IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v11y2014i12p12283-12303d42878.html
   My bibliography  Save this article

On the Analysis of a Repeated Measure Design in Genome-Wide Association Analysis

Author

Listed:
  • Young Lee

    (The Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Korea
    Department of Applied Statistics, Chung-Ang University, Seoul 156-756, Korea)

  • Suyeon Park

    (The Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Korea
    Department of Applied Statistics, Chung-Ang University, Seoul 156-756, Korea)

  • Sanghoon Moon

    (The Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Korea)

  • Juyoung Lee

    (The Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Korea)

  • Robert C. Elston

    (Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA)

  • Woojoo Lee

    (Department of Statistics, Inha University, Incheon 402-751, Korea)

  • Sungho Won

    (Department of Public Health Science, Seoul National University, Seoul 151-742, Korea)

Abstract

Longitudinal data enables detecting the effect of aging/time, and as a repeated measures design is statistically more efficient compared to cross-sectional data if the correlations between repeated measurements are not large. In particular, when genotyping cost is more expensive than phenotyping cost, the collection of longitudinal data can be an efficient strategy for genetic association analysis. However, in spite of these advantages, genome-wide association studies (GWAS) with longitudinal data have rarely been analyzed taking this into account. In this report, we calculate the required sample size to achieve 80% power at the genome-wide significance level for both longitudinal and cross-sectional data, and compare their statistical efficiency. Furthermore, we analyzed the GWAS of eight phenotypes with three observations on each individual in the Korean Association Resource (KARE). A linear mixed model allowing for the correlations between observations for each individual was applied to analyze the longitudinal data, and linear regression was used to analyze the first observation on each individual as cross-sectional data. We found 12 novel genome-wide significant disease susceptibility loci that were then confirmed in the Health Examination cohort, as well as some significant interactions between age/sex and SNPs.

Suggested Citation

  • Young Lee & Suyeon Park & Sanghoon Moon & Juyoung Lee & Robert C. Elston & Woojoo Lee & Sungho Won, 2014. "On the Analysis of a Repeated Measure Design in Genome-Wide Association Analysis," IJERPH, MDPI, vol. 11(12), pages 1-21, November.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:12:p:12283-12303:d:42878
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/11/12/12283/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/11/12/12283/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. M Kamran Ikram & Sim Xueling & Richard A Jensen & Mary Frances Cotch & Alex W Hewitt & M Arfan Ikram & Jie Jin Wang & Ronald Klein & Barbara E K Klein & Monique M B Breteler & Ning Cheung & Gerald Lie, 2010. "Four Novel Loci (19q13, 6q24, 12q24, and 5q14) Influence the Microcirculation In Vivo," PLOS Genetics, Public Library of Science, vol. 6(10), pages 1-12, October.
    2. Brendan Maher, 2008. "Personal genomes: The case of the missing heritability," Nature, Nature, vol. 456(7218), pages 18-21, November.
    3. Teri A. Manolio & Francis S. Collins & Nancy J. Cox & David B. Goldstein & Lucia A. Hindorff & David J. Hunter & Mark I. McCarthy & Erin M. Ramos & Lon R. Cardon & Aravinda Chakravarti & Judy H. Cho &, 2009. "Finding the missing heritability of complex diseases," Nature, Nature, vol. 461(7265), pages 747-753, October.
    4. Paul F O’Reilly & Clive J Hoggart & Yotsawat Pomyen & Federico C F Calboli & Paul Elliott & Marjo-Riitta Jarvelin & Lachlan J M Coin, 2012. "MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-1, May.
    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. von Stumm, Sophie & Kandaswamy, Radhika & Maxwell, Jessye, 2023. "Gene-environment interplay in early life cognitive development," Intelligence, Elsevier, vol. 98(C).
    2. Lucas Alvizi & Diogo Nani & Luciano Abreu Brito & Gerson Shigeru Kobayashi & Maria Rita Passos-Bueno & Roberto Mayor, 2023. "Neural crest E-cadherin loss drives cleft lip/palate by epigenetic modulation via pro-inflammatory gene–environment interaction," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. 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.
    4. Kai Wang, 2014. "Testing Genetic Association by Regressing Genotype over Multiple Phenotypes," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-9, September.
    5. Ilias Georgakopoulos-Soares & Chengyu Deng & Vikram Agarwal & Candace S. Y. Chan & Jingjing Zhao & Fumitaka Inoue & Nadav Ahituv, 2023. "Transcription factor binding site orientation and order are major drivers of gene regulatory activity," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Jose A Seoane & Colin Campbell & Ian N M Day & Juan P Casas & Tom R Gaunt, 2014. "Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-13, October.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Lin Yuan & Chang-An Yuan & De-Shuang Huang, 2017. "FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis," Complexity, Hindawi, vol. 2017, pages 1-10, September.
    12. Chang Lu & Jan Zaucha & Rihab Gam & Hai Fang & Smithers & Matt E. Oates & Miguel Bernabe-Rubio & James Williams & Natalie Zelenka & Arun Prasad Pandurangan & Himani Tandon & Hashem Shihab & Raju Kalai, 2023. "Hypothesis-free phenotype prediction within a genetics-first framework," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    13. Ian Barnett & Rajarshi Mukherjee & Xihong Lin, 2017. "The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 64-76, January.
    14. Bingxin Zhao & Fei Zou, 2022. "On polygenic risk scores for complex traits prediction," Biometrics, The International Biometric Society, vol. 78(2), pages 499-511, June.
    15. 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.
    16. 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.
    17. Janet Currie, 2011. "Ungleichheiten bei der Geburt: Einige Ursachen und Folgen," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 12(s1), pages 42-65, May.
    18. 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.
    19. Heejung Shim & Daniel I Chasman & Joshua D Smith & Samia Mora & Paul M Ridker & Deborah A Nickerson & Ronald M Krauss & Matthew Stephens, 2015. "A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
    20. Kettlewell, Nathan & Tymula, Agnieszka & Yoo, Hong Il, 2023. "The Heritability of Economic Preferences," IZA Discussion Papers 16633, Institute of Labor Economics (IZA).

    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:gam:jijerp:v:11:y:2014:i:12:p:12283-12303:d:42878. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.