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Home / Equine Research Bank / Genet Sel Evol / A comparison of methods for whole-genome QTL mapping using d...
Genetics, selection, evolution : GSE2015; 47(1); 6; doi: 10.1186/s12711-015-0087-7

A comparison of methods for whole-genome QTL mapping using dense markers in four livestock species.

Abstract: With dense genotyping, many choices exist for methods to detect quantitative trait loci (QTL) in livestock populations. However, no across-species study has been conducted on the performance of different methods using real data. We compared three methods that correct for relatedness either implicitly or explicitly: linkage and linkage disequilibrium haplotype-based analysis (LDLA), efficient mixed-model association (EMMA) analysis, and Bayesian whole-genome regression (BayesC). We analyzed one chromosome in each of five datasets (dairy cattle, beef cattle, sheep, horses, and pigs) using real genotypes based on dense single nucleotide polymorphisms and phenotypes. The P values corrected for multiple testing or Bayes factors greater than 150 were considered to be significant. To complete the real data study, we also simulated quantitative trait loci (QTL) for the same datasets based on the real genotypes. Several scenarios were chosen, with different QTL effects and linkage disequilibrium patterns. A pseudo-null statistical distribution was chosen to make the significance thresholds comparable across methods. Results: For the real data, the three methods generally agreed within 1 or 2 cM for the locations of QTL regions and disagreed when no signals were significant (e.g. in pigs). For certain datasets, LDLA had more significant signals than EMMA or BayesC, but they were concentrated around the same peaks. Therefore, the three methods detected approximately the same number of QTL regions. For the simulated data, LDLA was slightly less powerful and accurate than either EMMA or BayesC but this depended strongly on how thresholds were set in the simulations. Conclusions: All three methods performed similarly for real and simulated data. No method was clearly superior across all datasets or for any particular dataset. For computational efficiency and ease of interpretation, EMMA is recommended, but using more than one method is suggested.
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Publication Date: 2015-02-12 PubMed ID: 25885597PubMed Central: PMC4324410DOI: 10.1186/s12711-015-0087-7Google Scholar: Lookup
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  • Comparative Study
  • Journal Article
  • Research Support
  • Non-U.S. Gov't

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.

The research paper explores the effectiveness of three methods of detecting quantitative trait loci (QTL) in livestock populations using dense genotyping. It suggests that no method is distinctly superior and proposes using multiple methods to enhance computational efficiency and facilitate analysis.

Comparison of QTL Detection Methods

  • The study compared three methods that correct for relatedness in livestock populations: Linkage and Linkage Disequilibrium Haplotype-based Analysis (LDLA), Efficient Mixed-Model Association (EMMA) analysis, and Bayesian Whole-Genome Regression (BayesC).
  • The researchers evaluated the effectiveness of these methodologies by applying them to one chromosome in each of five datasets from different livestock (dairy cattle, beef cattle, sheep, horses, and pigs).

Data Analysis

  • Both genotypes and phenotypes were based on dense single nucleotide polymorphisms (SNPs), which are common types of genetic variations among individuals.
  • Those with P values corrected for multiple testing or Bayes factors greater than 150 were deemed significant.

Use of Simulated Data

  • Beyond real data, the researchers simulated quantitative trait loci (QTL) for the same datasets, relying upon a pseudo-null statistical distribution to make comparisons across methods.
  • Various scenarios were chosen, spanning different QTL effects and linkage disequilibrium patterns.

Research Findings

  • For real data, the three methods generally agreed within 1 or 2 cM for the locations of QTL regions, however, they differed when no signals were significant, as with pigs.
  • Notably, although LDLA sometimes had more signals, these signals predominantly clustered around the same peaks as the other two methods.
  • In simulated data, LDLA was found to be slightly less accurate and powerful than EMMA or BayesC, substantially influenced by how thresholds were established in the simulations.
  • Overall, all three methods performed similarly on both real and simulated data, with no significant advantage for any on any of the datasets.

Suggestions and Conclusions

  • The researchers recommend using EMMA for computational efficiency and ease of interpretation, but advise employing multiple methods as a best practice.

Cite This Article

APA
Legarra A, Croiseau P, Sanchez MP, Teyssèdre S, Sallé G, Allais S, Fritz S, Moreno CR, Ricard A, Elsen JM. (2015). A comparison of methods for whole-genome QTL mapping using dense markers in four livestock species. Genet Sel Evol, 47(1), 6. https://doi.org/10.1186/s12711-015-0087-7

Publication

Genetics, selection, evolution : GSE
ISSN: 1297-9686
NlmUniqueID: 9114088
Country: France
Language: English
Volume: 47
Issue: 1
Pages: 6
PII: 6

Researcher Affiliations

Legarra, Andres
  • INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. andres.legarra@toulouse.inra.fr.
Croiseau, Pascal
  • INRA, UMR 1313 GABI, Domaine de Vilvert, 78352, Jouy-en-Josas, France. pascal.croiseau@jouy.inra.fr.
Sanchez, Marie Pierre
  • INRA, UMR 1313 GABI, Domaine de Vilvert, 78352, Jouy-en-Josas, France. marie-pierre.sanchez@jouy.inra.fr.
Teyssèdre, Simon
  • INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. steyssedre@ragt.fr.
  • Current address: RAGT-R2n, Le bourg, 12510, Druelle, France. steyssedre@ragt.fr.
Sallé, Guillaume
  • INRA, UMR1282 Infectiologie et Santé Publique, F-37380, Nouzilly, France. guillaume.salle@tours.inra.fr.
  • Université François Rabelais de Tours, UMR1282 Infectiologie et Santé Publique, 37000, Tours, France. guillaume.salle@tours.inra.fr.
Allais, Sophie
  • Agrocampus Ouest, UMR1348 Pegase, F-35000, Rennes, France. sophie.allais@agrocampus-ouest.fr.
  • INRA, UMR1348 Pegase, F-35590, Saint-Gilles, France. sophie.allais@agrocampus-ouest.fr.
  • Université Européenne de Bretagne, Rennes, France. sophie.allais@agrocampus-ouest.fr.
Fritz, Sébastien
  • UNCEIA, Genetics Team, 75595, Paris, France. sebastien.fritz@jouy.inra.fr.
Moreno, Carole Rénée
  • INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. carole.moreno@toulouse.inra.fr.
Ricard, Anne
  • INRA, UMR 1313 GABI, Domaine de Vilvert, 78352, Jouy-en-Josas, France. anne.ricard@toulouse.inra.fr.
  • Recherche et Innovation, IFCE, 61310 Exmes, Paris, France. anne.ricard@toulouse.inra.fr.
Elsen, Jean-Michel
  • INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. jean-michel.elsen@toulouse.inra.fr.

MeSH Terms

  • Animals
  • Bayes Theorem
  • Cattle / genetics
  • Chromosome Mapping / methods
  • Genetic Linkage
  • Genetic Markers
  • Genome
  • Genotype
  • Haplotypes / genetics
  • Horses / genetics
  • Linkage Disequilibrium
  • Livestock / genetics
  • Models, Genetic
  • Phenotype
  • Polymorphism, Single Nucleotide
  • Quantitative Trait Loci / genetics
  • Sheep / genetics
  • Sus scrofa / genetics

References

This article includes 35 references
  1. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness.. Nat Genet 2006 Feb;38(2):203-8.
    doi: 10.1038/ng1702pubmed: 16380716google scholar: lookup
  2. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies.. Nat Genet 2006 Aug;38(8):904-9.
    doi: 10.1038/ng1847pubmed: 16862161google scholar: lookup
  3. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, Sabatti C, Eskin E. Variance component model to account for sample structure in genome-wide association studies.. Nat Genet 2010 Apr;42(4):348-54.
    doi: 10.1038/ng.548pmc: PMC3092069pubmed: 20208533google scholar: lookup
  4. Geyer CJ. Practical Markov Chain Monte Carlo. Stat Sci 1992;7:473–83.
    doi: 10.1214/ss/1177011137google scholar: lookup
  5. Browning SR, Thompson EA. Detecting rare variant associations by identity-by-descent mapping in case-control studies.. Genetics 2012 Apr;190(4):1521-31.
    doi: 10.1534/genetics.111.136937pmc: PMC3316661pubmed: 22267498google scholar: lookup
  6. Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps.. Genetics 2001 Apr;157(4):1819-29.
    pmc: PMC1461589pubmed: 11290733doi: 10.1093/genetics/157.4.1819google scholar: lookup
  7. Xu S. Estimating polygenic effects using markers of the entire genome.. Genetics 2003 Feb;163(2):789-801.
    pmc: PMC1462468pubmed: 12618414doi: 10.1093/genetics/163.2.789google scholar: lookup
  8. Yi N, Xu S. Bayesian LASSO for quantitative trait loci mapping.. Genetics 2008 Jun;179(2):1045-55.
    doi: 10.1534/genetics.107.085589pmc: PMC2429858pubmed: 18505874google scholar: lookup
  9. Sahana G, Guldbrandtsen B, Janss L, Lund MS. Comparison of association mapping methods in a complex pedigreed population.. Genet Epidemiol 2010 Jul;34(5):455-62.
    doi: 10.1002/gepi.20499pubmed: 20568276google scholar: lookup
  10. Druet T, Fritz S, Boussaha M, Ben-Jemaa S, Guillaume F, Derbala D, Zelenika D, Lechner D, Charon C, Boichard D, Gut IG, Eggen A, Gautier M. Fine mapping of quantitative trait loci affecting female fertility in dairy cattle on BTA03 using a dense single-nucleotide polymorphism map.. Genetics 2008 Apr;178(4):2227-35.
    doi: 10.1534/genetics.107.085035pmc: PMC2323811pubmed: 18430945google scholar: lookup
  11. Teyssèdre S, Elsen JM, Ricard A. Statistical distributions of test statistics used for quantitative trait association mapping in structured populations.. Genet Sel Evol 2012 Nov 12;44(1):32.
    doi: 10.1186/1297-9686-44-32pmc: PMC3817592pubmed: 23146127google scholar: lookup
  12. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies.. Nat Genet 2012 Jun 17;44(7):821-4.
    doi: 10.1038/ng.2310pmc: PMC3386377pubmed: 22706312google scholar: lookup
  13. Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Extension of the bayesian alphabet for genomic selection.. BMC Bioinformatics 2011 May 23;12:186.
    doi: 10.1186/1471-2105-12-186pmc: PMC3144464pubmed: 21605355google scholar: lookup
  14. VanRaden PM, Wiggans GR. Derivation, calculation, and use of national animal model information.. J Dairy Sci 1991 Aug;74(8):2737-46.
    doi: 10.3168/jds.S0022-0302(91)78453-1pubmed: 1918547google scholar: lookup
  15. Allais S, Levéziel H, Payet-Duprat N, Hocquette JF, Lepetit J, Rousset S, Denoyelle C, Bernard-Capel C, Journaux L, Bonnot A, Renand G. The two mutations, Q204X and nt821, of the myostatin gene affect carcass and meat quality in young heterozygous bulls of French beef breeds.. J Anim Sci 2010 Feb;88(2):446-54.
    doi: 10.2527/jas.2009-2385pubmed: 19966162google scholar: lookup
  16. Bishop SC, Morris CA. Genetics of disease resistance in sheep and goats. Small Ruminant Res 2007;70:48–59.
    doi: 10.1016/j.smallrumres.2007.01.006google scholar: lookup
  17. Sallé G, Jacquiet P, Gruner L, Cortet J, Sauvé C, Prévot F, Grisez C, Bergeaud JP, Schibler L, Tircazes A, François D, Pery C, Bouvier F, Thouly JC, Brunel JC, Legarra A, Elsen JM, Bouix J, Rupp R, Moreno CR. A genome scan for QTL affecting resistance to Haemonchus contortus in sheep.. J Anim Sci 2012 Dec;90(13):4690-705.
    doi: 10.2527/jas.2012-5121pubmed: 22767094google scholar: lookup
  18. Teyssèdre S, Dupuis MC, Guérin G, Schibler L, Denoix JM, Elsen JM, Ricard A. Genome-wide association studies for osteochondrosis in French Trotter horses.. J Anim Sci 2012 Jan;90(1):45-53.
    doi: 10.2527/jas.2011-4031pubmed: 21841084google scholar: lookup
  19. Sanchez MP, Tribout T, Iannuccelli N, Bouffaud M, Servin B, Tenghe A, Dehais P, Muller N, Del Schneider MP, Mercat MJ, Rogel-Gaillard C, Milan D, Bidanel JP, Gilbert H. A genome-wide association study of production traits in a commercial population of Large White pigs: evidence of haplotypes affecting meat quality.. Genet Sel Evol 2014 Feb 14;46(1):12.
    pmc: PMC3975960pubmed: 24528607doi: 10.1186/1297-9686-46-12google scholar: lookup
  20. Meuwissen TH, Karlsen A, Lien S, Olsaker I, Goddard ME. Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping.. Genetics 2002 May;161(1):373-9.
    pmc: PMC1462098pubmed: 12019251doi: 10.1093/genetics/161.1.373google scholar: lookup
  21. Druet T, Georges M. A hidden markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping.. Genetics 2010 Mar;184(3):789-98.
    doi: 10.1534/genetics.109.108431pmc: PMC2845346pubmed: 20008575google scholar: lookup
  22. Visscher PM. A note on the asymptotic distribution of likelihood ratio tests to test variance components.. Twin Res Hum Genet 2006 Aug;9(4):490-5.
    doi: 10.1375/twin.9.4.490pubmed: 16899155google scholar: lookup
  23. Grisart B, Coppieters W, Farnir F, Karim L, Ford C, Berzi P, Cambisano N, Mni M, Reid S, Simon P, Spelman R, Georges M, Snell R. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition.. Genome Res 2002 Feb;12(2):222-31.
    doi: 10.1101/gr.224202pubmed: 11827942google scholar: lookup
  24. VanRaden PM. Efficient methods to compute genomic predictions.. J Dairy Sci 2008 Nov;91(11):4414-23.
    doi: 10.3168/jds.2007-0980pubmed: 18946147google scholar: lookup
  25. Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH. BLUPF90 and related programs (BGF90). In Proceedings of the 7th World Congress on Genetics Applied to Livestock Production: 19–23 August 2002. Montpellier. CD-ROM Communication N° 28–07; 2002.
  26. Hoggart CJ, Whittaker JC, De Iorio M, Balding DJ. Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies.. PLoS Genet 2008 Jul 25;4(7):e1000130.
    doi: 10.1371/journal.pgen.1000130pmc: PMC2464715pubmed: 18654633google scholar: lookup
  27. Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc 1995;90:773–95.
    doi: 10.1080/01621459.1995.10476572google scholar: lookup
  28. Wakefield J. Bayes factors for genome-wide association studies: comparison with P-values.. Genet Epidemiol 2009 Jan;33(1):79-86.
    doi: 10.1002/gepi.20359pubmed: 18642345google scholar: lookup
  29. Legarra A, Ricardi A, Filangi O. GS3: Genomic selection, Gibbs sampling, Gauss-Seidel (and BayesCpi). 2011.
  30. Sorensen D, Gianola D. Likelihood, bayesian and MCMC methods in quantitative genetics. New York: Springer-Verlag; 2002.
  31. Boddicker N, Waide EH, Rowland RR, Lunney JK, Garrick DJ, Reecy JM, Dekkers JC. Evidence for a major QTL associated with host response to porcine reproductive and respiratory syndrome virus challenge.. J Anim Sci 2012 Jun;90(6):1733-46.
    doi: 10.2527/jas.2011-4464pubmed: 22205662google scholar: lookup
  32. Vidal O, Noguera JL, Amills M, Varona L, Gil M, Jiménez N, Dávalos G, Folch JM, Sánchez A. Identification of carcass and meat quality quantitative trait loci in a Landrace pig population selected for growth and leanness.. J Anim Sci 2005 Feb;83(2):293-300.
    pubmed: 15644499doi: 10.2527/2005.832293xgoogle scholar: lookup
  33. Harrell FE, Davis C. A new distribution-free quantile estimator. Biometrika 1982;69:635–40.
    doi: 10.1093/biomet/69.3.635google scholar: lookup
  34. Institute S. SAS/STAT 9.3 user’s guide. Cary, NC: SAS Institute; 2011.
  35. Sham PC, Purcell SM. Statistical power and significance testing in large-scale genetic studies.. Nat Rev Genet 2014 May;15(5):335-46.
    doi: 10.1038/nrg3706pubmed: 24739678google scholar: lookup

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  1. Ma L, Chen W, Li S, Qin M, Zeng Y. Identification and Functional Prediction of Circular RNAs Related to Growth Traits and Skeletal Muscle Development in Duroc pigs. Front Genet 2022;13:858763.
    doi: 10.3389/fgene.2022.858763pubmed: 36118900google scholar: lookup
  2. Sallé G, Deiss V, Marquis C, Tosser-Klopp G, Cortet J, Serreau D, Koch C, Marcon D, Bouvier F, Jacquiet P, Blanchard A, Mialon MM, Moreno-Romieux C. Genetic × environment variation in sheep lines bred for divergent resistance to strongyle infection. Evol Appl 2021 Nov;14(11):2591-2602.
    doi: 10.1111/eva.13294pubmed: 34815741google scholar: lookup
  3. Xavier A. Efficient Estimation of Marker Effects in Plant Breeding. G3 (Bethesda) 2019 Nov 5;9(11):3855-3866.
    doi: 10.1534/g3.119.400728pubmed: 31690600google scholar: lookup
  4. Maldonado C, Mora F, Scapim CA, Coan M. Genome-wide haplotype-based association analysis of key traits of plant lodging and architecture of maize identifies major determinants for leaf angle: hapLA4. PLoS One 2019;14(3):e0212925.
    doi: 10.1371/journal.pone.0212925pubmed: 30840677google scholar: lookup
  5. Pena RN, Noguera JL, García-Santana MJ, González E, Tejeda JF, Ros-Freixedes R, Ibáñez-Escriche N. Five genomic regions have a major impact on fat composition in Iberian pigs. Sci Rep 2019 Feb 14;9(1):2031.
    doi: 10.1038/s41598-019-38622-7pubmed: 30765794google scholar: lookup
  6. Legarra A, Ricard A, Varona L. GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait. G3 (Bethesda) 2018 Jul 2;8(7):2301-2308.
    doi: 10.1534/g3.118.200336pubmed: 29748199google scholar: lookup
  7. Spangler GL, Rosen BD, Ilori MB, Hanotte O, Kim ES, Sonstegard TS, Burke JM, Morgan JLM, Notter DR, Van Tassell CP. Whole genome structural analysis of Caribbean hair sheep reveals quantitative link to West African ancestry. PLoS One 2017;12(6):e0179021.
    doi: 10.1371/journal.pone.0179021pubmed: 28662044google scholar: lookup
  8. Babii A, Kovalchuk S, Glazko T, Kosovsky G, Glazko V. Helitrons and Retrotransposons Are Co-localized in Bos taurus Genomes. Curr Genomics 2017 Jun;18(3):278-286.
    doi: 10.2174/1389202918666161108143909pubmed: 28659723google scholar: lookup
  9. Lien CY, Tixier-Boichard M, Wu SW, Wang WF, Ng CS, Chen CF. Detection of QTL for traits related to adaptation to sub-optimal climatic conditions in chickens. Genet Sel Evol 2017 Apr 20;49(1):39.
    doi: 10.1186/s12711-017-0314-5pubmed: 28427323google scholar: lookup
  10. Bennewitz J, Edel C, Fries R, Meuwissen TH, Wellmann R. Application of a Bayesian dominance model improves power in quantitative trait genome-wide association analysis. Genet Sel Evol 2017 Jan 14;49(1):7.
    doi: 10.1186/s12711-017-0284-7pubmed: 28088170google scholar: lookup
  11. Han Y, Peñagaricano F. Unravelling the genomic architecture of bull fertility in Holstein cattle. BMC Genet 2016 Nov 14;17(1):143.
    doi: 10.1186/s12863-016-0454-6pubmed: 27842509google scholar: lookup
  12. Xavier A, Muir WM, Craig B, Rainey KM. Walking through the statistical black boxes of plant breeding. Theor Appl Genet 2016 Oct;129(10):1933-49.
    doi: 10.1007/s00122-016-2750-ypubmed: 27435734google scholar: lookup
  13. Michenet A, Saintilan R, Venot E, Phocas F. Insights into the genetic variation of maternal behavior and suckling performance of continental beef cows. Genet Sel Evol 2016 Jun 22;48(1):45.
    doi: 10.1186/s12711-016-0223-zpubmed: 27335091google scholar: lookup
  14. Michenet A, Barbat M, Saintilan R, Venot E, Phocas F. Detection of quantitative trait loci for maternal traits using high-density genotypes of Blonde d'Aquitaine beef cattle. BMC Genet 2016 Jun 21;17(1):88.
    doi: 10.1186/s12863-016-0397-ypubmed: 27328805google scholar: lookup
  15. Sato S, Uemoto Y, Kikuchi T, Egawa S, Kohira K, Saito T, Sakuma H, Miyashita S, Arata S, Kojima T, Suzuki K. SNP- and haplotype-based genome-wide association studies for growth, carcass, and meat quality traits in a Duroc multigenerational population. BMC Genet 2016 Apr 19;17:60.
    doi: 10.1186/s12863-016-0368-3pubmed: 27094516google scholar: lookup
  16. Ros-Freixedes R, Gol S, Pena RN, Tor M, Ibáñez-Escriche N, Dekkers JC, Estany J. Genome-Wide Association Study Singles Out SCD and LEPR as the Two Main Loci Influencing Intramuscular Fat Content and Fatty Acid Composition in Duroc Pigs. PLoS One 2016;11(3):e0152496.
    doi: 10.1371/journal.pone.0152496pubmed: 27023885google scholar: lookup
  17. Cheng HH, Perumbakkam S, Pyrkosz AB, Dunn JR, Legarra A, Muir WM. Fine mapping of QTL and genomic prediction using allele-specific expression SNPs demonstrates that the complex trait of genetic resistance to Marek's disease is predominantly determined by transcriptional regulation. BMC Genomics 2015 Oct 19;16:816.
    doi: 10.1186/s12864-015-2016-0pubmed: 26481588google scholar: lookup
  18. Liu Q, Li Z, Wang Z, Lu Y, Jiang S, Xia C, An P, Zhao L, Deng K, Xia Z, Wang W. Construction of an ultrahigh-density genetic linkage map for Manihot esculenta Crantz and identification of QTL for root quantity traits. BMC Plant Biol 2025 Apr 25;25(1):534.
    doi: 10.1186/s12870-025-06278-3pubmed: 40281418google scholar: lookup
  19. Ajithkumar M, D'Ambrosio J, Travers MA, Morvezen R, Degremont L. Genomic selection for resistance to one pathogenic strain of Vibrio splendidus in blue mussel Mytilus edulis. Front Genet 2024;15:1487807.
    doi: 10.3389/fgene.2024.1487807pubmed: 39831199google scholar: lookup
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