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Kertz NC, Banerjee P, Dyce PW, Diniz WJS. Harnessing Genomics and Transcriptomics Approaches to Improve Female Fertility in Beef Cattle-A Review. Animals (Basel) 2023; 13:3284. [PMID: 37894009 PMCID: PMC10603720 DOI: 10.3390/ani13203284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
Female fertility is the foundation of the cow-calf industry, impacting both efficiency and profitability. Reproductive failure is the primary reason why beef cows are sold in the U.S. and the cause of an estimated annual gross loss of USD 2.8 billion. In this review, we discuss the status of the genomics, transcriptomics, and systems genomics approaches currently applied to female fertility and the tools available to cow-calf producers to maximize genetic progress. We highlight the opportunities and limitations associated with using genomic and transcriptomic approaches to discover genes and regulatory mechanisms related to beef fertility. Considering the complex nature of fertility, significant advances in precision breeding will rely on holistic, multidisciplinary approaches to further advance our ability to understand, predict, and improve reproductive performance. While these technologies have advanced our knowledge, the next step is to translate research findings from bench to on-farm applications.
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Rocha RDFB, Garcia AO, Otto PI, Dos Santos MG, da Silva MVB, Martins MF, Machado MA, Panetto JCDC, Guimarães SEF. Single-step genome-wide association studies and post-GWAS analyses for the number of oocytes and embryos in Gir cattle. Mamm Genome 2023:10.1007/s00335-023-10009-0. [PMID: 37438444 DOI: 10.1007/s00335-023-10009-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/29/2023] [Indexed: 07/14/2023]
Abstract
Genome-Wide Association Studies (GWAS) are used for identification of quantitate trait loci (QTL) and genes associated with several traits. We aimed to identify genomic regions, genes, and biological processes associated with number of total and viable oocytes, and number of embryos in Gir dairy cattle. A dataset with 17,526 follicular aspirations, including the following traits: number of viable oocytes (VO), number of total oocytes (TO), and number of embryos (EMBR) from 1641 Gir donors was provided by five different stock farms. A genotype file with 2093 animals and 395,524 SNP markers was used to perform a single-step GWAS analysis for each trait. The top 10 windows with the highest percentage of additive genetic variance explained by 100 adjacent SNPs were selected. The genomic regions identified in our work were overlapped with QTLs from QTL database on chromosomes 1, 2, 5, 6, 7, 8, 9, 13, 17, 18, 20, 21, 22, 24, and 29. These QTLs were classified as External, Health, Meat and carcass, Production or Reproduction traits, and about 38% were related to Reproduction. In total, 117 genes were identified, of which 111 were protein-coding genes. Exclusively associations were observed for 42 genes with EMBR, and 1 with TO. Also, 42 genes were in common between VO and TO, 28 between VO and EMBR, and four genes were in common among all traits. In conclusion, great part of the identified genes plays a functional role in initial embryo development or general cell functions. The protein-coding genes ARNT, EGR1, HIF1A, AHR, and PAX2 are good markers for the production of oocytes and embryos in Gir cattle.
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Affiliation(s)
| | | | - Pamela Itajara Otto
- Department of Animal Science, Universidade Federal de Santa Maria, Santa Maria, RS, 97105-900, Brazil
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Machine Learning-Based Co-Expression Network Analysis Unravels Potential Fertility-Related Genes in Beef Cows. Animals (Basel) 2022; 12:ani12192715. [PMID: 36230456 PMCID: PMC9559512 DOI: 10.3390/ani12192715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/22/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
Simple Summary Female reproductive failure is still a challenge for the beef industry. Several biological processes that underlie fertility-related traits, such as the establishment of pregnancy and embryo survival, are still unclear. Increased availability of transcriptomic data has allowed a deep investigation of the potential mechanisms involved in fertility. This study investigated candidate gene biomarkers predictive of pregnancy status and underlying fertility-related networks. To this end, we integrated gene expression profiles through supervised machine learning and gene network modeling. We identified nine biologically relevant endometrial gene biomarkers that could discriminate against pregnancy status in cows. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. This study outlined key pathways involved with pregnancy success and provided predictive candidate biomarkers for pregnancy outcome in cows. Abstract Reproductive failure is still a challenge for beef producers and a significant cause of economic loss. The increased availability of transcriptomic data has shed light on the mechanisms modulating pregnancy success. Furthermore, new analytical tools, such as machine learning (ML), provide opportunities for data mining and uncovering new biological events that explain or predict reproductive outcomes. Herein, we identified potential biomarkers underlying pregnancy status and fertility-related networks by integrating gene expression profiles through ML and gene network modeling. We used public transcriptomic data from uterine luminal epithelial cells of cows retrospectively classified as pregnant (P, n = 25) and non-pregnant (NP, n = 18). First, we used a feature selection function from BioDiscML and identified SERPINE3, PDCD1, FNDC1, MRTFA, ARHGEF7, MEF2B, NAA16, ENSBTAG00000019474, and ENSBTAG00000054585 as candidate biomarker predictors of pregnancy status. Then, based on co-expression networks, we identified seven genes significantly rewired (gaining or losing connections) between the P and NP networks. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. We provided insights into the regulatory networks of fertility-related processes and demonstrated the potential of combining different analytical tools to prioritize candidate genes.
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Fathoni A, Boonkum W, Chankitisakul V, Duangjinda M. An Appropriate Genetic Approach for Improving Reproductive Traits in Crossbred Thai-Holstein Cattle under Heat Stress Conditions. Vet Sci 2022; 9:vetsci9040163. [PMID: 35448661 PMCID: PMC9031002 DOI: 10.3390/vetsci9040163] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/19/2022] [Accepted: 03/26/2022] [Indexed: 01/16/2023] Open
Abstract
Thailand is a tropical country affected by global climate change and has high temperatures and humidity that cause heat stress in livestock. A temperature−humidity index (THI) is required to assess and evaluate heat stress levels in livestock. One of the livestock types in Thailand experiencing heat stress due to extreme climate change is crossbred dairy cattle. Genetic evaluations of heat tolerance in dairy cattle have been carried out for reproductive traits. Heritability values for reproductive traits are generally low (<0.10) because environmental factors heavily influence them. Consequently, genetic improvement for these traits would be slow compared to production traits. Positive and negative genetic correlations were found between reproductive traits and reproductive traits and yield traits. Several selection methods for reproductive traits have been introduced, i.e., the traditional method, marker-assisted selection (MAS), and genomic selection (GS). GS is the most promising technique and provides accurate results with a high genetic gain. Single-step genomic BLUP (ssGBLUP) has higher accuracy than the multi-step equivalent for fertility traits or low-heritability traits.
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Affiliation(s)
- Akhmad Fathoni
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Department of Animal Breeding and Reproduction, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
| | - Wuttigrai Boonkum
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Network Center for Animal Breeding and OMICS Research, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Vibuntita Chankitisakul
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Network Center for Animal Breeding and OMICS Research, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Monchai Duangjinda
- Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand; (A.F.); (W.B.); (V.C.)
- Network Center for Animal Breeding and OMICS Research, Khon Kaen University, Khon Kaen 40002, Thailand
- Correspondence: ; Tel.: +66-81-872-4207
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