Evidence map›Paper›PMID 31317254›Full record

ReviewHuman genetics2020

Identifying causal variants and genes using functional genomics in specialized cell types and contexts.

Boxiang Liu, Stephen B Montgomery

Abstract readReview
In one paragraph

Review in Human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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

  1. Pooled it
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  9. Observational
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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

2 authors.

Boxiang LiuDepartment of Biology, Stanford University, Stanford, USA. jollier.liu@gmail.com.
Stephen B MontgomeryDepartment of Pathology, Stanford University, Stanford, USA. smontgom@stanford.edu.ORCID http://orcid.org/0000-0002-5200-3903

Funding

Optimizing imputation for diverse populations in a distributed frameworkU01HG009080 · NHGRI · STANFORD UNIVERSITY · PI KENNY, EIMEAR ELIZABETH · 2016 to 2020
$5.2M
Decoding the regulatory architecture of the human genome across cell types, individuals and diseaseU01HG009431 · NHGRI · STANFORD UNIVERSITY · PI PRITCHARD, JONATHAN K · 2017 to 2021
$3.5M
Methods for high-resolution analysis of genetic effects on gene expressionR01MH101814 · NIMH · UNIVERSITY OF GENEVA · PI BUSTAMANTE, CARLOS DANIEL, DERMITZAKIS, EMMANOUIL · 2013 to 2016
$2.3M
Identification of causal coronary heart disease variation in smooth muscle cellsR33HL120757 · NHLBI · STANFORD UNIVERSITY · PI QUERTERMOUS, THOMAS · 2016 to 2018
$1.6M
Predicting causal non-coding variants in a founder populationR01HG008150 · NHGRI · STANFORD UNIVERSITY · PI MONTGOMERY, STEPHEN · 2015 to 2017
$1.4M
Integrative multi-omics in whole genome studies of HLBS disordersR01HL142015 · NHLBI · STANFORD UNIVERSITY · PI MONTGOMERY, STEPHEN · 2018 to 2019
$882k
NHGRI NIH HHS R01 HG008150NHGRI NIH HHS U01 HG009080NHGRI NIH HHS U01 HG009431NHLBI NIH HHS R01 HL142015NHLBI NIH HHS R33 HL120757NIH HHS R01HG008150NIMH NIH HHS R01 MH101814
6 · The paper itself

Abstract

A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a variety of statistical and experimental approaches have been developed. These approaches often borrow information from functional genomics assays such as ATAC-seq, ChIP-seq and RNA-seq to annotate functional variants and identify regulatory relationships between variants and genes. While such approaches are powerful, given the diversity of cell types and environments, it is paramount to select disease-relevant contexts for follow-up analyses. In this review, we discuss the latest developments, challenges, and best practices for determining the causal mechanisms of polygenic disease risk variants with functional genomics data from specialized cell types.

Indexed as

Genome, HumanGenome-Wide Association StudyPolymorphism, Single NucleotideQuantitative Trait LociCell LineageGenesGenetic Predisposition to DiseaseGenomicsHumans

Identifiers

PMID31317254
PMCPMC6942616

What OpenQuestion holds

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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.