Genetic Epidemiology, Translational Neurogenomics, Psychiatric Genetics and Statistical Genetics Laboratories investigate the pattern of disease in families, particularly identical and non-identical twins, to assess the relative importance of genes and environment in a variety of important health problems.
QIMR Home Page
GenEpi Home Page
Publications
Contacts
Research
Staff Index
Collaborators
Software Tools
Computing Resources
Studies
Search
GenEpi Intranet
PMID
30914942
TITLE
Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data.
ABSTRACT
Imaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods. Accelerated Permutation inference for ACE (APACE) and fast permutation heritability inference (FPHI), employ fast, non-iterative approximation-based methods. We performed this evaluation in a simulated twin-sibling pedigree and phenotypes and in diffusion tensor imaging (DTI) data from three twin-sibling cohorts, the human connectome project (HCP), netherlands twin register (NTR) and BrainSCALE projects provided as a part of the enhancing neuro imaging genetics analysis (ENIGMA) consortium. We observed that heritability estimate may differ depending on the underlying method and dataset. The heritability estimates from the two MLE approaches provided excellent agreement in both simulated and imaging data. The heritability estimates for two approximation approaches showed reduced heritability estimates in datasets with deviations from data normality. We propose a data homogenization approach (implemented in solar-eclipse; www.solar-eclipse-genetics.org) to improve the convergence of heritability estimates across different methods. The homogenization steps include consistent regression of any nuisance covariates and enforcing normality on the trait data using inverse Gaussian transformation. Under these conditions, the heritability estimates for simulated and DTI phenotypes produced converging heritability estimates regardless of the method. Thus, using these simple suggestions may help new heritability studies to provide outcomes that are comparable regardless of software package.
DATE PUBLISHED
2019
HISTORY
PUBSTATUS PUBSTATUSDATE
received 2017/07/05
accepted 2019/02/25
entrez 2019/03/28 06:00
pubmed 2019/03/28 06:00
medline 2019/03/28 06:01
AUTHORS
NAME COLLECTIVENAME LASTNAME FORENAME INITIALS AFFILIATION AFFILIATIONINFO
Kochunov P Kochunov Peter P Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States.
Patel B Patel Binish B Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States.
Ganjgahi H Ganjgahi Habib H Department of Statistics, University of Oxford, Oxford, United Kingdom.
Donohue B Donohue Brian B Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States.
Ryan M Ryan Meghann M Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States.
Hong EL Hong Elliot L EL Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States.
Chen X Chen Xu X Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands.
Adhikari B Adhikari Bhim B Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States.
Jahanshad N Jahanshad Neda N Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, United States.
Thompson PM Thompson Paul M PM Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, United States.
Van't Ent D Van't Ent Dennis D Department of Biological Psychology, VU University, Amsterdam, Netherlands.
den Braber A den Braber Anouk A Department of Biological Psychology, VU University, Amsterdam, Netherlands.
de Geus EJC de Geus Eco J C EJC Department of Biological Psychology, VU University, Amsterdam, Netherlands.
Brouwer RM Brouwer Rachel M RM Department of Biological Psychology, VU University, Amsterdam, Netherlands.
Boomsma DI Boomsma Dorret I DI Department of Biological Psychology, VU University, Amsterdam, Netherlands.
Hulshoff Pol HE Hulshoff Pol Hilleke E HE Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands.
de Zubicaray GI de Zubicaray Greig I GI Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, QLD, Australia.
McMahon KL McMahon Katie L KL Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia.
Martin NG Martin Nicholas G NG QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
Wright MJ Wright Margaret J MJ Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
Nichols TE Nichols Thomas E TE Big Data Institute, University of Oxford, Oxford, United Kingdom.
INVESTIGATORS
JOURNAL
VOLUME: 13
ISSUE:
TITLE: Frontiers in neuroinformatics
ISOABBREVIATION: Front Neuroinform
YEAR: 2019
MONTH:
DAY:
MEDLINEDATE:
SEASON:
CITEDMEDIUM: Print
ISSN: 1662-5196
ISSNTYPE: Print
MEDLINE JOURNAL
MEDLINETA: Front Neuroinform
COUNTRY: Switzerland
ISSNLINKING: 1662-5196
NLMUNIQUEID: 101477957
PUBLICATION TYPE
PUBLICATIONTYPE TEXT
Journal Article
COMMENTS AND CORRECTIONS
GRANTS
GRANTID AGENCY COUNTRY
R01 EB015611 NIBIB NIH HHS United States
S10 OD023696 NIH HHS United States
GENERAL NOTE
KEYWORDS
KEYWORD
DTI
computational methods
genetics
heritability
imaging genetics
population
reproducability
MESH HEADINGS
SUPPLEMENTARY MESH
GENE SYMBOLS
CHEMICALS
OTHER ID's