Evidence map›Paper›PMID 33462486›Full record

ArticleNature genetics2021

Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power.

Elizabeth G Atkinson, Adam X Maihofer, Masahiro Kanai, Alicia R Martin, Konrad J Karczewski, Marcos L Santoro, Jacob C Ulirsch, Yoichiro Kamatani, Yukinori Okada, Hilary K Finucane and 4 more

Abstract read
In one paragraph

Article in Nature genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 193 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
193citing papers in PubMed, 9 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

193 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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133 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Elizabeth G AtkinsonAnalytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. eatkinso@broadinstitute.org.ORCID http://orcid.org/0000-0002-6308-776X
Adam X MaihoferDepartment of Psychiatry, University of California, San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0003-4517-1623
Masahiro KanaiAnalytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0001-5165-4408
Alicia R MartinAnalytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0003-0241-3522
Konrad J KarczewskiAnalytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0003-2878-4671
Marcos L SantoroStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-5714-9509
Jacob C UlirschAnalytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0002-7947-0827
Yoichiro KamataniLaboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Yukinori OkadaDepartment of Statistical Genetics, Graduate School of Medicine, Osaka University, Suita, Japan.ORCID http://orcid.org/0000-0002-0311-8472
Hilary K FinucaneAnalytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0003-3864-9828
Karestan C KoenenStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Caroline M Nievergelt *Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0001-5766-8923
Mark J Daly *Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
Benjamin M Neale *Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0003-1513-6077

Funding

Pilot & Feasibility ProgramP30DK043351 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Curtis Huttenhower · 1991 to 2026
$35.3M
Training Program in Psychiatric Genetics and Translational ResearchT32MH017119 · NIMH · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI KARESTAN C KOENEN, Jukka-Pekka Onnela · 1985 to 2026
$8.9M
Psychiatric Genomics Consortium for PTSDR01MH106595 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KARESTAN C KOENEN, Caroline M Nievergelt · 2016 to 2026
$8.4M
Statistical methods to localize disease heritability and identify biological mechanismsR37MH107649 · NIMH · BROAD INSTITUTE, INC. · PI Benjamin Michael Neale · 2019 to 2026
$7.0M
4/7 Psychiatric Genomics Consortium: Advancing Discovery and ImpactR01MH124847 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NIEVERGELT, CAROLINE M, SEBAT, JONATHAN · 2021 to 2025
$2.1M
Generalizing polygenic risk prediction methods across populations for insights into psychiatric diseaseR00MH117229 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI MARTIN, ALICIA · 2020 to 2022
$724k
A framework enabling the genomic analysis of psychiatric traits across admixed populations.K01MH121659 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI ATKINSON, ELIZABETH GRACE · 2019 to 2022
$719k
Generalizing polygenic risk prediction methods across populations for insights into psychiatric diseaseK99MH117229 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI MARTIN, ALICIA · 2018 to 2019
$262k
NIDDK NIH HHS P30 DK043351NIMH NIH HHS K01 MH121659NIMH NIH HHS K99 MH117229NIMH NIH HHS R00 MH117229NIMH NIH HHS R01 MH106595NIMH NIH HHS R01 MH124847NIMH NIH HHS R37 MH107649NIMH NIH HHS T32 MH017119
6 · The paper itself

Abstract

Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants.

Indexed as

Models, GeneticSoftwareBlack or African AmericanCholesterolGenome-Wide Association StudyGuanine Nucleotide Exchange FactorsHaplotypesHomeodomain ProteinsHumansLipidsPedigreePolymorphism, Single NucleotideTranscription FactorsWhite PeopleCholesterolDOCK6 protein, humanGuanine Nucleotide Exchange FactorsHomeodomain ProteinsLipidsMEIS2 protein, humanTranscription Factors

Identifiers

PMID33462486
PMCPMC7867648

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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.