Name
Title | ||
|---|---|---|
Variance decomposition of MRI-based covariance maps using genetically informative samples and structural equation modeling. |
Abstract | ||
|---|---|---|
The role of genetics in driving intracortical relationships is an important question that has rarely been studied in humans. In particular, there are no extant high-resolution imaging studies on genetic covariance. In this article, we describe a novel method that combines classical quantitative genetic methodologies for variance decomposition with recently developed semi-multivariate algorithms for high-resolution measurement of phenotypic covariance. Using these tools, we produced correlational maps of genetic and environmental (i.e. nongenetic) relationships between several regions of interest and the cortical surface in a large pediatric sample of 600 twins, siblings, and singletons. These analyses demonstrated high, fairly uniform, statistically significant genetic correlations between the entire cortex and global mean cortical thickness. In agreement with prior reports on phenotypic covariance using similar methods, we found that mean cortical thickness was most strongly correlated with association cortices. However, the present study suggests that genetics plays a large role in global brain patterning of cortical thickness in this manner. Further, using specific gyri with known high heritabilities as seed regions, we found a consistent pattern of high bilateral genetic correlations between structural homologues, with environmental correlations more restricted to the same hemisphere as the seed region, suggesting that interhemispheric covariance is largely genetically mediated. These findings are consistent with the limited existing knowledge on the genetics of cortical variability as well as our prior multivariate studies on cortical gyri. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.neuroimage.2008.06.039 | NeuroImage |
Keywords | Field | DocType |
genetic variation,genetic correlation,multivariate analysis,region of interest,structural equation model,phenotype,statistical significance,variance decomposition,quantitative genetics,high resolution,magnetic resonance imaging,genetics,algorithms | Variance decomposition of forecast errors,Developmental psychology,Structural equation modeling,Multivariate statistics,Genetic variation,Psychology,Extant taxon,Multivariate analysis,Covariance | Journal |
Volume | Issue | ISSN |
47 | 1 | 1053-8119 |
Citations | PageRank | References |
8 | 0.57 | 9 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| j eric schmitt | 1 | 26 | 3.01 |
| Rhoshel K. Lenroot | 2 | 31 | 2.85 |
| Sarah E. Ordaz | 3 | 8 | 0.57 |
| Gregory L Wallace | 4 | 20 | 1.94 |
| Jason P Lerch | 5 | 388 | 31.42 |
| Alan C. Evans | 6 | 3045 | 574.95 |
| Elizabeth C. Prom | 7 | 8 | 0.57 |
| kenneth s kendler | 8 | 41 | 5.01 |
| Michael C. Neale | 9 | 64 | 7.93 |
| Jay N Giedd | 10 | 376 | 30.78 |