Dataset Information

Deletion mapping of regulatory elements for GATA3 in T cells reveals a distal enhancer involved in allergic diseases.

ABSTRACT: GATA3 is essential for T cell differentiation and is surrounded by genome-wide association study (GWAS) hits for immune traits. Interpretation of these GWAS hits is challenging because gene expression quantitative trait locus (eQTL) studies lack power to detect variants with small effects on gene expression in specific cell types and the genome region containing GATA3 contains dozens of potential regulatory sequences. To map regulatory sequences for GATA3, we performed a high-throughput tiling deletion screen of a 2 Mb genome region in Jurkat T cells. This revealed 23 candidate regulatory sequences, all but one of which is within the same topological-associating domain (TAD) as GATA3. We then performed a lower-throughput deletion screen to precisely map regulatory sequences in primary T helper 2 (Th2) cells. We tested 25 sequences with ∼100 bp deletions and validated five of the strongest hits with independent deletion experiments. Additionally, we fine-mapped GWAS hits for allergic diseases in a distal regulatory element, 1 Mb downstream of GATA3, and identified 14 candidate causal variants. Small deletions spanning the candidate variant rs725861 decreased GATA3 levels in Th2 cells, and luciferase reporter assays showed regulatory differences between its two alleles, suggesting a causal mechanism for this variant in allergic diseases. Our study demonstrates the power of integrating GWAS signals with deletion mapping and identifies critical regulatory sequences for GATA3.

SUBMITTER: Chen HV

PROVIDER: S-EPMC10119147 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Deletion mapping of regulatory elements for GATA3 in T cells reveals a distal enhancer involved in allergic diseases.

Chen Hsiuyi V HV Lorenzini Michael H MH Lavalle Shanna N SN Sajeev Karthyayani K Fonseca Ariana A Fiaux Patrick C PC Sen Arko A Luthra Ishika I Ho Aaron J AJ Chen Aaron R AR Guruvayurappan Karthik K O'Connor Carolyn C McVicker Graham G

American journal of human genetics 20230328 4

GATA3 is essential for T cell differentiation and is surrounded by genome-wide association study (GWAS) hits for immune traits. Interpretation of these GWAS hits is challenging because gene expression quantitative trait locus (eQTL) studies lack power to detect variants with small effects on gene expression in specific cell types and the genome region containing GATA3 contains dozens of potential regulatory sequences. To map regulatory sequences for GATA3, we performed a high-throughput tiling d ...[more]

PMID: 36990085

Similar Datasets

Project description:BackgroundThe data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model's complexity. In our system AIKYATAN, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).ResultsWe develop a suite of ML models, under the banner AIKYATAN, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-'a-vis the competition. Specifically, AIKYATAN-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.ConclusionsOur exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our AIKYATAN suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

Dataset Information

Deletion mapping of regulatory elements for GATA3 in T cells reveals a distal enhancer involved in allergic diseases.

Publications

Deletion mapping of regulatory elements for GATA3 in T cells reveals a distal enhancer involved in allergic diseases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets