Genetic analysis of cortical thickness and fractional anisotropy of water diffusion in the brain.

Peter Kochunov,Elliot L Hong,Valeria Kochunov,Henry H Holcomb,Joanne E Curran,Jack Kent,Thomas E Nichols,John Blangero,Melanie A Carless,Paul M Thompson,Matthew P Johnson,Jason L Stein,David C Glahn,Anderson M Winkler,Rene L Olvera,Shelley A Cole

doi:10.3389/fnins.2011.00120

Abstract

Objectives: The thickness of the brain’s cortical gray matter (GM) and the fractional anisotropy (FA) of the cerebral white matter (WM) each follow an inverted U-shape trajectory with age. The two measures are positively correlated and may be modulated by common biological mechanisms. We employed four types of genetic analyses to localize individual genes acting pleiotropically upon these phenotypes. Methods: Whole-brain and regional GM thickness and FA values were measured from high-resolution anatomical and diffusion tensor MR images collected from 712, Mexican American participants (438 females, age = 47.9 ± 13.2 years) recruited from 73 (9.7 ± 9.3 individuals/family) large families. The significance of the correlation between two traits was estimated using a bivariate genetic correlation analysis. Localization of chromosomal regions that jointly influenced both traits was performed using whole-genome quantitative trait loci (QTL) analysis. Gene localization was performed using SNP genotyping on Illumina 1M chip and correlation with leukocyte-based gene-expression analyses. The gene-expressions were measured using the Illumina BeadChip. These data were available for 371 subjects. Results: Significant genetic correlation was observed among GM thickness and FA values. Significant logarithm of odds (LOD ≥ 3.0) QTLs were localized within chromosome 15q22–23. More detailed localization reported no significant association (p < 5·10−5) for 1565 SNPs located within the QTLs. Post hoc analysis indicated that 40% of the potentially significant (p ≤ 10−3) SNPs were localized to the related orphan receptor alpha (RORA) and NARG2 genes. A potentially significant association was observed for the rs2456930 polymorphism reported as a significant GWAS finding in Alzheimer’s disease neuroimaging initiative subjects. The expression levels for RORA and ADAM10 genes were significantly (p < 0.05) correlated with both FA and GM thickness. NARG2 expressions were significantly correlated with GM thickness (p < 0.05) but failed to show a significant correlation (p = 0.09) with FA. Discussion: This study identified a novel, significant QTL at 15q22–23. SNP correlation with gene-expression analyses indicated that RORA, NARG2, and ADAM10 jointly influence GM thickness and WM–FA values.

Highlights

The human cerebrum is a complex, multi-compartmental structure whose anatomy and function are influenced by individual genetic variations (Thompson et al, 2001; Glahn et al, 2007)
The positive sign of the phenotypic correlation coefficient suggested that the same genetic factors associated with higher fractional anisotropy (FA) values were linked to progressively higher gray matter (GM) thickness values
There are 126 possible GM–FA pairs between the 14 regional GM thickness values and nine regional FA values (Table 2). 114 out of 126 GM–FA correlation analyses showed a significant (p < 0.05) phenotypic correlation, and 101 of the 126 were significant when corrected for multiple comparisons (p < 0.0004, Bonferroni correction for 126 tests)

Summary

Introduction

The human cerebrum is a complex, multi-compartmental structure whose anatomy and function are influenced by individual genetic variations (Thompson et al, 2001; Glahn et al, 2007). Understanding the genetic mechanisms that control inter-subject cerebral variability is critical for deciphering the brain’s normal and pathological function. Recent discoveries of genetic factors that can increase the likelihood of developing neurodegenerative disorders such as dementias has emphasized the need to identify genes that influence integrity of cerebral tissue (Glahn et al, 2007; Meyer-Lindenberg, 2010; Stein et al, 2010a). Statistical genetic methods have been developed to measure the genetic modulation of inter-subject variability. By combining neuroimaging with genetics clinical investigators have promoted a better understanding of cortical variability (Walsh, 2000; Farnham et al, 2004; Gaitanis and Walsh, 2004; Jones et al, 2004; Edenberg et al, 2005; Klein et al, 2005; Kamarajan et al, 2006; Brouwer et al, 2010; Kochunov et al, 2010a; Joshi et al, 2011)

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