Genetic and genomic analysis of reproduction traits in holstein cattle using SNP chip data and imputed sequence level genotypes.
Journal
BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258
Informations de publication
Date de publication:
19 Sep 2024
19 Sep 2024
Historique:
received:
02
04
2024
accepted:
09
09
2024
medline:
20
9
2024
pubmed:
20
9
2024
entrez:
19
9
2024
Statut:
epublish
Résumé
Reproductive performance plays an important role in animal welfare, health and profitability in animal husbandry and breeding. It is well established that there is a negative correlation between performance and reproduction in dairy cattle. This relationship is being increasingly considered in breeding programs. By elucidating the genetic architecture of underlying reproduction traits, it will be possible to make a more detailed contribution to this. Our study followed two approaches to elucidate this area; in a first part, variance components were estimated for 14 different calving and fertility traits, and then genome-wide association studies were performed for 13 reproduction traits on imputed sequence-level genotypes with subsequent enrichment analyses. Variance components analyses showed a low to moderate heritability (h Our results confirm previous findings of other authors in a comprehensive cohort including 13 different traits at the same time. Additionally, we identified new candidate genes involved in dairy cattle reproduction and made initial suggestions regarding their potential impact, with special regard to the X chromosome as a putative information source for further research. This work can make a contribution to reveal the genetic architecture of reproduction traits in context of trait specific interactions.
Sections du résumé
BACKGROUND
BACKGROUND
Reproductive performance plays an important role in animal welfare, health and profitability in animal husbandry and breeding. It is well established that there is a negative correlation between performance and reproduction in dairy cattle. This relationship is being increasingly considered in breeding programs. By elucidating the genetic architecture of underlying reproduction traits, it will be possible to make a more detailed contribution to this. Our study followed two approaches to elucidate this area; in a first part, variance components were estimated for 14 different calving and fertility traits, and then genome-wide association studies were performed for 13 reproduction traits on imputed sequence-level genotypes with subsequent enrichment analyses.
RESULTS
RESULTS
Variance components analyses showed a low to moderate heritability (h
CONCLUSION
CONCLUSIONS
Our results confirm previous findings of other authors in a comprehensive cohort including 13 different traits at the same time. Additionally, we identified new candidate genes involved in dairy cattle reproduction and made initial suggestions regarding their potential impact, with special regard to the X chromosome as a putative information source for further research. This work can make a contribution to reveal the genetic architecture of reproduction traits in context of trait specific interactions.
Identifiants
pubmed: 39300329
doi: 10.1186/s12864-024-10782-5
pii: 10.1186/s12864-024-10782-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
880Informations de copyright
© 2024. The Author(s).
Références
Walsh SW, Williams EJ, Evans ACO. A review of the causes of poor fertility in high milk producing dairy cows. Anim Reprod Sci. 2011;123:127–38. https://doi.org/10.1016/j.anireprosci.2010.12.001
doi: 10.1016/j.anireprosci.2010.12.001
pubmed: 21255947
Pryce JE, Royal MD, Garnsworthy PC, Mao IL. Fertility in the high-producing dairy cow. Livest Prod Sci. 2004;86:125–35. https://doi.org/10.1016/S0301-6226(03)00145-3
doi: 10.1016/S0301-6226(03)00145-3
Hoekstra J, van der Lugt A, van der Werf J, Ouweltjes W. Genetic and phenotypic parameters for milk production and fertility traits in upgraded dairy cattle. Livest Prod Sci. 1994;40:225–32. https://doi.org/10.1016/0301-6226(94)90090-6
doi: 10.1016/0301-6226(94)90090-6
Berry DP, Wall E, Pryce JE. Genetics and genomics of reproductive performance in dairy and beef cattle. Animal. 2014;8(Suppl 1):105–21. https://doi.org/10.1017/S1751731114000743
doi: 10.1017/S1751731114000743
pubmed: 24703258
Hansen LB, Freeman AE, Berger PJ. Yield and Fertility relationships in dairy cattle. J Dairy Sci. 1983;66:293–305. https://doi.org/10.3168/jds.S0022-0302(83)81789-5
doi: 10.3168/jds.S0022-0302(83)81789-5
pubmed: 6833601
Simianer H, Solbu H, Schaeffer LR. Estimated genetic correlations between disease and yield traits in dairy cattle. J Dairy Sci. 1991;74:4358–65. https://doi.org/10.3168/jds.S0022-0302(91)78632-3
doi: 10.3168/jds.S0022-0302(91)78632-3
pubmed: 1787205
VIT. Jahresbericht 2020: Vereinigte Informationssysteme Tierhaltung w. V. 2021. https://www.vit.de/fileadmin/Wir-sind-vit/Jahresberichte/vit-JB2020-gesamt.pdf . Accessed 14 Sep 2023.
Hardie LC, Haagen IW, Heins BJ, Dechow CD. Genetic parameters and association of national evaluations with breeding values for health traits in US organic holstein cows. J Dairy Sci. 2022;105:495–508. https://doi.org/10.3168/jds.2021-20588
doi: 10.3168/jds.2021-20588
pubmed: 34656345
Egger-Danner C, Cole JB, Pryce JE, Gengler N, Heringstad B, Bradley A, Stock KF. Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits. Animal. 2015;9:191–207. https://doi.org/10.1017/S1751731114002614
doi: 10.1017/S1751731114002614
pubmed: 25387784
Wangler A, Blum E, Böttcher I, Sanftleben P. Lebensleistung und Nutzungsdauer von Milchkühen aus der Sicht einer effizienten Milchproduktion. Züchtungskunde. 09.2009;2009:341–60.
VIT. Estimation of Breeding Values for Milk Production Traits, Somatic Cell Score, Conformation, Productive Life and Reproduction Traits in German Dairy Cattle. 2022. https://www.vit.de/fileadmin/DE/Zuchtwertschaetzung/Zws_Bes_eng.pdf . Accessed 1 Feb 2024.
Ashwell MS, Heyen DW, Sonstegard TS, van Tassell CP, Da Y, VanRaden PM, et al. Detection of Quantitative Trait Loci Affecting Milk Production, Health, and Reproductive traits in Holstein cattle. J Dairy Sci. 2004;87:468–75. https://doi.org/10.3168/jds.S0022-0302(04)73186-0
doi: 10.3168/jds.S0022-0302(04)73186-0
pubmed: 14762090
Goddard ME, Hayes BJ, Meuwissen THE. Genomic selection in livestock populations. Genet Res (Camb). 2010;92:413–21. https://doi.org/10.1017/S0016672310000613
doi: 10.1017/S0016672310000613
pubmed: 21429272
Abo-Ismail MK, Brito LF, Miller SP, Sargolzaei M, Grossi DA, Moore SS, et al. Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle. Genet Sel Evol. 2017;49:82. https://doi.org/10.1186/s12711-017-0356-8
doi: 10.1186/s12711-017-0356-8
pubmed: 29115939
pmcid: 6389134
Seidenspinner T, Bennewitz J, Reinhardt F, Thaller G. Need for sharp phenotypes in QTL detection for calving traits in dairy cattle. J Anim Breed Genet. 2009;126:455–62. https://doi.org/10.1111/j.1439-0388.2009.00804.x
doi: 10.1111/j.1439-0388.2009.00804.x
pubmed: 19912419
Müller M-P, Rothammer S, Seichter D, Russ I, Hinrichs D, Tetens J, et al. Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18. J Dairy Sci. 2017;100:1987–2006. https://doi.org/10.3168/jds.2016-11506
doi: 10.3168/jds.2016-11506
pubmed: 28109604
Druet T, Macleod IM, Hayes BJ. Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity. 2014;112:39–47. https://doi.org/10.1038/hdy.2013.13
doi: 10.1038/hdy.2013.13
pubmed: 23549338
Uffelmann E, Huang QQ, Munung NS, de Vries J, Okada Y, Martin AR, et al. Genome-wide association studies. Nat Rev Methods Primers. 2021;1:1–21. https://doi.org/10.1038/s43586-021-00056-9
doi: 10.1038/s43586-021-00056-9
Daetwyler HD, Capitan A, Pausch H, Stothard P, van Binsbergen R, Brøndum RF, et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat Genet. 2014;46:858–65. https://doi.org/10.1038/ng.3034
doi: 10.1038/ng.3034
pubmed: 25017103
Wientjes YCJ, Bijma P, Calus MPL, Zwaan BJ, Vitezica ZG, van den Heuvel J. The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture. Genet Sel Evol. 2022;54:19. https://doi.org/10.1186/s12711-022-00709-7
doi: 10.1186/s12711-022-00709-7
pubmed: 35255802
pmcid: 8900405
Xiang R, MacLeod IM, Daetwyler HD, Jong G, de, O’Connor E, Schrooten C, et al. Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations. Nat Commun. 2021;12:860. https://doi.org/10.1038/s41467-021-21001-0
doi: 10.1038/s41467-021-21001-0
pubmed: 33558518
pmcid: 7870883
Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010;11:499–511. https://doi.org/10.1038/nrg2796
doi: 10.1038/nrg2796
pubmed: 20517342
van Binsbergen R, Bink MC, Calus MP, van Eeuwijk FA, Hayes BJ, Hulsegge I, Veerkamp RF. Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle. Genet Sel Evol. 2014;46:41. https://doi.org/10.1186/1297-9686-46-41
doi: 10.1186/1297-9686-46-41
pubmed: 25022768
pmcid: 4226983
Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL. Advantages and pitfalls in the application of mixed-model association methods. Nat Genet. 2014;46:100–6. https://doi.org/10.1038/ng.2876
doi: 10.1038/ng.2876
pubmed: 24473328
pmcid: 3989144
Schneider H, Segelke D, Tetens J, Thaller G, Bennewitz J. A genomic assessment of the correlation between milk production traits and claw and udder health traits in Holstein dairy cattle. J Dairy Sci. 2023;106:1190–205. https://doi.org/10.3168/jds.2022-22312
doi: 10.3168/jds.2022-22312
pubmed: 36460501
Jairath L, Dekkers J, Schaeffer LR, Liu Z, Burnside EB, Kolstad B. Genetic evaluation for Herd Life in Canada. J Dairy Sci. 1998;81:550–62. https://doi.org/10.3168/jds.S0022-0302(98)75607-3
doi: 10.3168/jds.S0022-0302(98)75607-3
pubmed: 9532510
Zengting Liu Y, Masuda. A deregression method for single-step genomic model using all genotype data. IB. 2021:41–51.
Westwood CT, Lean IJ, Garvin JK. Factors influencing fertility of holstein dairy cows: a Multivariate description. J Dairy Sci. 2002;85:3225–37. https://doi.org/10.3168/jds.S0022-0302(02)74411-1
doi: 10.3168/jds.S0022-0302(02)74411-1
pubmed: 12512596
Segelke D, Chen J, Liu Z, Reinhardt F, Thaller G, Reents R. Reliability of genomic prediction for German holsteins using imputed genotypes from low-density chips. J Dairy Sci. 2012;95:5403–11. https://doi.org/10.3168/jds.2012-5466
doi: 10.3168/jds.2012-5466
pubmed: 22916947
Browning BL, Zhou Y, Browning SR. A one-penny Imputed Genome from Next-Generation reference panels. Am J Hum Genet. 2018;103:338–48. https://doi.org/10.1016/j.ajhg.2018.07.015
doi: 10.1016/j.ajhg.2018.07.015
pubmed: 30100085
pmcid: 6128308
Browning BL, Browning SR. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet. 2009;84:210–23. https://doi.org/10.1016/j.ajhg.2009.01.005
doi: 10.1016/j.ajhg.2009.01.005
pubmed: 19200528
pmcid: 2668004
Križanac A-M, Reimer C, Heise J, Liu Z, Pryce J, Bennewitz J et al. Sequence-based GWAS in 180 000 German Holstein cattle reveals new candidate genes for milk production traits. Preprint at https://doi.org/10.1101/2023.12.06.570350 .
Pacheco HA, Rezende FM, Peñagaricano F. Gene mapping and genomic prediction of bull fertility using sex chromosome markers. J Dairy Sci. 2020;103:3304–11. https://doi.org/10.3168/jds.2019-17767
doi: 10.3168/jds.2019-17767
pubmed: 32063375
Sanchez MP, Escouflaire C, Baur A, Hozé C, Capitan A. Sequence-based association analyses on X chromosome in six dairy cattle breeds. Rotterdam, Netherlands; 2022.
Hadfield JD. MCMC methods for Multi-response Generalized Linear mixed models: the MCMCglmmR Package. J Stat Soft. 2010. https://doi.org/10.18637/jss.v033.i02
doi: 10.18637/jss.v033.i02
Villemereuil Pde. Tutorial - Estimation of a biological trait heritability using the animal model: How to use the MCMCglmm R package; 2012.
Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.
doi: 10.1016/j.ajhg.2010.11.011
pubmed: 21167468
pmcid: 3014363
Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–9. https://doi.org/10.1038/ng.608
doi: 10.1038/ng.608
pubmed: 20562875
pmcid: 3232052
Jiang J, Ma L, Prakapenka D, VanRaden PM, Cole JB, Da Y. A large-scale genome-wide Association study in U.S. Holstein cattle. Front Genet. 2019;10:412. https://doi.org/10.3389/fgene.2019.00412
doi: 10.3389/fgene.2019.00412
pubmed: 31139206
pmcid: 6527781
Arishima T, Sasaki S, Isobe T, Ikebata Y, Shimbara S, Ikeda S, et al. Maternal variant in the upstream of FOXP3 gene on the X chromosome is associated with recurrent infertility in Japanese black cattle. BMC Genet. 2017;18:103. https://doi.org/10.1186/s12863-017-0573-8
doi: 10.1186/s12863-017-0573-8
pubmed: 29212449
pmcid: 5719641
Wickham H. Ggplot2: elegant graphics for data analysis. Cham: Springer international publishing; 2016.
doi: 10.1007/978-3-319-24277-4
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing; 2022.
McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, et al. The Ensembl variant effect predictor. Genome Biol. 2016;17:122. https://doi.org/10.1186/s13059-016-0974-4
doi: 10.1186/s13059-016-0974-4
pubmed: 27268795
pmcid: 4893825
Tweedie S, Braschi B, Gray K, Jones TEM, Seal RL, Yates B, Bruford EA. Genenames.org: the HGNC and VGNC resources in 2021. Nucleic Acids Res. 2021;49:D939–46. https://doi.org/10.1093/nar/gkaa980
doi: 10.1093/nar/gkaa980
pubmed: 33152070
Marc Carlson. org.Bt.eg.db: genome wide annotation for bovine. Bioconductor; 2017.
Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7. https://doi.org/10.1089/omi.2011.0118
doi: 10.1089/omi.2011.0118
pubmed: 22455463
pmcid: 3339379
Yu G, Wang L-G, Yan G-R, He Q-Y. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinf (Oxford England). 2015;31:608–9. https://doi.org/10.1093/bioinformatics/btu684
doi: 10.1093/bioinformatics/btu684
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. https://doi.org/10.1093/nar/28.1.27
doi: 10.1093/nar/28.1.27
pubmed: 10592173
pmcid: 102409
Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, Vilo J. G:profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019;47:W191–8. https://doi.org/10.1093/nar/gkz369
doi: 10.1093/nar/gkz369
pubmed: 31066453
pmcid: 6602461
Hanbo Chen. VennDiagram: Generate High-Resolution Venn and Euler Plots: R package version 1.7.3. 2022. https://CRAN.R-project.org/package=VennDiagram . Accessed 4 Mar 2024.
Thomsen H, Reinsch N, Xu N, Looft C, Grupe S, Kuhn C, et al. Comparison of estimated breeding values, daughter yield deviations and de-regressed proofs within a whole genome scan for QTL. J Anim Breed Genet. 2001;118:357–70. https://doi.org/10.1046/j.1439-0388.2001.00302.x
doi: 10.1046/j.1439-0388.2001.00302.x
Liu Z, Reinhardt F, Reents R. The effective daughter contribution concept applied to multiple trait models for approximating reliability of estimated breeding values. IB. 2001:41.
Kolbehdari D, Wang Z, Grant JR, Murdoch B, Prasad A, Xiu Z, et al. A whole-genome scan to map quantitative trait loci for conformation and functional traits in Canadian holstein bulls. J Dairy Sci. 2008;91:2844–56. https://doi.org/10.3168/jds.2007-0585
doi: 10.3168/jds.2007-0585
pubmed: 18565942
O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733–45. https://doi.org/10.1093/nar/gkv1189
doi: 10.1093/nar/gkv1189
pubmed: 26553804
Larson RL. Heifer development: reproduction and nutrition. Vet Clin North Am Food Anim Pract. 2007;23:53–68. https://doi.org/10.1016/j.cvfa.2006.11.003
doi: 10.1016/j.cvfa.2006.11.003
pubmed: 17382841
D’Hondt V, Lacroix-Triki M, Jarlier M, Boissiere-Michot F, Puech C, Coopman P, et al. High PTPN13 expression in high grade serous ovarian carcinoma is associated with a better patient outcome. Oncotarget. 2017;8:95662–73. https://doi.org/10.18632/oncotarget.21175
doi: 10.18632/oncotarget.21175
pubmed: 29221157
pmcid: 5707051
Révillion F, Puech C, Rabenoelina F, Chalbos D, Peyrat J-P, Freiss G. Expression of the putative tumor suppressor gene PTPN13/PTPL1 is an independent prognostic marker for overall survival in breast cancer. Int J Cancer. 2009;124:638–43. https://doi.org/10.1002/ijc.23989
doi: 10.1002/ijc.23989
pubmed: 19004008
pmcid: 2740876
Parker Gaddis KL, Null DJ, Cole JB. Explorations in genome-wide association studies and network analyses with dairy cattle fertility traits. J Dairy Sci. 2016;99:6420–35. https://doi.org/10.3168/jds.2015-10444
doi: 10.3168/jds.2015-10444
pubmed: 27209127
Fortes MRS, Reverter A, Nagaraj SH, Zhang Y, Jonsson NN, Barris W, et al. A single nucleotide polymorphism-derived regulatory gene network underlying puberty in 2 tropical breeds of beef cattle. J Anim Sci. 2011;89:1669–83. https://doi.org/10.2527/jas.2010-3681
doi: 10.2527/jas.2010-3681
pubmed: 21357453
Alves BCA, Tobo PR, Rodrigues R, Ruiz JC, de Lima VFMH, Moreira-Filho CA. Characterization of bovine transcripts preferentially expressed in testis and with a putative role in spermatogenesis. Theriogenology. 2011;76:991–8. https://doi.org/10.1016/j.theriogenology.2011.04.027
doi: 10.1016/j.theriogenology.2011.04.027
pubmed: 21664671
Strucken EM, Bortfeldt RH, Tetens J, Thaller G, Brockmann GA. Genetic effects and correlations between production and fertility traits and their dependency on the lactation-stage in Holstein friesians. BMC Genet. 2012;13:108. https://doi.org/10.1186/1471-2156-13-108
doi: 10.1186/1471-2156-13-108
pubmed: 23244492
pmcid: 3561121
Koh YQ, Peiris HN, Vaswani K, Almughlliq FB, Meier S, Burke CR, et al. Proteome profiling of exosomes derived from plasma of heifers with divergent genetic merit for fertility. J Dairy Sci. 2018;101:6462–73. https://doi.org/10.3168/jds.2017-14190
doi: 10.3168/jds.2017-14190
pubmed: 29705424
Lee Y-L, Takeda H, Costa Monteiro Moreira G, Karim L, Mullaart E, Coppieters W, et al. 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. https://doi.org/10.1371/journal.pgen.1009331
doi: 10.1371/journal.pgen.1009331
pubmed: 34288907
pmcid: 8328317
Olsen HG, Knutsen TM, Lewandowska-Sabat AM, Grove H, Nome T, Svendsen M, et al. Fine mapping of a QTL on bovine chromosome 6 using imputed full sequence data suggests a key role for the group-specific component (GC) gene in clinical mastitis and milk production. Genet Sel Evol. 2016;48:79. https://doi.org/10.1186/s12711-016-0257-2
doi: 10.1186/s12711-016-0257-2
pubmed: 27760518
pmcid: 5072345
Naeem A, Drackley JK, Stamey J, Loor JJ. Role of metabolic and cellular proliferation genes in ruminal development in response to enhanced plane of nutrition in neonatal holstein calves. J Dairy Sci. 2012;95:1807–20. https://doi.org/10.3168/jds.2011-4709
doi: 10.3168/jds.2011-4709
pubmed: 22459829
Pacheco HA, da Silva S, Sigdel A, Mak CK, Galvão KN, Texeira RA, et al. Gene mapping and gene-set analysis for milk fever incidence in holstein dairy cattle. Front Genet. 2018;9:465. https://doi.org/10.3389/fgene.2018.00465
doi: 10.3389/fgene.2018.00465
pubmed: 30364193
pmcid: 6192420
Grundmann M, von Versen-Höynck F. Vitamin D - roles in women’s reproductive health? Reprod Biol Endocrinol. 2011;9:146. https://doi.org/10.1186/1477-7827-9-146
doi: 10.1186/1477-7827-9-146
pubmed: 22047005
pmcid: 3239848
Jones KS, Assar S, Prentice A, Schoenmakers I. Vitamin D expenditure is not altered in pregnancy and lactation despite changes in vitamin D metabolite concentrations. Sci Rep. 2016;6:26795. https://doi.org/10.1038/srep26795
doi: 10.1038/srep26795
pubmed: 27222109
pmcid: 4879580
Ankö M-L, Panula P. Regulation of endogenous human NPFF2 receptor by neuropeptide FF in SK-N-MC neuroblastoma cell line. J Neurochem. 2006;96:573–84. https://doi.org/10.1111/j.1471-4159.2005.03581.x
doi: 10.1111/j.1471-4159.2005.03581.x
pubmed: 16336216
Bonini JA, Jones KA, Adham N, Forray C, Artymyshyn R, Durkin MM, et al. Identification and characterization of two G protein-coupled receptors for neuropeptide FF. J Biol Chem. 2000;275:39324–31. https://doi.org/10.1074/jbc.M004385200
doi: 10.1074/jbc.M004385200
pubmed: 11024015
Genazzani AR, Genazzani AD, Volpogni C, Pianazzi F, Li GA, Surico N, Petraglia F. Opioid control of gonadotrophin secretion in humans. Hum Reprod. 1993;8(Suppl 2):151–3. https://doi.org/10.1093/humrep/8.suppl_2.151
doi: 10.1093/humrep/8.suppl_2.151
pubmed: 8276950
Goodman RL, Coolen LM, Anderson GM, Hardy SL, Valent M, Connors JM, et al. Evidence that dynorphin plays a major role in mediating progesterone negative feedback on gonadotropin-releasing hormone neurons in sheep. Endocrinology. 2004;145:2959–67. https://doi.org/10.1210/en.2003-1305
doi: 10.1210/en.2003-1305
pubmed: 14988383
Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010;38:D355–60. https://doi.org/10.1093/nar/gkp896
doi: 10.1093/nar/gkp896
pubmed: 19880382
Kusnadi EP, Timpone C, Topisirovic I, Larsson O, Furic L. Regulation of gene expression via translational buffering. Biochim Biophys Acta Mol Cell Res. 2022;1869:119140. https://doi.org/10.1016/j.bbamcr.2021.119140
doi: 10.1016/j.bbamcr.2021.119140
pubmed: 34599983
Cue RI, Monardes HG, Hayes JF. Relationships of calving ease with type traits. J Dairy Sci. 1990;73:3586–90. https://doi.org/10.3168/jds.S0022-0302(90)79060-1
doi: 10.3168/jds.S0022-0302(90)79060-1
pubmed: 2099378
Reimand J, Kull M, Peterson H, Hansen J, Vilo J. G:Profiler–a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 2007;35:W193–200. https://doi.org/10.1093/nar/gkm226
doi: 10.1093/nar/gkm226
pubmed: 17478515
pmcid: 1933153
Mila M, Alvarez-Mora MI, Madrigal I, Rodriguez-Revenga L. Fragile X syndrome: an overview and update of the FMR1 gene. Clin Genet. 2018;93:197–205. https://doi.org/10.1111/cge.13075
doi: 10.1111/cge.13075
pubmed: 28617938
Oberlé I, Rousseau F, Heitz D, Kretz C, Devys D, Hanauer A, et al. Instability of a 550-base pair DNA segment and abnormal methylation in fragile X syndrome. Science. 1991;252:1097–102. https://doi.org/10.1126/science.252.5009.1097
doi: 10.1126/science.252.5009.1097
pubmed: 2031184
Verkerk AJ, Pieretti M, Sutcliffe JS, Fu Y-H, Kuhl DP, Pizzuti A, et al. Identification of a gene (FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile X syndrome. Cell. 1991;65:905–14. https://doi.org/10.1016/0092-8674(91)90397-H
doi: 10.1016/0092-8674(91)90397-H
pubmed: 1710175
Sullivan AK, Marcus M, Epstein MP, Allen EG, Anido AE, Paquin JJ, et al. Association of FMR1 repeat size with ovarian dysfunction. Hum Reprod. 2005;20:402–12. https://doi.org/10.1093/humrep/deh635
doi: 10.1093/humrep/deh635
pubmed: 15608041
Sherman SL. Premature ovarian failure in the fragile X syndrome. Am J Med Genet. 2000;97:189–94. https://doi.org/10.1002/1096-8628(200023)97:33C%189::AID-AJMG10363E%3.0.CO;2-J
Mihm M, Good TE, Ireland JL, Ireland JJ, Knight PG, Roche JF. Decline in serum follicle-stimulating hormone concentrations alters key intrafollicular growth factors involved in selection of the dominant follicle in heifers. Biol Reprod. 1997;57:1328–37. https://doi.org/10.1095/biolreprod57.6.1328
doi: 10.1095/biolreprod57.6.1328
pubmed: 9408237
Goetz T, Arslan A, Wisden W, Wulff P. GABAA receptors: structure and function in the basal ganglia. Prog Brain Res. 2007;160:21–41. https://doi.org/10.1016/S0079-6123(06)60003-4
doi: 10.1016/S0079-6123(06)60003-4
pubmed: 17499107
pmcid: 2648504
Atack JR, Hutson PH, Collinson N, Marshall G, Bentley G, Moyes C, et al. Anxiogenic properties of an inverse agonist selective for alpha3 subunit-containing GABA A receptors. Br J Pharmacol. 2005;144:357–66. https://doi.org/10.1038/sj.bjp.0706056
doi: 10.1038/sj.bjp.0706056
pubmed: 15655523
pmcid: 1576012
Dias R, Sheppard WFA, Fradley RL, Garrett EM, Stanley JL, Tye SJ, et al. Evidence for a significant role of alpha 3-containing GABAA receptors in mediating the anxiolytic effects of benzodiazepines. J Neurosci. 2005;25:10682–8. https://doi.org/10.1523/JNEUROSCI.1166-05.2005
doi: 10.1523/JNEUROSCI.1166-05.2005
pubmed: 16291941
pmcid: 6725841
Rudolph U, Möhler H. Analysis of GABAA receptor function and dissection of the pharmacology of benzodiazepines and general anesthetics through mouse genetics. Annu Rev Pharmacol Toxicol. 2004;44:475–98. https://doi.org/10.1146/annurev.pharmtox.44.101802.121429
doi: 10.1146/annurev.pharmtox.44.101802.121429
pubmed: 14744255
Xiang F, Buervenich S, Nicolao P, Bailey ME, Zhang Z, Anvret M. Mutation screening in Rett syndrome patients. J Med Genet. 2000;37:250–5. https://doi.org/10.1136/jmg.37.4.250
doi: 10.1136/jmg.37.4.250
pubmed: 10745042
pmcid: 1734556
Brunton PJ, Russell JA, Hirst JJ. Allopregnanolone in the brain: protecting pregnancy and birth outcomes. Prog Neurobiol. 2014;113:106–36. https://doi.org/10.1016/j.pneurobio.2013.08.005
doi: 10.1016/j.pneurobio.2013.08.005
pubmed: 24012715
Rosahl TW, Spillane D, Missler M, Herz J, Selig DK, Wolff JR, et al. Essential functions of synapsins I and II in synaptic vesicle regulation. Nature. 1995;375:488–93. https://doi.org/10.1038/375488a0
doi: 10.1038/375488a0
pubmed: 7777057
Mirza FJ, Zahid S. The role of synapsins in Neurological disorders. Neurosci Bull. 2018;34:349–58. https://doi.org/10.1007/s12264-017-0201-7
doi: 10.1007/s12264-017-0201-7
pubmed: 29282612
Revest J-M, Kaouane N, Mondin M, Le Roux A, Rougé-Pont F, Vallée M, et al. The enhancement of stress-related memory by glucocorticoids depends on synapsin-Ia/Ib. Mol Psychiatry. 2010;15(1125):1140–51. https://doi.org/10.1038/mp.2010.40
doi: 10.1038/mp.2010.40
pmcid: 2990189
Ayrout M, Simon V, Bernard V, Binart N, Cohen-Tannoudji J, Lombès M, Chauvin S. A novel non genomic glucocorticoid signaling mediated by a membrane palmitoylated glucocorticoid receptor cross talks with GnRH in gonadotrope cells. Sci Rep. 2017;7:1537. https://doi.org/10.1038/s41598-017-01777-2
doi: 10.1038/s41598-017-01777-2
pubmed: 28484221
pmcid: 5431531
Leslie KE, Doig PA, Bosu WT, Curtis RA, Martin SW. Effects of gonadotrophin releasing hormone on reproductive performance of dairy cows with retained placenta. Can J Comp Med. 1984;48:354–9.
pubmed: 6391640
pmcid: 1236082
Besbaci M, Abdelli A, Minviel JJ, Belabdi I, Kaidi R, Raboisson D. Association of pregnancy per artificial insemination with gonadotropin-releasing hormone and human chorionic gonadotropin administered during the luteal phase after artificial insemination in dairy cows: a meta-analysis. J Dairy Sci. 2020;103:2006–18. https://doi.org/10.3168/jds.2019-16439
doi: 10.3168/jds.2019-16439
pubmed: 31785879
Kimura K, Goff JP, Kehrli ME, Reinhardt TA. Decreased neutrophil function as a cause of retained placenta in dairy cattle. J Dairy Sci. 2002;85:544–50. https://doi.org/10.3168/jds.S0022-0302(02)74107-6
doi: 10.3168/jds.S0022-0302(02)74107-6
pubmed: 11949858
Jing F, Ruan X, Liu X, Yang C, Di Wang, Zheng J, et al. The PABPC5/HCG15/ZNF331 feedback Loop regulates Vasculogenic Mimicry of Glioma via STAU1-Mediated mRNA decay. Mol Therapy - Oncolytics. 2020;17:216–31. https://doi.org/10.1016/j.omto.2020.03.017
doi: 10.1016/j.omto.2020.03.017
Bhattacharjee RB, Bag J. Depletion of nuclear poly(A) binding protein PABPN1 produces a compensatory response by cytoplasmic PABP4 and PABP5 in cultured human cells. PLoS ONE. 2012;7:e53036. https://doi.org/10.1371/journal.pone.0053036
doi: 10.1371/journal.pone.0053036
pubmed: 23300856
pmcid: 3534090
Blanco P, Sargent CA, Boucher CA, Howell G, Ross M, Affara NA. A novel poly(A)-binding protein gene (PABPC5) maps to an X-specific subinterval in the Xq21.3/Yp11.2 homology block of the human sex chromosomes. Genomics. 2001;74:1–11. https://doi.org/10.1006/geno.2001.6530
doi: 10.1006/geno.2001.6530
pubmed: 11374897
Venables JP, Eperon I. The roles of RNA-binding proteins in spermatogenesis and male infertility. Curr Opin Genet Dev. 1999;9:346–54. https://doi.org/10.1016/s0959-437x(99)80052-5
doi: 10.1016/s0959-437x(99)80052-5
pubmed: 10377282
López-Gatius F, Santolaria P, Yániz J, Rutllant J, López-Béjar M. Factors affecting pregnancy loss from gestation day 38 to 90 in lactating dairy cows from a single herd. Theriogenology. 2002;57:1251–61. https://doi.org/10.1016/S0093-691X(01)00715-4
doi: 10.1016/S0093-691X(01)00715-4
pubmed: 12013445
Spencer TE. Early pregnancy: concepts, challenges, and potential solutions. Anim Front. 2013;3:48–55. https://doi.org/10.2527/af.2013-0033
doi: 10.2527/af.2013-0033
Druet T, Legarra A. Theoretical and empirical comparisons of expected and realized relationships for the X-chromosome. Genet Sel Evol. 2020;52:50. https://doi.org/10.1186/s12711-020-00570-6
doi: 10.1186/s12711-020-00570-6
pubmed: 32819272
pmcid: 7441635