Recent studies strongly suggest that there are common, genetically determined pathways to risk for psychiatric and neurodevelopmental disorders including autism, intellectual disability, attention deficit disorder, and schizophrenia, but no study has investigated the relationship between genetic variation and human brain development prior to the age at which clinical symptoms are first recognized. The primary objective of the current application is to use cutting-edge techniques in genomics to identify common and rare genetic variants which impact brain development in the early postnatal period, an extremely dynamic time which may be critical in the etiology of neurodevelopmental disorders. Intracranial volume (ICV), total white matter, total gray matter, lateral ventricle volume, and maturation of white matter tracts are heritable in neonates. The proposed project will test several major genetic mechanisms which could explain this high heritability. (1) We will test whether variation in structural brain phenotypes is predicted by common variants of moderate to large effect size by genotyping approximately 1 million single nucleotide polymorphisms (SNPs) and a genome-wide set of copy number variation (CNV) probes in a large (900 subject) and well-characterized population sample of children assessed with high-resolution MRI of the brain at 2 weeks of age with T1- weighted, T2-weighted, and diffusion tensor imaging sequences. Analysis will be completed in 2 stages, a hypothesis driven test of a defined set of genetic variants previously implicated in brain development and a hypothesis-generating unbiased search of the genome to identify previously unsuspected variants affecting brain development. (2) Using the same data set we will test whether variation in structural brain phenotypes is predicted by the combined effects of many common variants each with a small effect size through pathway analysis and advanced multi-marker association models. (3) We will also use this data set to test if the total burden of rare genic CNVs predicts variation in brain structure. (4) We will test whether rare SNPs and/or small insertions and deletions are associated with brain development by performing full exome sequencing in a subgroup of 20 children with enlarged ventricles, a phenotype which is highly relevant to neurodevelopmental disorders. While the focus of this grant is on the neonatal period, participants are also returning for follow-up scans and detailed developmental assessments at 1, 2, 4 and 6 yrs of age as part of 2 already funded studies. Thus, ultimately, the information generated in this grant can be used to study genetic determinates of the trajectories of structural and functional brain development across the critical transitional period of infancy and early childhood. This is an unprecedented opportunity to identify genetic variants which impact brain development, potentially mediating risk for psychiatric and neurodevelopmental disorders. A better understanding of such genetic mechanisms has the potential to inspire new approaches to prevention, diagnosis, and treatment which are urgently needed.

Public Health Relevance

The primary goal of the current application is to identify common and rare genetic variants which predict early brain development in humans;a period which may be critical in the development of psychiatric and neurodevelopmental disorders such as autism, schizophrenia, intellectual disability and attention-deficit disorder. Results will significantly improve our understanding of how genes shape neurodevelopmental trajectories in a way which increases the risk for psychiatric and neurodevelopmental disorders. Ultimately, identifying the genes involved in brain development has the potential to significantly improve diagnosis, guide research efforts into environmental risk factors, and generate completely new treatment possibilities for individuals with psychiatric and neurodevelopmental conditions.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
1R01MH092335-01A1
Application #
8187749
Study Section
Developmental Brain Disorders Study Section (DBD)
Program Officer
Zehr, Julia L
Project Start
2011-07-01
Project End
2016-04-30
Budget Start
2011-07-01
Budget End
2012-04-30
Support Year
1
Fiscal Year
2011
Total Cost
$504,632
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Psychiatry
Type
Schools of Medicine
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Park, Yeonhee; Su, Zhihua; Zhu, Hongtu (2017) Groupwise envelope models for imaging genetic analysis. Biometrics 73:1243-1253
Zhu, Wensheng; Yuan, Ying; Zhang, Jingwen et al. (2017) Genome-wide association analysis of secondary imaging phenotypes from the Alzheimer's disease neuroimaging initiative study. Neuroimage 146:983-1002
Huang, Chao; Thompson, Paul; Wang, Yalin et al. (2017) FGWAS: Functional genome wide association analysis. Neuroimage 159:107-121
Jha, Shaili C; Meltzer-Brody, Samantha; Steiner, Rachel J et al. (2016) Antenatal depression, treatment with selective serotonin reuptake inhibitors, and neonatal brain structure: A propensity-matched cohort study. Psychiatry Res 253:43-53
Shen, Dan; Shen, Haipeng; Zhu, Hongtu et al. (2016) The Statistics and Mathematics of High Dimension Low Sample Size Asymptotics. Stat Sin 26:1747-1770
Huang, Meiyan; Nichols, Thomas; Huang, Chao et al. (2015) FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data. Neuroimage 118:613-27
Huang, Chao; Styner, Martin; Zhu, Hongtu (2015) Clustering High-Dimensional Landmark-based Two-dimensional Shape Data(‡). J Am Stat Assoc 110:946-961
Luo, Xinchao; Zhu, Lixing; Kong, Linglong et al. (2015) Functional Nonlinear Mixed Effects Models for Longitudinal Image Data. Inf Process Med Imaging 24:794-805
Lu, Zhao-Hua; Zhu, Hongtu; Knickmeyer, Rebecca C et al. (2015) Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection. Genet Epidemiol 39:664-77
Shen, Dan; Zhu, Hongtu (2015) Spatially Weighted Principal Component Regression for High-Dimensional Prediction. Inf Process Med Imaging 24:758-69

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