Letter | Published:

Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals

Nature Geneticsvolume 50pages362367 (2018) | Download Citation

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Abstract

Stature is affected by many polymorphisms of small effect in humans1. In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes2,3. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10−8) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP–seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals.

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Acknowledgements

H.D.D., A.J.C., P.J.B. and B.J.H. would like to acknowledge the Dairy Futures Cooperative Research Centre for funding. H.P. and R.F. acknowledge funding from the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr ‘Synbreed—Synergistic Plant and Animal Breeding’ (grant 0315527B). H.P., R.F., R.E. and K.-U.G. acknowledge the Arbeitsgemeinschaft Süddeutscher Rinderzüchter, the Arbeitsgemeinschaft Österreichischer Fleckviehzüchter and ZuchtData EDV Dienstleistungen for providing genotype data. A. Bagnato acknowledges the European Union (EU) Collaborative Project LowInputBreeds (grant agreement 222623) for providing Brown Swiss genotypes. Braunvieh Schweiz is acknowledged for providing Brown Swiss phenotypes. H.P. and R.F. acknowledge the German Holstein Association (DHV) and the Confederación de Asociaciones de Frisona Española (CONCAFE) for sharing genotype data. H.P. was financially supported by a postdoctoral fellowship from the Deutsche Forschungsgemeinschaft (DFG) (grant PA 2789/1-1). D.B. and D.C.P. acknowledge funding from the Research Stimulus Fund (11/S/112) and Science Foundation Ireland (14/IA/2576). M.S. and F.S.S. acknowledge the Canadian Dairy Network (CDN) for providing the Holstein genotypes. P.S. acknowledges funding from the Genome Canada project entitled ‘Whole Genome Selection through Genome Wide Imputation in Beef Cattle’ and acknowledges WestGrid and Compute/Calcul Canada for providing computing resources. J.F.T. was supported by the National Institute of Food and Agriculture, US Department of Agriculture, under awards 2013-68004-20364 and 2015-67015-23183. A. Bagnato, F.P., M.D. and J.W. acknowledge EU Collaborative Project Quantomics (grant 516 agreement 222664) for providing Brown Swiss and Finnish Ayrshire sequences and genotypes. A.C.B. and R.F.V. acknowledge funding from the public–private partnership ‘Breed4Food’ (code BO-22.04-011-001-ASG-LR) and EU FP7 IRSES SEQSEL (grant 317697). A.C.B. and R.F.V. acknowledge CRV (Arnhem, the Netherlands) for providing data on Dutch and New Zealand Holstein and Jersey bulls.

Author information

Affiliations

  1. Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Wageningen, the Netherlands

    • Aniek C. Bouwman
    •  & Roel F. Veerkamp
  2. AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia

    • Hans D. Daetwyler
    • , Amanda J. Chamberlain
    • , Carla Hurtado Ponce
    • , Hubert Pausch
    • , Phil J. Bowman
    • , Min Wang
    • , Christy Vander Jagt
    • , Mike E. Goddard
    •  & Ben J. Hayes
  3. School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, Australia

    • Hans D. Daetwyler
    •  & Min Wang
  4. Faculty of Land and Food Resources, University of Melbourne, Parkville, Victoria, Australia

    • Carla Hurtado Ponce
    •  & Mike E. Goddard
  5. Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada

    • Mehdi Sargolzaei
    •  & Flavio S. Schenkel
  6. The Semex Alliance, Guelph, Ontario, Canada

    • Mehdi Sargolzaei
  7. Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark

    • Goutam Sahana
    • , Rasmus F. Brøndum
    • , Bernt Guldbrandtsen
    •  & Mogens S. Lund
  8. GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France

    • Armelle Govignon-Gion
    • , Chris Hozé
    • , Mekki Boussaha
    • , Marie-Pierre Sanchez
    • , Dominique Rocha
    • , Aurelien Capitan
    • , Thierry Tribout
    • , Anne Barbat
    • , Pascal Croiseau
    •  & Didier Boichard
  9. Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark

    • Simon Boitard
    • , Bo Thomsen
    • , Frank Panitz
    • , Christian Bendixen
    •  & Lars-Erik Holm
  10. Platform of Bioinformatics and Statistics, University of Veterinary Medicine, Vienna, Austria

    • Marlies Dolezal
  11. Chair of Animal Breeding, Technische Universität München, Freising-Weihenstephan, Germany

    • Hubert Pausch
    •  & Ruedi Fries
  12. Animal Genomics, ETH Zurich, Zurich, Switzerland

    • Hubert Pausch
  13. GenPhySE, Université de Toulouse, INRA, INPT, INP-ENVT, Castanet-Tolosan, France

    • Bertrand Servin
  14. Department of Animal Science, Iowa State University, Ames, IA, USA

    • Dorian J. Garrick
    •  & James Reecy
  15. Green Technology, Natural Resources Institute Finland (Luke), Jokioinen, Finland

    • Johanna Vilkki
  16. Department of Veterinary Medicine, University of Milan, Milan, Italy

    • Alessandro Bagnato
  17. Division of Animal Sciences, University of Missouri, Columbia, MO, USA

    • Jesse L. Hoff
    • , Robert D. Schnabel
    •  & Jeremy F. Taylor
  18. University of Queensland, Institute for Molecular Bioscience, St Lucia, Queensland, Australia

    • Anna A. E. Vinkhuyzen
  19. University of Queensland, Queensland Brain Institute, St Lucia, Queensland, Australia

    • Anna A. E. Vinkhuyzen
  20. Qualitas AG, Zug, Switzerland

    • Birgit Gredler
    •  & Mirjam Frischknecht
  21. Allice, Paris, France

    • Chris Hozé
    •  & Aurelien Capitan
  22. Institute of Genetics, University of Bern, Bern, Switzerland

    • Cord Drögemüller
    •  & Vidhya Jagannathan
  23. Canadian Beef Breeds Council, Calgary, Alberta, Canada

    • John J. Crowley
  24. Research Institute of Organic Agriculture (FiBL), Frick, Switzerland

    • Anna Bieber
  25. Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Ireland

    • Deirdre C. Purfield
    •  & Donagh P. Berry
  26. Institute of Animal Breeding, Bavarian State Research Centre for Agriculture, Poing, Germany

    • Reiner Emmerling
    •  & Kay-Uwe Götz
  27. Tierzuchtforschung, Poing, Germany

    • Ingolf Russ
  28. University of Natural Resources and Life Sciences, Vienna, Austria

    • Johann Sölkner
  29. Animal Genomics and Improvement Laboratory, Agricultural Research Service, US Department of Agriculture, Beltsville, MD, USA

    • Curtis P. Van Tassell
  30. Department of Agricultural, Food and Nutritional Science/Livestock Gentec, University of Alberta, Edmonton, Alberta, Canada

    • Paul Stothard
  31. Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, University of Queensland, St Lucia, Queensland, Australia

    • Ben J. Hayes

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Contributions

A.C.B. conducted the meta-analysis and contributed to writing the manuscript. H.D.D., A.J.C. and C.V.J. ran the 1000 Bull Genomes pipeline and extracted sequence variants, and A.J.C. and C.V.J. performed the eQTL analysis. C.H.P. sourced samples for miniature cattle and generated whole-genome sequence alignments for these. M.S., D.P.B., P.J.B. and F.S.S. contributed to genotype imputation and writing of the manuscript. M.S., F.S.S., G.S., D.C.P., H.P., J.V., B. Gredler, J.J.C., J.L.H. and R.F.B. performed GWAS analysis. S.B., B.S. and M.D. performed selection signature analysis. R.E. and K.-U.G. prepared daughter yield deviations and yield deviaitons of Fleckvieh animals, and the Intergenomics Consortium contributed genotypes. A.G.-G., C.H., M.-P.S., A.C., T.T., A. Bieber, P.C. and A. Barbat prepared phenotypes and genotypes for French cattle and ran GWAS. M.F., I.R. and J.S. prepared phenotypes and genotypes for Swiss and Austrian cattle and ran GWAS. A.A.E.V. contributed to across-species identification of stature-related genes. M.B., M.W., P.S., D.R., V.J. and R.D.S. performed variant annotation. B.J.H., D.J.G., J.F.T., C.B., J.R., A. Bagnato, F.P., B.T., L.-E.H., C.D., R.F., C.P.V.T., R.F.V., D.B., P.S., M.E.G., B. Guldbrandtsen and M.S.L. conceived the experimental design, analyzed stature data for contributed breeds and wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Ben J. Hayes.

Integrated supplementary information

  1. Supplementary Figure 1 Accuracy of imputing sequence variants in cattle populations.

    The accuracy of imputing sequence variant genotypes was assessed in 61 Jersey, 59 Brown Swiss, 187 Fleckvieh, 93 Angus and 312 Holstein animals from 1000 Bull Genomes Run4. a, A principal-components analysis of the genomic relationship matrix that was built using 1,147 animals and 20,211,021 autosomal sequence variants with minor allele frequency greater than 0.01 clearly separated the animals by breed. The genomic relationship matrix was built using PLINK1.9, and GCTA6 was used to perform the principal-components analysis. (b) The overall accuracy of imputing sequence variant genotypes was assessed for 2,231,805, 1,036,588 and 622,011 sequence variants, respectively, on chromosomes 1, 20 and 25 using FImpute45 and Minimac44. The accuracy of imputing genotypes was calculated by 15-fold cross-validation, where the sequence genotypes of 25 randomly selected animals per breed from 1000 Bull Genomes Run4 were masked to those on the Illumina BovineHD BeadChip and all the sequence variants were then imputed using FImpute or Minimac with all other sequences (n = 1,122) as a reference. The random selection of animals was performed 15 times for each chromosome and breed analysis. The squared correlation (r2) between the real sequence variant called genotypes and the imputed variant genotypes was taken as the accuracy of imputation. Box plots show the overall accuracy of imputing sequence variants per breed using FImpute or Minimac. The interquartile range defines the height of the box, and whiskers extend to 1.5 times the interquartile range. In each box, the median is represented by the horizontal black bar. c,d, Accuracy of imputing sequence variant genotypes using Minimac (c) or FImpute (d) by minor allele frequency. The dotted and solid lines in c represent the squared correlation between true genotypes (0, 1 or 2) and imputed dosages and best-guess genotypes, respectively.

  2. Supplementary Figure 2 The accuracy of imputing sequence variant genotypes assessed in Montebeliarde, Normande and Danish Red cattle using 1000 Bull Genomes Run4.

    a, Accuracy was assessed for sequence variants on chromosomes 1, 20 and 25 using FImpute. The accuracy of imputing genotypes was calculated by masking the sequence genotypes of 14 randomly selected animals per breed from 1000 Bull Genomes Run4 to those on the Illumina BovineHD BeadChip and then imputing all sequence variants using FImpute45 with all other sequences (n = 1,133) as a reference. The squared correlation (r2) between the real sequence variant called genotypes and the imputed variant genotypes was taken as the accuracy of imputation. The accuracy of imputing sequence variant genotypes is plotted by minor allele frequency. b, The accuracy of imputation to sequence variants in Holstein and Jersey cattle was compared for FImpute and Beagle. The accuracy was assessed for sequence variants on chromosome 14. The accuracy of imputing genotypes was calculated by masking the sequence genotypes of 25 randomly selected animals per breed from 1000 Bull Genomes Run4 to those on the Illumina BovineHD BeadChip and then imputing all sequence variants using FImpute and Beagle with all other sequences (n = 1,122) as a reference.

  3. Supplementary Figure 3 Association testing with imputed sequence variants in the region of the GHR gene.

    ac, Sequence variant genotypes were imputed in 6,777 Fleckvieh, 5,204 Holstein and 1,646 Brown Swiss animals using the 1000 Bull Genomes Run4 multi-breed reference population with Minimac44. Association tests were performed between imputed sequence variant genotypes on chromosome 20 and daughter-derived values for protein percentage. Association testing was carried out with EMMAX using the -Z flag to consider predicted allele dosages for the imputed sequence variants. a, In Fleckvieh, the GHR p.Y279F mutation (rs385640152) was the second most strongly associated marker. b,c, The association testing revealed the causal GHR p.Y279F mutation (rs385640152)55 (in the growth hormone receptor gene) to be the most significantly associated variant in Holstein (b) and Brown Swiss (c) cattle. The frequency of the minor allele was 0.16, 0.08 and 0.12 in Holstein, Fleckvieh and Brown Swiss cattle, respectively. Data were used from Pausch et al.10.

  4. Supplementary Figure 4 Association testing with imputed sequence variants in the region of the DGAT1 gene.

    a,b, Sequence variant genotypes were imputed in 6,777 Fleckvieh and 5,204 Holstein animals using the 1000 Bull Genomes Run4 multi-breed reference population with Minimac44. Association tests were performed between imputed sequence variant genotypes on chromosome 14 and daughter-derived values for fat percentage. Association testing was carried out with EMMAX5 using the -Z flag to consider predicted allele dosages for the imputed sequence variants. The association testing revealed the causal mutation (p.A232K) in the DGAT1 gene56 to be the most significantly associated variant in Fleckvieh (a) and Holstein (b) cattle, as has been demonstrated in an earlier run of the 1000 Bull Genomes project4. The frequency of the minor allele was 0.31, 0.07 and 0.12 in Holstein and Fleckvieh cattle, respectively. Data were used from Pausch et al10.

  5. Supplementary Figure 5 Association testing with imputed sequence variants in the region of the ABCG2 gene.

    Sequence variant genotypes were imputed into 5,204 Holstein animals using the 1000 Bull Genomes Run4 multi-breed reference population with Minimac44. Association tests were performed between imputed sequence variant genotypes on chromosome 6 and daughter-derived values for protein percentage. Association testing was carried out with EMMAX5 using the -Z flag to consider predicted allele dosages for the imputed sequence variants. The association testing revealed the causal p.Y581S variant in ABCG256 to be the most significantly associated variant in Holstein cattle. The frequency of the minor allele was 0.013. Data were used from Pausch et al10.

  6. Supplementary Figure 6

    The number of breeds that each variant segregates in plotted against the size of the variant effect estimated in the meta-analysis for the 163 lead variants.

  7. Supplementary Figure 7 Selection signature analysis.

    Signatures of selection based on haplotype (hapFLK statistical test35; top) or single-SNP (FLK statistical test36; bottom) tests for differentiation (–log10P values), with n = 380 animals. Darker colors indicate significant hits at an FDR of 5%. Vertical red lines highlight the positions of 163 lead variants from the stature GWAS meta-analysis.

  8. Supplementary Figure 8

    The distance of the most significant SNP from the gene for 659 genes with eQTLs.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–8 and Supplementary Tables 1, 3, 5 and 7–9.

  2. Life Sciences Reporting Summary

  3. Supplementary Table 2

    Positions and effects of the most significant SNPs, confidence intervals, overlap with human genes for stature, overlap with eQTLs from whole blood and indication of the ancestral allele.

  4. Supplementary Table 4

    Validation of lead SNPs in ten populations encompassing eight breeds.

  5. Supplementary Table 6

    Proportion of bootstrap samples in which the lead variant from the original meta-analysis remained the lead variant in the bootstrap sample.

About this article

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DOI

https://doi.org/10.1038/s41588-018-0056-5

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