Common Genetic Variation Important in Early Subcortical Brain Development
Cullen, H.; Dimitrakopoulou, K.; Patel, H.; Curtis, C.; Batalle, D.; Gale-Grant, O.; Cordero-Grande, L.; Price, A.; Hajnal, J.; Edwards, A. D.
Show abstract
1.Recent genome-wide association studies have identified numerous single nucleotide polymorphisms (SNPs) associated with subcortical brain volumes. These studies have been undertaken in largely adult cohorts. To better understand the role of genetic variability in foetal and perinatal brain development, we investigate how common genetic variation affects subcortical brain development in a cohort of 208 term-born infants from the Developing Human Connectome Project. We examine six SNPs, that have shown robust associations with subcortical brain volumes in adult studies and ask if these associations exist at birth. We then investigate whether genome-wide polygenic scores (GPSs) for adult subcortical brain volumes are predictive of the corresponding neonatal brain volume. Finally, we explore the relationship between GPSs for psychiatric disorders and subcortical brain volume at birth. We find the association between SNP rs945270 and putamen volume, seen in adults, is present at birth (p=3.67x10-3, {beta}=0.13, SE=0.04). The associations between SNP rs61921502 and hippocampal volume and SNP rs11111090 and brainstem volume are also nominally present in our neonatal cohort. We show that neonatal hippocampal, brainstem, putamen and thalamic volume are all significantly associated with the GPSs for their corresponding volume in adults. Finally, we find that GPSs for five psychiatric disorders and a cross-disorder score are not significantly predictive of subcortical brain volumes or total brain volume at birth. Our results indicate that SNPs important in shaping adult subcortical brain volume are also significant in foetal and perinatal brain development. Key PointsO_LIWe show that the association between the single nucleotide polymorphism, rs945270 and putamen volume, seen in adults, is present in neonates. C_LIO_LIWe show that neonatal hippocampal, putamen, brainstem and thalamic volumes are all significantly predicted by the genome-wide polygenic scores for corresponding adult brain volumes. C_LIO_LIWe do not find any robust association between genome-wide polygenic scores for psychiatric disorders and neonatal brain volume although we observe several nominal associations. C_LI
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Replicability of Structural Brain Alterations Associated with General Psychopathology: Evidence from a Population-Representative Birth Cohort 95%
- Gene dosage effects of 22q11.2 copy number variants on in-vivo measures of white matter axonal density and dispersion 95%
- Structural deviations of the posterior fossa and the cerebellum and their cognitive links in a neurodevelopmental deletion syndrome 93%
Similar papers in this journal
- Parsing brain-behavior heterogeneity in very preterm born children using integrated similarity networks 95%
- Maternal depressive symptoms, neonatal white matter, and toddler social-emotional development 95%
- Transdiagnostic dimensions of psychopathology explain individuals' unique deviations from normative neurodevelopment in brain structure 94%
Similar papers in this journal
- Functional connectivity in the social perception pathway at birth is linked with attention to faces at 4 months 92%
- Convergent and Divergent Cerebellar Alterations in 22q11.2 Copy Number Variants 92%
- Sex-specific effects of early life unpredictability on hippocampal and amygdala responses to novelty in adolescents 92%
Similar papers in this journal
- Analysis of diffusion tensor imaging data from UK Biobank confirms dosage effect of 15q11.2 copy-number variation on white matter and shows association with cognition 95%
- Brain-Wide Mendelian Randomization Study of Anxiety Disorders and Symptoms 95%
- Unique functional neuroimaging signatures of genetic versus clinical high risk for psychosis 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.