IDEAS home Printed from https://ideas.repec.org/a/plo/pgen00/1004198.html
   My bibliography  Save this article

A Multi-Trait, Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature, Fatness and Reproduction in Beef Cattle

Author

Listed:
  • Sunduimijid Bolormaa
  • Jennie E Pryce
  • Antonio Reverter
  • Yuandan Zhang
  • William Barendse
  • Kathryn Kemper
  • Bruce Tier
  • Keith Savin
  • Ben J Hayes
  • Michael E Goddard

Abstract

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t'V−1t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.Author Summary: We describe novel methods for finding significant associations between a genome wide panel of SNPs and multiple complex traits, and further for distinguishing between genes with effects on multiple traits and multiple linked genes affecting different traits. The method uses a meta-analysis based on estimates of SNP effects from independent single trait genome wide association studies (GWAS). The method could therefore be widely used to combine already published GWAS results. The method was applied to 32 traits that describe growth, body composition, feed intake and reproduction in 10,191 beef cattle genotyped for approximately 700,000 SNP. The genes found to be associated with these traits can be arranged into 4 groups that differ in their pattern of effects and hence presumably in their physiological mechanism of action. For instance, one group of genes affects weight and fatness in the opposite direction and can be described as a group of genes affecting mature size, while another group affects weight and fatness in the same direction.

Suggested Citation

  • Sunduimijid Bolormaa & Jennie E Pryce & Antonio Reverter & Yuandan Zhang & William Barendse & Kathryn Kemper & Bruce Tier & Keith Savin & Ben J Hayes & Michael E Goddard, 2014. "A Multi-Trait, Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature, Fatness and Reproduction in Beef Cattle," PLOS Genetics, Public Library of Science, vol. 10(3), pages 1-23, March.
  • Handle: RePEc:plo:pgen00:1004198
    DOI: 10.1371/journal.pgen.1004198
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004198
    Download Restriction: no

    File URL: https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1004198&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pgen.1004198?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anirene G. T. Pereira & Yuri T Utsunomiya & Marco Milanesi & Rafaela B P Torrecilha & Adriana S Carmo & Haroldo H R Neves & Roberto Carvalheiro & Paolo Ajmone-Marsan & Tad S Sonstegard & Johann Sölkne, 2016. "Pleiotropic Genes Affecting Carcass Traits in Bos indicus (Nellore) Cattle Are Modulators of Growth," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-13, July.
    2. Aline Camporez Crispim & Matthew John Kelly & Simone Eliza Facioni Guimarães & Fabyano Fonseca e Silva & Marina Rufino Salinas Fortes & Raphael Rocha Wenceslau & Stephen Moore, 2015. "Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
    3. Theo Meuwissen & Ben Hayes & Iona MacLeod & Michael Goddard, 2022. "Identification of Genomic Variants Causing Variation in Quantitative Traits: A Review," Agriculture, MDPI, vol. 12(10), pages 1-11, October.
    4. Ziyi Xiong & Xingjian Gao & Yan Chen & Zhanying Feng & Siyu Pan & Haojie Lu & Andre G. Uitterlinden & Tamar Nijsten & Arfan Ikram & Fernando Rivadeneira & Mohsen Ghanbari & Yong Wang & Manfred Kayser , 2022. "Combining genome-wide association studies highlight novel loci involved in human facial variation," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    5. Haeil Park & Xiaoyin Li & Yeunjoo E Song & Karen Y He & Xiaofeng Zhu, 2016. "Multivariate Analysis of Anthropometric Traits Using Summary Statistics of Genome-Wide Association Studies from GIANT Consortium," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-17, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pgen00:1004198. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosgenetics (email available below). General contact details of provider: https://journals.plos.org/plosgenetics/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.