Evidence map›Paper›PMID 32275883›Full record

ArticleAmerican journal of human genetics2020

Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases.

Christopher DeBoever, Yosuke Tanigawa, Matthew Aguirre, Greg McInnes, Adam Lavertu, Manuel A Rivas

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 4 pooled it
8.3field-weighted citation impact, top 2% of its field
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

43 citing papers in PubMed, 4 syntheses or guidelines pooled it, 64 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Christopher DeBoeverDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Yosuke TanigawaDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Matthew AguirreDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Greg McInnesDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Adam LavertuDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Manuel A RivasDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA. Electronic address: mrivas@stanford.edu.
Stanford University · US

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Optimizing imputation for diverse populations in a distributed frameworkU01HG009080 · NHGRI · STANFORD UNIVERSITY · PI KENNY, EIMEAR ELIZABETH · 2016 to 2020
$5.2M
SOFTWARE FOR LARGE-SCALE INFERENCE OF THE GENETICS OF LIFESTYLE MEASURES, BIOMARKERS, AND COMMON AND RARE DISEASESR01HG010140 · NHGRI · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL, MONTGOMERY, STEPHEN · 2018 to 2022
$2.0M
Biomedical Data Science Graduate Training at StanfordT32LM012409 · NLM · STANFORD UNIVERSITY · PI PLEVRITIS, SYLVIA KATINA · 2016 to 2020
$1.5M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS R01 HG010140NHGRI NIH HHS U01 HG009080NLM NIH HHS T15 LM007033NLM NIH HHS T32 LM012409
6 · The paper itself

Abstract

Population-scale biobanks that combine genetic data and high-dimensional phenotyping for a large number of participants provide an exciting opportunity to perform genome-wide association studies (GWAS) to identify genetic variants associated with diverse quantitative traits and diseases. A major challenge for GWAS in population biobanks is ascertaining disease cases from heterogeneous data sources such as hospital records, digital questionnaire responses, or interviews. In this study, we use genetic parameters, including genetic correlation, to evaluate whether GWAS performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of disease implicate similar disease genetics across a range of effect sizes. We find that hospital record and questionnaire GWAS largely identify similar genetic effects for many complex phenotypes and that combining together both phenotyping methods improves power to detect genetic associations. We also show that family history GWAS using cases ascertained on family history of disease agrees with combined hospital record and questionnaire GWAS and that family history GWAS has better power to detect genetic associations for some phenotypes. Overall, this work demonstrates that digital phenotyping and unstructured phenotype data can be combined with structured data such as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies to identify genetic associations.

Indexed as

Genome-Wide Association StudyPhenotypeAsthmaDatabases, FactualDiseaseFemaleGenetics, MedicalGenotypeHumansMaleNeoplasmsUnited Kingdomdigital phenotypinggenetic correlationgeneticsgenome-wide association studyGWASmixture modelsphenotypingpopulation cohortsUK Biobank

Identifiers

PMID32275883
PMCPMC7212271
OpenAlexW3015900446

What Socratic holds

Textmetadata
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 Socratic graph.