IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0181269.html
   My bibliography  Save this article

Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome

Author

Listed:
  • Malka Gorfine
  • Sonja I Berndt
  • Jenny Chang-Claude
  • Michael Hoffmeister
  • Loic Le Marchand
  • John Potter
  • Martha L Slattery
  • Nir Keret
  • Ulrike Peters
  • Li Hsu

Abstract

The popular Genome-wide Complex Trait Analysis (GCTA) software uses the random-effects models for estimating the narrow-sense heritability based on GWAS data of unrelated individuals without knowing and identifying the causal loci. Many methods have since extended this approach to various situations. However, since the proportion of causal loci among the variants is typically very small and GCTA uses all variants to calculate the similarities among individuals, the estimation of heritability may be unstable, resulting in a large variance of the estimates. Moreover, if the causal SNPs are not genotyped, GCTA sometimes greatly underestimates the true heritability. We present a novel narrow-sense heritability estimator, named HERRA, using well-developed ultra-high dimensional machine-learning methods, applicable to continuous or dichotomous outcomes, as other existing methods. Additionally, HERRA is applicable to time-to-event or age-at-onset outcome, which, to our knowledge, no existing method can handle. Compared to GCTA and LDAK for continuous and binary outcomes, HERRA often has a smaller variance, and when causal SNPs are not genotyped, HERRA has a much smaller empirical bias. We applied GCTA, LDAK and HERRA to a large colorectal cancer dataset using dichotomous outcome (4,312 cases, 4,356 controls, genotyped using Illumina 300K), the respective heritability estimates of GCTA, LDAK and HERRA are 0.068 (SE = 0.017), 0.072 (SE = 0.021) and 0.110 (SE = 5.19 x 10−3). HERRA yields over 50% increase in heritability estimate compared to GCTA or LDAK.

Suggested Citation

  • Malka Gorfine & Sonja I Berndt & Jenny Chang-Claude & Michael Hoffmeister & Loic Le Marchand & John Potter & Martha L Slattery & Nir Keret & Ulrike Peters & Li Hsu, 2017. "Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-19, August.
  • Handle: RePEc:plo:pone00:0181269
    DOI: 10.1371/journal.pone.0181269
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181269
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0181269&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0181269?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. Gerhard Moser & Sang Hong Lee & Ben J Hayes & Michael E Goddard & Naomi R Wray & Peter M Visscher, 2015. "Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model," PLOS Genetics, Public Library of Science, vol. 11(4), pages 1-22, April.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. The Tien Mai, 2023. "Reliable Genetic Correlation Estimation via Multiple Sample Splitting and Smoothing," Mathematics, MDPI, vol. 11(9), pages 1-13, May.

    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. Carla Márquez-Luna & Steven Gazal & Po-Ru Loh & Samuel S. Kim & Nicholas Furlotte & Adam Auton & Alkes L. Price, 2021. "Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Ganwen Zhang & Jianini Zhao & Jieru Wang & Guo Lin & Lin Li & Fengfei Ban & Meiting Zhu & Yangjun Wen & Jin Zhang, 2024. "An Improved Expectation–Maximization Bayesian Algorithm for GWAS," Mathematics, MDPI, vol. 12(13), pages 1-14, June.
    3. M. S. Clark & J. I. Hoffman & L. S. Peck & L. Bargelloni & D. Gande & C. Havermans & B. Meyer & T. Patarnello & T. Phillips & K. R. Stoof-Leichsenring & D. L. J. Vendrami & A. Beck & G. Collins & M. W, 2023. "Multi-omics for studying and understanding polar life," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    4. 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.
    5. Ye, Mao & Zhang, Peng & Nie, Lizhen, 2018. "Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 32-49.
    6. Cox Lwaka Tamba & Yuan-Li Ni & Yuan-Ming Zhang, 2017. "Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-20, January.
    7. Gao Wang & Abhishek Sarkar & Peter Carbonetto & Matthew Stephens, 2020. "A simple new approach to variable selection in regression, with application to genetic fine mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1273-1300, December.
    8. Marion Patxot & Daniel Trejo Banos & Athanasios Kousathanas & Etienne J. Orliac & Sven E. Ojavee & Gerhard Moser & Alexander Holloway & Julia Sidorenko & Zoltan Kutalik & Reedik Mägi & Peter M. Vissch, 2021. "Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    9. Katharina B Böndel & Susanne A Kraemer & Toby Samuels & Deirdre McClean & Josianne Lachapelle & Rob W Ness & Nick Colegrave & Peter D Keightley, 2019. "Inferring the distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii," PLOS Biology, Public Library of Science, vol. 17(6), pages 1-24, June.

    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:pone00:0181269. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.