Evidence map›Paper›PMID 26072481›Full record

ArticleBioinformatics (Oxford, England)2015

Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses.

Danny S Park, Brielin Brown, Celeste Eng, Scott Huntsman, Donglei Hu, Dara G Torgerson, Esteban G Burchard, Noah Zaitlen

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Genetics and Genomics of Coronary Artery Disease.Current cardiology reports · 2016
    Review
  7. Article
  8. 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

8 authors.

Danny S ParkDepartment of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Brielin BrownDepartment of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Celeste EngDepartment of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Scott HuntsmanDepartment of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Donglei HuDepartment of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Dara G TorgersonDepartment of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Esteban G BurchardDepartment of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Noah ZaitlenDepartment of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.

Funding

Understanding Asthma EndotypesU19AI077439 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI David J Erle · 2008 to 2026
$41.1M
New Approaches for Empowering Studies of Asthma in Populations of African DescentR01HL104608 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI BARNES, KATHLEEN C, KENNY, EIMEAR ELIZABETH · 2011 to 2022
$23.1M
CLINICAL, PHARMACOLOGY, DRUG ACTION &PHARMACOKINETICST32GM007546 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Radojka Savic · 1985 to 2026
$8.9M
PHARMACEUTICAL SCIENCES AND PHARMACOGENOMICST32GM007175 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI AHITUV, NADAV, KROETZ, DEANNA L · 1985 to 2021
$7.1M
Social Interventions to address disparities In young adult tobacco useP60MD006902 · NIMHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HIATT, ROBERT A · 2012 to 2016
$6.4M
Gene-environments and Admixture in Latino Asthmatics (GALA 2)R01ES015794 · NIEHS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BURCHARD, ESTEBAN GONZALEZ · 2008 to 2012
$5.4M
Postdoctoral training in tobacco controlR25CA113710 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GLANTZ, STANTON ARNOLD · 2006 to 2015
$5.4M
Whole Genome Analyses for Asthma in Latino PopulationsR01HL088133 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BURCHARD, ESTEBAN GONZALEZ · 2008 to 2012
$3.8M
Case-control Association Studies and Genetic ConfoundingR01HL078885 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BURCHARD, ESTEBAN GONZALEZ · 2005 to 2009
$1.9M
Richer Models of Asthma Risk: Bridging the Environment-Genetics DivideK25HL121295 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ZAITLEN, NOAH A · 2014 to 2018
$784k
Genetics of Asthma In Latino AmericansK23HL004464 · NHLBI · UNIVERSITY OF CALIFORNIA SAN FRANCISCO · PI BURCHARD, ESTEBAN GONZALEZ · 2001 to 2005
$609k
NCI NIH HHS R25 CA113710NCI NIH HHS R25-CA113710NHLBI NIH HHS K23 HL004464NHLBI NIH HHS K25 HL121295NHLBI NIH HHS K25HL121295NHLBI NIH HHS R01-HL004464NHLBI NIH HHS R01 HL078885NHLBI NIH HHS R01-HL078885NHLBI NIH HHS R01 HL088133NHLBI NIH HHS R01-HL088133NHLBI NIH HHS R01 HL104608NHLBI NIH HHS R01-HL104608NIAID NIH HHS U19 AI077439NIAID NIH HHS U19-AI077439NIEHS NIH HHS R01 ES015794NIEHS NIH HHS R01-ES015794NIGMS NIH HHS T32 GM007175NIGMS NIH HHS T32 GM007546NIGMS NIH HHS T32-GM007546NIMHD NIH HHS P60 MD006902NIMHD NIH HHS P60MD006902
6 · The paper itself

Abstract

motivationApproaches to identifying new risk loci, training risk prediction models, imputing untyped variants and fine-mapping causal variants from summary statistics of genome-wide association studies are playing an increasingly important role in the human genetics community. Current summary statistics-based methods rely on global 'best guess' reference panels to model the genetic correlation structure of the dataset being studied. This approach, especially in admixed populations, has the potential to produce misleading results, ignores variation in local structure and is not feasible when appropriate reference panels are missing or small. Here, we develop a method, Adapt-Mix, that combines information across all available reference panels to produce estimates of local genetic correlation structure for summary statistics-based methods in arbitrary populations.

resultsWe applied Adapt-Mix to estimate the genetic correlation structure of both admixed and non-admixed individuals using simulated and real data. We evaluated our method by measuring the performance of two summary statistics-based methods: imputation and joint-testing. When using our method as opposed to the current standard of 'best guess' reference panels, we observed a 28% decrease in mean-squared error for imputation and a 73.7% decrease in mean-squared error for joint-testing. AVAILABILITY AND IMPLEMENTATION: Our method is publicly available in a software package called ADAPT-Mix available at https://github.com/dpark27/adapt_mix.

Indexed as

AlgorithmsCoronary Artery DiseaseData Interpretation, StatisticalGenome-Wide Association StudyGenotypeHumansPhenotypePolymorphism, Single NucleotideSoftware

Identifiers

PMID26072481
PMCPMC4553832

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.