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Articles by K. K Nicodemus
Total Records ( 2 ) for K. K Nicodemus
  K. K Nicodemus , A. J Law , E Radulescu , A Luna , B Kolachana , R Vakkalanka , D Rujescu , I Giegling , R. E Straub , K McGee , B Gold , M Dean , P Muglia , J. H Callicott , H. Y Tan and D. R. Weinberger
 

Context  NRG1 is a schizophrenia candidate gene and plays an important role in brain development and neural function. Schizophrenia is a complex disorder, with etiology likely due to epistasis.

Objective  To examine epistasis between NRG1 and selected N-methyl-d-aspartate–glutamate pathway partners implicated in its effects, including ERBB4, AKT1, DLG4, NOS1, and NOS1AP.

Design  Schizophrenia case-control sample analyzed using machine learning algorithms and logistic regression with follow-up using neuroimaging on an independent sample of healthy controls.

Participants  A referred sample of schizophrenic patients (n = 296) meeting DSM-IV criteria for schizophrenia spectrum disorder and a volunteer sample of controls for case-control comparison (n = 365) and a separate volunteer sample of controls for neuroimaging (n = 172).

Main Outcome Measures  Epistatic association between single-nucleotide polymorphisms (SNPs) and case-control status; epistatic association between SNPs and the blood oxygen level–dependent physiological response during working memory measured by functional magnetic resonance imaging.

Results  We observed interaction between NRG1 5' and 3' SNPs rs4560751 and rs3802160 (likelihood ratio test P = .00020) and schizophrenia, which was validated using functional magnetic resonance imaging of working memory in healthy controls; carriers of risk-associated genotypes showed inefficient processing in the dorsolateral prefrontal cortex (P = .015, familywise error corrected). We observed epistasis between NRG1 (rs10503929; Thr286/289/294Met) and its receptor ERBB4 (rs1026882; likelihood ratio test P = .035); a 3-way interaction with these 2 SNPs and AKT1 (rs2494734) was also observed (odds ratio, 27.13; 95% confidence interval, 3.30-223.03; likelihood ratio test P = .042). These same 2- and 3-way interactions were further biologically validated via functional magnetic resonance imaging: healthy individuals carrying risk genotypes for NRG1 and ERBB4, or these 2 together with AKT1, were disproportionately less efficient in dorsolateral prefrontal cortex processing. Lower-level interactions were not observed between NRG1 /ERBB4 and AKT1 in association or neuroimaging, consistent with biological evidence that NRG1 x ERBB4 interaction modulates downstream AKT1 signaling.

Conclusion  Our data suggest complex epistatic effects implicating an NRG1 molecular pathway in cognitive brain function and the pathogenesis of schizophrenia.

  K. K Nicodemus and J. D. Malley
 

Motivation: The advent of high-throughput genomics has produced studies with large numbers of predictors (e.g. genome-wide association, microarray studies). Machine learning algorithms (MLAs) are a computationally efficient way to identify phenotype-associated variables in high-dimensional data. There are important results from mathematical theory and numerous practical results documenting their value. One attractive feature of MLAs is that many operate in a fully multivariate environment, allowing for small-importance variables to be included when they act cooperatively. However, certain properties of MLAs under conditions common in genomic-related data have not been well-studied—in particular, correlations among predictors pose a problem.

Results: Using extensive simulation, we showed considering correlation within predictors is crucial in making valid inferences using variable importance measures (VIMs) from three MLAs: random forest (RF), conditional inference forest (CIF) and Monte Carlo logic regression (MCLR). Using a case–control illustration, we showed that the RF VIMs—even permutation-based—were less able to detect association than other algorithms at effect sizes encountered in complex disease studies. This reduction occurred when ‘causal’ predictors were correlated with other predictors, and was sharpest when RF tree building used the Gini index. Indeed, RF Gini VIMs are biased under correlation, dependent on predictor correlation strength/number and over-trained to random fluctuations in data when tree terminal node size was small. Permutation-based VIM distributions were less variable for correlated predictors and are unbiased, thus may be preferred when predictors are correlated. MLAs are a powerful tool for high-dimensional data analysis, but well-considered use of algorithms is necessary to draw valid conclusions.

 
 
 
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