Genetic architecture of hippocampal subfields on standard resolution MRI: How the parts relate to the whole

Abstract

The human hippocampus can be subdivided into subfields with unique functional properties and differential vulnerability to disease or neuropsychiatric conditions. Identifying genes that confer susceptibility to such processes is an important goal in developing treatments. Recent advances in automatic subfield segmentation from magnetic resonance images make it possible to use these measures as phenotypes in large‐scale genome‐wide association studies. Such analyses are likely to rely largely on standard resolution (~1 mm isotropic) T1‐weighted images acquired on 3.0T scanners. Determining whether the genetic architecture of subfields can be detected from such images is therefore an important step. We used Freesurfer v6.0 to segment hippocampal subfields in two large twin studies, the Vietnam Era Twin Study of Aging and the Human Connectome Project. We estimated heritability of subfields and the genetic overlap with total hippocampal volume. Heritability was similar across samples, but little genetic variance remained after accounting for genetic influences on total hippocampal volume. Importantly, we examined genetic relationships between subfields to determine whether subfields can be grouped based on a smaller number of underlying, genetically independent factors. We identified three genetic factors in both samples, but the high degree of cross loadings precluded formation of genetically distinct groupings of subfields. These results confirm the reliability of Freesurfer v6.0 generated subfields across samples for phenotypic analyses. However, the current results suggest that it will be difficult for large‐scale genetic analyses to identify subfield‐specific genes that are distinct from both total hippocampal volume and other subfields using segmentations generated from standard resolution T1‐weighted images.

Document Details

Document Type
Pub Defense Publication
Publication Date
Nov 15, 2018
Source ID
10.1002/hbm.24464

Entities

People

  • Anders M. Dale
  • Carol E Franz
  • Christine Fennema‐notestine
  • Donald J. Hagler Jr.
  • Jeremy A. Elman
  • Linda K. Mcevoy
  • Lisa T. Eyler
  • Matthew S Panizzon
  • Michael C. Neale
  • Michael J. Lyons
  • Nathan A Gillespie
  • William S. Kremen

Organizations

  • Blueprint for Neuroscience Research
  • Boston University
  • Cancer Genomics Centre
  • McDonnell Center for Systems Neuroscience
  • National Academy of Sciences
  • National Archives and Records Administration
  • National Institute on Aging
  • National Institutes of Health
  • National Research Council
  • Temple University
  • United States Department of Defense
  • United States Department of Veterans Affairs
  • University of California, San Diego
  • Virginia Commonwealth University

Tags

Fields of Study

  • Biology

Readers

  • Molecular and genetic basis of cancer.
  • Neuroscience
  • Regression Analysis.

Technology Areas

  • Biotechnology