCID: PMC6106898 DOI: 10.1186/s12711-018-0414-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 07/30/2018] [Indexed: 12/22/2022] Open
Abstract
Background Runs of homozygosity (ROH) islands are stretches of homozygous sequence in the genome of a large proportion of individuals in a population. Algorithms for the detection of ROH depend on the similarity of haplotypes. Coverage gaps and copy number variants (CNV) may result in incorrect identification of such similarity, leading to the detection of ROH islands where none exists. Misidentified hemizygous regions will also appear as homozygous based on sequence variation alone. Our aim was to identify ROH islands influenced by marker coverage gaps or CNV, using Illumina BovineHD BeadChip (777 K) single nucleotide polymorphism (SNP) data for Austrian Brown Swiss, Tyrol Grey and Pinzgauer cattle. Methods ROH were detected using clustering, and ROH islands were determined from population inbreeding levels for each marker. CNV were detected using a multivariate copy number analysis method and a hidden Markov model. SNP coverage gaps were defined as genomic regions with intermarker distances on average longer than 9.24 kb. ROH islands that overlapped CNV regions (CNVR) or SNP coverage gaps were considered as potential artefacts. Permutation tests were used to determine if overlaps between CNVR with copy losses and ROH islands were due to chance. Diversity of the haplotypes in the ROH islands was assessed by haplotype analyses. Results In Brown Swiss, Tyrol Grey and Pinzgauer, we identified 13, 22, and 24 ROH islands covering 26.6, 389.0 and 35.8 Mb, respectively, and we detected 30, 50 and 71 CNVR derived from CNV by using both algorithms, respectively. Overlaps between ROH islands, CNVR or coverage gaps occurred for 7, 14 and 16 ROH islands, respectively. About 37, 44 and 52% of the ROH islands coverage in Brown Swiss, Tyrol Grey and Pinzgauer, respectively, were affected by copy loss. Intersections between ROH islands and CNVR were small, but significantly larger compared to ROH islands at random locations across the genome, implying an association between ROH islands and CNVR. Haplotype diversity for reliable ROH islands was lower than for ROH islands that intersected with copy loss CNVR. Conclusions Our findings show that a significant proportion of the ROH islands in the bovine genome are artefacts due to CNV or SNP coverage gaps. Electronic supplementary material The online version of this article (10.1186/s12711-018-0414-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wilson Nandolo
- Division of Livestock Sciences (NUWI), University of Natural Resources and Life Sciences, Gregor-Mendel Strasse 33, 1180, Vienna, Austria.,Lilongwe University of Agriculture and Natural Resources, P. O. Box 219, Lilongwe, Malawi
| | - Yuri T Utsunomiya
- School of Agricultural and Veterinarian Sciences, Jaboticabal, Department of Preventive Veterinary Medicine and Animal Reproduction, São Paulo State University (UNESP), São Paulo, Brazil
| | - Gábor Mészáros
- Division of Livestock Sciences (NUWI), University of Natural Resources and Life Sciences, Gregor-Mendel Strasse 33, 1180, Vienna, Austria.
| | - Maria Wurzinger
- Division of Livestock Sciences (NUWI), University of Natural Resources and Life Sciences, Gregor-Mendel Strasse 33, 1180, Vienna, Austria
| | - Negar Khayadzadeh
- Division of Livestock Sciences (NUWI), University of Natural Resources and Life Sciences, Gregor-Mendel Strasse 33, 1180, Vienna, Austria
| | - Rafaela B P Torrecilha
- School of Agricultural and Veterinarian Sciences, Jaboticabal, Department of Preventive Veterinary Medicine and Animal Reproduction, São Paulo State University (UNESP), São Paulo, Brazil
| | - Henry A Mulindwa
- National Livestock Resources Research Institute, P.O Box 96, Tororo, Uganda
| | - Timothy N Gondwe
- Lilongwe University of Agriculture and Natural Resources, P. O. Box 219, Lilongwe, Malawi
| | - Patrik Waldmann
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07, Uppsala, Sweden
| | - Maja Ferenčaković
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000, Zagreb, Croatia
| | - José F Garcia
- School of Agricultural and Veterinarian Sciences, Jaboticabal, Department of Preventive Veterinary Medicine and Animal Reproduction, São Paulo State University (UNESP), São Paulo, Brazil.,School of Veterinary Medicine, Araçatuba, Department of Support, Production and Animal Health, São Paulo State University (UNESP), São Paulo, Brazil
| | - Benjamin D Rosen
- Animal Genomics and Improvement Laboratory, Beltsville, MD, 20705-2350, USA
| | - Derek Bickhart
- Animal Genomics and Improvement Laboratory, Beltsville, MD, 20705-2350, USA
| | - Curt P van Tassell
- Animal Genomics and Improvement Laboratory, Beltsville, MD, 20705-2350, USA
| | - Ino Curik
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000, Zagreb, Croatia
| | - Johann Sölkner
- Division of Livestock Sciences (NUWI), University of Natural Resources and Life Sciences, Gregor-Mendel Strasse 33, 1180, Vienna, Austria
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