Evidence map›Paper›PMID 35943817›Full record

ReviewHuman molecular genetics2022

Scalable approaches for functional analyses of whole-genome sequencing non-coding variants.

Pavel P Kuksa, Emily Greenfest-Allen, Jeffrey Cifello, Matei Ionita, Hui Wang, Heather Nicaretta, Po-Liang Cheng, Wan-Ping Lee, Li-San Wang, Yuk Yee Leung

Abstract readReview
In one paragraph

Review in Human molecular genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Article
  6. Review
  7. The Role of Genetic Testing in Adult CKD.Journal of the American Society of Nephrology : JASN · 2024
    Article
  8. Article
  9. Article
  10. Review
  11. Human Molecular Genetics Review Issue 2022.Human molecular genetics · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Pavel P KuksaPenn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Emily Greenfest-AllenPenn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Jeffrey CifelloPenn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Matei IonitaPenn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Hui WangPenn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Heather NicarettaPenn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Po-Liang ChengPenn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Wan-Ping LeePenn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0002-5305-1181
Li-San WangPenn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Yuk Yee LeungPenn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0002-3047-5440

Funding

National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD)U24AG021886 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI TATIANA M. FOROUD · 2002 to 2026
$119.8M
Alzheimer's Disease Genetics ConsortiumU01AG032984 · NIA · UNIVERSITY OF PENNSYLVANIA · PI SCHELLENBERG, GERARD DAVID · 2009 to 2024
$60.4M
THE NIA GENETICS OF ALZHEIMER'S DISEASE DATA STORAGE SITEU24AG041689 · NIA · UNIVERSITY OF PENNSYLVANIA · PI LI-SAN WANG · 2012 to 2026
$42.3M
Genome Center for Alzheimer's Disease (GCAD)U54AG052427 · NIA · UNIVERSITY OF PENNSYLVANIA · PI NAJ, ADAM CHRISTIAN · 2016 to 2025
$32.8M
Copy Number Variation Identification and Association Study on Alzheimer's Disease Whole Genome Sequencing DataRF1AG074328 · NIA · UNIVERSITY OF PENNSYLVANIA · PI LEE, WAN-PING, TZENG, JUNG-YING · 2021 to 2021
$2.3M
NIA NIH HHS RF1 AG074328NIA NIH HHS U01 AG032984NIA NIH HHS U24 AG021886NIA NIH HHS U24 AG041689NIA NIH HHS U54 AG052427NIH HHS U24-AG041689
6 · The paper itself

Abstract

Non-coding genetic variants outside of protein-coding genome regions play an important role in genetic and epigenetic regulation. It has become increasingly important to understand their roles, as non-coding variants often make up the majority of top findings of genome-wide association studies (GWAS). In addition, the growing popularity of disease-specific whole-genome sequencing (WGS) efforts expands the library of and offers unique opportunities for investigating both common and rare non-coding variants, which are typically not detected in more limited GWAS approaches. However, the sheer size and breadth of WGS data introduce additional challenges to predicting functional impacts in terms of data analysis and interpretation. This review focuses on the recent approaches developed for efficient, at-scale annotation and prioritization of non-coding variants uncovered in WGS analyses. In particular, we review the latest scalable annotation tools, databases and functional genomic resources for interpreting the variant findings from WGS based on both experimental data and in silico predictive annotations. We also review machine learning-based predictive models for variant scoring and prioritization. We conclude with a discussion of future research directions which will enhance the data and tools necessary for the effective functional analyses of variants identified by WGS to improve our understanding of disease etiology.

Indexed as

Epigenesis, GeneticGenome-Wide Association StudyGenomicsWhole Genome Sequencing

Identifiers

PMID35943817
PMCPMC9585666

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.