Machine learning shows association between genetic variability in <i>PPARG</i> and cerebral connectivity in preterm infants.

ABSTRACT: Preterm infants show abnormal structural and functional brain development, and have a high risk of long-term neurocognitive problems. The molecular and cellular mechanisms involved are poorly understood, but novel methods now make it possible to address them by examining the relationship between common genetic variability and brain endophenotype. We addressed the hypothesis that variability in the Peroxisome Proliferator Activated Receptor (PPAR) pathway would be related to brain development. We employed machine learning in an unsupervised, unbiased, combined analysis of whole-brain diffusion tractography together with genomewide, single-nucleotide polymorphism (SNP)-based genotypes from a cohort of 272 preterm infants, using Sparse Reduced Rank Regression (sRRR) and correcting for ethnici

SUBMITTER: Krishnan ML 

PROVIDER: S-EPMC5748164 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature