A…transcriptional map of primate (macaque) #brain development http://www.Nature.com/nature/journal/vaop/ncurrent/full/nature18637.html Gene expression changes more rapidly before birth
Nature (2016) doi:10.1038/nature18637
The #Brain #Somatic Mosaicism Network
http://science.ScienceMag.org/content/356/6336/eaal1641 Long lifespan of neurons accentuates impact of individual somatic mutations
QT:{{”
Neuropsychiatric disorders have a complex genetic architecture. Human genetic population-based studies have identified numerous heritable sequence and structural genomic variants associated with
susceptibility to neuropsychiatric disease. However, these germline variants do not fully account for disease risk. During brain development, progenitor cells undergo billions of cell divisions to generate the ~80 billion neurons in the brain. The failure to accurately repair DNA damage arising during replication,
transcription, and cellular metabolism amid this dramatic cellular expansion can lead to somatic mutations. Somatic mutations that alter subsets of neuronal transcriptomes and proteomes can, in turn, affect cell proliferation and survival and lead to neurodevelopmental disorders. The long life span of individual neurons and the direct relationship between neural circuits and behavior suggest that somatic mutations in small populations of neurons can significantly affect individual neurodevelopment. The Brain Somatic Mosaicism Network has been founded to study somatic mosaicism both in neurotypical human brains and in the context of complex neuropsychiatric disorders.” “}}
Partitioning heritability of regulatory…variants across 11 common diseases http://www.Cell.com/ajhg/abstract/S0002-9297(14)00426-1 Almost 80% #noncoding v 10% coding
The paper below claims to find most of the heritability of 11 common diseases in regulatory regions (79% of heritability found in regulatory regions, <10% in protein coding regions).
Partitioning heritability of regulatory and cell-type-specific variants across 11 common
diseases.
Gusev A, Lee SH, Trynka G, Finucane H, Vilhjálmsson BJ, Xu H, Zang C, Ripke S, Bulik-Sullivan B, Stahl E; Schizophrenia Working Group of the Psychiatric Genomics Consortium; SWE-SCZ Consortium, Kähler AK, Hultman CM, Purcell SM, McCarroll SA, Daly M, Pasaniuc B, Sullivan PF, Neale BM, Wray NR, Raychaudhuri S, Price AL; Schizophrenia Working Group of the Psychiatric Genomics Consortium; SWE-SCZ Consortium.
Am J Hum Genet. 2014 Nov 6;95(5):535-52. doi:
10.1016/j.ajhg.2014.10.004. Epub 2014 Nov 6.
Building a Brain in the Lab
https://www.ScientificAmerican.com/article/building-a-brain-in-the-lab/ Nice summary of the development of organoids & their promise for personalized treatments
Genome-wide…analysis implicates miRNA dysregulation in #ASD http://www.nature.com/neuro/journal/v19/n11/full/nn.4373.html 58 diff. expr. miRNAs incl 17 strongly down in cases
http://www.nature.com/neuro/journal/v19/n11/full/nn.4373.html
QT:{{”
The miRNA expression profiles were very similar between the frontal and temporal cortex, but were distinct in the cerebellum
(Supplementary Fig. 2a–f), consistent with previous observations for mRNAs11, 12. We therefore combined 95 covariate-matched samples (47 samples from 28 ASD cases and 48 samples from 28 controls;
Supplementary Fig. 1c and Supplementary Table 1) from the FC and TC for differential gene expression (DGE) analysis, comparing ASD and CTL using a linear mixed-effects regression framework to control for potential confounders (Online Methods). We identified 58 miRNAs showing significant (false discovery rate (FDR) < 0.05) expression changes between ASD and CTL: 17 were downregulated and 41 were upregulated in ASD cortex (Fig. 1b and Supplementary Table 2). The fold changes for the differentially expressed miRNAs were highly concordant between the FC and TC (Pearson correlation coefficient R = 0.96, P < 2.2 × 10−16; Fig. 1c).
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The impact of #SVs on…gene expression
http://biorxiv.org/content/early/2016/06/09/055962 24k in 147 people in GTEx pilot act as causal variants in 3-7% of ~25k eQTLs
The impact of structural variation on human gene expression
Colby Chiang, Alexandra J Scott, Joe R Davis, Emily
K Tsang, Xin Li, Yungil Kim, Farhan N Damani, Liron Ganel, GTEx Consortium, Stephen B Montgomery, Alexis Battle, Donald F Conrad, Ira M Hall
doi: https://doi.org/10.1101/055962
Your an adult. Your brain, not so much by @CarlZimmer
http://www.nytimes.com/2016/12/21/science/youre-an-adult-your-brain-not-so-much.html Non-obvious ethical implications of developmental neuroscience
QT:{{”
“The human brain reaches its adult volume by age 10, but the neurons that make it up continue to change for years after that. The connections between neighboring neurons get pruned back, as new links emerge between more widely separated areas of the brain.
Eventually this reshaping slows, a sign that the brain is maturing. But it happens at different rates in different parts of the brain.
The pruning in the occipital lobe, at the back of the brain, tapers off by age 20. In the frontal lobe, in the front of the brain, new links are still forming at age 30, if not beyond.
“It challenges the notion of what ‘done’ really means,” Dr. Somerville said.
As the anatomy of the brain changes, its activity changes as well. In a child’s brain, neighboring regions tend to work together. By adulthood, distant regions start acting in concert. Neuroscientists have speculated that this long-distance harmony lets the adult brain work more efficiently and process more information.”
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Gene expr. elucidates functional impact of…risk for SCZ
http://www.nature.com/neuro/journal/vaop/ncurrent/full/nn.4399.html CMC resource contains >2.1M #eQTLs involving ~1.6M SNPs
NATURE NEUROSCIENCE | ARTICLE
Gene expression elucidates functional impact of polygenic risk for schizophrenia
Menachem Fromer,
Panos Roussos,
Solveig K Sieberts,
Jessica S Johnson,
David H Kavanagh,
Thanneer M Perumal,
Douglas M Ruderfer,
….
Eric E Schadt,
Keisuke Hirai,
Kathryn Roeder,
Kristen J Brennand,
Nicholas Katsanis,
Enrico Domenici,
Bernie Devlin
& Pamela Sklar
There are BrainSpan genotypes available through dbGAP:
https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000406.v1.p1