IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v136y2018icp83-86.html
   My bibliography  Save this article

Statistical methods and challenges in connectome genetics

Author

Listed:
  • Pluta, Dustin
  • Yu, Zhaoxia
  • Shen, Tong
  • Chen, Chuansheng
  • Xue, Gui
  • Ombao, Hernando

Abstract

The study of genetic influences on brain connectivity, known as connectome genetics, is an exciting new direction of research in imaging genetics. We here review recent results and current statistical methods in this area, and discuss some of the persistent challenges and possible directions for future work.

Suggested Citation

  • Pluta, Dustin & Yu, Zhaoxia & Shen, Tong & Chen, Chuansheng & Xue, Gui & Ombao, Hernando, 2018. "Statistical methods and challenges in connectome genetics," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 83-86.
    • Handle: RePEc:eee:stapro:v:136:y:2018:i:c:p:83-86
    DOI: 10.1016/j.spl.2018.02.048
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715218300932
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2018.02.048?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dawei Liu & Xihong Lin & Debashis Ghosh, 2007. "Semiparametric Regression of Multidimensional Genetic Pathway Data: Least-Squares Kernel Machines and Linear Mixed Models," Biometrics, The International Biometric Society, vol. 63(4), pages 1079-1088, December.
    2. Claudia Kirch & Birte Muhsal & Hernando Ombao, 2015. "Detection of Changes in Multivariate Time Series With Application to EEG Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1197-1216, September.
    3. Hua Zhou & Lexin Li & Hongtu Zhu, 2013. "Tensor Regression with Applications in Neuroimaging Data Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 540-552, June.
    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. Reid, Nancy, 2018. "Statistical science in the world of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 42-45.
    2. Bowman, Adrian W., 2018. "Big questions, informative data, excellent science," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 34-36.

    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. Lin Liu, 2021. "Matrix‐based introduction to multivariate data analysis, by KoheiAdachi 2nd edition. Singapore: Springer Nature, 2020. pp. 457," Biometrics, The International Biometric Society, vol. 77(4), pages 1498-1500, December.
    2. Anastasiou, Andreas & Cribben, Ivor & Fryzlewicz, Piotr, 2022. "Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity," LSE Research Online Documents on Economics 112148, London School of Economics and Political Science, LSE Library.
    3. Casini, Alessandro & Perron, Pierre, 2024. "Change-point analysis of time series with evolutionary spectra," Journal of Econometrics, Elsevier, vol. 242(2).
    4. Cui Guo & Jian Kang & Timothy D. Johnson, 2022. "A spatial Bayesian latent factor model for image‐on‐image regression," Biometrics, The International Biometric Society, vol. 78(1), pages 72-84, March.
    5. Bo Wei & Limin Peng & Ying Guo & Amita Manatunga & Jennifer Stevens, 2023. "Tensor response quantile regression with neuroimaging data," Biometrics, The International Biometric Society, vol. 79(3), pages 1947-1958, September.
    6. Maria A. Veretennikova & Alla Sikorskii & Michael J. Boivin, 2018. "Parameters of stochastic models for electroencephalogram data as biomarkers for child’s neurodevelopment after cerebral malaria," Journal of Statistical Distributions and Applications, Springer, vol. 5(1), pages 1-12, December.
    7. Zaili Fang & Inyoung Kim & Jeesun Jung, 2018. "Semiparametric Kernel-Based Regression for Evaluating Interaction Between Pathway Effect and Covariate," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 129-152, March.
    8. Yao Lei Xu & Kriton Konstantinidis & Danilo P. Mandic, 2022. "Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling," Papers 2211.05581, arXiv.org.
    9. Buddhananda Banerjee & Satyaki Mazumder, 2018. "A more powerful test identifying the change in mean of functional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 691-715, June.
    10. Tianqi Sun & Weiyu Li & Lu Lin, 2024. "Matrix-variate generalized linear model with measurement error," Statistical Papers, Springer, vol. 65(6), pages 3935-3958, August.
    11. Wen‐Yu Hua & Debashis Ghosh, 2015. "Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies," Biometrics, The International Biometric Society, vol. 71(3), pages 812-820, September.
    12. Wenbiao Zhao & Lixing Zhu & Falong Tan, 2024. "Multiple change point detection for high-dimensional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(3), pages 809-846, September.
    13. Xiaoshan Li & Da Xu & Hua Zhou & Lexin Li, 2018. "Tucker Tensor Regression and Neuroimaging Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 520-545, December.
    14. Arnab Maity & Xihong Lin, 2011. "Powerful Tests for Detecting a Gene Effect in the Presence of Possible Gene–Gene Interactions Using Garrote Kernel Machines," Biometrics, The International Biometric Society, vol. 67(4), pages 1271-1284, December.
    15. Hayato Maki & Sakriani Sakti & Hiroki Tanaka & Satoshi Nakamura, 2018. "Quality prediction of synthesized speech based on tensor structured EEG signals," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.
    16. Zhao, Junlong & Niu, Lu & Zhan, Shushi, 2017. "Trace regression model with simultaneously low rank and row(column) sparse parameter," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 1-18.
    17. Jade Xiaoqing Wang & Yimei Li & Wilburn E. Reddick & Heather M. Conklin & John O. Glass & Arzu Onar‐Thomas & Amar Gajjar & Cheng Cheng & Zhao‐Hua Lu, 2023. "A high‐dimensional mediation model for a neuroimaging mediator: Integrating clinical, neuroimaging, and neurocognitive data to mitigate late effects in pediatric cancer," Biometrics, The International Biometric Society, vol. 79(3), pages 2430-2443, September.
    18. Kim, Jonathan & Sandri, Brian J. & Rao, Raghavendra B. & Lock, Eric F., 2023. "Bayesian predictive modeling of multi-source multi-way data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
    19. Long Qu & Tobias Guennel & Scott L. Marshall, 2013. "Linear Score Tests for Variance Components in Linear Mixed Models and Applications to Genetic Association Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 883-892, December.
    20. Chelsey Hill & James Li & Matthew J. Schneider & Martin T. Wells, 2021. "The tensor auto‐regressive model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 636-652, July.

    More about this item

    Keywords

    Connectivity; Genetics;

    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:eee:stapro:v:136:y:2018:i:c:p:83-86. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.