Evidence map›Paper›PMID 42443949›Full record

ArticleGenome biology2026

CIT-Lasso: a scalable approach beyond guilty by association for identifying causal variants from genome-wide summary statistics.

Zihuai He, Benjamin Chu, James Yang, Jiaqi Gu, Zhaomeng Chen, Linxi Liu, Tim Morrison, Michael E Belloy, Xinran Qi, Nima Hejazi and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Zihuai He *Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA. zihuai@stanford.edu.
Benjamin Chu *Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.
James Yang *Department of Statistics, Stanford University, Stanford, CA, 94305, USA.
Jiaqi Gu *Department of Mathematics and Statistics, University of South Florida, Tampa, FL, 33620, USA.
Zhaomeng ChenDepartment of Statistics, Stanford University, Stanford, CA, 94305, USA.
Linxi LiuDepartment of Statistics, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
Tim MorrisonDepartment of Statistics, Stanford University, Stanford, CA, 94305, USA.
Michael E BelloyDepartment of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA.
Xinran QiDepartment of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA.
Nima HejaziDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Maya MathurQuantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, 94305, USA.
Yann Le GuenQuantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, 94305, USA.
Hua TangDepartment of Pediatrics, Stanford University, Stanford, CA, 94305, USA.
Trevor HastieDepartment of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.
Iuliana Ionita-LazaDepartment of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, 10032, USA.
Emmanuel CandèsDepartment of Statistics, Stanford University, Stanford, CA, 94305, USA. candes@stanford.edu.
Chiara SabattiDepartment of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA. sabatti@stanford.edu.

Funding

Stanford Alzheimer's Disease Research CenterAdmin Supp: Developing iPSC models for AD and PDP30AG066515 · NIA · STANFORD UNIVERSITY · PI Victor Henderson · 2020 to 2026
$29.0M
New Statistical Methods for Medical Signals and ImagesR01EB001988 · NIBIB · STANFORD UNIVERSITY · PI JOHNSTONE, IAIN M · 2003 to 2018
$6.1M
Statistical and computational methods for integrative analysis of Alzheimer's Disease geneticsR01AG066206 · NIA · STANFORD UNIVERSITY · PI HE, ZIHUAI · 2019 to 2023
$3.5M
Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omicsR00AG075238 · NIA · WASHINGTON UNIVERSITY · PI BELLOY, MICHAEL · 2024 to 2025
$727k
Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omicsK99AG075238 · NIA · STANFORD UNIVERSITY · PI BELLOY, MICHAEL · 2022 to 2023
$264k
NIA NIH HHS K99 AG075238NIA NIH HHS P30 AG066515NIA NIH HHS R00 AG075238NIA NIH HHS R01 AG066206NIBIB NIH HHS R01 EB001988NIH HHS AG066515NIH HHS AG075238NIH HHS EB001988-21NIH/NIA AG066206Simonsen Foundation 814641
6 · The paper itself

Abstract

We present CIT-Lasso, a framework that uses only summary statistics to identify, genome-wide, sets of variants carrying non-redundant information on a phenotype, distinguishing likely causal variants from correlated variants that are merely associated. The open-source implementation completes genome-wide analysis in under 15 min on one CPU. In simulations, it outperforms existing methods in false discovery rate control, power, and fine-mapping resolution. Applied to an Alzheimer's disease meta-analysis, it identified 82 loci, 37 beyond conventional GWAS; prior MPRA and CRISPR-Cas9 studies corroborate prioritized variants. Results on other 67 large-scale GWAS reveal the method's generalizability to make discoveries beyond conventional GWAS pipeline.

Indexed as

Genetic VariationGenome-Wide Association StudyAlzheimer DiseaseHumansPhenotypePolymorphism, Single Nucleotide

Identifiers

PMID42443949
PMCPMC13359564

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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.