Multi-breed genome-wide association studies across countries for electronically recorded behavior traits in local dual-purpose cows

English version

Autoři: Tong Yin ^aff001; Maria Jaeger ^aff001; Carsten Scheper ^aff001; Gregorz Grodkowski ^aff002; Tomasz Sakowski ^aff002; Marija Klopčič ^aff003; Beat Bapst ^aff004; Sven König ^aff001
Působiště autorů: Institute of Animal Breeding and Genetics, Justus-Liebig-University of Gießen, Gießen, Germany ^aff001; Institute of Genetics and Animal Breeding of the Polish Academy of Sciences, Jastrzębiec, Poland ^aff002; University of Ljubljana, Biotechnical Faculty, Department of Animal Science, Domzale, Slovenia ^aff003; Genetic evaluation center, Qualitas AG, Switzerland ^aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0221973

Souhrn

Basic bovine behavior is a crucial parameter influencing cattle domestication. In addition, behavior has an impact on cattle productivity, welfare and adaptation. The aim of the present study was to infer quantitative genetic and genomic mechanisms contributing to natural dual-purpose cow behavior in grazing systems. In this regard, we genotyped five dual-purpose breeds for a dense SNP marker panel from four different European countries. All cows from the across-country study were equipped with the same electronic recording devices. In this regard, we analyzed 97,049 longitudinal sensor behavior observations from 319 local dual-purpose cows for rumination, feeding, basic activity, high active, not active and ear temperature. According to the specific sensor behaviors and following a welfare protocol, we computed two different welfare indices. For genomic breed characterizations and multi-breed genome-wide association studies, sensor traits and test-day production records were merged with 35,826 SNP markers per cow. For the estimation of variance components, we used the pedigree relationship matrix and a combined similarity matrix that simultaneously included both pedigree and genotypes. Heritabilities for feeding, high active and not active were in a moderate range from 0.16 to 0.20. Estimates were very similar from both relationship matrix-modeling approaches and had quite small standard errors. Heritabilities for the remaining sensor traits (feeding, basic activity, ear temperature) and welfare indices were lower than 0.09. Five significant SNPs on chromosomes 11, 17, 27 and 29 were associated with rumination, and two different SNPs significantly influenced the sensor traits “not active” (chromosome 13) and “feeding” (chromosome 23). Gene annotation analyses inferred 22 potential candidate genes with a false discovery rate lower than 20%, mostly associated with rumination (13 genes) and feeding (8 genes). Mendelian randomization based on genomic variants (i.e., the instrumental variables) was used to infer causal inference between an exposure and an outcome. Significant regression coefficients among behavior traits indicate that all specific behavioral mechanisms contribute to similar physiological processes. The regression coefficients of rumination and feeding on milk yield were 0.10 kg/% and 0.12 kg/%, respectively, indicating their positive influence on dual-purpose cow productivity. Genomically, an improved welfare behavior of grazing cattle, i.e., a higher score for welfare indices, was significantly associated with increased fat and protein percentages.

Klíčová slova:

Cattle – Ears – Europe – Fats – Genome-wide association studies – Grazing – Heredity – Molecular genetics

Zdroje

1. Hohenboken WD. Inheritance of behavioural characteristics in livestock: A review. Anim Breed Abstr. 1986; 54: 623–639.

2. Launchbaugh K. L., Walker J. W., Taylor C. A. Foraging behaviour: experience or inheritance: Grazing behaviour of livestock and wildlife. 70th ed. Moscow ID, USA: University of Idaho; 1999.

3. Snowder GD, Walker JW, Launchbaugh KL, van Vleck LD. Genetic and phenotypic parameters for dietary selection of mountain big sagebrush (Artemisia tridentata Nutt. ssp. vaseyana [Rydb] Beetle) in Rambouillet sheep. J Anim Sci. 2001; 79: 486. doi: 10.2527/2001.792486x 11219459

4. Schutz MM, Pajor EA. Genetic Control of Dairy Cattle Behavior. Journal of Dairy Science. 2001; 84: E31–E38. doi: 10.3168/jds.S0022-0302(01)70194-4

5. Løvendahl P, Munksgaard L. An investigation into genetic and phenotypic variation in time budgets and yield of dairy cows. Journal of Dairy Science. 2016; 99: 408–417. doi: 10.3168/jds.2015-9838 26519973

6. Byskov MV, Fogh A, Løvendahl P. Genetic parameters of rumination time and feed efficiency traits in primiparous Holstein cows under research and commercial conditions. Journal of Dairy Science. 2017; 100: 9635–9642. doi: 10.3168/jds.2016-12511 28941822

7. Adamczyk K, Pokorska J, Makulska J, Earley B, Mazurek M. Genetic analysis and evaluation of behavioural traits in cattle. Livestock Science. 2013; 154: 1–12. doi: 10.1016/j.livsci.2013.01.016

8. Alam T, Bahar B, Waters SM, McGee M, Sweeney T. Analysis of multiple polymorphisms in the bovine neuropeptide Y5 receptor gene and structural modelling of the encoded protein. Mol Biol Rep. 2012; 39: 4411–4421. doi: 10.1007/s11033-011-1229-9 21947839

9. Seong J, Suh DS, Park KD, Lee HK, Kong HS. Identification and analysis of MC4R polymorphisms and their association with economic traits of Korean cattle (Hanwoo). Mol Biol Rep. 2012; 39: 3597–3601. doi: 10.1007/s11033-011-1133-3 21735104

10. Raven L-A, Cocks BG, Hayes BJ. Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle. BMC Genomics. 2014; 15: 62. doi: 10.1186/1471-2164-15-62 24456127

11. Roos AP de, Hayes BJ, Spelman RJ, Goddard ME. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics. 2008; 179: 1503–1512. doi: 10.1534/genetics.107.084301 18622038

12. Bovine HapMap Consortium, Gibbs RA, Taylor JF, van Tassell CP, Barendse W, Eversole KA, et al. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science. 2009; 324: 528–532. doi: 10.1126/science.1167936 19390050

13. Habier D, Tetens J, Seefried F-R, Lichtner P, Thaller G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet Sel Evol. 2010; 42: 5. doi: 10.1186/1297-9686-42-5 20170500

14. Sutter NB, Eberle MA, Parker HG, Pullar BJ, Kirkness EF, Kruglyak L, et al. Extensive and breed-specific linkage disequilibrium in Canis familiaris. Genome Res. 2004; 14: 2388–2396. doi: 10.1101/gr.3147604 15545498

15. Gutiérrez-Gil B, Arranz JJ, Wiener P. An interpretive review of selective sweep studies in Bos taurus cattle populations: identification of unique and shared selection signals across breeds. Front Genet. 2015; 6: 167. doi: 10.3389/fgene.2015.00167 26029239

16. Sempéré G, Moazami-Goudarzi K, Eggen A, Laloë D, Gautier M, Flori L. WIDDE: a Web-Interfaced next generation database for genetic diversity exploration, with a first application in cattle. BMC Genomics. 2015; 16: 940. doi: 10.1186/s12864-015-2181-1 26573482

17. Gautier M, Laloë D, Moazami-Goudarzi K. Insights into the genetic history of French cattle from dense SNP data on 47 worldwide breeds. PLoS ONE. 2010; 5. doi: 10.1371/journal.pone.0013038 20927341

18. Lund MS, Su G, Janss L, Guldbrandtsen B, Brøndum RF. Genomic evaluation of cattle in a multi-breed context. Livestock Science. 2014; 166: 101–110. doi: 10.1016/j.livsci.2014.05.008

19. Chen M, Pan D, Ren H, Fu J, Li J, Su G, et al. Identification of selective sweeps reveals divergent selection between Chinese Holstein and Simmental cattle populations. Genetics Selection Evolution. 2016; 48: 76. doi: 10.1186/s12711-016-0254-5 27716022

20. Mügge, B., Lutz, W.E., Südbeck, H., Zelfel, S. Deutsche Holsteins. Die Geschichte einer Zucht.: Verlag Eugen Ulmer.; 1999.

21. Jaeger M., Scheper C., König S., Brügemann K. Studien zur Inzucht und Verwandtschaft beim 'Deutschen Schwarzbunten Niederungsrind' (DSN) auf Basis eigens berechneter Rasseanteile. Züchtungskunde. 2018; 90: 262–279.

22. Qanbari S, Pimentel ECG, Tetens J, Thaller G, Lichtner P, Sharifi AR, et al. A genome-wide scan for signatures of recent selection in Holstein cattle. Anim Genet. 2010; 41: 377–389. doi: 10.1111/j.1365-2052.2009.02016.x 20096028

23. Payne WJA, Hodges J. Tropical cattle: origins, breeds and breeding policies: Blackwell Science Ltd, Oxford, UK; 1997.

24. Gamarra D, Lopez-Oceja A, Pancorbo MM de. Genetic characterization and founder effect analysis of recently introduced Salers cattle breed population. Animal. 2017; 11: 24–32. doi: 10.1017/S1751731116001063 27278345

25. Heath-Agnew E. History of Hereford Cattle. London: Gerald Duckworth & Co Ltd; 1983.

26. Parker Gaddis KL, Cole JB, Clay JS, Maltecca C. Genomic selection for producer-recorded health event data in US dairy cattle. Journal of Dairy Science. 2014; 97: 3190–3199. doi: 10.3168/jds.2013-7543 24612803

27. Cesarani A, Gaspa G, Correddu F, Cellesi M, Dimauro C, Macciotta NPP. Genomic selection of milk fatty acid composition in Sarda dairy sheep: Effect of different phenotypes and relationship matrices on heritability and breeding value accuracy. Journal of Dairy Science. 2019; 102: 3189–3203. doi: 10.3168/jds.2018-15333 30799105

28. Nørgaard P, Nadeau E, Randby ÅT. A new Nordic structure evaluation system for diets fed to dairy cows: a meta analysis. In: Sauvant D, Milgen J, Faverdin P, Friggens N, editors. Modelling nutrient digestion and utilisation in farm animals. Wageningen: Wageningen Academic Publishers; 2011. pp. 112–120.

29. Mertens DR. Creating a System for Meeting the Fiber Requirements of Dairy Cows. Journal of Dairy Science. 1997; 80: 1463–1481. doi: 10.3168/jds.S0022-0302(97)76075-2 9241608

30. Krause KM, Combs DK, Beauchemin KA. Effects of Forage Particle Size and Grain Fermentability in Midlactation Cows. II. Ruminal pH and Chewing Activity. Journal of Dairy Science. 2002; 85: 1947–1957. doi: 10.3168/jds.S0022-0302(02)74271-9 12214987

31. Gregorini P, Waghorn GC, Kuhn-Sherlock B, Romera AJ, Macdonald KA. Short communication: grazing pattern of dairy cows that were selected for divergent residual feed intake as calves. Journal of Dairy Science. 2015; 98: 6486–6491. doi: 10.3168/jds.2015-9614 26162793

32. European Commission. An overview of estimates of costs of production and gross margins of milk production in the EU: EU Agricultural and Farm Economics Briefs. Available: https://ec.europa.eu/agriculture/sites/agriculture/files/rural-area-economics/briefs/pdf/016_en.pdf. Accessed 23 August 2018.

33. Veerkamp RF. Selection for Economic Efficiency of Dairy Cattle Using Information on Live Weight and Feed Intake: A Review. Journal of Dairy Science. 1998; 81: 1109–1119. doi: 10.3168/jds.S0022-0302(98)75673-5 9594400

34. Robinson DL, Oddy VH. Genetic parameters for feed efficiency, fatness, muscle area and feeding behaviour of feedlot finished beef cattle. Livestock Production Science. 2004; 90: 255–270. doi: 10.1016/j.livprodsci.2004.06.011

35. Fregonesi JA, Leaver JD. Behaviour, performance and health indicators of welfare for dairy cows housed in strawyard or cubicle systems. Livestock Production Science. 2001; 68: 205–216. doi: 10.1016/S0301-6226(00)00234-7

36. Bewley JM, Boyce RE, Hockin J, Munksgaard L, Eicher SD, Einstein ME, et al. Influence of milk yield, stage of lactation, and body condition on dairy cattle lying behaviour measured using an automated activity monitoring sensor. J Dairy Res. 2010; 77: 1–6. doi: 10.1017/S0022029909990227 19758477

37. Schöpke K, Weigel KA. Use of Accelerometer Data for Genetic Evaluation in Dairy Cattle. Interbull Bulletin. 2014; 48: 68–72.

38. Ismael A, Strandberg E, Kargo M, Fogh A, Løvendahl P. Estrus traits derived from activity measurements are heritable and closely related to the time from calving to first insemination. Journal of Dairy Science. 2015; 98: 3470–3477. doi: 10.3168/jds.2014-8940 25747826

39. Marinelli M, Pappa I, Bustamante M, Bonilla C, Suarez A, Tiesler CM, et al. Heritability and Genome-Wide Association Analyses of Sleep Duration in Children: The EAGLE Consortium. Sleep. 2016; 39: 1859–1869. doi: 10.5665/sleep.6170 27568811

40. Lopez-Minguez J, Morosoli JJ, Madrid JA, Garaulet M, Ordoñana JR. Heritability of siesta and night-time sleep as continuously assessed by a circadian-related integrated measure. Sci Rep; 7: 12340. doi: 10.1038/s41598-017-12460-x 28951572

41. Dikmen S, Cole JB, Null DJ, Hansen PJ. Heritability of rectal temperature and genetic correlations with production and reproduction traits in dairy cattle. Journal of Dairy Science. 2012; 95: 3401–3405. doi: 10.3168/jds.2011-4306 22612974

42. Adnan Jabbar Jadoa Al-Kanaan. Heat stress response for physiological traits in dairy and dual-purpose cattle populations on phenotypic and genetic scales. PhD Thesis, Department of Animal Breeding, Faculty of Organic Agriculture, Kassel University. 2016.

43. Loyau T, Zerjal T, Rodenburg TB, Fablet J, Tixier-Boichard M, Pinard-van der Laan MH, et al. Heritability of body surface temperature in hens estimated by infrared thermography at normal or hot temperatures and genetic correlations with egg and feather quality. Animal. 2016; 10: 1594–1601. doi: 10.1017/S1751731116000616 27095244

44. Seath DM. Heritability of Heat Tolerance in Dairy Cattle. 1947: 137–144.

45. McClure MC, Ramey HR, Rolf MM, McKay SD, Decker JE, Chapple RH, et al. Genome-wide association analysis for quantitative trait loci influencing Warner-Bratzler shear force in five taurine cattle breeds. Anim Genet. 2012; 43: 662–673. doi: 10.1111/j.1365-2052.2012.02323.x 22497286

46. Sanchez M-P, Govignon-Gion A, Croiseau P, Fritz S, Hozé C, Miranda G, et al. Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle. Genet Sel Evol. 2017; 49: 68. doi: 10.1186/s12711-017-0344-z 28923017

47. Speed D, Hemani G, Johnson MR, Balding DJ. Improved heritability estimation from genome-wide SNPs. Am J Hum Genet. 2012; 91: 1011–1021. doi: 10.1016/j.ajhg.2012.10.010 23217325

48. Bakshi A, Zhu Z, Vinkhuyzen AAE, Hill WD, McRae AF, Visscher PM, et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Sci Rep. 2016; 6: 32894. doi: 10.1038/srep32894 27604177

49. Chen Y, Gondro C, Quinn K, Herd RM, Parnell PF, Vanselow B. Global gene expression profiling reveals genes expressed differentially in cattle with high and low residual feed intake. Anim Genet. 2011; 42: 475–490. doi: 10.1111/j.1365-2052.2011.02182.x 21906099

50. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2018; 46: D754–D761. doi: 10.1093/nar/gkx1098 29155950

51. Do DN, Strathe AB, Ostersen T, Jensen J, Mark T, Kadarmideen HN. Genome-wide association study reveals genetic architecture of eating behavior in pigs and its implications for humans obesity by comparative mapping. PLoS ONE. 2013; 8: e71509. doi: 10.1371/journal.pone.0071509 23977060

52. Haeseleer F, Imanishi Y, Maeda T, Possin DE, Maeda A, Lee A, et al. Essential role of Ca2+ -binding protein 4, a Cav 1.4 channel regulator, in photoreceptor synaptic function. Nature Neuroscience. 2004: 1079–1087. doi: 10.1038/nn1320 15452577

53. Valdmanis PN, Dupré N, Lachance M, Stochmanski SJ, Belzil VV, Dion PA, et al. A mutation in the RNF170 gene causes autosomal dominant sensory ataxia. Brain. 2011; 134: 602–607. doi: 10.1093/brain/awq329 21115467

54. Chauhan A, Maurya S, Yadav AS, Sonwane A, Shukla SK, Prakash C. Differential expression profile of innate immune response genes between indigenous and crossbred cattle. Journal of Pure and Applied Microbiology. 2015; 9: 2913+.

55. Meade KG, Gormley E, Doyle MB, Fitzsimons T, O'Farrelly C, Costello E, et al. Innate gene repression associated with Mycobacterium bovis infection in cattle: toward a gene signature of disease. BMC Genomics. 2007; 8: 400. doi: 10.1186/1471-2164-8-400 17974019

56. Li G, Khateeb K, Schaeffer E, Zhang B, Khatib H. Genes of the transforming growth factor-beta signalling pathway are associated with pre-implantation embryonic development in cattle. J Dairy Res. 2012; 79: 310–317. doi: 10.1017/S0022029912000210 22687279

57. Haeseleer F, Sokal I, Verlinde CLMJ, Erdjument-Bromage H, Tempst P, Pronin AN, et al. Five Members of a Novel Ca2+-binding Protein (CABP) Subfamily with Similarity to Calmodulin*. J Biol Chem. 2000; 275: 1247–1260. doi: 10.1074/jbc.275.2.1247 10625670

58. Zeitz C, Kloeckener-Gruissem B, Forster U, Kohl S, Magyar I, Wissinger B, et al. Mutations in CABP4, the gene encoding the Ca2+-binding protein 4, cause autosomal recessive night blindness. Am J Hum Genet. 2006; 79: 657–667. doi: 10.1086/508067 16960802

59. Keustermans GC, Kofink D, Eikendal A, Jager W de, Meerding J, Nuboer R, et al. Monocyte gene expression in childhood obesity is associated with obesity and complexity of atherosclerosis in adults. Sci Rep. 2017; 7: 16826. doi: 10.1038/s41598-017-17195-3 29203885

60. Morris MC, Gilliam EA, Li L. Innate immune programing by endotoxin and its pathological consequences. Front Immunol. 2014; 5: 680. doi: 10.3389/fimmu.2014.00680 25610440

61. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics. 2016; 54: 1.30.1–1.30.33. doi: 10.1002/cpbi.5 27322403

62. Boztepe T, Gulec S. Investigation of the influence of high glucose on molecular and genetic responses: an in vitro study using a human intestine model. Genes Nutr. 2018; 13: 11. doi: 10.1186/s12263-018-0602-x 29736189

63. Jiang Z, Michal JJ, Chen J, Daniels TF, Kunej T, Garcia MD, et al. Discovery of novel genetic networks associated with 19 economically important traits in beef cattle. Int. J. Biol. Sci. 2009; 5: 528–542. doi: 10.7150/ijbs.5.528 19727437

64. Harris SE, Fox H, Wright AF, Hayward C, Starr JM, Whalley LJ, et al. A genetic association analysis of cognitive ability and cognitive ageing using 325 markers for 109 genes associated with oxidative stress or cognition. BMC Genet. 2007; 8: 43. doi: 10.1186/1471-2156-8-43 17601350

65. Starr JM, Shiels PG, Harris SE, Pattie A, Pearce MS, Relton CL, et al. Oxidative stress, telomere length and biomarkers of physical aging in a cohort aged 79 years from the 1932 Scottish Mental Survey. Mech Ageing Dev. 2008; 129: 745–751. doi: 10.1016/j.mad.2008.09.020 18977241

66. Yu XX, Mao W, Zhong A, Schow P, Brush J, Sherwood SW, et al. Characterization of novel UCP5/BMCP1 isoforms and differential regulation of UCP4 and UCP5 expression through dietary or temperature manipulation. FASEB J. 2000; 14: 1611–1618. doi: 10.1096/fj.14.11.1611 10928996

67. Mao W, Yu XX, Zhong A, Li W, Brush J, Sherwood SW, et al. UCP4, a novel brain-specific mitochondrial protein that reduces membrane potential in mammalian cells. FEBS Letters. 1999; 443: 326–330. doi: 10.1016/s0014-5793(98)01713-x 10025957

68. Jaeger M, Brügemann K, Brandt H, König S. Associations between precision sensor data with productivity, health and welfare indicator traits in native black and white dual-purpose cattle under grazing conditions. Applied Animal Behaviour Science. 2019; 212: 9–18. doi: 10.1016/j.applanim.2019.01.008

69. Soriani N, Panella G, Calamari L. Rumination time during the summer season and its relationships with metabolic conditions and milk production. Journal of Dairy Science. 2013; 96: 5082–5094. doi: 10.3168/jds.2013-6620 23791488

70. Walker SL, Smith RF, Routly JE, Jones DN, Morris MJ, Dobson H. Lameness, activity time-budgets, and estrus expression in dairy cattle. Journal of Dairy Science. 2008; 91: 4552–4559. doi: 10.3168/jds.2008-1048 19038930

71. West JW. Effects of Heat-Stress on Production in Dairy Cattle. Journal of Dairy Science. 2003; 86: 2131–2144. doi: 10.3168/jds.S0022-0302(03)73803-X 12836950

72. Piccione G, Caola G, Refinetti R. Daily and estrous rhythmicity of body temperature in domestic cattle. BMC Physiol. 2003; 3: 7. doi: 10.1186/1472-6793-3-7 12882649

73. Yin T, Bapst B, Borstel UUv, Simianer H, König S. Genetic parameters for gaussian and categorical traits in organic and low input dairy cattle herds based on random regression methodology. Livestock Science. 2012; 147: 159–169. doi: 10.1016/j.livsci.2012.04.017

74. Roche JR, Friggens NC, Kay JK, Fisher MW, Stafford KJ, Berry DP. Invited review: Body condition score and its association with dairy cow productivity, health, and welfare. Journal of Dairy Science. 2009; 92: 5769–5801. doi: 10.3168/jds.2009-2431 19923585

75. Friggens NC, Ridder C, Løvendahl P. On the use of milk composition measures to predict the energy balance of dairy cows. Journal of Dairy Science. 2007; 90: 5453–5467. doi: 10.3168/jds.2006-821 18024736

76. Welfare assessment protocol. Welfare Quality® Assessment protocol for cattle. ASG Veehouderij BV, Lelystad, The Netherlands.; 2009. Available: www.welfarequality.net/media/1017/cattle_protocol_without_veal.PDF. Accessed 23 May 2018.

77. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81: 559–575. doi: 10.1086/519795 17701901

78. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007; 81: 1084–1097. doi: 10.1086/521987 17924348

79. 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 21167468

80. Madsen P, Jensen J. A user's guide to DMU: A package for analysing multivariate mixed models.; 2013. Accessed 26 June 2018.

81. Legarra A, Aguilar I, Misztal I. A relationship matrix including full pedigree and genomic information. Journal of Dairy Science. 2009; 92: 4656–4663. doi: 10.3168/jds.2009-2061 19700729

82. Garrick DJ, Taylor JF, Fernando RL. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genet Sel Evol. 2009; 41: 55. doi: 10.1186/1297-9686-41-55 20043827

83. Yin T, König S. Genome-wide associations and detection of potential candidate genes for direct genetic and maternal genetic effects influencing dairy cattle body weight at different ages. Genet Sel Evol. 2019; 51: 4. doi: 10.1186/s12711-018-0444-4 30727969

84. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological). 1995; 57: 289–300.

85. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology. Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25: 25–29. doi: 10.1038/75556 10802651

86. The Gene Ontology Consortium. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 2017; 45: D331–D338. doi: 10.1093/nar/gkw1108 27899567

87. Geer LY, Marchler-Bauer A, Geer RC, Han L, He J, He S, et al. The NCBI BioSystems database. Nucleic Acids Res. 2010; 38: D492–6. doi: 10.1093/nar/gkp858 19854944

88. Chen C, Huang H, Wu CH. Protein Bioinformatics Databases and Resources. Methods Mol Biol. 2017; 1558: 3–39. doi: 10.1007/978-1-4939-6783-4_1 28150231

89. Smith GD, Ebrahim S. 16. Mendelian Randomization: Genetic Variants as Instruments for Strengthening Causal Inference in Observational Studies. In: Weinstein M, Vaupel JW, Wachter KW, editors. Biosocial Surveys. National Research Council (US) Committee on Advances in Collecting and Utilizing Biological Indicators and Genetic Information in Social Science Surveys. Washington (DC); 2008.

90. Wooldridge JM. Introductory econometrics. A modern approach. 5th ed. Mason: Cengage Learning; 2013.

91. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016; 48: 481–487. doi: 10.1038/ng.3538 27019110

92. Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nature Communications; 9: 224. doi: 10.1038/s41467-017-02317-2 29335400

Multi-breed genome-wide association studies across countries for electronically recorded behavior traits in local dual-purpose cows

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Klíčová slova:

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