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Animals : an open access journal from MDPI2019; 10(1); 53; doi: 10.3390/ani10010053

Whole-Genome Signatures of Selection in Sport Horses Revealed Selection Footprints Related to Musculoskeletal System Development Processes.

Abstract: Selective breeding has led to gradual changes at the genome level of horses. Deciphering selective pressure patterns is progressive to understand how breeding strategies have shaped the sport horse genome; although, little is known about the genomic regions under selective pressures in sport horse breeds. The major goal of this study was to shed light on genomic regions and biological pathways under selective pressures in sport horses. In this study, whole-genome sequences of 16 modern sport and 35 non-sport horses were used to investigate the genomic selective signals of sport performance, by employing fixation index, nucleotide diversity, and Tajima's D approaches. A total number of 49 shared genes were identified using these approaches. The functional enrichment analysis for candidate genes revealed novel significant biological processes related to musculoskeletal system development, such as limb development and morphogenesis, having been targeted by selection in sport breeds.
Publication Date: 2019-12-26 PubMed ID: 31888018PubMed Central: PMC7023322DOI: 10.3390/ani10010053Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research investigates the genetic changes brought about by selective breeding in sport horses. It identifies regions within their genomes that have been influenced by this breeding, particularly presenting an emphasis on processes related to the development of the musculoskeletal system.

Research Aim

The goal of the research was to identify genomic regions and biological pathways under selective pressures in sport horses. This is to understand how breeding strategies employed over the course of history have altered the sport horse genome.

Methodology

  • The research used whole-genome sequences from 16 modern sport horses and 35 non-sport horses. This comparison between sport and non-sport horse genomes allows researchers to isolate genomic signals associated specifically with sport performance.
  • The researchers applied three different scientific methods to their analysis: fixation index, nucleotide diversity, and Tajima’s D approaches.
    • The fixation index measures population differentiation due to genetic structure. It can be used to identify areas of the genome under selective pressure.
    • Nucleotide diversity is a measure of genetic variation within a population. It can be used to identify regions of the genome that have undergone selection because these areas often show lower diversity.
    • Tajima’s D is a test of neutrality of mutations. It can identify regions of the genome that have experienced recent selection pressure resulting in skewed mutation rates.

Findings

  • A total of 49 shared genes were identified across the genomes of sport horses using the above methods. These genes are likely to have undergone genetic changes based on breeding practices that favor sport performance traits.
  • The findings suggest that selective pressure is evident in genes associated with the musculoskeletal system’s development, highlighting processes such as limb development and morphogenesis. This indicates that breeding strategies have targeted these aspects to enhance physical attributes beneficial for sport horses, like strength, speed, and endurance.

Conclusions

This research provides valuable insights into the genomic impact of selective breeding on sport horses. By identifying and understanding how selection pressures have acted on their genomes, it can help pinpoint what biological processes and traits have been emphasized in their development over time. Notably, these findings further reinforce the perception of selective breeding as a potent force in shaping physical characteristics needed for sporting performance in horses.

Cite This Article

APA
Salek Ardestani S, Aminafshar M, Zandi Baghche Maryam MB, Banabazi MH, Sargolzaei M, Miar Y. (2019). Whole-Genome Signatures of Selection in Sport Horses Revealed Selection Footprints Related to Musculoskeletal System Development Processes. Animals (Basel), 10(1), 53. https://doi.org/10.3390/ani10010053

Publication

ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 10
Issue: 1
PII: 53

Researcher Affiliations

Salek Ardestani, Siavash
  • Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran.
Aminafshar, Mehdi
  • Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran.
Zandi Baghche Maryam, Mohammad Bagher
  • Department of Animal Science, University of Zanjan, Zanjan 4537138791, Iran.
Banabazi, Mohammad Hossein
  • Department of Biotechnology, Animal Science Research Institute of Iran (ASRI), Agricultural Research, Education & Extension Organization (AREEO), Karaj 3146618361, Iran.
Sargolzaei, Mehdi
  • Department of Pathobiology, Veterinary College, University of Guelph, Guelph, ON NIG2W1, Canada.
  • Select Sires Inc., Plain City, OH 43064, USA.
Miar, Younes
  • Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS B2N5E3, Canada.

Conflict of Interest Statement

The authors declare no competing financial interests.

References

This article includes 53 references
  1. Bowling AT, Ruvinsky A. The Genetics of the Horse. .
  2. Koenen E, Aldridge L, Philipsson J. An overview of breeding objectives for warmblood sport horses. Livest. Prod. Sci. 2004;88:77–84.
  3. Nolte W, Thaller G, Kuehn C. Selection signatures in four German warmblood horse breeds: Tracing breeding history in the modern sport horse.. PLoS One 2019;14(4):e0215913.
  4. Yang J, Li WR, Lv FH, He SG, Tian SL, Peng WF, Sun YW, Zhao YX, Tu XL, Zhang M, Xie XL, Wang YT, Li JQ, Liu YG, Shen ZQ, Wang F, Liu GJ, Lu HF, Kantanen J, Han JL, Li MH, Liu MJ. Whole-Genome Sequencing of Native Sheep Provides Insights into Rapid Adaptations to Extreme Environments.. Mol Biol Evol 2016 Oct;33(10):2576-92.
    doi: 10.1093/molbev/msw129pmc: PMC5026255pubmed: 27401233google scholar: lookup
  5. Wang X, Liu J, Zhou G, Guo J, Yan H, Niu Y, Li Y, Yuan C, Geng R, Lan X, An X, Tian X, Zhou H, Song J, Jiang Y, Chen Y. Whole-genome sequencing of eight goat populations for the detection of selection signatures underlying production and adaptive traits.. Sci Rep 2016 Dec 12;6:38932.
    doi: 10.1038/srep38932pmc: PMC5150979pubmed: 27941843google scholar: lookup
  6. Asadollahpour Nanaei H, Ayatollahi Mehrgardi A, Esmailizadeh A. Comparative population genomics unveils candidate genes for athletic performance in Hanoverians.. Genome 2019 Apr;62(4):279-285.
    doi: 10.1139/gen-2018-0151pubmed: 30779599google scholar: lookup
  7. Stock KF, Jönsson L, Ricard A, Mark T. Genomic applications in horse breeding. Anim. Front. 2016;6:45–52.
    doi: 10.2527/af.2016-0007google scholar: lookup
  8. Makvandi-Nejad S, Hoffman GE, Allen JJ, Chu E, Gu E, Chandler AM, Loredo AI, Bellone RR, Mezey JG, Brooks SA, Sutter NB. Four loci explain 83% of size variation in the horse.. PLoS One 2012;7(7):e39929.
  9. Metzger J, Tonda R, Beltran S, Agueda L, Gut M, Distl O. Next generation sequencing gives an insight into the characteristics of highly selected breeds versus non-breed horses in the course of domestication.. BMC Genomics 2014 Jul 4;15(1):562.
    doi: 10.1186/1471-2164-15-562pmc: PMC4097168pubmed: 24996778google scholar: lookup
  10. Kader A, Li Y, Dong K, Irwin DM, Zhao Q, He X, Liu J, Pu Y, Gorkhali NA, Liu X, Jiang L, Li X, Guan W, Zhang Y, Wu DD, Ma Y. Population Variation Reveals Independent Selection toward Small Body Size in Chinese Debao Pony.. Genome Biol Evol 2015 Dec 3;8(1):42-50.
    doi: 10.1093/gbe/evv245pmc: PMC4758242pubmed: 26637467google scholar: lookup
  11. Metzger J, Rau J, Naccache F, Bas Conn L, Lindgren G, Distl O. Genome data uncover four synergistic key regulators for extremely small body size in horses.. BMC Genomics 2018 Jun 25;19(1):492.
    doi: 10.1186/s12864-018-4877-5pmc: PMC6019228pubmed: 29940849google scholar: lookup
  12. Rubin CJ, Megens HJ, Martinez Barrio A, Maqbool K, Sayyab S, Schwochow D, Wang C, Carlborg Ö, Jern P, Jørgensen CB, Archibald AL, Fredholm M, Groenen MA, Andersson L. Strong signatures of selection in the domestic pig genome.. Proc Natl Acad Sci U S A 2012 Nov 27;109(48):19529-36.
    doi: 10.1073/pnas.1217149109pmc: PMC3511700pubmed: 23151514google scholar: lookup
  13. Li X, Su R, Wan W, Zhang W, Jiang H, Qiao X, Fan Y, Zhang Y, Wang R, Liu Z, Wang Z, Liu B, Ma Y, Zhang H, Zhao Q, Zhong T, Di R, Jiang Y, Chen W, Wang W, Dong Y, Li J. Identification of selection signals by large-scale whole-genome resequencing of cashmere goats.. Sci Rep 2017 Nov 9;7(1):15142.
    doi: 10.1038/s41598-017-15516-0pmc: PMC5680388pubmed: 29123196google scholar: lookup
  14. Metzger J, Karwath M, Tonda R, Beltran S, Águeda L, Gut M, Gut IG, Distl O. Runs of homozygosity reveal signatures of positive selection for reproduction traits in breed and non-breed horses.. BMC Genomics 2015 Oct 9;16:764.
    doi: 10.1186/s12864-015-1977-3pmc: PMC4600213pubmed: 26452642google scholar: lookup
  15. Moon S, Lee JW, Shin D, Shin KY, Kim J, Choi IY, Kim J, Kim H. A Genome-wide Scan for Selective Sweeps in Racing Horses.. Asian-Australas J Anim Sci 2015 Nov;28(11):1525-31.
    doi: 10.5713/ajas.14.0696pmc: PMC4647090pubmed: 26333666google scholar: lookup
  16. Petersen JL, Mickelson JR, Rendahl AK, Valberg SJ, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Capomaccio S, Cappelli K, Cothran EG, Distl O, Fox-Clipsham L, Graves KT, Guérin G, Haase B, Hasegawa T, Hemmann K, Hill EW, Leeb T, Lindgren G, Lohi H, Lopes MS, McGivney BA, Mikko S, Orr N, Penedo MC, Piercy RJ, Raekallio M, Rieder S, Røed KH, Swinburne J, Tozaki T, Vaudin M, Wade CM, McCue ME. Genome-wide analysis reveals selection for important traits in domestic horse breeds.. PLoS Genet 2013;9(1):e1003211.
  17. Frischknecht M, Flury C, Leeb T, Rieder S, Neuditschko M. Selection signatures in Shetland ponies.. Anim Genet 2016 Jun;47(3):370-2.
    doi: 10.1111/age.12416pubmed: 26857482google scholar: lookup
  18. Gurgul A, Jasielczuk I, Semik-Gurgul E, Pawlina-Tyszko K, Stefaniuk-Szmukier M, Szmatoła T, Polak G, Tomczyk-Wrona I, Bugno-Poniewierska M. A genome-wide scan for diversifying selection signatures in selected horse breeds.. PLoS One 2019;14(1):e0210751.
  19. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data.. Bioinformatics 2014 Aug 1;30(15):2114-20.
  20. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.. Bioinformatics 2009 Jul 15;25(14):1754-60.
  21. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.. Genome Res 2010 Sep;20(9):1297-303.
    doi: 10.1101/gr.107524.110pmc: PMC2928508pubmed: 20644199google scholar: lookup
  22. Cook DE, Andersen EC. VCF-kit: assorted utilities for the variant call format.. Bioinformatics 2017 May 15;33(10):1581-1582.
  23. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses.. Am J Hum Genet 2007 Sep;81(3):559-75.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  24. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals.. Genome Res 2009 Sep;19(9):1655-64.
    doi: 10.1101/gr.094052.109pmc: PMC2752134pubmed: 19648217google scholar: lookup
  25. Zhang C, Dong SS, Xu JY, He WM, Yang TL. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files.. Bioinformatics 2019 May 15;35(10):1786-1788.
    doi: 10.1093/bioinformatics/bty875pubmed: 30321304google scholar: lookup
  26. Weir BS, Cockerham CC. ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE.. Evolution 1984 Nov;38(6):1358-1370.
  27. Nei M, Li WH. Mathematical model for studying genetic variation in terms of restriction endonucleases.. Proc Natl Acad Sci U S A 1979 Oct;76(10):5269-73.
    doi: 10.1073/pnas.76.10.5269pmc: PMC413122pubmed: 291943google scholar: lookup
  28. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.. Genetics 1989 Nov;123(3):585-95.
    pmc: PMC1203831pubmed: 2513255doi: 10.1093/genetics/123.3.585google scholar: lookup
  29. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R. The variant call format and VCFtools.. Bioinformatics 2011 Aug 1;27(15):2156-8.
  30. Andam C, Challagundla L, Azarian T, Hanage W, Robinson D. Population Structure of Pathogenic Bacteria. 2017.
  31. McCue ME, Bannasch DL, Petersen JL, Gurr J, Bailey E, Binns MM, Distl O, Guérin G, Hasegawa T, Hill EW, Leeb T, Lindgren G, Penedo MC, Røed KH, Ryder OA, Swinburne JE, Tozaki T, Valberg SJ, Vaudin M, Lindblad-Toh K, Wade CM, Mickelson JR. A high density SNP array for the domestic horse and extant Perissodactyla: utility for association mapping, genetic diversity, and phylogeny studies.. PLoS Genet 2012 Jan;8(1):e1002451.
  32. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Distl O, Felicetti M, Fox-Clipsham L, Graves KT, Guérin G, Haase B, Hasegawa T, Hemmann K, Hill EW, Leeb T, Lindgren G, Lohi H, Lopes MS, McGivney BA, Mikko S, Orr N, Penedo MC, Piercy RJ, Raekallio M, Rieder S, Røed KH, Silvestrelli M, Swinburne J, Tozaki T, Vaudin M, M Wade C, McCue ME. Genetic diversity in the modern horse illustrated from genome-wide SNP data.. PLoS One 2013;8(1):e54997.
  33. Zhang C, Ni P, Ahmad HI, Gemingguli M, Baizilaitibei A, Gulibaheti D, Fang Y, Wang H, Asif AR, Xiao C, Chen J, Ma Y, Liu X, Du X, Zhao S. Detecting the Population Structure and Scanning for Signatures of Selection in Horses (Equus caballus) From Whole-Genome Sequencing Data.. Evol Bioinform Online 2018;14:1176934318775106.
    doi: 10.1177/1176934318775106pmc: PMC5990873pubmed: 29899660google scholar: lookup
  34. Dutson J. Storey’s Illustrated Guide to 96 Horse Breeds of North America. .
  35. Georgescu SE, Manea MA, Dudu A, Costache M. Phylogenetic relationships of the Hucul horse from Romania inferred from mitochondrial D-loop variation.. Genet Mol Res 2011 Oct 31;10(4):4104-13.
    doi: 10.4238/2011.October.31.7pubmed: 22057995google scholar: lookup
  36. Ardlie KG, Kruglyak L, Seielstad M. Patterns of linkage disequilibrium in the human genome.. Nat Rev Genet 2002 Apr;3(4):299-309.
    doi: 10.1038/nrg777pubmed: 11967554google scholar: lookup
  37. Nalls MA, Guerreiro RJ, Simon-Sanchez J, Bras JT, Traynor BJ, Gibbs JR, Launer L, Hardy J, Singleton AB. Extended tracts of homozygosity identify novel candidate genes associated with late-onset Alzheimer's disease.. Neurogenetics 2009 Jul;10(3):183-90.
    doi: 10.1007/s10048-009-0182-4pmc: PMC2908484pubmed: 19271249google scholar: lookup
  38. Purfield DC, McParland S, Wall E, Berry DP. The distribution of runs of homozygosity and selection signatures in six commercial meat sheep breeds.. PLoS One 2017;12(5):e0176780.
  39. Rietbroek NJ, Dingboom EG, Joosten BJ, Eizema K, Everts ME. Effect of show jumping training on the development of locomotory muscle in young horses.. Am J Vet Res 2007 Nov;68(11):1232-8.
    doi: 10.2460/ajvr.68.11.1232pubmed: 17975979google scholar: lookup
  40. de Simoni Gouveia JJ, da Silva MV, Paiva SR, de Oliveira SM. Identification of selection signatures in livestock species.. Genet Mol Biol 2014 Jun;37(2):330-42.
  41. Frischknecht M, Jagannathan V, Plattet P, Neuditschko M, Signer-Hasler H, Bachmann I, Pacholewska A, Drögemüller C, Dietschi E, Flury C, Rieder S, Leeb T. A Non-Synonymous HMGA2 Variant Decreases Height in Shetland Ponies and Other Small Horses.. PLoS One 2015;10(10):e0140749.
  42. Ablondi M, Viklund Å, Lindgren G, Eriksson S, Mikko S. Signatures of selection in the genome of Swedish warmblood horses selected for sport performance.. BMC Genomics 2019 Sep 18;20(1):717.
    doi: 10.1186/s12864-019-6079-1pmc: PMC6751828pubmed: 31533613google scholar: lookup
  43. Bordbari MH, Penedo MCT, Aleman M, Valberg SJ, Mickelson J, Finno CJ. Deletion of 2.7 kb near HOXD3 in an Arabian horse with occipitoatlantoaxial malformation.. Anim Genet 2017 Jun;48(3):287-294.
    doi: 10.1111/age.12531pmc: PMC5441686pubmed: 28111759google scholar: lookup
  44. Gmel AI, Druml T, von Niederhäusern R, Leeb T, Neuditschko M. Genome-Wide Association Studies Based on Equine Joint Angle Measurements Reveal New QTL Affecting the Conformation of Horses.. Genes (Basel) 2019 May 14;10(5).
    doi: 10.3390/genes10050370pmc: PMC6562990pubmed: 31091839google scholar: lookup
  45. Lawrence LA. Horse Conformation Analysis. .
  46. Clayton HM, Hobbs SJ. An exploration of strategies used by dressage horses to control moments around the center of mass when performing passage.. PeerJ 2017;5:e3866.
    doi: 10.7717/peerj.3866pmc: PMC5623309pubmed: 28970972google scholar: lookup
  47. Mallo M, Alonso CR. The regulation of Hox gene expression during animal development.. Development 2013 Oct;140(19):3951-63.
    doi: 10.1242/dev.068346pubmed: 24046316google scholar: lookup
  48. Pineault KM, Wellik DM. Hox genes and limb musculoskeletal development.. Curr Osteoporos Rep 2014 Dec;12(4):420-7.
    doi: 10.1007/s11914-014-0241-0pmc: PMC4216602pubmed: 25266923google scholar: lookup
  49. Xu B, Wellik DM. Axial Hox9 activity establishes the posterior field in the developing forelimb.. Proc Natl Acad Sci U S A 2011 Mar 22;108(12):4888-91.
    doi: 10.1073/pnas.1018161108pmc: PMC3064354pubmed: 21383175google scholar: lookup
  50. Barreto Rda S, Rodrigues MN, Carvalho RC, De Oliveira E Silva FM, Rigoglio NN, Jacob JC, Gastal EL, Miglino MA. Organogenesis of the Musculoskeletal System in Horse Embryos and Early Fetuses.. Anat Rec (Hoboken) 2016 Jun;299(6):722-9.
    doi: 10.1002/ar.23339pubmed: 26934175google scholar: lookup
  51. Bobbert MF, Santamaría S. Contribution of the forelimbs and hindlimbs of the horse to mechanical energy changes in jumping.. J Exp Biol 2005 Jan;208(Pt 2):249-60.
    doi: 10.1242/jeb.01373pubmed: 15634844google scholar: lookup
  52. Schröder W, Klostermann A, Distl O. Candidate genes for physical performance in the horse.. Vet J 2011 Oct;190(1):39-48.
    doi: 10.1016/j.tvjl.2010.09.029pubmed: 21115378google scholar: lookup
  53. Stock KF, Distl O. Genetic correlations between conformation traits and radiographic findings in the limbs of German Warmblood riding horses.. Genet Sel Evol 2006 Nov-Dec;38(6):657-71.
    pmc: PMC2689269pubmed: 17129565doi: 10.1186/1297-9686-38-6-657google scholar: lookup

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  1. Lindsay-McGee V, Sanchez-Molano E, Banos G, Clark EL, Piercy RJ, Psifidi A. Genetic characterisation of the Connemara pony and the Warmblood horse using a within-breed clustering approach.. Genet Sel Evol 2023 Aug 17;55(1):60.
    doi: 10.1186/s12711-023-00827-wpubmed: 37592264google scholar: lookup
  2. Colpitts J, McLoughlin PD, Poissant J. Runs of homozygosity in Sable Island feral horses reveal the genomic consequences of inbreeding and divergence from domestic breeds.. BMC Genomics 2022 Jul 12;23(1):501.
    doi: 10.1186/s12864-022-08729-9pubmed: 35820826google scholar: lookup
  3. Liu X, Zhang Y, Liu W, Li Y, Pan J, Pu Y, Han J, Orlando L, Ma Y, Jiang L. A single-nucleotide mutation within the TBX3 enhancer increased body size in Chinese horses.. Curr Biol 2022 Jan 24;32(2):480-487.e6.
    doi: 10.1016/j.cub.2021.11.052pubmed: 34906355google scholar: lookup
  4. Ghoreishifar SM, Rochus CM, Moghaddaszadeh-Ahrabi S, Davoudi P, Salek Ardestani S, Zinovieva NA, Deniskova TE, Johansson AM. Shared Ancestry and Signatures of Recent Selection in Gotland Sheep.. Genes (Basel) 2021 Mar 17;12(3).
    doi: 10.3390/genes12030433pubmed: 33802939google scholar: lookup
  5. Ghoreishifar SM, Eriksson S, Johansson AM, Khansefid M, Moghaddaszadeh-Ahrabi S, Parna N, Davoudi P, Javanmard A. Signatures of selection reveal candidate genes involved in economic traits and cold acclimation in five Swedish cattle breeds.. Genet Sel Evol 2020 Sep 4;52(1):52.
    doi: 10.1186/s12711-020-00571-5pubmed: 32887549google scholar: lookup
  6. Mancin E, Ablondi M, Mantovani R, Pigozzi G, Sabbioni A, Sartori C. Genetic Variability in the Italian Heavy Draught Horse from Pedigree Data and Genomic Information.. Animals (Basel) 2020 Jul 30;10(8).
    doi: 10.3390/ani10081310pubmed: 32751586google scholar: lookup
  7. Ablondi M, Dadousis C, Vasini M, Eriksson S, Mikko S, Sabbioni A. Genetic Diversity and Signatures of Selection in a Native Italian Horse Breed Based on SNP Data.. Animals (Basel) 2020 Jun 8;10(6).
    doi: 10.3390/ani10061005pubmed: 32521830google scholar: lookup