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Forutan M, Engle BN, Chamberlain AJ, Ross EM, Nguyen LT, D'Occhio MJ, Snr AC, Kho EA, Fordyce G, Speight S, Goddard ME, Hayes BJ. Genome-wide association and expression quantitative trait loci in cattle reveals common genes regulating mammalian fertility. Commun Biol 2024; 7:724. [PMID: 38866948 PMCID: PMC11169601 DOI: 10.1038/s42003-024-06403-2] [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: 04/20/2023] [Accepted: 05/31/2024] [Indexed: 06/14/2024] Open
Abstract
Most genetic variants associated with fertility in mammals fall in non-coding regions of the genome and it is unclear how these variants affect fertility. Here we use genome-wide association summary statistics for Heifer puberty (pubertal or not at 600 days) from 27,707 Bos indicus, Bos taurus and crossbred cattle; multi-trait GWAS signals from 2119 indicine cattle for four fertility traits, including days to calving, age at first calving, pregnancy status, and foetus age in weeks (assessed by rectal palpation of the foetus); and expression quantitative trait locus for whole blood from 489 indicine cattle, to identify 87 putatively functional genes affecting cattle fertility. Our analysis reveals a significant overlap between the set of cattle and previously reported human fertility-related genes, impling the existence of a shared pool of genes that regulate fertility in mammals. These findings are crucial for developing approaches to improve fertility in cattle and potentially other mammals.
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Affiliation(s)
- Mehrnush Forutan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
| | - Bailey N Engle
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
- USDA,ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933, USA
| | - Amanda J Chamberlain
- Agriculture Victoria, Centre for AgriBiosciences, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
| | - Elizabeth M Ross
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Loan T Nguyen
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Michael J D'Occhio
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW, Australia
| | - Alf Collins Snr
- Collins Belah Valley Brahman Stud, Marlborough, 4705, QLD, Australia
| | - Elise A Kho
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Geoffry Fordyce
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | | | - Michael E Goddard
- Agriculture Victoria, Centre for AgriBiosciences, Bundoora, VIC, Australia
- University of Melbourne, Melbourne, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
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van den Berg I, Chamberlain AJ, MacLeod IM, Nguyen TV, Goddard ME, Xiang R, Mason B, Meier S, Phyn CVC, Burke CR, Pryce JE. Using expression data to fine map QTL associated with fertility in dairy cattle. Genet Sel Evol 2024; 56:42. [PMID: 38844868 PMCID: PMC11154999 DOI: 10.1186/s12711-024-00912-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 05/13/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Female fertility is an important trait in dairy cattle. Identifying putative causal variants associated with fertility may help to improve the accuracy of genomic prediction of fertility. Combining expression data (eQTL) of genes, exons, gene splicing and allele specific expression is a promising approach to fine map QTL to get closer to the causal mutations. Another approach is to identify genomic differences between cows selected for high and low fertility and a selection experiment in New Zealand has created exactly this resource. Our objective was to combine multiple types of expression data, fertility traits and allele frequency in high- (POS) and low-fertility (NEG) cows with a genome-wide association study (GWAS) on calving interval in Australian cows to fine-map QTL associated with fertility in both Australia and New Zealand dairy cattle populations. RESULTS Variants that were significantly associated with calving interval (CI) were strongly enriched for variants associated with gene, exon, gene splicing and allele-specific expression, indicating that there is substantial overlap between QTL associated with CI and eQTL. We identified 671 genes with significant differential expression between POS and NEG cows, with the largest fold change detected for the CCDC196 gene on chromosome 10. Our results provide numerous candidate genes associated with female fertility in dairy cattle, including GYS2 and TIGAR on chromosome 5 and SYT3 and HSD17B14 on chromosome 18. Multiple QTL regions were located in regions with large numbers of copy number variants (CNV). To identify the causal mutations for these variants, long read sequencing may be useful. CONCLUSIONS Variants that were significantly associated with CI were highly enriched for eQTL. We detected 671 genes that were differentially expressed between POS and NEG cows. Several QTL detected for CI overlapped with eQTL, providing candidate genes for fertility in dairy cattle.
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Affiliation(s)
- Irene van den Berg
- Agriculture Victoria, AgriBio, Centre of AgriBioscience, 5 Ring Road, Bundoora, VIC, 3082, Australia.
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre of AgriBioscience, 5 Ring Road, Bundoora, VIC, 3082, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
| | - Iona M MacLeod
- Agriculture Victoria, AgriBio, Centre of AgriBioscience, 5 Ring Road, Bundoora, VIC, 3082, Australia
| | - Tuan V Nguyen
- Agriculture Victoria, AgriBio, Centre of AgriBioscience, 5 Ring Road, Bundoora, VIC, 3082, Australia
| | - Mike E Goddard
- Agriculture Victoria, AgriBio, Centre of AgriBioscience, 5 Ring Road, Bundoora, VIC, 3082, Australia
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Ruidong Xiang
- Agriculture Victoria, AgriBio, Centre of AgriBioscience, 5 Ring Road, Bundoora, VIC, 3082, Australia
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Brett Mason
- Agriculture Victoria, AgriBio, Centre of AgriBioscience, 5 Ring Road, Bundoora, VIC, 3082, Australia
| | | | | | | | - Jennie E Pryce
- Agriculture Victoria, AgriBio, Centre of AgriBioscience, 5 Ring Road, Bundoora, VIC, 3082, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
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Tang Y, Zhang J, Li W, Liu X, Chen S, Mi S, Yang J, Teng J, Fang L, Yu Y. Identification and characterization of whole blood gene expression and splicing quantitative trait loci during early to mid-lactation of dairy cattle. BMC Genomics 2024; 25:445. [PMID: 38711039 DOI: 10.1186/s12864-024-10346-7] [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: 09/30/2023] [Accepted: 04/25/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Characterization of regulatory variants (e.g., gene expression quantitative trait loci, eQTL; gene splicing QTL, sQTL) is crucial for biologically interpreting molecular mechanisms underlying loci associated with complex traits. However, regulatory variants in dairy cattle, particularly in specific biological contexts (e.g., distinct lactation stages), remain largely unknown. In this study, we explored regulatory variants in whole blood samples collected during early to mid-lactation (22-150 days after calving) of 101 Holstein cows and analyzed them to decipher the regulatory mechanisms underlying complex traits in dairy cattle. RESULTS We identified 14,303 genes and 227,705 intron clusters expressed in the white blood cells of 101 cattle. The average heritability of gene expression and intron excision ratio explained by cis-SNPs is 0.28 ± 0.13 and 0.25 ± 0.13, respectively. We identified 23,485 SNP-gene expression pairs and 18,166 SNP-intron cluster pairs in dairy cattle during early to mid-lactation. Compared with the 2,380,457 cis-eQTLs reported to be present in blood in the Cattle Genotype-Tissue Expression atlas (CattleGTEx), only 6,114 cis-eQTLs (P < 0.05) were detected in the present study. By conducting colocalization analysis between cis-e/sQTL and the results of genome-wide association studies (GWAS) from four traits, we identified a cis-e/sQTL (rs109421300) of the DGAT1 gene that might be a key marker in early to mid-lactation for milk yield, fat yield, protein yield, and somatic cell score (PP4 > 0.6). Finally, transcriptome-wide association studies (TWAS) revealed certain genes (e.g., FAM83H and TBC1D17) whose expression in white blood cells was significantly (P < 0.05) associated with complex traits. CONCLUSIONS This study investigated the genetic regulation of gene expression and alternative splicing in dairy cows during early to mid-lactation and provided new insights into the regulatory mechanisms underlying complex traits of economic importance.
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Affiliation(s)
- Yongjie Tang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jinning Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Wenlong Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Xueqin Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Siqian Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Siyuan Mi
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jinyan Yang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, 8000, Denmark.
| | - Ying Yu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
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Everman ER, Macdonald SJ. Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster. G3 (BETHESDA, MD.) 2024; 14:jkae015. [PMID: 38262701 PMCID: PMC11021028 DOI: 10.1093/g3journal/jkae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/25/2024]
Abstract
Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.
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Affiliation(s)
- Elizabeth R Everman
- School of Biological Sciences, The University of Oklahoma, 730 Van Vleet Oval, Norman, OK 73019, USA
| | - Stuart J Macdonald
- Molecular Biosciences, University of Kansas, 1200 Sunnyside Ave, Lawrence, KS 66045, USA
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Diniz WJS, Afonso J, Kertz NC, Dyce PW, Banerjee P. Mapping Expression Quantitative Trait Loci Targeting Candidate Genes for Pregnancy in Beef Cows. Biomolecules 2024; 14:150. [PMID: 38397387 PMCID: PMC10886872 DOI: 10.3390/biom14020150] [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: 12/09/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/25/2024] Open
Abstract
Despite collective efforts to understand the complex regulation of reproductive traits, no causative genes and/or mutations have been reported yet. By integrating genomics and transcriptomics data, potential regulatory mechanisms may be unveiled, providing opportunities to dissect the genetic factors governing fertility. Herein, we identified regulatory variants from RNA-Seq data associated with gene expression regulation in the uterine luminal epithelial cells of beef cows. We identified 4676 cis and 7682 trans eQTLs (expression quantitative trait loci) affecting the expression of 1120 and 2503 genes, respectively (FDR < 0.05). These variants affected the expression of transcription factor coding genes (71 cis and 193 trans eQTLs) and genes previously reported as differentially expressed between pregnant and nonpregnant cows. Functional over-representation analysis highlighted pathways related to metabolism, immune response, and hormone signaling (estrogen and GnRH) affected by eQTL-regulated genes (p-value ≤ 0.01). Furthermore, eQTLs were enriched in QTL regions for 13 reproduction-related traits from the CattleQTLdb (FDR ≤ 0.05). Our study provides novel insights into the genetic basis of reproductive processes in cattle. The underlying causal mechanisms modulating the expression of uterine genes warrant further investigation.
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Affiliation(s)
- Wellison J. S. Diniz
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA; (N.C.K.); (P.W.D.); (P.B.)
| | - Juliana Afonso
- Embrapa Pecuária Sudeste, Rodovia Washington Luiz, Km 234, s/n, Fazenda Canchim, São Carlos 13560-970, SP, Brazil;
| | - Nicholas C. Kertz
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA; (N.C.K.); (P.W.D.); (P.B.)
| | - Paul W. Dyce
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA; (N.C.K.); (P.W.D.); (P.B.)
| | - Priyanka Banerjee
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA; (N.C.K.); (P.W.D.); (P.B.)
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Li C, Li J, Wang H, Zhang R, An X, Yuan C, Guo T, Yue Y. Genomic Selection for Live Weight in the 14th Month in Alpine Merino Sheep Combining GWAS Information. Animals (Basel) 2023; 13:3516. [PMID: 38003134 PMCID: PMC10668700 DOI: 10.3390/ani13223516] [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: 08/29/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Alpine Merino Sheep is a novel breed reared from Australian Merino Sheep as the father and Gansu Alpine Fine-Wool Sheep as the mother, living all year in cold and arid alpine areas with exceptional wool quality and meat performance. Body weight is an important economic trait of the Alpine Merino Sheep, but there is limited research on identifying the genes associated with live weight in the 14th month for improving the accuracy of the genomic prediction of this trait. Therefore, this study's sample comprised 1310 Alpine Merino Sheep ewes, and the Fine Wool Sheep 50K Panel was used for genome-wide association study (GWAS) analysis to identify candidate genes. Moreover, the trial population (1310 ewes) in this study was randomly divided into two groups. One group was used as the population for GWAS analysis and screened for the most significant top 5%, top 10%, top 15%, and top 20% SNPs to obtain prior marker information. The other group was used to estimate the genetic parameters based on the weight assigned by heritability combined with different prior marker information. The aim of this study was to compare the accuracy of genomic breeding value estimation when combined with prior marker information from GWAS analysis with the optimal linear unbiased prediction method for genome selection (GBLUP) for the breeding value of target traits. Finally, the accuracy was evaluated using the five-fold cross-validation method. This research provides theoretical and technical support to improve the accuracy of sheep genome selection and better guide breeding. The results demonstrated that eight candidate genes were associated with GWAS analysis, and the gene function query and literature search results suggested that FAM184B, NCAPG, MACF1, ANKRD44, DCAF16, FUK, LCORL, and SYN3 were candidate genes affecting live weight in the 14th month (WT), which regulated the growth of muscle and bone in sheep. In genome selection analysis, the heritability of GBLUP to calculate the WT was 0.335-0.374, the accuracy after five-fold cross-verification was 0.154-0.190, and after assigning different weights to the top 5%, top 10%, top 15%, and top 20% of the GWAS results in accordance with previous information to construct the G matrix, the accuracy of the WT in the GBLUP model was improved by 2.59-7.79%.
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Affiliation(s)
- Chenglan Li
- Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; (C.L.)
- Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou 730050, China
| | - Jianye Li
- Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; (C.L.)
- Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou 730050, China
| | - Haifeng Wang
- Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; (C.L.)
- Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou 730050, China
| | - Rui Zhang
- Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; (C.L.)
- Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou 730050, China
| | - Xuejiao An
- Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; (C.L.)
- Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou 730050, China
| | - Chao Yuan
- Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; (C.L.)
- Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou 730050, China
| | - Tingting Guo
- Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; (C.L.)
- Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou 730050, China
| | - Yaojing Yue
- Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China; (C.L.)
- Sheep Breeding Engineering Technology Research Center of Chinese Academy of Agricultural Sciences, Lanzhou 730050, China
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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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Cai W, Zhang Y, Chang T, Wang Z, Zhu B, Chen Y, Gao X, Xu L, Zhang L, Gao H, Song J, Li J. The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle. J Anim Sci Biotechnol 2023; 14:78. [PMID: 37165455 PMCID: PMC10173583 DOI: 10.1186/s40104-023-00876-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 04/05/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle. RESULTS We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits. CONCLUSIONS The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.
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Affiliation(s)
- Wentao Cai
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yapeng Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Tianpeng Chang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Zezhao Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Bo Zhu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yan Chen
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Xue Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lingyang Xu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lupei Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Huijiang Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiuzhou Song
- Department of Animal and Avian Science, University of Maryland, College Park, MD, 20742, USA.
| | - Junya Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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Marrella MA, Biase FH. Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing. J Anim Sci Biotechnol 2023; 14:62. [PMID: 37143150 PMCID: PMC10161580 DOI: 10.1186/s40104-023-00861-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 03/05/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND A gap currently exists between genetic variants and the underlying cell and tissue biology of a trait, and expression quantitative trait loci (eQTL) studies provide important information to help close that gap. However, two concerns that arise with eQTL analyses using RNA-sequencing data are normalization of data across samples and the data not following a normal distribution. Multiple pipelines have been suggested to address this. For instance, the most recent analysis of the human and farm Genotype-Tissue Expression (GTEx) project proposes using trimmed means of M-values (TMM) to normalize the data followed by an inverse normal transformation. RESULTS In this study, we reasoned that eQTL analysis could be carried out using the same framework used for differential gene expression (DGE), which uses a negative binomial model, a statistical test feasible for count data. Using the GTEx framework, we identified 35 significant eQTLs (P < 5 × 10-8) following the ANOVA model and 39 significant eQTLs (P < 5 × 10-8) following the additive model. Using a differential gene expression framework, we identified 930 and six significant eQTLs (P < 5 × 10-8) following an analytical framework equivalent to the ANOVA and additive model, respectively. When we compared the two approaches, there was no overlap of significant eQTLs between the two frameworks. Because we defined specific contrasts, we identified trans eQTLs that more closely resembled what we expect from genetic variants showing complete dominance between alleles. Yet, these were not identified by the GTEx framework. CONCLUSIONS Our results show that transforming RNA-sequencing data to fit a normal distribution prior to eQTL analysis is not required when the DGE framework is employed. Our proposed approach detected biologically relevant variants that otherwise would not have been identified due to data transformation to fit a normal distribution.
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Affiliation(s)
- Mackenzie A Marrella
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Fernando H Biase
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
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Lopdell TJ. Using QTL to Identify Genes and Pathways Underlying the Regulation and Production of Milk Components in Cattle. Animals (Basel) 2023; 13:ani13050911. [PMID: 36899768 PMCID: PMC10000085 DOI: 10.3390/ani13050911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Milk is a complex liquid, and the concentrations of many of its components are under genetic control. Many genes and pathways are known to regulate milk composition, and the purpose of this review is to highlight how the discoveries of quantitative trait loci (QTL) for milk phenotypes can elucidate these pathways. The main body of this review focuses primarily on QTL discovered in cattle (Bos taurus) as a model species for the biology of lactation, and there are occasional references to sheep genetics. The following section describes a range of techniques that can be used to help identify the causative genes underlying QTL when the underlying mechanism involves the regulation of gene expression. As genotype and phenotype databases continue to grow and diversify, new QTL will continue to be discovered, and although proving the causality of underlying genes and variants remains difficult, these new data sets will further enhance our understanding of the biology of lactation.
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Ravi Kumar D, Nandhini PB, Joel Devadasan M, Sivalingam J, Mengistu DW, Verma A, Gupta ID, Niranjan SK, Kataria RS, Tantia MS. Genome-wide association study revealed suggestive QTLs for production and reproduction traits in Indian Murrah buffalo. 3 Biotech 2023; 13:100. [PMID: 36866324 PMCID: PMC9971368 DOI: 10.1007/s13205-023-03505-2] [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: 09/06/2022] [Accepted: 01/31/2023] [Indexed: 03/03/2023] Open
Abstract
The present study was aimed to identify the genome-wide SNPs associated with production and reproduction traits in 96 Indian Murrah buffalo genotyped based on ddRAD approach using Genome-Wide Association Study (GWAS) along with phenotypes of contemporary animals using mixed linear model for production and reproduction traits. A total of 27,735 SNPs identified using ddRAD approach in 96 Indian Murrah buffaloes were used for GWAS. A total of 28 SNPs were found to be associated with production and reproductive traits. Among these, 14 SNPs were present in the intronic region of AK5, BACH2, DIRC2, ECPAS, MPZL1, MYO16, QRFPR, RASGRF1, SLC9A4, TANC1, and TRIM67 genes and one SNP in long non-coding region of LOC102414911. Out of these 28 SNPs, 9 SNPs were found to have pleiotropic effect over milk production traits and were present in chromosome number BBU 1, 2, 4, 6, 9, 10, 12, 19, and 20. SNPs in the intronic region of AK5, TRIM67 genes were found to be associated with milk production traits. Eleven and five SNPs in the intergenic region were associated with milk production and reproduction traits respectively. The above genomic information may be used for selection of Murrah animals for genetic improvement.
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Affiliation(s)
- D. Ravi Kumar
- ICAR-National Dairy Research Institute, Karnal, Haryana India
| | - P. B. Nandhini
- ICAR-National Dairy Research Institute, Karnal, Haryana India
| | | | - Jayakumar Sivalingam
- ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana India
- ICAR-Directorate of Poultry Research, Hyderabad, Telangana India
| | | | - Archana Verma
- ICAR-National Dairy Research Institute, Karnal, Haryana India
| | - I. D. Gupta
- ICAR-National Dairy Research Institute, Karnal, Haryana India
| | - S. K. Niranjan
- ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana India
| | - R. S. Kataria
- ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana India
| | - M. S. Tantia
- ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana India
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Cis-eQTL Analysis and Functional Validation of Candidate Genes for Carcass Yield Traits in Beef Cattle. Int J Mol Sci 2022; 23:ijms232315055. [PMID: 36499383 PMCID: PMC9736101 DOI: 10.3390/ijms232315055] [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: 10/18/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
Carcass yield traits are of considerable economic importance for farm animals, which act as a major contributor to the world’s food supply. Genome-wide association studies (GWASs) have identified many genetic variants associated with carcass yield traits in beef cattle. However, their functions are not effectively illustrated. In this study, we performed an integrative analysis of gene-based GWAS with expression quantitative trait locus (eQTL) analysis to detect candidate genes for carcass yield traits and validate their effects on bovine skeletal muscle satellite cells (BSCs). The gene-based GWAS and cis-eQTL analysis revealed 1780 GWAS and 1538 cis-expression genes. Among them, we identified 153 shared genes that may play important roles in carcass yield traits. Notably, the identified cis-eQTLs of PON3 and PRIM2 were significantly (p < 0.001) enriched in previous GWAS loci for carcass traits. Furthermore, overexpression of PON3 and PRIM2 promoted the BSCs’ proliferation, increased the expression of MYOD and downregulated the expression of MYOG, which indicated that these genes may inhibit myogenic differentiation. In contrast, PON3 and PRIM2 were significantly downregulated during the differentiation of BSCs. These findings suggested that PON3 and PRIM2 may promote the proliferation of BSCs and inhibit them in the pre-differentiation stage. Our results further contribute to the understanding of the molecular mechanisms of carcass yield traits in beef cattle.
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Ribeiro G, Baldi F, Cesar ASM, Alexandre PA, Peripolli E, Ferraz JBS, Fukumasu H. Detection of potential functional variants based on systems-biology: the case of feed efficiency in beef cattle. BMC Genomics 2022; 23:774. [PMID: 36434498 PMCID: PMC9700932 DOI: 10.1186/s12864-022-08958-y] [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: 08/25/2021] [Accepted: 10/20/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Potential functional variants (PFVs) can be defined as genetic variants responsible for a given phenotype. Ultimately, these are the best DNA markers for animal breeding and selection, especially for polygenic and complex phenotypes. Herein, we described the identification of PFVs for complex phenotypes (in this case, Feed Efficiency in beef cattle) using a systems-biology driven approach based on RNA-seq data from physiologically relevant organs. RESULTS The systems-biology coupled with deep molecular phenotyping by RNA-seq of liver, muscle, hypothalamus, pituitary, and adrenal glands of animals with high and low feed efficiency (FE) measured by residual feed intake (RFI) identified 2,000,936 uniquely variants. Among them, 9986 variants were significantly associated with FE and only 78 had a high impact on protein expression and were considered as PFVs. A set of 169 significant uniquely variants were expressed in all five organs, however, only 27 variants had a moderate impact and none of them a had high impact on protein expression. These results provide evidence of tissue-specific effects of high-impact PFVs. The PFVs were enriched (FDR < 0.05) for processing and presentation of MHC Class I and II mediated antigens, which are an important part of the adaptive immune response. The experimental validation of these PFVs was demonstrated by the increased prediction accuracy for RFI using the weighted G matrix (ssGBLUP+wG; Acc = 0.10 and b = 0.48) obtained in the ssGWAS in comparison to the unweighted G matrix (ssGBLUP; Acc = 0.29 and b = 1.10). CONCLUSION Here we identified PFVs for FE in beef cattle using a strategy based on systems-biology and deep molecular phenotyping. This approach has great potential to be used in genetic prediction programs, especially for polygenic phenotypes.
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Affiliation(s)
- Gabriela Ribeiro
- grid.11899.380000 0004 1937 0722Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Sao Paulo, 13635-900 Brazil
| | - Fernando Baldi
- grid.410543.70000 0001 2188 478XDepartment of Animal Science, São Paulo State University (UNESP), Jaboticabal, São Paulo, Brazil
| | - Aline S. M. Cesar
- grid.11899.380000 0004 1937 0722Escola Superior de Agricultura “Luiz de Queiroz”, University of Sao Paulo, Piracicaba, São Paulo, Brazil
| | - Pâmela A. Alexandre
- grid.11899.380000 0004 1937 0722Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Sao Paulo, 13635-900 Brazil ,CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD 4067 Australia
| | - Elisa Peripolli
- grid.11899.380000 0004 1937 0722Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Sao Paulo, 13635-900 Brazil ,grid.410543.70000 0001 2188 478XDepartment of Animal Science, São Paulo State University (UNESP), Jaboticabal, São Paulo, Brazil
| | - José B. S. Ferraz
- grid.11899.380000 0004 1937 0722Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Sao Paulo, 13635-900 Brazil
| | - Heidge Fukumasu
- grid.11899.380000 0004 1937 0722Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Sao Paulo, 13635-900 Brazil
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14
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van den Berg I, Ho PN, Nguyen TV, Haile-Mariam M, MacLeod IM, Beatson PR, O'Connor E, Pryce JE. GWAS and genomic prediction of milk urea nitrogen in Australian and New Zealand dairy cattle. Genet Sel Evol 2022; 54:15. [PMID: 35183113 PMCID: PMC8858489 DOI: 10.1186/s12711-022-00707-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/31/2022] [Indexed: 11/24/2022] Open
Abstract
Background Urinary nitrogen leakage is an environmental concern in dairy cattle. Selection for reduced urinary nitrogen leakage may be done using indicator traits such as milk urea nitrogen (MUN). The result of a previous study indicated that the genetic correlation between MUN in Australia (AUS) and MUN in New Zealand (NZL) was only low to moderate (between 0.14 and 0.58). In this context, an alternative is to select sequence variants based on genome-wide association studies (GWAS) with a view to improve genomic prediction accuracies. A GWAS can also be used to detect quantitative trait loci (QTL) associated with MUN. Therefore, our objectives were to perform within-country GWAS and a meta-GWAS for MUN using records from up to 33,873 dairy cows and imputed whole-genome sequence data, to compare QTL detected in the GWAS for MUN in AUS and NZL, and to use sequence variants selected from the meta-GWAS to improve the prediction accuracy for MUN based on a joint AUS-NZL reference set. Results Using the meta-GWAS, we detected 14 QTL for MUN, located on chromosomes 1, 6, 11, 14, 19, 22, 26 and the X chromosome. The three most significant QTL encompassed the casein genes on chromosome 6, PAEP on chromosome 11 and DGAT1 on chromosome 14. We selected 50,000 sequence variants that had the same direction of effect for MUN in AUS and MUN in NZL and that were most significant in the meta-analysis for the GWAS. The selected sequence variants yielded a genetic correlation between MUN in AUS and MUN in NZL of 0.95 and substantially increased prediction accuracy in both countries. Conclusions Our results demonstrate how the sharing of data between two countries can increase the power of a GWAS and increase the accuracy of genomic prediction using a multi-country reference population and sequence variants selected based on a meta-GWAS. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-022-00707-9.
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Affiliation(s)
- Irene van den Berg
- Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Bundoora, AgriBioVIC, 3083, Australia.
| | - Phuong N Ho
- Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Bundoora, AgriBioVIC, 3083, Australia
| | - Tuan V Nguyen
- Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Bundoora, AgriBioVIC, 3083, Australia
| | - Mekonnen Haile-Mariam
- Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Bundoora, AgriBioVIC, 3083, Australia
| | - Iona M MacLeod
- Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Bundoora, AgriBioVIC, 3083, Australia
| | | | | | - Jennie E Pryce
- Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Bundoora, AgriBioVIC, 3083, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
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15
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Johnsson M, Jungnickel MK. Evidence for and localization of proposed causative variants in cattle and pig genomes. Genet Sel Evol 2021; 53:67. [PMID: 34461824 PMCID: PMC8404348 DOI: 10.1186/s12711-021-00662-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 08/20/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND This paper reviews the localization of published potential causative variants in contemporary pig and cattle reference genomes, and the evidence for their causality. In spite of the difficulties inherent to the identification of causative variants from genetic mapping and genome-wide association studies, researchers in animal genetics have proposed putative causative variants for several traits relevant to livestock breeding. RESULTS For this review, we read the literature that supports potential causative variants in 13 genes (ABCG2, DGAT1, GHR, IGF2, MC4R, MSTN, NR6A1, PHGK1, PRKAG3, PLRL, RYR1, SYNGR2 and VRTN) in cattle and pigs, and localized them in contemporary reference genomes. We review the evidence for their causality, by aiming to separate the evidence for the locus, the proposed causative gene and the proposed causative variant, and report the bioinformatic searches and tactics needed to localize the sequence variants in the cattle or pig genome. CONCLUSIONS Taken together, there is usually good evidence for the association at the locus level, some evidence for a specific causative gene at eight of the loci, and some experimental evidence for a specific causative variant at six of the loci. We recommend that researchers who report new potential causative variants use referenced coordinate systems, show local sequence context, and submit variants to repositories.
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Affiliation(s)
- Martin Johnsson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07 Uppsala, Sweden
| | - Melissa K. Jungnickel
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG Scotland, UK
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16
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Lee YL, Takeda H, Costa Monteiro Moreira G, Karim L, Mullaart E, Coppieters W, Appeltant R, Veerkamp RF, Groenen MAM, Georges M, Bosse M, Druet T, Bouwman AC, Charlier C. A 12 kb multi-allelic copy number variation encompassing a GC gene enhancer is associated with mastitis resistance in dairy cattle. PLoS Genet 2021; 17:e1009331. [PMID: 34288907 PMCID: PMC8328317 DOI: 10.1371/journal.pgen.1009331] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 08/02/2021] [Accepted: 06/08/2021] [Indexed: 12/24/2022] Open
Abstract
Clinical mastitis (CM) is an inflammatory disease occurring in the mammary glands of lactating cows. CM is under genetic control, and a prominent CM resistance QTL located on chromosome 6 was reported in various dairy cattle breeds. Nevertheless, the biological mechanism underpinning this QTL has been lacking. Herein, we mapped, fine-mapped, and discovered the putative causal variant underlying this CM resistance QTL in the Dutch dairy cattle population. We identified a ~12 kb multi-allelic copy number variant (CNV), that is in perfect linkage disequilibrium with a lead SNP, as a promising candidate variant. By implementing a fine-mapping and through expression QTL mapping, we showed that the group-specific component gene (GC), a gene encoding a vitamin D binding protein, is an excellent candidate causal gene for the QTL. The multiplicated alleles are associated with increased GC expression and low CM resistance. Ample evidence from functional genomics data supports the presence of an enhancer within this CNV, which would exert cis-regulatory effect on GC. We observed that strong positive selection swept the region near the CNV, and haplotypes associated with the multiplicated allele were strongly selected for. Moreover, the multiplicated allele showed pleiotropic effects for increased milk yield and reduced fertility, hinting that a shared underlying biology for these effects may revolve around the vitamin D pathway. These findings together suggest a putative causal variant of a CM resistance QTL, where a cis-regulatory element located within a CNV can alter gene expression and affect multiple economically important traits.
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Affiliation(s)
- Young-Lim Lee
- Wageningen University & Research, Animal Breeding and Genomics, Wageningen, the Netherlands
| | - Haruko Takeda
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | | | - Latifa Karim
- GIGA Genomics Platform, GIGA Institute, University of Liège, Liège, Belgium
| | | | - Wouter Coppieters
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
- GIGA Genomics Platform, GIGA Institute, University of Liège, Liège, Belgium
| | | | - Ruth Appeltant
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Roel F. Veerkamp
- Wageningen University & Research, Animal Breeding and Genomics, Wageningen, the Netherlands
| | - Martien A. M. Groenen
- Wageningen University & Research, Animal Breeding and Genomics, Wageningen, the Netherlands
| | - Michel Georges
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Mirte Bosse
- Wageningen University & Research, Animal Breeding and Genomics, Wageningen, the Netherlands
| | - Tom Druet
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Aniek C. Bouwman
- Wageningen University & Research, Animal Breeding and Genomics, Wageningen, the Netherlands
| | - Carole Charlier
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
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Kumar H, Panigrahi M, Saravanan KA, Rajawat D, Parida S, Bhushan B, Gaur GK, Dutt T, Mishra BP, Singh RK. Genome-wide detection of copy number variations in Tharparkar cattle. Anim Biotechnol 2021; 34:448-455. [PMID: 34191685 DOI: 10.1080/10495398.2021.1942027] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Copy number variations (CNVs) are major forms of genetic variation with an increasing importance in animal genomics. This study used the Illumina BovineSNP 50 K BeadChip to detect the genome-wide CNVs in the Tharparkar cattle. With the aid of PennCNV software, we noticed a total of 447 copy number variation regions (CNVRs) across the autosomal genome, occupying nearly 2.17% of the bovine genome. The average size of detected CNVRs was found to be 122.2 kb, the smallest CNVR being 50.02 kb in size, to the largest being 1,232.87 Kb. Enrichment analyses of the genes in these CNVRs gave significant associations with molecular adaptation-related Gene Ontology (GO) terms. Most CNVR genes were significantly enriched for specific biological functions; signaling pathways, sensory responses to stimuli, and various cellular processes. In addition, QTL analysis of CNVRs described them to be linked with economically essential traits in cattle. The findings here provide crucial information for constructing a more comprehensive CNVR map for the indigenous cattle genome.
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Affiliation(s)
- Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Subhashree Parida
- Division of Pharmacology & Toxicology, ICAR-Indian Veterinary Research Institute, Bareilly, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - G K Gaur
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Triveni Dutt
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Bareilly, India
| | - B P Mishra
- Division of Animal Biotechnology, ICAR-Indian Veterinary Research Institute, Bareilly, India
| | - R K Singh
- Division of Animal Biotechnology, ICAR-Indian Veterinary Research Institute, Bareilly, India
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18
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Schmidtmann C, Schönherz A, Guldbrandtsen B, Marjanovic J, Calus M, Hinrichs D, Thaller G. Assessing the genetic background and genomic relatedness of red cattle populations originating from Northern Europe. Genet Sel Evol 2021; 53:23. [PMID: 33676402 PMCID: PMC7936461 DOI: 10.1186/s12711-021-00613-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 02/08/2021] [Indexed: 12/20/2022] Open
Abstract
Background Local cattle breeds need special attention, as they are valuable reservoirs of genetic diversity. Appropriate breeding decisions and adequate genomic management of numerically smaller populations are required for their conservation. At this point, the analysis of dense genome-wide marker arrays provides encompassing insights into the genomic constitution of livestock populations. We have analyzed the genetic characterization of ten cattle breeds originating from Germany, The Netherlands and Denmark belonging to the group of red dairy breeds in Northern Europe. The results are intended to provide initial evidence on whether joint genomic breeding strategies of these populations will be successful. Results Traditional Danish Red and Groningen White-Headed were the most genetically differentiated breeds and their populations showed the highest levels of inbreeding. In contrast, close genetic relationships and shared ancestry were observed for the populations of German Red and White Dual-Purpose, Dutch Meuse-Rhine-Yssel, and Dutch Deep Red breeds, reflecting their common histories. A considerable amount of gene flow from Red Holstein to German Angler and to German Red and White Dual-Purpose was revealed, which is consistent with frequent crossbreeding to improve productivity of these local breeds. In Red Holstein, marked genomic signatures of selection were reported on chromosome 18, suggesting directed selection for important breeding goal traits. Furthermore, tests for signatures of selection between Red Holstein, Red and White Dual-Purpose, and Meuse-Rhine-Yssel uncovered signals for all investigated pairs of populations. The corresponding genomic regions, which were putatively under different selection pressures, harboured various genes which are associated with traits such as milk and beef production, mastitis and female fertility. Conclusions This study provides comprehensive knowledge on the genetic constitution and genomic connectedness of divergent red cattle populations in Northern Europe. The results will help to design and optimize breeding strategies. A joint genomic evaluation including some of the breeds studied here seems feasible.
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Affiliation(s)
- Christin Schmidtmann
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, 24098, Kiel, Germany.
| | - Anna Schönherz
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.,Department of Animal Science, Aarhus University, 8830, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.,Department of Animal Sciences, Department of Animal Breeding and Husbandry, University of Bonn, 53115, Bonn, Germany
| | - Jovana Marjanovic
- Animal Breeding and Genomics, Wageningen University and Research, 6700AH, Wageningen, The Netherlands
| | - Mario Calus
- Animal Breeding and Genomics, Wageningen University and Research, 6700AH, Wageningen, The Netherlands
| | - Dirk Hinrichs
- Department of Animal Breeding, University of Kassel, 37213, Witzenhausen, Germany
| | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, 24098, Kiel, Germany
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19
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Naji MM, Utsunomiya YT, Sölkner J, Rosen BD, Mészáros G. Investigation of ancestral alleles in the Bovinae subfamily. BMC Genomics 2021; 22:108. [PMID: 33557747 PMCID: PMC7871596 DOI: 10.1186/s12864-021-07412-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 01/27/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND In evolutionary theory, divergence and speciation can arise from long periods of reproductive isolation, genetic mutation, selection and environmental adaptation. After divergence, alleles can either persist in their initial state (ancestral allele - AA), co-exist or be replaced by a mutated state (derived alleles -DA). In this study, we aligned whole genome sequences of individuals from the Bovinae subfamily to the cattle reference genome (ARS.UCD-1.2) for defining ancestral alleles necessary for selection signatures study. RESULTS Accommodating independent divergent of each lineage from the initial ancestral state, AA were defined based on fixed alleles on at least two groups of yak, bison and gayal-gaur-banteng resulting in ~ 32.4 million variants. Using non-overlapping scanning windows of 10 Kb, we counted the AA observed within taurine and zebu cattle. We focused on the extreme points, regions with top 0. 1% (high count) and regions without any occurrence of AA (null count). High count regions preserved gene functions from ancestral states that are still beneficial in the current condition, while null counts regions were linked to mutated ones. For both cattle, high count regions were associated with basal lipid metabolism, essential for survival of various environmental pressures. Mutated regions were associated to productive traits in taurine, i.e. higher metabolism, cell development and behaviors and in immune response domain for zebu. CONCLUSIONS Our findings suggest that retaining and losing AA in some regions are varied and made it species-specific with possibility of overlapping as it depends on the selective pressure they had to experience.
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Affiliation(s)
- Maulana M. Naji
- University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
| | - Yuri T. Utsunomiya
- São Paulo State University (Unesp), School of Veterinary Medicine, Department of Production and Animal Health, Araçatuba, São Paulo Brazil
- International Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics, Araçatuba, São Paulo Brazil
- AgroPartners Consulting. R. Floriano Peixoto, 120-Sala 43A-Centro, Araçatuba, SP 16010-220 Brazil
- Personal-PEC. R. Sebastiao Lima, 1336-Centro, Campo Grande, MS 79004-600 Brazil
| | - Johann Sölkner
- University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
| | | | - Gábor Mészáros
- University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
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20
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Yuan Z, Sunduimijid B, Xiang R, Behrendt R, Knight MI, Mason BA, Reich CM, Prowse-Wilkins C, Vander Jagt CJ, Chamberlain AJ, MacLeod IM, Li F, Yue X, Daetwyler HD. Expression quantitative trait loci in sheep liver and muscle contribute to variations in meat traits. Genet Sel Evol 2021; 53:8. [PMID: 33461502 PMCID: PMC7812657 DOI: 10.1186/s12711-021-00602-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/08/2021] [Indexed: 11/10/2022] Open
Abstract
Background Variants that regulate transcription, such as expression quantitative trait loci (eQTL), have shown enrichment in genome-wide association studies (GWAS) for mammalian complex traits. However, no study has reported eQTL in sheep, although it is an important agricultural species for which many GWAS of complex meat traits have been conducted. Using RNA sequence data produced from liver and muscle from 149 sheep and imputed whole-genome single nucleotide polymorphisms (SNPs), our aim was to dissect the genetic architecture of the transcriptome by associating sheep genotypes with three major molecular phenotypes including gene expression (geQTL), exon expression (eeQTL) and RNA splicing (sQTL). We also examined these three types of eQTL for their enrichment in GWAS of multi-meat traits and fatty acid profiles. Results Whereas a relatively small number of molecular phenotypes were significantly heritable (h2 > 0, P < 0.05), their mean heritability ranged from 0.67 to 0.73 for liver and from 0.71 to 0.77 for muscle. Association analysis between molecular phenotypes and SNPs within ± 1 Mb identified many significant cis-eQTL (false discovery rate, FDR < 0.01). The median distance between the eQTL and transcription start sites (TSS) ranged from 68 to 153 kb across the three eQTL types. The number of common variants between geQTL, eeQTL and sQTL within each tissue, and the number of common variants between liver and muscle within each eQTL type were all significantly (P < 0.05) larger than expected by chance. The identified eQTL were significantly (P < 0.05) enriched in GWAS hits associated with 56 carcass traits and fatty acid profiles. For example, several geQTL in muscle mapped to the FAM184B gene, hundreds of sQTL in liver and muscle mapped to the CAST gene, and hundreds of sQTL in liver mapped to the C6 gene. These three genes are associated with body composition or fatty acid profiles. Conclusions We detected a large number of significant eQTL and found that the overlap of variants between eQTL types and tissues was prevalent. Many eQTL were also QTL for meat traits. Our study fills a gap in the knowledge on the regulatory variants and their role in complex traits for the sheep model.
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Affiliation(s)
- Zehu Yuan
- State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Grassland Agriculture Engineering Center, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China.,Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.,Institutes of Agricultural Science and Technology Development (Joint International Research Laboratory of Agriculture & Agri-Product Safety), Yangzhou University, Yangzhou, 225000, People's Republic of China
| | - Bolormaa Sunduimijid
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Ruidong Xiang
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.,Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Ralph Behrendt
- Agriculture Victoria, Hamilton Centre, Hamilton, VIC, 3300, Australia
| | - Matthew I Knight
- Agriculture Victoria, Hamilton Centre, Hamilton, VIC, 3300, Australia
| | - Brett A Mason
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Coralie M Reich
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Claire Prowse-Wilkins
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Christy J Vander Jagt
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Iona M MacLeod
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Fadi Li
- State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Grassland Agriculture Engineering Center, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China
| | - Xiangpeng Yue
- State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Grassland Agriculture Engineering Center, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, People's Republic of China.
| | - 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.
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21
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van den Berg I, Xiang R, Jenko J, Pausch H, Boussaha M, Schrooten C, Tribout T, Gjuvsland AB, Boichard D, Nordbø Ø, Sanchez MP, Goddard ME. Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds. Genet Sel Evol 2020; 52:37. [PMID: 32635893 PMCID: PMC7339598 DOI: 10.1186/s12711-020-00556-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 06/26/2020] [Indexed: 12/14/2022] Open
Abstract
Background Sequence-based genome-wide association studies (GWAS) provide high statistical power to identify candidate causal mutations when a large number of individuals with both sequence variant genotypes and phenotypes is available. A meta-analysis combines summary statistics from multiple GWAS and increases the power to detect trait-associated variants without requiring access to data at the individual level of the GWAS mapping cohorts. Because linkage disequilibrium between adjacent markers is conserved only over short distances across breeds, a multi-breed meta-analysis can improve mapping precision. Results To maximise the power to identify quantitative trait loci (QTL), we combined the results of nine within-population GWAS that used imputed sequence variant genotypes of 94,321 cattle from eight breeds, to perform a large-scale meta-analysis for fat and protein percentage in cattle. The meta-analysis detected (p ≤ 10−8) 138 QTL for fat percentage and 176 QTL for protein percentage. This was more than the number of QTL detected in all within-population GWAS together (124 QTL for fat percentage and 104 QTL for protein percentage). Among all the lead variants, 100 QTL for fat percentage and 114 QTL for protein percentage had the same direction of effect in all within-population GWAS. This indicates either persistence of the linkage phase between the causal variant and the lead variant across breeds or that some of the lead variants might indeed be causal or tightly linked with causal variants. The percentage of intergenic variants was substantially lower for significant variants than for non-significant variants, and significant variants had mostly moderate to high minor allele frequencies. Significant variants were also clustered in genes that are known to be relevant for fat and protein percentages in milk. Conclusions Our study identified a large number of QTL associated with fat and protein percentage in dairy cattle. We demonstrated that large-scale multi-breed meta-analysis reveals more QTL at the nucleotide resolution than within-population GWAS. Significant variants were more often located in genic regions than non-significant variants and a large part of them was located in potentially regulatory regions.
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Affiliation(s)
- Irene van den Berg
- Agriculture Victoria Research, AgriBio, 5 Ring Road, Bundoora, VIC, 3083, Australia.
| | - Ruidong Xiang
- Agriculture Victoria Research, AgriBio, 5 Ring Road, Bundoora, VIC, 3083, Australia.,Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Janez Jenko
- GENO SA, Storhamargata 44, 2317, Hamar, Norway
| | | | - Mekki Boussaha
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | | | - Thierry Tribout
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | | | - Didier Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | | | - Marie-Pierre Sanchez
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Mike E Goddard
- Agriculture Victoria Research, AgriBio, 5 Ring Road, Bundoora, VIC, 3083, Australia.,Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia
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22
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Leal-Gutiérrez JD, Elzo MA, Mateescu RG. Identification of eQTLs and sQTLs associated with meat quality in beef. BMC Genomics 2020; 21:104. [PMID: 32000679 PMCID: PMC6993519 DOI: 10.1186/s12864-020-6520-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 01/20/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Transcription has a substantial genetic control and genetic dissection of gene expression could help us understand the genetic architecture of complex phenotypes such as meat quality in cattle. The objectives of the present research were: 1) to perform eQTL and sQTL mapping analyses for meat quality traits in longissimus dorsi muscle; 2) to uncover genes whose expression is influenced by local or distant genetic variation; 3) to identify expression and splicing hot spots; and 4) to uncover genomic regions affecting the expression of multiple genes. RESULTS Eighty steers were selected for phenotyping, genotyping and RNA-seq evaluation. A panel of traits related to meat quality was recorded in longissimus dorsi muscle. Information on 112,042 SNPs and expression data on 8588 autosomal genes and 87,770 exons from 8467 genes were included in an expression and splicing quantitative trait loci (QTL) mapping (eQTL and sQTL, respectively). A gene, exon and isoform differential expression analysis previously carried out in this population identified 1352 genes, referred to as DEG, as explaining part of the variability associated with meat quality traits. The eQTL and sQTL mapping was performed using a linear regression model in the R package Matrix eQTL. Genotype and year of birth were included as fixed effects, and population structure was accounted for by including as a covariate the first PC from a PCA analysis on genotypic data. The identified QTLs were classified as cis or trans using 1 Mb as the maximum distance between the associated SNP and the gene being analyzed. A total of 8377 eQTLs were identified, including 75.6% trans, 10.4% cis, 12.5% DEG trans and 1.5% DEG cis; while 11,929 sQTLs were uncovered: 66.1% trans, 16.9% DEG trans, 14% cis and 3% DEG cis. Twenty-seven expression master regulators and 13 splicing master regulators were identified and were classified as membrane-associated or cytoskeletal proteins, transcription factors or DNA methylases. These genes could control the expression of other genes through cell signaling or by a direct transcriptional activation/repression mechanism. CONCLUSION In the present analysis, we show that eQTL and sQTL mapping makes possible positional identification of gene and isoform expression regulators.
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Affiliation(s)
| | - Mauricio A Elzo
- Department of Animal Sciences, University of Florida, Gainesville, FL, USA
| | - Raluca G Mateescu
- Department of Animal Sciences, University of Florida, Gainesville, FL, USA
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23
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Liu S, Fang L, Zhou Y, Santos DJA, Xiang R, Daetwyler HD, Chamberlain AJ, Cole JB, Li CJ, Yu Y, Ma L, Zhang S, Liu GE. Analyses of inter-individual variations of sperm DNA methylation and their potential implications in cattle. BMC Genomics 2019; 20:888. [PMID: 31752687 PMCID: PMC6873545 DOI: 10.1186/s12864-019-6228-6] [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: 06/19/2019] [Accepted: 10/28/2019] [Indexed: 12/18/2022] Open
Abstract
Background DNA methylation has been shown to be involved in many biological processes, including X chromosome inactivation in females, paternal genomic imprinting, and others. Results Based on the correlation patterns of methylation levels of neighboring CpG sites among 28 sperm whole genome bisulfite sequencing (WGBS) data (486 × coverage), we obtained 31,272 methylation haplotype blocks (MHBs). Among them, we defined conserved methylated regions (CMRs), variably methylated regions (VMRs) and highly variably methylated regions (HVMRs) among individuals, and showed that HVMRs might play roles in transcriptional regulation and function in complex traits variation and adaptive evolution by integrating evidence from traditional and molecular quantitative trait loci (QTL), and selection signatures. Using a weighted correlation network analysis (WGCNA), we also detected a co-regulated module of HVMRs that was significantly associated with reproduction traits, and enriched for glycosyltransferase genes, which play critical roles in spermatogenesis and fertilization. Additionally, we identified 46 VMRs significantly associated with reproduction traits, nine of which were regulated by cis-SNPs, implying the possible intrinsic relationships among genomic variations, DNA methylation, and phenotypes. These significant VMRs were co-localized (± 10 kb) with genes related to sperm motility and reproduction, including ZFP36L1, CRISP2 and HGF. We provided further evidence that rs109326022 within a predominant QTL on BTA18 might influence the reproduction traits through regulating the methylation level of nearby genes JOSD2 and ASPDH in sperm. Conclusion In summary, our results demonstrated associations of sperm DNA methylation with reproduction traits, highlighting the potential of epigenomic information in genomic improvement programs for cattle.
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Affiliation(s)
- Shuli Liu
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.,USDA-ARS, Animal Genomics and Improvement Laboratory, Beltsville, MD, 20705, USA
| | - Lingzhao Fang
- USDA-ARS, Animal Genomics and Improvement Laboratory, Beltsville, MD, 20705, USA.,Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.,Medical Research Council Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Yang Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Education Ministry of China, Huazhong Agricultural University, Wuhan, 430070, Hubei, China
| | - Daniel J A Santos
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Ruidong Xiang
- Faculty of Veterinary & Agricultural Science, The University of Melbourne, Parkville, Victoria, 3052, Australia.,Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, 3083, Australia
| | - Hans D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, 3083, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, 3083, Australia
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, 3083, Australia
| | - John B Cole
- USDA-ARS, Animal Genomics and Improvement Laboratory, Beltsville, MD, 20705, USA
| | - Cong-Jun Li
- USDA-ARS, Animal Genomics and Improvement Laboratory, Beltsville, MD, 20705, USA
| | - Ying Yu
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Shengli Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
| | - George E Liu
- USDA-ARS, Animal Genomics and Improvement Laboratory, Beltsville, MD, 20705, USA.
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