Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 114, Issue 52, Pages 13744-13749Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1704907114
Keywords
brain development; preterm; magnetic resonance imaging; machine learning; PPARG
Categories
Funding
- National Institute for Health Research (NIHR) Programme Grants for Applied Research Programme [RP-PG-0707-10154]
- NIHR comprehensive Biomedical Research Centre Award
- Medical Research Council (MRC)
- MRC [MR/N026063/1, MR/K006355/1, MR/J014311/1] Funding Source: UKRI
- National Institutes of Health Research (NIHR) [RP-PG-0707-10154] Funding Source: National Institutes of Health Research (NIHR)
- Medical Research Council [MR/N026063/1, MR/K006355/1, MR/J014311/1] Funding Source: researchfish
- National Institute for Health Research [RP-PG-0707-10154] Funding Source: researchfish
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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 ethnicity and age at birth and imaging. Empirical selection frequencies for SNPs associated with cerebral connectivity ranged from 0.663 to zero, with multiple highly selected SNPs mapping to genes for PPARG (six SNPs), ITGA6 (four SNPs), and FXR1 (two SNPs). SNPs in PPARG were significantly over-represented (ranked 7-11 and 67 of 556,000 SNPs; P < 2.2 x 10(-7)), and were mostly in introns or regulatory regions with predicted effects including protein coding and nonsense-mediated decay. Edge-centric graph-theoretic analysis showed that highly selected white-matter tracts were consistent across the group and important for information transfer (P < 2.2 x 10(-17)); they most often connected to the insula (P < 6 x 10(-17)). These results suggest that the inhibited brain development seen in humans exposed to the stress of a premature extrauterine environment is modulated by genetic factors, and that PPARG signaling has a previously unrecognized role in cerebral development.
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