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Statistical methods and challenges in connectome genetics

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Bowman, Adrian W., 2018. "Big questions, informative data, excellent science," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 34-36.
    2. Reid, Nancy, 2018. "Statistical science in the world of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 42-45.

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    Keywords

    Connectivity; Genetics;

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