scholarly journals Linking genes, circuits, and behavior: network connectivity as a novel endophenotype of externalizing

2018 ◽  
Vol 49 (11) ◽  
pp. 1905-1913 ◽  
Author(s):  
Naomi Sadeh ◽  
Jeffrey M. Spielberg ◽  
Mark W. Logue ◽  
Jasmeet P. Hayes ◽  
Erika J. Wolf ◽  
...  

AbstractBackgroundExternalizing disorders are known to be partly heritable, but the biological pathways linking genetic risk to the manifestation of these costly behaviors remain under investigation. This study sought to identify neural phenotypes associated with genomic vulnerability for externalizing disorders.MethodsOne-hundred fifty-five White, non-Hispanic veterans were genotyped using a genome-wide array and underwent resting-state functional magnetic resonance imaging. Genetic susceptibility was assessed using an independently developed polygenic score (PS) for externalizing, and functional neural networks were identified using graph theory based network analysis. Tasks of inhibitory control and psychiatric diagnosis (alcohol/substance use disorders) were used to measure externalizing phenotypes.ResultsA polygenic externalizing disorder score (PS) predicted connectivity in a brain circuit (10 nodes, nine links) centered on left amygdala that included several cortical [bilateral inferior frontal gyrus (IFG) pars triangularis, left rostral anterior cingulate cortex (rACC)] and subcortical (bilateral amygdala, hippocampus, and striatum) regions. Directional analyses revealed that bilateral amygdala influenced left prefrontal cortex (IFG) in participants scoring higher on the externalizing PS, whereas the opposite direction of influence was observed for those scoring lower on the PS. Polygenic variation was also associated with higher Participation Coefficient for bilateral amygdala and left rACC, suggesting that genes related to externalizing modulated the extent to which these nodes functioned as communication hubs.ConclusionsFindings suggest that externalizing polygenic risk is associated with disrupted connectivity in a neural network implicated in emotion regulation, impulse control, and reinforcement learning. Results provide evidence that this network represents a genetically associated neurobiological vulnerability for externalizing disorders.

2008 ◽  
Vol 147B (6) ◽  
pp. 690-698 ◽  
Author(s):  
Cindy L. Ehlers ◽  
David A. Gilder ◽  
Wendy S. Slutske ◽  
Penelope A. Lind ◽  
Kirk C. Wilhelmsen

2014 ◽  
Vol 226 (03) ◽  
Author(s):  
F Ponthan ◽  
D Pal ◽  
J Vormoor ◽  
O Heidenreich
Keyword(s):  

2007 ◽  
Vol 30 (4) ◽  
pp. 86
Author(s):  
M. Lanktree ◽  
J. Robinson ◽  
J. Creider ◽  
H. Cao ◽  
D. Carter ◽  
...  

Background: In Dunnigan-type familial partial lipodystrophy (FPLD) patients are born with normal fat distribution, but subcutaneous fat from extremities and gluteal regions are lost during puberty. The abnormal fat distribution leads to the development of metabolic syndrome (MetS), a cluster of phenotypes including hyperglycemia, dyslipidemia, hypertension, and visceral obesity. The study of FPLD as a monogenic model of MetS may uncover genetic risk factors of the common MetS which affects ~30% of adult North Americans. Two molecular forms of FPLD have been identified including FPLD2, resulting from heterozygous mutations in the LMNA gene, and FPLD3, resulting from both heterozygous dominant negative and haploinsufficiency mutations in the PPARG gene. However, many patients with clinically diagnosed FPLD have no mutation in either LMNA or PPARG, suggesting the involvement of additional genes in FPLD etiology. Methods: Here, we report the results of an Affymetrix 10K GeneChip microarray genome-wide linkage analysis study of a German kindred displaying the FPLD phenotype and no known lipodystrophy-causing mutations. Results: The investigation identified three chromosomal loci, namely 1q, 3p, and 9q, with non-parametric logarithm of odds (NPL) scores >2.7. While not meeting the criteria for genome-wide significance, it is interesting to note that the 1q and 3p peaks contain the LMNA and PPARG genes respectively. Conclusions: Three possible conclusions can be drawn from these results: 1) the peaks identified are spurious findings, 2) additional genes physically close to LMNA, PPARG, or within 9q, are involved in FPLD etiology, or 3) alternative disease causing mechanisms not identified by standard exon sequencing approaches, such as promoter mutations, alternative splicing, or epigenetics, are also responsible for FPLD.


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