Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay

Shigaki, Dustin; Adato, Orit; Adhikari, Aashish N.; Dong, Shengcheng; Hawkins-Hooker, Alex; Inoue, Fumitaka; Juven‐Gershon, Tamar; Kenlay, Henry; Martin, Beth; Patra, Ayoti; Penzar, Dmitry; Schubach, Max; Xiong, Chenling; Yan, Zhongxia; Boyle, Alan P.; Kreimer, Anat; Kulakovskiy, Ivan V.; Reid, John E.; Unger, Ron; Yosef, Nir; Shendure, Jay; Ahituv, Nadav; Kircher, Martin; Beer, M

doi:10.1002/humu.23797

Cited by 52 publications

(63 citation statements)

References 34 publications

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“…We trained a separate model for the effects of variants in promoter sequences and in gene‐ distal sequences (Section 2). The assessors of this challenge (Shigaki et al, ) evaluated accuracy using Pearson correlation of the predicted labels (−1, 0, 1) with the continuous MPRA expression impact scores. They also calculated the AUROC treating this as a discretized classification task (e.g., 1 vs. [0 and −1]).…”

Section: Resultsmentioning

confidence: 99%

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Meta‐analysis of massively parallel reporter assays enables prediction of regulatory function across cell types

et al. 2019

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Deciphering the potential of noncoding loci to influence gene regulation has been the subject of intense research, with important implications in understanding genetic underpinnings of human diseases. Massively parallel reporter assays (MPRAs) can measure regulatory activity of thousands of DNA sequences and their variants in a single experiment. With increasing number of publically available MPRA data sets, one can now develop data‐driven models which, given a DNA sequence, predict its regulatory activity. Here, we performed a comprehensive meta‐analysis of several MPRA data sets in a variety of cellular contexts. We first applied an ensemble of methods to predict MPRA output in each context and observed that the most predictive features are consistent across data sets. We then demonstrate that predictive models trained in one cellular context can be used to predict MPRA output in another, with loss of accuracy attributed to cell‐type‐specific features. Finally, we show that our approach achieves top performance in the Fifth Critical Assessment of Genome Interpretation “Regulation Saturation” Challenge for predicting effects of single‐nucleotide variants. Overall, our analysis provides insights into how MPRA data can be leveraged to highlight functional regulatory regions throughout the genome and can guide effective design of future experiments by better prioritizing regions of interest.

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Section: Resultsmentioning

confidence: 99%

“…We trained a separate model for the effects of variants in promoter sequences and in gene-distal sequences (Section 2). The assessors of this challenge (Shigaki et al, 2019) Table S7).…”

Section: Studying the Effects Of Small Genetic Variants On Mpra Outmentioning

confidence: 99%

Meta‐analysis of massively parallel reporter assays enables prediction of regulatory function across cell types

et al. 2019

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“…We also found two other novel impact UTR variants in PLEC (the gene has an autosomal recessive inheritance pattern in OMIM), with an OMIM disease description of epidermolysis bullosa with pyloric atresia, related to the patient's fragile skin and food intolerance symptoms. As discussed above, present methods for identifying noncoding impact variants are not mature. Recent strong CAGI results for predicting which variants affect expression are encouraging in this regard (Shigaki et al, ), and it would be interesting to see how some of the more successful methods used there perform on the SickKids data. Nonstandard descriptions in clinical reports: an advantage of the Sickkids data is the use of HPO terms to describe patients’ symptoms (Girdea et al, ). That greatly facilitated the identification of candidate genes, and its broader adoption by other analysis centers will improve performance.…”

Section: Discussionmentioning

confidence: 99%

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype‐weighted knowledge in the CAGI SickKids5 clinical genomes challenge

et al. 2019

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Precise identification of causative variants from whole-genome sequencing data, including both coding and noncoding variants, is challenging. The Critical Assessment of Genome Interpretation 5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of the 24 whole-genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state-of-the-art pipeline.The patients have a range of eye, neurological, and connective-tissue disorders. We used a gene-centric approach to address this problem, assigning each gene a multiphenotype-matching score. Mutations in the top-scoring genes for each phenotype profile were ranked on a 6-point scale of pathogenicity probability, resulting in an approximately equal number of top-ranked coding and noncoding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the postsubmission phase, after careful screening of the genes in the correct genome, we identified additional potential diagnostic variants, a high proportion of which are noncoding.

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“…The Critical Assessment of Genome Interpretation 5 (CAGI5) consortium performed an MPRA of saturation-mutagenized human regulatory elements and disease-associated promoters in numerous cell types. A subset of these data were provided to analysts, who were then challenged to computationally predict functionality and effect sizes of heldout variants (58). The end project focused on identifying the most informative datasets (e.g., chromatin modification data from ENCODE) for cell-type specific regulatory variant prediction.…”

Section: Mpras Enable Identification Of Functional Regulators and Varmentioning

confidence: 99%

The Oft-Overlooked Massively Parallel Reporter Assay: Where, When, and Which Psychiatric Genetic Variants are Functional?

Mulvey¹,

Lagunas²,

Dougherty³

2020

Preprint

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Neuropsychiatric phenotypes have been long known to be influenced by heritable risk factors. The past decade of genetic studies have confirmed this directly, revealing specific common and rare genetic variants enriched in disease cohorts. However, the early hope for these studies-that only a small set of genes would be responsible for a given disorder-proved false. The picture that has emerged is far more complex: a given disorder may be influenced by myriad coding and noncoding variants of small effect size, and/or by rare but severe variants of large effect size, many de novo.Noncoding genomic sequences harbor a large portion of these variants, the molecular functions of which cannot usually be inferred from sequence alone. This creates a substantial barrier to understanding the higher-order molecular and biological systems underlying disease risk. Fortunately, a proliferation of genetic technologies-namely, scalable oligonucleotide synthesis, high-throughput RNA sequencing, CRISPR, and CRISPR derivatives-have opened novel avenues to experimentally identify biologically significant variants en masse. These advances have yielded an especially versatile technique adaptable to large-scale functional assays of variation in both untranscribed and untranslated regulatory features: Massively Parallel Reporter Assays (MPRAs). MPRAs are powerful molecular genetic tools that can be used to screen tens of thousands of predefined sequences for functional effects in a single experiment. This approach has several ideal features for psychiatric genetics, but remains underutilized in the field to date. To emphasize the opportunities MPRA holds for dissecting psychiatric polygenicity, we review here its applications in the literature, discuss its ability to test several biological variables implicated in psychiatric disorders, illustrate this flexibility with a proof-of-principle, in vivo cell-type specific implementation of the assay, and envision future outcomes of applying MPRA to both computational and experimental neurogenetics.

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Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay

Cited by 52 publications

References 34 publications

Meta‐analysis of massively parallel reporter assays enables prediction of regulatory function across cell types

Meta‐analysis of massively parallel reporter assays enables prediction of regulatory function across cell types

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype‐weighted knowledge in the CAGI SickKids5 clinical genomes challenge

The Oft-Overlooked Massively Parallel Reporter Assay: Where, When, and Which Psychiatric Genetic Variants are Functional?

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