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Machine learning shows association between genetic variability in PPARG and cerebral connectivity in preterm infants.
Krishnan, Michelle L; Wang, Zi; Aljabar, Paul; Ball, Gareth; Mirza, Ghazala; Saxena, Alka; Counsell, Serena J; Hajnal, Joseph V; Montana, Giovanni; Edwards, A David.
Affiliation
  • Krishnan ML; Centre for the Developing Brain, King's College London, St. Thomas' Hospital, London SE1 7EH, United Kingdom.
  • Wang Z; Department of Biomedical Engineering, King's College London, St. Thomas' Hospital, London SE1 7EH, United Kingdom.
  • Aljabar P; Centre for the Developing Brain, King's College London, St. Thomas' Hospital, London SE1 7EH, United Kingdom.
  • Ball G; Centre for the Developing Brain, King's College London, St. Thomas' Hospital, London SE1 7EH, United Kingdom.
  • Mirza G; Genomics Core Facility, National Institute for Health Research Biomedical Research Centre, Guy's and St. Thomas' National Health Service Foundation Trust, London SE1 9RT, United Kingdom.
  • Saxena A; Genomics Core Facility, National Institute for Health Research Biomedical Research Centre, Guy's and St. Thomas' National Health Service Foundation Trust, London SE1 9RT, United Kingdom.
  • Counsell SJ; Centre for the Developing Brain, King's College London, St. Thomas' Hospital, London SE1 7EH, United Kingdom.
  • Hajnal JV; Centre for the Developing Brain, King's College London, St. Thomas' Hospital, London SE1 7EH, United Kingdom.
  • Montana G; Department of Biomedical Engineering, King's College London, St. Thomas' Hospital, London SE1 7EH, United Kingdom.
  • Edwards AD; Department of Biomedical Engineering, King's College London, St. Thomas' Hospital, London SE1 7EH, United Kingdom.
Proc Natl Acad Sci U S A ; 114(52): 13744-13749, 2017 12 26.
Article in En | MEDLINE | ID: mdl-29229843
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 overrepresented (ranked 7-11 and 67 of 556,000 SNPs; P < 2.2 × 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 × 10-17); they most often connected to the insula (P < 6 × 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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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain / Infant, Premature / Polymorphism, Single Nucleotide / PPAR gamma / Diffusion Tensor Imaging / Connectome / Machine Learning Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male / Newborn Language: En Journal: Proc Natl Acad Sci U S A Year: 2017 Document type: Article Affiliation country: United kingdom Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain / Infant, Premature / Polymorphism, Single Nucleotide / PPAR gamma / Diffusion Tensor Imaging / Connectome / Machine Learning Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male / Newborn Language: En Journal: Proc Natl Acad Sci U S A Year: 2017 Document type: Article Affiliation country: United kingdom Country of publication: United States