Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Computationally efficient mixed effect model for genetic analysis of high dimensional neuroimaging data

Tools
- Tools
+ Tools

Ganjgahi, Habib (2016) Computationally efficient mixed effect model for genetic analysis of high dimensional neuroimaging data. PhD thesis, University of Warwick.

[img]
Preview
PDF
WRAP_Theses_Ganjgahi_2016.pdf - Submitted Version - Requires a PDF viewer.

Download (7Mb) | Preview
Official URL: http://webcat.warwick.ac.uk/record=b3084178~S15

Request Changes to record.

Abstract

A new research direction in the neuroimaging discipline, so called imaging genetic, has emerged recently concerns describing individual differences in imaging phenotypes using genetic and environmental factors. The large number of voxel- and vertex-wise measurements in imaging genetics studies present a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot perform essential genetic analyses including heritability and association estimations and testings, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel- wise or cluster-wise P-values. Moreover, available genetic tools rely on P-values that can be inaccurate with usual parametric inference methods.

In this thesis computationally efficient linear mixed effect model for voxel-wise genetic analyses of high-dimensional imaging phenotypes are developed. Specifically, fast estimation and inference procedures for heritability and association analyses are introduced using orthogonal transformations that dramatically simplify the likelihood and restricted likelihood functions of mixed effect model. We review the family of score, likelihood ratio and Wald tests and propose novel inference methods for fixed and random effect terms in the mixed effect models. To address problems with inaccuracies with the standard results used to find P-values, we propose different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate different significance tests for heritability and association, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability and genome-wide association studies in the massive data setting.

Item Type: Thesis or Dissertation (PhD)
Subjects: Q Science > QA Mathematics
R Medicine > RC Internal medicine
Library of Congress Subject Headings (LCSH): Nervous system -- Imaging, Brain -- Imaging -- Statistical methods, Imaging systems in genetics, Genetics -- Statistical methods, Multilevel models (Statistics)
Official Date: November 2016
Dates:
DateEvent
November 2016Submitted
Institution: University of Warwick
Theses Department: Department of Statistics
Thesis Type: PhD
Publication Status: Unpublished
Supervisor(s)/Advisor: Nichols, Thomas E.
Format of File: pdf
Extent: xiii, 113 leaves : illustrations, charts
Language: eng

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us