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

Liang, Dan; Elwell, Angela L.; Aygün, Nil; Lafferty, Michael; Krupa, Oleh; Cheek, Kerry E.; Courtney, Kenan P.; Yusupova, Marianna; Garrett, Melanie E.; Ashley–Koch, Allison; Crawford, Gregory E.; Love, Michael I.; Torre-Ubieta, Luis de la; Geschwind, Daniel H.; Stein, Jason L.

doi:10.1101/2020.01.13.904862

Cited by 10 publications

(21 citation statements)

References 132 publications

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“…To investigate the target gene(s) affected by allelic variation at rs7001340, we used two expression quantitative trait loci (eQTL) datasets derived from fetal cortical brain tissue 34 and adult dorsolateral prefrontal cortex 33 . We also used chromatin accessibility profiles from primary human neural progenitor cells and their differentiated neuronal progeny 51 , as well as HEK293T cells (GSM1008573) 52 . Further information is provided in Supplementary Methods.…”

Section: Functional Annotation Of Rs7001340 Locus With Multi-omic Datmentioning

confidence: 99%

“…4a, b, Supplementary Table 13), demonstrating the regulatory potential of this SNP and suggesting its causal role in psychiatric disorders, including ASD. While MPRA was performed in HEK cells, the SNP was located in a regulatory element present in both HEK cells and neural progenitors, with higher chromatin accessibility in human neural progenitors compared with postmitotic neurons 51 (Fig. 4a, Supplementary Fig.…”

Section: Functional Validation To Fine-map Causal Variants and Priorimentioning

confidence: 99%

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Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism

et al. 2020

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Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder. Large genetically informative cohorts of individuals with ASD have led to the identification of a limited number of common genome-wide significant (GWS) risk loci to date. However, many more common genetic variants are expected to contribute to ASD risk given the high heritability. Here, we performed a genome-wide association study (GWAS) on 6222 casepseudocontrol pairs from the Simons Foundation Powering Autism Research for Knowledge (SPARK) dataset to identify additional common genetic risk factors and molecular mechanisms underlying risk for ASD. We identified one novel GWS locus from the SPARK GWAS and four significant loci, including an additional novel locus from metaanalysis with a previous GWAS. We replicated the previous observation of significant enrichment of ASD heritability within regulatory regions of the developing cortex, indicating that disruption of gene regulation during neurodevelopment is critical for ASD risk. We further employed a massively parallel reporter assay (MPRA) and identified a putative causal variant at the novel locus from SPARK GWAS with strong impacts on gene regulation (rs7001340). Expression quantitative trait loci data demonstrated an association between the risk allele and decreased expression of DDHD2 (DDHD domain containing 2) in both adult and prenatal brains. In conclusion, by integrating genetic association data with multi-omic gene regulatory annotations and experimental validation, we fine-mapped a causal risk variant and demonstrated that DDHD2 is a novel gene associated with ASD risk.

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Section: Functional Annotation Of Rs7001340 Locus With Multi-omic Datmentioning

confidence: 99%

Section: Functional Validation To Fine-map Causal Variants and Priorimentioning

confidence: 99%

Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism

et al. 2020

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“…Practically, brain structure traits have higher power than neuropsychiatric disorders so smaller sample sizes will be required to achieve equivalent gains in genetic discovery. [Liang et al, 2020]. This is likely because multiple mechanisms can influence gene expression including transcription factor binding, miRNA expression levels, methylation, and RNA degradation [Li et al, 2016].…”

Section: Discussionmentioning

confidence: 99%

Evaluating brain structure traits as endophenotypes using polygenicity and discoverability

Matoba

Love

Stein

2020

Preprint

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Human brain structure traits have been hypothesized to be broad endophenotypes for neuropsychiatric disorders, implying that brain structure traits are comparatively 'closer to the underlying biology'. Genome-wide association studies from large sample sizes allow for the comparison of common variant genetic architectures between traits to test the evidence supporting this claim. Endophenotypes, compared to neuropsychiatric disorders, are hypothesized to have less polygenicity, with greater effect size of each susceptible SNP, requiring smaller sample sizes to discover them. Here, we compare polygenicity and discoverability of brain structure traits, neuropsychiatric disorders, and other traits (89 in total) to directly test this hypothesis. We found reduced polygenicity (FDR = 0.01) and increased discoverability of cortical brain structure traits, as compared to neuropsychiatric disorders (FDR = 3.68x10-9). We predict that ~8M samples will be required to explain the full heritability of cortical surface area by genome-wide significant SNPs, whereas sample sizes over 20M will be required to explain the full heritability of major depressive disorder. In conclusion, we find reduced polygenicity and increased discoverability of cortical structure compared to neuropsychiatric disorders, which is consistent with brain structure satisfying the higher power criterion of endophenotypes.

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“…Similar to other intermediate molecular phenotypes, features of chromatin conformation can also be the basis for QTL mapping. The first such studies have recently been carried out and led to the identification of chromatin QTLs that influence chromatin loop strength (56) and chromatin accessibility (57).…”

Section: Nuclear Landscapesmentioning

confidence: 99%

Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal

Uffelmann

Posthuma

2021

Biological Psychiatry

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Most neuropsychiatric disorders are highly polygenic, implicating hundreds to thousands of causal genetic variants that span much of the genome. This widespread polygenicity complicates biological understanding because no single variant can explain disease etiology. A strategy to advance biological insight is to seek convergent functions among the large set of variants and map them to a smaller set of disease-relevant genes and pathways. Accordingly, functional genomic resources that provide data on intermediate molecular phenotypes, such as gene-expression and methylation status, can be leveraged to functionally annotate variants and map them to genes. Such molecular quantitative trait locus mappings can be integrated with genome-wide association studies to make sense of the polygenic signal that underlies complex disease. Other resources that provide data on the 3-dimensional structure of chromatin and functional importance of specific genomic regions can be integrated similarly. In addition, mapped genes can then be tested for convergence in biological function, tissue, cell type, or developmental stage. In this review, we provide an overview of functional genomic resources and methods that can be used to interpret results from genome-wide association studies, and we discuss current challenges for biological understanding and future requirements to overcome them.

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

Cited by 10 publications

References 132 publications

Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism

Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism

Evaluating brain structure traits as endophenotypes using polygenicity and discoverability

Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal

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