Activation of cryptic splicing in bovine WDR19 is associated with reduced semen quality and male fertility
Autoři:
Maya Hiltpold aff001; Guanglin Niu aff002; Naveen Kumar Kadri aff001; Danang Crysnanto aff001; Zih-Hua Fang aff001; Mirjam Spengeler aff003; Fritz Schmitz-Hsu aff004; Christian Fuerst aff005; Hermann Schwarzenbacher aff005; Franz R. Seefried aff003; Frauke Seehusen aff006; Ulrich Witschi aff004; Angelika Schnieke aff002; Ruedi Fries aff007; Heinrich Bollwein aff008; Krzysztof Flisikowski aff002; Hubert Pausch aff001
Působiště autorů:
Animal Genomics, ETH Zürich, Lindau, Switzerland
aff001; Livestock Biotechnology, TU München, Freising, Germany
aff002; QualitasAG, Zug, Switzerland
aff003; Swissgenetics, Zollikofen, Switzerland
aff004; ZuchtData, Wien, Austria
aff005; Institute of Veterinary Pathology, University of Zurich, Zurich, Switzerland
aff006; Animal Breeding, TU München, Freising, Germany
aff007; Clinic of Reproductive Medicine, University of Zurich, Zürich, Switzerland
aff008
Vyšlo v časopise:
Activation of cryptic splicing in bovine WDR19 is associated with reduced semen quality and male fertility. PLoS Genet 16(5): e32767. doi:10.1371/journal.pgen.1008804
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008804
Souhrn
Cattle are ideally suited to investigate the genetics of male reproduction, because semen quality and fertility are recorded for all ejaculates of artificial insemination bulls. We analysed 26,090 ejaculates of 794 Brown Swiss bulls to assess ejaculate volume, sperm concentration, sperm motility, sperm head and tail anomalies and insemination success. The heritability of the six semen traits was between 0 and 0.26. Genome-wide association testing on 607,511 SNPs revealed a QTL on bovine chromosome 6 that was associated with sperm motility (P = 2.5 x 10−27), head (P = 2.0 x 10−44) and tail anomalies (P = 7.2 x 10−49) and insemination success (P = 9.9 x 10−13). The QTL harbors a recessive allele that compromises semen quality and male fertility. We replicated the effect of the QTL on fertility (P = 7.1 x 10−32) in an independent cohort of 2481 Brown Swiss bulls. The analysis of whole-genome sequencing data revealed that a synonymous variant (BTA6:58373887C>T, rs474302732) in WDR19 encoding WD repeat-containing protein 19 was in linkage disequilibrium with the fertility-associated haplotype. WD repeat-containing protein 19 is a constituent of the intraflagellar transport complex that is essential for the physiological function of motile cilia and flagella. Bioinformatic and transcription analyses revealed that the BTA6:58373887 T-allele activates a cryptic exonic splice site that eliminates three evolutionarily conserved amino acids from WDR19. Western blot analysis demonstrated that the BTA6:58373887 T-allele decreases protein expression. We make the remarkable observation that, in spite of negative effects on semen quality and bull fertility, the BTA6:58373887 T-allele has a frequency of 24% in the Brown Swiss population. Our findings are the first to uncover a variant that is associated with quantitative variation in semen quality and male fertility in cattle.
Klíčová slova:
Cattle – Haplotypes – Heredity – Insemination – Semen – Sperm – Sperm head – Variant genotypes
Zdroje
1. Esslemont RJ, Kossaibati MA, Allcock J. Economics of fertility in dairy cows. BSAP Occasional Publication. 2001;26: 19–29. doi: 10.1017/S0263967X00033565
2. Inchaisri C, Jorritsma R, Vos PLAM, van der Weijden GC, Hogeveen H. Economic consequences of reproductive performance in dairy cattle. Theriogenology. 2010;74: 835–846. doi: 10.1016/j.theriogenology.2010.04.008 20580069
3. De Vries A. Economic Value of Pregnancy in Dairy Cattle. Journal of Dairy Science. 2006;89: 3876–3885. doi: 10.3168/jds.S0022-0302(06)72430-4 16960063
4. Fuerst C, Gredler B. Genetic evaluation for fertility traits in Austria and Germany. Interbull Bulletin. 2009;40:3–9.
5. Norman HD, Hutchison JL, VanRaden PM. Evaluations for service-sire conception rate for heifer and cow inseminations with conventional and sexed semen. Journal of Dairy Science. 2011;94: 6135–6142. doi: 10.3168/jds.2010-3875 22118101
6. Nadarajah K, Burnside EB, Schaeffer LR. Genetic Parameters for Fertility of Dairy Bulls. Journal of Dairy Science. 1988;71: 2730–2734. doi: 10.3168/jds.S0022-0302(88)79866-5 3204190
7. Tiezzi F, Maltecca C, Penasa M, Cecchinato A, Bittante G. Short communication: Genetic analysis of dairy bull fertility from field data of Brown Swiss cattle. Journal of Dairy Science. 2013;96: 7325–7328. doi: 10.3168/jds.2013-6885 23992975
8. Rezende FM, Nani JP, Peñagaricano F. Genomic prediction of bull fertility in US Jersey dairy cattle. Journal of Dairy Science. 2019;102: 3230–3240. doi: 10.3168/jds.2018-15810 30712930
9. Mathevon M, Buhr MM, Dekkers JCM. Environmental, Management, and Genetic Factors Affecting Semen Production in Holstein Bulls. Journal of Dairy Science. 1998;81: 3321–3330. doi: 10.3168/jds.S0022-0302(98)75898-9 9891279
10. Druet T, Fritz S, Sellem E, Basso B, Gérard O, Salas‐Cortes L, et al. Estimation of genetic parameters and genome scan for 15 semen characteristics traits of Holstein bulls. Journal of Animal Breeding and Genetics. 2009;126: 269–277. doi: 10.1111/j.1439-0388.2008.00788.x 19630877
11. Berry DP, Eivers B, Dunne G, McParland S. Genetics of bull semen characteristics in a multi-breed cattle population. Theriogenology. 2019;123: 202–208. doi: 10.1016/j.theriogenology.2018.10.006 30317043
12. Burren A, Joerg H, Erbe M, Gilmour AR, Witschi U, Schmitz‐Hsu F. Genetic parameters for semen production traits in Swiss dairy bulls. Reproduction in Domestic Animals. 2019;54: 1177–1181. doi: 10.1111/rda.13492 31206856
13. Rodríguez-Martínez H. State of the art in farm animal sperm evaluation. Reproduction, Fertility and Development. 2006;19: 91–101. doi: 10.1071/RD06104 17389138
14. Vincent P, Underwood SL, Dolbec C, Bouchard N, Kroetsch T, Blondin P. Bovine Semen Quality Control in Artificial Insemination Centers. In: Hopper RM, editor. Bovine Reproduction. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2014. pp. 685–695. doi: 10.1002/9781118833971.ch74
15. Thundathil JC, Dance AL, Kastelic JP. Fertility management of bulls to improve beef cattle productivity. Theriogenology. 2016;86: 397–405. doi: 10.1016/j.theriogenology.2016.04.054 27173954
16. García-Ruiz A, Cole JB, VanRaden PM, Wiggans GR, Ruiz-López FJ, Van Tassell CP. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proceedings of the National Academy of Sciences of the United States of America. 2016;113: E3995–E4004. doi: 10.1073/pnas.1519061113 27354521
17. 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. Nature Genetics. 2014;46: 858–865. doi: 10.1038/ng.3034 25017103
18. Pausch H, MacLeod IM, Fries R, Emmerling R, Bowman PJ, Daetwyler HD, et al. Evaluation of the accuracy of imputed sequence variant genotypes and their utility for causal variant detection in cattle. Genetics Selection Evolution. 2017;49: 24. doi: 10.1186/s12711-017-0301-x 28222685
19. Fortes MRS, DeAtley KL, Lehnert SA, Burns BM, Reverter A, Hawken RJ, et al. Genomic regions associated with fertility traits in male and female cattle: Advances from microsatellites to high-density chips and beyond. Animal Reproduction Science. 2013;141: 1–19. doi: 10.1016/j.anireprosci.2013.07.002 23932163
20. Puglisi R, Gaspa G, Balduzzi D, Severgnini A, Vanni R, Macciotta NPP, et al. Genomewide analysis of bull sperm quality and fertility traits. Reproduction in Domestic Animals. 2016;51: 840–843. doi: 10.1111/rda.12747 27550832
21. Qin C, Yin H, Zhang X, Sun D, Zhang Q, Liu J, et al. Genome-wide association study for semen traits of the bulls in Chinese Holstein. Animal Genetics. 2017;48: 80–84. doi: 10.1111/age.12433 27610941
22. Fortes MRS, Reverter A, Kelly M, McCulloch R, Lehnert SA. Genome-wide association study for inhibin, luteinizing hormone, insulin-like growth factor 1, testicular size and semen traits in bovine species. Andrology. 2013;1: 644–650. doi: 10.1111/j.2047-2927.2013.00101.x 23785023
23. Hering DM, Olenski K, Kaminski S. Genome-wide association study for poor sperm motility in Holstein-Friesian bulls. Animal Reproduction Science. 2014;146: 89–97. doi: 10.1016/j.anireprosci.2014.01.012 24612955
24. Han Y, Peñagaricano F. Unravelling the genomic architecture of bull fertility in Holstein cattle. BMC Genetics. 2016;17: 143. doi: 10.1186/s12863-016-0454-6 27842509
25. Ferenčaković M, Sölkner J, Kapš M, Curik I. Genome-wide mapping and estimation of inbreeding depression of semen quality traits in a cattle population. Journal of Dairy Science. 2017;100: 4721–4730. doi: 10.3168/jds.2016-12164 28434751
26. Lan XY, Peñagaricano F, DeJung L, Weigel KA, Khatib H. Short communication: A missense mutation in the PROP1 (prophet of Pit 1) gene affects male fertility and milk production traits in the US Holstein population. Journal of Dairy Science. 2013;96: 1255–1257. doi: 10.3168/jds.2012-6019 23245960
27. Pausch H, Wurmser C, Reinhardt F, Emmerling R, Fries R. Short communication: Validation of 4 candidate causative trait variants in 2 cattle breeds using targeted sequence imputation. Journal of Dairy Science. 2015;98: 4162–4167. doi: 10.3168/jds.2015-9402 25892690
28. Pausch H, Kölle S, Wurmser C, Schwarzenbacher H, Emmerling R, Jansen S, et al. A Nonsense Mutation in TMEM95 Encoding a Nondescript Transmembrane Protein Causes Idiopathic Male Subfertility in Cattle. PLoS Genetics. 2014;10. doi: 10.1371/journal.pgen.1004044 24391514
29. Pausch H, Venhoranta H, Wurmser C, Hakala K, Iso-Touru T, Sironen A, et al. A frameshift mutation in ARMC3 is associated with a tail stump sperm defect in Swedish Red (Bos taurus) cattle. BMC Genetics. 2016;17: 49. doi: 10.1186/s12863-016-0356-7 26923438
30. Iso-Touru T, Wurmser C, Venhoranta H, Hiltpold M, Savolainen T, Sironen A, et al. A splice donor variant in CCDC189 is associated with asthenospermia in Nordic Red dairy cattle. BMC Genomics. 2019;20: 286. doi: 10.1186/s12864-019-5628-y 30975085
31. Goddard ME, Hayes BJ. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics. 2009;10: 381–391. doi: 10.1038/nrg2575 19448663
32. Weitze KF. 7 Andrologie beim Bullen. In: Walter B, Holzmann A, editors. Veterinärmedizinische Andrologie: Physiologie und Pathologie der Fortpflanzung bei männlichen Tieren. Stuttgart, Deutschland: Schattauer Verlag; 2001. pp. 119–214.
33. Harris T, Marquez B, Suarez S, Schimenti J. Sperm motility defects and infertility in male mice with a mutation in Nsun7, a member of the Sun domain-containing family of putative RNA methyltransferases. Biology of Reproduction. 2007;77: 376–382. doi: 10.1095/biolreprod.106.058669 17442852
34. Khosronezhad N, Hosseinzadeh Colagar A, Mortazavi SM. The Nsun7 (A11337)-deletion mutation, causes reduction of its protein rate and associated with sperm motility defect in infertile men. Journal of Assisted Reproduction and Genetics. 2015;32: 807–815. doi: 10.1007/s10815-015-0443-0 25702163
35. Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative Genomics Viewer. Nature Biotechnology. 2011;29: 24–26. doi: 10.1038/nbt.1754 21221095
36. Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics. 2018;34: 867–868. doi: 10.1093/bioinformatics/btx699 29096012
37. Pausch H, Schwarzenbacher H, Burgstaller J, Flisikowski K, Wurmser C, Jansen S, et al. Homozygous haplotype deficiency reveals deleterious mutations compromising reproductive and rearing success in cattle. BMC Genomics. 2015;16: 312. doi: 10.1186/s12864-015-1483-7 25927203
38. Zhang Q, Calus MPL, Bosse M, Sahana G, Lund MS, Guldbrandtsen B. Human-Mediated Introgression of Haplotypes in a Modern Dairy Cattle Breed. Genetics. 2018;209: 1305–1317. doi: 10.1534/genetics.118.301143 29848486
39. Dam TJP van, Townsend MJ, Turk M, Schlessinger A, Sali A, Field MC, et al. Evolution of modular intraflagellar transport from a coatomer-like progenitor. Proceedings of the National Academy of Sciences of the United States of America. 2013;110: 6943–6948. doi: 10.1073/pnas.1221011110 23569277
40. Efimenko E, Blacque OE, Ou G, Haycraft CJ, Yoder BK, Scholey JM, et al. Caenorhabditis elegans DYF-2, an Orthologue of Human WDR19, Is a Component of the Intraflagellar Transport Machinery in Sensory Cilia. Molecular Biology of the Cell. 2006;17: 4801–4811. doi: 10.1091/mbc.E06-04-0260 16957054
41. Wang Y, Hu X-J, Zou X-D, Wu X-H, Ye Z-Q, Wu Y-D. WDSPdb: a database for WD40-repeat proteins. Nucleic Acids Research. 2015;43: D339–D344. doi: 10.1093/nar/gku1023 25348404
42. Taylor JF, Schnabel RD, Sutovsky P. Review: Genomics of bull fertility. Animal. 2018;12: s172–s183. doi: 10.1017/S1751731118000599 29618393
43. Barth AD, Waldner CL. Factors affecting breeding soundness classification of beef bulls examined at the Western College of Veterinary Medicine. Canadian Veterinary Journal. 2002;43: 274–284. 11963661
44. Ax RL, Gilbert GR, Shook GE. Sperm in Poor Quality Semen from Bulls During Heat Stress Have a Lower Affinity for Binding Hydrogen-3 Heparin1. Journal of Dairy Science. 1987;70: 195–200. doi: 10.3168/jds.S0022-0302(87)79994-9 3571622
45. Auer PL, Reiner AP, Leal SM. The effect of phenotypic outliers and non-normality on rare-variant association testing. European Journal of Human Genetics. 2016;24: 1188–1194. doi: 10.1038/ejhg.2015.270 26733287
46. Vitezica ZG, Reverter A, Herring W, Legarra A. Dominance and epistatic genetic variances for litter size in pigs using genomic models. Genetics Selection Evolution. 2018;50: 71. doi: 10.1186/s12711-018-0437-3 30577727
47. Gruhot T, Gray K, Brown V, Huang Y, Kachman SD, Spangler ML, et al. Genetic relationships among sperm quality traits of Duroc boars collected during the summer season. Animal Reproduction Science. 2019;206: 85–92. doi: 10.1016/j.anireprosci.2019.05.012 31151862
48. Kadri NK, Sahana G, Charlier C, Iso-Touru T, Guldbrandtsen B, Karim L, et al. A 660-Kb Deletion with Antagonistic Effects on Fertility and Milk Production Segregates at High Frequency in Nordic Red Cattle: Additional Evidence for the Common Occurrence of Balancing Selection in Livestock. PLoS Genetics. 2014;10. doi: 10.1371/journal.pgen.1004049 24391517
49. Fang Z-H, Pausch H. Multi-trait meta-analyses reveal 25 quantitative trait loci for economically important traits in Brown Swiss cattle. BMC Genomics. 2019;20: 695. doi: 10.1186/s12864-019-6066-6 31481029
50. Chun S, Fay JC. Evidence for Hitchhiking of Deleterious Mutations within the Human Genome. PLoS Genetics. 2011;7. doi: 10.1371/journal.pgen.1002240 21901107
51. Bouwman AC, Daetwyler HD, Chamberlain AJ, Ponce CH, Sargolzaei M, Schenkel FS, et al. Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nature Genetics. 2018;50: 362–367. doi: 10.1038/s41588-018-0056-5 29459679
52. Pausch H, Emmerling R, Gredler-Grandl B, Fries R, Daetwyler HD, Goddard ME. Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution. BMC Genomics. 2017;18. doi: 10.1186/s12864-017-4263-8 29121857
53. Signer-Hasler H, Burren A, Neuditschko M, Frischknecht M, Garrick D, Stricker C, et al. Population structure and genomic inbreeding in nine Swiss dairy cattle populations. Genetics Selection Evolution. 2017;49. doi: 10.1186/s12711-017-0358-6 29115934
54. Sartelet A, Druet T, Michaux C, Fasquelle C, Géron S, Tamma N, et al. A Splice Site Variant in the Bovine RNF11 Gene Compromises Growth and Regulation of the Inflammatory Response. PLoS Genetics. 2012;8. doi: 10.1371/journal.pgen.1002581 22438830
55. Drögemüller M, Jagannathan V, Becker D, Drögemüller C, Schelling C, Plassais J, et al. A Mutation in the FAM83G Gene in Dogs with Hereditary Footpad Hyperkeratosis (HFH). PLoS Genet. 2014;10. doi: 10.1371/journal.pgen.1004370 24832243
56. McClure MC, Bickhart D, Null D, VanRaden P, Xu L, Wiggans G, et al. Bovine Exome Sequence Analysis and Targeted SNP Genotyping of Recessive Fertility Defects BH1, HH2, and HH3 Reveal a Putative Causative Mutation in SMC2 for HH3. PLOS ONE. 2014;9: e92769. doi: 10.1371/journal.pone.0092769 24667746
57. Schwarzenbacher H, Burgstaller J, Seefried FR, Wurmser C, Hilbe M, Jung S, et al. A missense mutation in TUBD1 is associated with high juvenile mortality in Braunvieh and Fleckvieh cattle. BMC Genomics. 2016;17: 400. doi: 10.1186/s12864-016-2742-y 27225349
58. Pausch H, Ammermüller S, Wurmser C, Hamann H, Tetens J, Drögemüller C, et al. A nonsense mutation in the COL7A1 gene causes epidermolysis bullosa in Vorderwald cattle. BMC Genetics. 2016;17: 149. doi: 10.1186/s12863-016-0458-2 27905875
59. Bhati M, Kadri NK, Crysnanto D, Pausch H. Assessing genomic diversity and signatures of selection in Original Braunvieh cattle using whole-genome sequencing data. BMC Genomics. 2020;21: 27. doi: 10.1186/s12864-020-6446-y 31914939
60. Reinartz S, Distl O. Short communication: Lethal mutations in Vorderwald cattle through Montbéliarde incrossings. Journal of Dairy Science. 2020;103: 613–618. doi: 10.3168/jds.2019-17213 31733870
61. Blencowe BJ. Exonic splicing enhancers: mechanism of action, diversity and role in human genetic diseases. Trends in Biochemical Sciences. 2000;25: 106–110. doi: 10.1016/s0968-0004(00)01549-8 10694877
62. Wei Q, Zhang Y, Li Y, Zhang Q, Ling K, Hu J. The BBSome controls IFT assembly and turnaround in cilia. Nature Cell Biology. 2012;14: 950–957. doi: 10.1038/ncb2560 22922713
63. Iomini C, Li L, Esparza JM, Dutcher SK. Retrograde Intraflagellar Transport Mutants Identify Complex A Proteins With Multiple Genetic Interactions in Chlamydomonas reinhardtii. Genetics. 2009;183: 885–896. doi: 10.1534/genetics.109.101915 19720863
64. Coussa RG, Otto EA, Gee H-Y, Arthurs P, Ren H, Lopez I, et al. WDR19: An ancient, retrograde, intraflagellar ciliary protein is mutated in autosomal recessive retinitis pigmentosa and in Senior-Loken syndrome. Clinical Genetics. 2013;84: 150–159. doi: 10.1111/cge.12196 23683095
65. Lee JM, Ahn YH, Kang HG, Ha IS, Lee K, Moon KC, et al. Nephronophthisis 13: implications of its association with Caroli disease and altered intracellular localization of WDR19 in the kidney. Pediatric Nephrology. 2015;30: 1451–1458. doi: 10.1007/s00467-015-3068-8 25726036
66. Bredrup C, Saunier S, Oud MM, Fiskerstrand T, Hoischen A, Brackman D, et al. Ciliopathies with Skeletal Anomalies and Renal Insufficiency due to Mutations in the IFT-A Gene WDR19. The American Journal of Human Genetics. 2011;89: 634–643. doi: 10.1016/j.ajhg.2011.10.001 22019273
67. Fehrenbach H, Decker C, Eisenberger T, Frank V, Hampel T, Walden U, et al. Mutations in WDR19 encoding the intraflagellar transport component IFT144 cause a broad spectrum of ciliopathies. Pediatric Nephrology. 2014;29: 1451–1456. doi: 10.1007/s00467-014-2762-2 24504730
68. Arts H, Knoers N. Cranioectodermal Dysplasia. In: Adam MP, Ardinger HH, Pagon RA, Wallace SE, Bean LJ, Stephens K, et al., editors. GeneReviews®. Seattle (WA): University of Washington, Seattle; 1993. Available: http://www.ncbi.nlm.nih.gov/books/NBK154653/
69. Xu C, Min J. Structure and function of WD40 domain proteins. Protein Cell. 2011;2: 202–214. doi: 10.1007/s13238-011-1018-1 21468892
70. Zhang Y, Liu H, Li W, Zhang Z, Zhang S, Teves ME, et al. Intraflagellar transporter protein 140 (IFT140), a component of IFT-A complex, is essential for male fertility and spermiogenesis in mice. Cytoskeleton. 2018;75: 70–84. doi: 10.1002/cm.21427 29236364
71. Wang X, Sha Y, Wang W, Cui Y, Chen J, Yan W, et al. Novel IFT140 variants cause spermatogenic dysfunction in humans. Molecular Genetics & Genomic Medicine. 2019;7: e920. doi: 10.1002/mgg3.920 31397098
72. Rosen BD, Bickhart DM, Schnabel RD, Koren S, Elsik CG, Tseng E, et al. De novo assembly of the cattle reference genome with single-molecule sequencing. Gigascience. 2020;9. doi: 10.1093/gigascience/giaa021 32191811
73. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4: 7. doi: 10.1186/s13742-015-0047-8 25722852
74. Browning BL, Zhou Y, Browning SR. A One-Penny Imputed Genome from Next-Generation Reference Panels. The American Journal of Human Genetics. 2018;103: 338–348. doi: 10.1016/j.ajhg.2018.07.015 30100085
75. Loh P-R, Danecek P, Palamara PF, Fuchsberger C, A Reshef Y, K Finucane H, et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nature Genetics. 2016;48: 1443–1448. doi: 10.1038/ng.3679 27694958
76. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nature Genetics. 2012;44: 955–959. doi: 10.1038/ng.2354 22820512
77. Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH. BLUPF90 and related programs (BGF90). Proceedings of the 7th world congress on genetics applied to livestock production. 2002. pp. 743–744.
78. Houle D, Meyer K. Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood. Journal of Evolutionary Biology. 2015;28: 1542–1549. doi: 10.1111/jeb.12674 26079756
79. Schaeffer LR. Evaluation of Bulls for Nonreturn Rates Within Artificial Insemination Organizations. Journal of Dairy Science. 1993;76: 837–842. doi: 10.3168/jds.S0022-0302(93)77409-3
80. Baes CF, Dolezal MA, Koltes JE, Bapst B, Fritz-Waters E, Jansen S, et al. Evaluation of variant identification methods for whole genome sequencing data in dairy cattle. BMC Genomics. 2014;15: 948. doi: 10.1186/1471-2164-15-948 25361890
81. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34: i884–i890. doi: 10.1093/bioinformatics/bty560 30423086
82. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:13033997 [q-bio]. 2013 [cited 4 Nov 2019]. Available: http://arxiv.org/abs/1303.3997
83. Picard Tools—By Broad Institute. [cited 4 Nov 2019]. Available: https://broadinstitute.github.io/picard/
84. Tarasov A, Vilella AJ, Cuppen E, Nijman IJ, Prins P. Sambamba: fast processing of NGS alignment formats. Bioinformatics. 2015;31: 2032–2034. doi: 10.1093/bioinformatics/btv098 25697820
85. DePristo MA, Banks E, Poplin RE, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics. 2011;43: 491–498. doi: 10.1038/ng.806 21478889
86. Crysnanto D, Wurmser C, Pausch H. Accurate sequence variant genotyping in cattle using variation-aware genome graphs. Genetics Selection Evolution. 2019;51: 21. doi: 10.1186/s12711-019-0462-x 31092189
87. Rausch T, Zichner T, Schlattl A, Stütz AM, Benes V, Korbel JO. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics. 2012;28: i333–i339. doi: 10.1093/bioinformatics/bts378 22962449
88. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biology. 2016;17: 122. doi: 10.1186/s13059-016-0974-4 27268795
89. Gao Y, Li S, Lai Z, Zhou Z, Wu F, Huang Y, et al. Analysis of Long Non-Coding RNA and mRNA Expression Profiling in Immature and Mature Bovine (Bos taurus) Testes. Frontiers in Genetics. 2019;10. doi: 10.3389/fgene.2019.00646 31333723
90. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology. 2016;34: 525–527. doi: 10.1038/nbt.3519 27043002
91. Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2016;4. doi: 10.12688/f1000research.7563.2 26925227
92. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29: 15–21. doi: 10.1093/bioinformatics/bts635 23104886
93. Reese MG, Eeckman FH, Kulp D, Haussler D. Improved Splice Site Detection in Genie. Journal of Computational Biology. 1997;4: 311–323. doi: 10.1089/cmb.1997.4.311 9278062
94. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Molecular Systems Biology. 2011;7: 539. doi: 10.1038/msb.2011.75 21988835
95. Madeira F, Park Y mi, Lee J, Buso N, Gur T, Madhusoodanan N, et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Research. 2019;47: W636–W641. doi: 10.1093/nar/gkz268 30976793
96. Ma J, An K, Zhou J-B, Wu N-S, Wang Y, Ye Z-Q, et al. WDSPdb: an updated resource for WD40 proteins. Bioinformatics. 2019;35: 4824–4826. doi: 10.1093/bioinformatics/btz460 31161214
Článek vyšel v časopise
PLOS Genetics
2020 Číslo 5
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- „Jednohubky“ z klinického výzkumu – 2024/44
Nejčtenější v tomto čísle
- The domesticated transposase ALP2 mediates formation of a novel Polycomb protein complex by direct interaction with MSI1, a core subunit of Polycomb Repressive Complex 2 (PRC2)
- Polyploidy breaks speciation barriers in Australian burrowing frogs Neobatrachus
- The phosphorelay BarA/SirA activates the non-cognate regulator RcsB in Salmonella enterica
- Congenital hearing impairment associated with peripheral cochlear nerve dysmyelination in glycosylation-deficient muscular dystrophy
