Biology subjects

Elwell, A. L.

Publications and source records attributed to Elwell, A. L..

3 recordsLinked to original sources

bioRxiv2020

Genetic influences on cell type specific gene expression and splicing during neurogenesis elucidate regulatory mechanisms of brain traits

Interpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing is generally performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements active during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map expression quantitative trait loci (eQTLs), splicing QTLs (sQTLs), and allele specific expression in primary human neural progenitors (n=85) and their sorted neuronal progeny (n=74), identifying numerous loci not detected in either bulk developing cortical wall or adult cortex. Using colocalization and genetic imputation via transcriptome wide association, we uncover cell-type specific regulatory mechanisms underlying risk for brain-relevant traits that are active during neocortical differentiation. Specifically, we identified a progenitor-specific eQTL for CENPW co-localized with common variant associations of cortical area and educational attainment.

genetics↗

bioRxiv2020

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

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. HighlightsO_LIGenetic variation alters chromatin architecture during human cortical development C_LIO_LIGenetic effects on chromatin accessibility are highly cell-type specific C_LIO_LIAlleles disrupting TF motifs generally decrease accessibility, except for repressors C_LIO_LIcaQTLs facilitate fine-mapping and inference of regulatory mechanisms of GWAS loci C_LI

genetics↗

bioRxiv2019

Tradeoff between more cells and higher read depth for single-cell RNA-seq spatial ordering analysis of the liver lobule

As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), we evaluate their ability to recapitulate biological signals in the context of pseudo-spatial reconstruction. Overall, we find gene expression profiles after spatial-reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seqs higher sensitivity and read-depth allows for analysis of lower expressed genes and isoforms. Additionally, we evaluate trade-offs for each protocol by performing subsampling analyses, and find that optimizing the balance between sequencing depth and number of cells within a protocol is important for efficient use of resources. Our analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.

genomics↗