IDEAS home Printed from https://ideas.repec.org/a/plo/pgen00/1008995.html
   My bibliography  Save this article

Pan-genomic open reading frames: A potential supplement of single nucleotide polymorphisms in estimation of heritability and genomic prediction

Author

Listed:
  • Zhengcao Li
  • Henner Simianer

Abstract

Pan-genomic open reading frames (ORFs) potentially carry protein-coding gene or coding variant information in a population. In this study, we suggest that pan-genomic ORFs are promising to be utilized in estimation of heritability and genomic prediction. A Saccharomyces cerevisiae dataset with whole-genome SNPs, pan-genomic ORFs, and the copy numbers of those ORFs is used to test the effectiveness of ORF data as a predictor in three prediction models for 35 traits. Our results show that the ORF-based heritability can capture more genetic effects than SNP-based heritability for all traits. Compared to SNP-based genomic prediction (GBLUP), pan-genomic ORF-based genomic prediction (OBLUP) is distinctly more accurate for all traits, and the predictive abilities on average are more than doubled across all traits. For four traits, the copy number of ORF-based prediction(CBLUP) is more accurate than OBLUP. When using different numbers of isolates in training sets in ORF-based prediction, the predictive abilities for all traits increased as more isolates are added in the training sets, suggesting that with very large training sets the prediction accuracy will be in the range of the square root of the heritability. We conclude that pan-genomic ORFs have the potential to be a supplement of single nucleotide polymorphisms in estimation of heritability and genomic prediction.Author summary: The properties of single nucleotide polymorphisms (SNPs) as a main source of genetic variability for estimation of heritability and genomic prediction have been widely studied over the past years. This data type remarkably accelerated the development of medical diagnosis in human genetics and prediction of breeding values in livestock and crop breeding field. However, due to the inherent pitfalls of SNP-based prediction, e.g. imperfect LD between markers and causal variants, seeking new genomic datasets of causal variants has become imperative. Our study point out some of the superiorities of pan-genomic open reading frames as independent variables in estimation of heritability and genomic prediction.

Suggested Citation

  • Zhengcao Li & Henner Simianer, 2020. "Pan-genomic open reading frames: A potential supplement of single nucleotide polymorphisms in estimation of heritability and genomic prediction," PLOS Genetics, Public Library of Science, vol. 16(8), pages 1-19, August.
    • Handle: RePEc:plo:pgen00:1008995
    DOI: 10.1371/journal.pgen.1008995
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008995
    Download Restriction: no
    File URL: https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1008995&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pgen.1008995?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
    ---><---

    References listed on IDEAS

    as
    1. Kaspar Märtens & Johan Hallin & Jonas Warringer & Gianni Liti & Leopold Parts, 2016. "Predicting quantitative traits from genome and phenome with near perfect accuracy," Nature Communications, Nature, vol. 7(1), pages 1-8, September.
    2. Agnieszka A. Golicz & Philipp E. Bayer & Guy C. Barker & Patrick P. Edger & HyeRan Kim & Paula A. Martinez & Chon Kit Kenneth Chan & Anita Severn-Ellis & W. Richard McCombie & Isobel A. P. Parkin & An, 2016. "The pangenome of an agronomically important crop plant Brassica oleracea," Nature Communications, Nature, vol. 7(1), pages 1-8, December.
    3. Brendan Maher, 2008. "Personal genomes: The case of the missing heritability," Nature, Nature, vol. 456(7218), pages 18-21, November.
    4. Joshua S. Bloom & Ian M. Ehrenreich & Wesley T. Loo & Thúy-Lan Võ Lite & Leonid Kruglyak, 2013. "Finding the sources of missing heritability in a yeast cross," Nature, Nature, vol. 494(7436), pages 234-237, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Takeshi Matsui & Martin N. Mullis & Kevin R. Roy & Joseph J. Hale & Rachel Schell & Sasha F. Levy & Ian M. Ehrenreich, 2022. "The interplay of additivity, dominance, and epistasis on fitness in a diploid yeast cross," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Chuong B Do & David A Hinds & Uta Francke & Nicholas Eriksson, 2012. "Comparison of Family History and SNPs for Predicting Risk of Complex Disease," PLOS Genetics, Public Library of Science, vol. 8(10), pages 1-16, October.
    3. Iuliana Ionita-Laza & Joseph D Buxbaum & Nan M Laird & Christoph Lange, 2011. "A New Testing Strategy to Identify Rare Variants with Either Risk or Protective Effect on Disease," PLOS Genetics, Public Library of Science, vol. 7(2), pages 1-6, February.
    4. Aida Bianco & Eusebio Chiefari & Carmelo G A Nobile & Daniela Foti & Maria Pavia & Antonio Brunetti, 2015. "The Association between HMGA1 rs146052672 Variant and Type 2 Diabetes: A Transethnic Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-15, August.
    5. Yumei Yang & Qishan Wang & Qiang Chen & Rongrong Liao & Xiangzhe Zhang & Hongjie Yang & Youmin Zheng & Zhiwu Zhang & Yuchun Pan, 2014. "A New Genotype Imputation Method with Tolerance to High Missing Rate and Rare Variants," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-7, June.
    6. Chung-Feng Kao & Jia-Rou Liu & Hung Hung & Po-Hsiu Kuo, 2015. "A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-14, April.
    7. Dominic Russ & John A Williams & Victor Roth Cardoso & Laura Bravo-Merodio & Samantha C Pendleton & Furqan Aziz & Animesh Acharjee & Georgios V Gkoutos, 2022. "Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-19, February.
    8. von Stumm, Sophie & Kandaswamy, Radhika & Maxwell, Jessye, 2023. "Gene-environment interplay in early life cognitive development," Intelligence, Elsevier, vol. 98(C).
    9. Charles-Elie Rabier & Philippe Barre & Torben Asp & Gilles Charmet & Brigitte Mangin, 2016. "On the Accuracy of Genomic Selection," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-23, June.
    10. Xiaoling Tong & Min-Jin Han & Kunpeng Lu & Shuaishuai Tai & Shubo Liang & Yucheng Liu & Hai Hu & Jianghong Shen & Anxing Long & Chengyu Zhan & Xin Ding & Shuo Liu & Qiang Gao & Bili Zhang & Linli Zhou, 2022. "High-resolution silkworm pan-genome provides genetic insights into artificial selection and ecological adaptation," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    11. Janet Currie, 2011. "Ungleichheiten bei der Geburt: Einige Ursachen und Folgen," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 12(s1), pages 42-65, May.
    12. Karen Kapur & Toby Johnson & Noam D Beckmann & Joban Sehmi & Toshiko Tanaka & Zoltán Kutalik & Unnur Styrkarsdottir & Weihua Zhang & Diana Marek & Daniel F Gudbjartsson & Yuri Milaneschi & Hilma Holm , 2010. "Genome-Wide Meta-Analysis for Serum Calcium Identifies Significantly Associated SNPs near the Calcium-Sensing Receptor (CASR) Gene," PLOS Genetics, Public Library of Science, vol. 6(7), pages 1-12, July.
    13. Kettlewell, Nathan & Tymula, Agnieszka & Yoo, Hong Il, 2023. "The Heritability of Economic Preferences," IZA Discussion Papers 16633, Institute of Labor Economics (IZA).
    14. Gang Fang & Majda Haznadar & Wen Wang & Haoyu Yu & Michael Steinbach & Timothy R Church & William S Oetting & Brian Van Ness & Vipin Kumar, 2012. "High-Order SNP Combinations Associated with Complex Diseases: Efficient Discovery, Statistical Power and Functional Interactions," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-15, April.
    15. Guadagno, C.R. & Millar, D. & Lai, R. & Mackay, D.S. & Pleban, J.R. & McClung, C.R. & Weinig, C. & Wang, D.R. & Ewers, B.E., 2020. "Use of transcriptomic data to inform biophysical models via Bayesian networks," Ecological Modelling, Elsevier, vol. 429(C).
    16. Eltohamy A A Yousef & Thomas Müller & Andreas Börner & Karl J Schmid, 2018. "Comparative analysis of genetic diversity and differentiation of cauliflower (Brassica oleracea var. botrytis) accessions from two ex situ genebanks," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-19, February.
    17. Kozlitina Julia & Schucany William R., 2015. "A robust distribution-free test for genetic association studies of quantitative traits," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(5), pages 443-464, November.
    18. Le Rouzic, Arnaud & Skaug, Hans J. & Hansen, Thomas F., 2010. "Estimating genetic architectures from artificial-selection responses: A random-effect framework," Theoretical Population Biology, Elsevier, vol. 77(2), pages 119-130.
    19. Danesh Moradigaravand & Martin Palm & Anne Farewell & Ville Mustonen & Jonas Warringer & Leopold Parts, 2018. "Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-17, December.
    20. Janet Currie, 2011. "Inequality at Birth: Some Causes and Consequences," American Economic Review, American Economic Association, vol. 101(3), pages 1-22, May.

    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:pgen00:1008995. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.