2015-07-14
279
Just as imaging genetics will continue to incorporate increasingly sophisticated analytic methodologies, so too will imaging genetics evolve to incorporate increasingly sophisticated models of genetic risk, reflective of the increasingly apparent polygenic complexity of psychiatric syndromes. Genome-wide association studies (GWAS) have indicated a highly significant polygenic component of schizophrenia risk, possibly involving up to thousands of common alleles of very small effect, at the population level.71
While early imaging genetics used intermediate phenotypes to assess the impact of single gene variants, recent studies have increasingly tended towards epistatic models of gene interaction. In 2007, Tan et al reported a two-way risk variant epistasis: the COMT-Val risk allele, associated with reduced prefrontal dopamine, and the GRM3 risk allele, related to suboptimal glutamatergic function, interacted to give disproportionately inefficient DLPFC activation in a working memory task.72 Further, the inefficiency was associated with reduced frontoparietal functional connectivity. Nicodemus et al reported the first 3-way interaction using neuroimaging genetics to assess the risk susceptibility of the NRGI molecular pathway, finding epistasis between NRGI, and its tyrosine kinase receptor ERBB4, in a 3-way interaction with a variant of AKT1.73 The statistical interaction was biologically validated by fMRI, in which healthy individuals carrying all three at-risk genotypes for NRGI, ERBB4, and AKT1 were disproportionately less efficient in DLPFC processing than any other combinations of one or two at-risk genotypes. Of note, lower-level interactions were not observed between NRGI, ERBB4, and AKTI, suggesting that the interaction, and the NRGI pathway, was necessary for the observed fMRI effect of inefficiency. Other reports of epistasis in neuroimaging genetics include association of variants of with altered DLPFC activation, during working memory tasks including DISCI-CIT-NDELI, MTHFR-COMT, and COMTRGS4.74-76
|
|
|
Imaging genetics is further evolving towards modeling increasing genetic complexity, by utilizing a polygenic risk score or propensity score of genetic risk for schizophrenia in fMRI studies. A range of options for constructing a polygenic score may be considered, selection of markers according to their P -values in association studies, and different methods for weighting markers in the score.77 Only a handful of studies utilizing a polygenic risk score have been reported to date, using both functional and structural neuroimaging, and for multiple psychiatric syndromes. Walton et al calculated a genetic risk score for schizophrenia, the additive effect of 41 SNPS from 34 putative risk genes, and found a positive relationship between the genetic risk score and left DLPFC inefficiency during a working memory task.78 Holmes et al reported a structural anatomic association with polygenic risk for Major Depressive Disorder (MDD) In a sample of 1050 healthy young adults with no history of psychiatric illness. Using risk scores derived from large MDD GWAS analyses, an MDD polygenic score was found to be associated with reduced cortical thickness in the left medial prefrontal cortex, a structural variation that is believed to influence vulnerability to MDD.79 In a third study, increasing polygenic risk allele load for bipolar affective disorder (BPAD) was associated with increased activation in limbic regions previously implicated in BPAD, including the anterior cingulate cortex and amygdala during a verbal fluency task.80 So, while a few early imaging genetics studies have employed the polygenic risk score, use of the polygenic score approach remains to be assessed and validated in larger-scale, more robust studies, with an explicit focus on schizophrenia and with various models of the risk score calculation possible.
Go to:
