A fundamental challenge in analyzing exome-sequence data is distinguishing pathogenic mutations from background polymorphisms. To address this problem in the context of a genetically heterogeneous disease, retinitis pigmentosa (RP), we devised a candidate-gene prioritization strategy called cis-regulatory mapping that utilizes ChIP-seq data for the photoreceptor transcription factor CRX to rank candidate genes. Exome sequencing combined with this approach identified a homozygous nonsense mutation in male germ cell-associated kinase (MAK) in the single affected member of a consanguineous Turkish family with RP. MAK encodes a cilium-associated mitogen-activated protein kinase whose function is conserved from the ciliated alga, Chlamydomonas reinhardtii, to humans. Mutations in MAK orthologs in mice and other model organisms result in abnormally long cilia and, in mice, rapid photoreceptor degeneration. Subsequent sequence analyses of additional individuals with RP identified five probands with missense mutations in MAK. Two of these mutations alter amino acids that are conserved in all known kinases, and an in vitro kinase assay indicates that these mutations result in a loss of kinase activity. Thus, kinase activity appears to be critical for MAK function in humans. This study highlights a previously underappreciated role for CRX as a direct transcriptional regulator of ciliary genes in photoreceptors. In addition, it demonstrates the effectiveness of CRX-based cis-regulatory mapping in prioritizing candidate genes from exome data and suggests that this strategy should be generally applicable to a range of retinal diseases.
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http://dx.doi.org/10.1016/j.ajhg.2011.07.005 | DOI Listing |
Despite extensive mapping of cis-regulatory elements (cREs) across cellular contexts with chromatin accessibility assays, the sequence syntax and genetic variants that regulate transcription factor (TF) binding and chromatin accessibility at context-specific cREs remain elusive. We introduce ChromBPNet, a deep learning DNA sequence model of base-resolution accessibility profiles that detects, learns and deconvolves assay-specific enzyme biases from regulatory sequence determinants of accessibility, enabling robust discovery of compact TF motif lexicons, cooperative motif syntax and precision footprints across assays and sequencing depths. Extensive benchmarks show that ChromBPNet, despite its lightweight design, is competitive with much larger contemporary models at predicting variant effects on chromatin accessibility, pioneer TF binding and reporter activity across assays, cell contexts and ancestry, while providing interpretation of disrupted regulatory syntax.
View Article and Find Full Text PDFAm J Hum Genet
January 2025
Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA; Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA. Electronic address:
cis-regulatory elements (CREs) control gene transcription dynamics across cell types and in response to the environment. In asthma, multiple immune cell types play an important role in the inflammatory process. Genetic variants in CREs can also affect gene expression response dynamics and contribute to asthma risk.
View Article and Find Full Text PDFGenome-wide association studies (GWAS) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping cell-type specific chromatin interactions.
View Article and Find Full Text PDFNat Genet
January 2025
Calico Life Sciences LLC, South San Francisco, CA, USA.
Sequence-based machine-learning models trained on genomics data improve genetic variant interpretation by providing functional predictions describing their impact on the cis-regulatory code. However, current tools do not predict RNA-seq expression profiles because of modeling challenges. Here, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence.
View Article and Find Full Text PDFbioRxiv
December 2024
Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression. Previously, The ENCODE consortium mapped biochemical signals across many cell types and tissues and integrated these data to develop a Registry of 0.9 million human and 300 thousand mouse candidate cis-Regulatory Elements (cCREs) annotated with potential functions.
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