Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex

Abstract

All available non-identifying information was recorded for each specimen in Table S1. In total, 176 postmortem brain specimens (104 male, 72 female; postmortem interval of 21.7 ± 15.9 (mean ± SD) hours and pH, 6.41 ± 0.35) ranging in age from 6 post-conception weeks (PCW) to 20 postnatal years (PY) ( Figure 1 and Table S1) were included in this study. Fetal age was extrapolated based on the date of the mother's last menstruation, characteristics of the fetus noted upon ultrasonography scanning, foot length of the fetus, and visual inspection. The postmortem interval (PMI) was defined as hours between time of death and time when tissue samples were frozen. METHOD DETAILS Tissue dissectionTissue was dissected as described previously (Kang et al. 2011). Samples collected from 6 -9 PCW specimens contained the entire thickness of the cerebral wall. Samples collected from 12 -22 PCW specimens contained the cortical plate. Samples from 35 PCW -20 PY specimens were dissected such that the entire gray matter (layer 1-6) and part of the underlying subplate (4 -12 postnatal months) or white matter (1 -20 PY) were collected. RNA extraction and quality assessmentTotal RNA was extracted using mirVana kit (Ambion) with some modifications, as given in the following text, to the manufacturer's protocol, as described below. Each tissue sample was

Autism genetics perturb prenatal neurodevelopment through a hierarchy of broadly-expressed and brain-specific genes

“…Moreover, by emphasizing the gene regulatory networks, these alternative models suggest that, to the extent that the gene regulatory networks are preserved, the dysregulation could also be reflected in non-neuronal cells . Supporting these ideas, many rASD genes are broadly-expressed across tissues and strongly enriched for gene expression regulators such as chromatin remodelers, transcription factors, and modulators of signaling pathways (Courchesne et al, 2020;Courchesne et al, 2019;Werling et al, 2020). Similarly, genome wide association studies (GWAS) and whole genome sequencing both highlight the important role of broadly functional eQTLs and gene regulatory networks in ASD liability (Brandler et al, 2018;Courchesne et al, 2020;Grove et al, 2019).The fundamental unanswered question, therefore, is how heterogenous rASD genes connect with the molecular, cellular, and developmental perturbations in ASD.…”

A selective inference approach for FDR control using multi-omics covariates yields insights into disease risk

“…The second source was the BrainVar study of dorsolateral prefrontal cortex samples across a developmental span (Werling et al 2019). BrainVar included cortical tissue from 176 individuals falling into two developmental periods: pre-natal, 112 individuals; and post-natal, 60 individuals.…”

“…First, Figure S1 displays the partial dependence plot of the effect size under the alternative on BD z-statistics, yielding a similar relationship to Figure 4(C) from Results. Figure S2 Additionally, in Figure S3 we display the p-value distributions comparing the enrichment for membership in the different WGCNA modules reported by Werling et al (2019). While many of the WGCNA modules lack clear evidence or contain too few eSNPs, as denoted by their respective y-axes, the cyan and salmon modules display noticeable enrichment.…”

“…In all 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations and it is especially apparent using gene expression information from the developing human prefontal cortex (Werling et al 2019), as compared to adult tissue samples from the GTEx Consortium. We interpret these results in light of recent theories about the polygenic nature of SCZ.…”

“…Specifically, we apply AdaPT to GWAS for de-tecting single nucleotide polymorphisms (SNPs) associated with schizophrenia (SCZ) using bipolar disorder (BD) GWAS results from an independent sample as a covariate. Additionally, we incorporate results from the recent BrainVar study to identify a set of expression-SNPs (eSNPs) based on 176 neurotypical brains, sampled from pre-and post-natal tissue from the human dorsolateral prefrontal cortex (Werling et al 2019). Along with the genetically correlated BD z-statistics, we create additional features from this complementary data source by summarizing the associated developmental gene expression quantitative trait loci (eQTL) slopes and membership in gene coexpression networks.…”