Genetic effects on brain traits impact cell-type specific gene regulation during neurogenesis

Aygün, Nil; Elwell, Angela L.; Liang, Dan; Lafferty, Michael; Cheek, Kerry E.; Courtney, Kenan P.; Mory, Jessica; Hadden-Ford, Ellie; Krupa, Oleh; Torre-Ubieta, Luis de la; Geschwind, Daniel H.; Love, Michael I.; Stein, Jason L.

doi:10.1101/2020.10.21.349019

Cited by 4 publications

(3 citation statements)

References 124 publications

(249 reference statements)

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“…As neural progenitors are the actively dividing cells in the developing brain, their expressed genes may explain the observed genetic variants for head size. 32 Indeed, genes at or near the head size loci were enriched in differentially expressed neural progenitor cell genes ( Figure 4 D; Table S23 ). Subsequently, we looked at a single-cell RNA-sequencing (scRNA-seq) dataset from cell types in the human cortex.…”

Section: Resultsmentioning

confidence: 99%

“… 34 https://organoidreportcard.cells.ucsc.edu e/sQTLs, and allele-specific expression in cultured primary human neural progenitors and their sorted neuronal progeny Aygün et al. 32 https://bitbucket.org/steinlabunc/expression_splicing_qtls_public/src/master/ …”

Section: Methodsmentioning

confidence: 99%

“…To assess whether the identified genes in the current study are enriched for genes differentially expressed in human progenitors versus neurons, we utilized differential gene expression data of those cell lines, derived from a previously published sample population (N donor = 85 in progenitors and N donor = 74 in neurons). 32 Using genes with at least 10 counts in more than 5% of the cell-type specific donors in either cell-type (resulting in 16,172 protein-coding genes out of 28,785 genes in total), we performed a paired differential gene expression analysis with design matrix: model.matrix(∼ CellType + as.factor(DonorID) + RIN, data) as described previously, 32 using the limma R package. 86 We detected 1,095/1,420 protein genes upregulated in progenitors/neurons, respectively, for abs(logFC) > 1.5 and adjusted p value < 0.05.…”

Section: Methodsmentioning

confidence: 99%

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Genetic variants for head size share genes and pathways with cancer

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confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Cell-type-specific effects of genetic variation on chromatin accessibility during human neuronal differentiation

et al. 2021

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Common genetic risk for neuropsychiatric disorders is enriched in regulatory elements active during cortical neurogenesis. However, the mechanisms mediating the effects of genetic variants on gene regulation are poorly understood. To determine the functional impact of common genetic variation on the non-coding genome longitudinally during human cortical development, we performed a chromatin accessibility quantitative trait loci (caQTL) analysis in neural progenitor cells and their differentiated neuronal progeny from 92 donors. We identified 8,111 caQTLs in progenitors and 3,676 caQTLs in neurons, with highly temporal, cell-type specific effects. A subset (~20%) of caQTLs were also associated with changes in gene expression. Motif-disrupting alleles of transcriptional activators generally led to decreases in chromatin accessibility, whereas motif-disrupting alleles of repressors led to increases in chromatin accessibility. By integrating cell-type specific caQTLs and brain-relevant genome-wide association data, we were able to fine-map loci and identify regulatory mechanisms underlying non-coding neuropsychiatric disorder risk variants.

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Limited overlap of eQTLs and GWAS hits due to systematic differences in discovery

Mostafavi

Spence

Naqvi

et al. 2022

Preprint

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Most signals in genome-wide association studies (GWAS) of complex traits point to noncoding genetic variants with putative gene regulatory effects. However, currently identified expression quantitative trait loci (eQTLs) explain only a small fraction of GWAS signals. By analyzing GWAS hits for complex traits in the UK Biobank, and cis-eQTLs from the GTEx consortium, we show that these assays systematically discover different types of genes and variants: eQTLs cluster strongly near transcription start sites, while GWAS hits do not. Genes near GWAS hits are enriched in numerous functional annotations, are under strong selective constraint and have a complex regulatory landscape across different tissue/cell types, while genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally-relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variants, and support the use of complementary functional approaches alongside the next generation of eQTL studies.

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Genetic effects on brain traits impact cell-type specific gene regulation during neurogenesis

Cited by 4 publications

References 124 publications

Genetic variants for head size share genes and pathways with cancer

Genetic variants for head size share genes and pathways with cancer

Cell-type-specific effects of genetic variation on chromatin accessibility during human neuronal differentiation

Limited overlap of eQTLs and GWAS hits due to systematic differences in discovery

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