Evidence map›Paper›PMID 41758904›Full record

ArticlePLoS genetics2026

Genetic correlation-guided mega-analysis of DO mice provides mechanistic insight and candidate genes for age-related pathologies.

Martin N Mullis, Austin E Y T Lefebvre, Kathyayini Sivasubramanian, Angela Luo, Florian Schmid, Matt Sooknah, Kevin M Wright, Anil Raj, José Zavala-Solorio, Chunlian Zhang and 3 more

Abstract read
In one paragraph

Article in PLoS genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

13 authors.

Martin N MullisCalico Life Sciences LLC, South San Francisco, California, United States of America.ORCID https://orcid.org/0000-0002-5134-7528
Austin E Y T LefebvreCalico Life Sciences LLC, South San Francisco, California, United States of America.ORCID https://orcid.org/0000-0001-8148-5369
Kathyayini SivasubramanianCalico Life Sciences LLC, South San Francisco, California, United States of America.
Angela LuoCalico Life Sciences LLC, South San Francisco, California, United States of America.
Florian SchmidCalico Life Sciences LLC, South San Francisco, California, United States of America.ORCID https://orcid.org/0000-0003-1892-4255
Matt SooknahCalico Life Sciences LLC, South San Francisco, California, United States of America.ORCID https://orcid.org/0009-0003-7145-4691
Kevin M WrightCalico Life Sciences LLC, South San Francisco, California, United States of America.
Anil RajCalico Life Sciences LLC, South San Francisco, California, United States of America.ORCID https://orcid.org/0000-0003-4412-0883
José Zavala-SolorioCalico Life Sciences LLC, South San Francisco, California, United States of America.ORCID https://orcid.org/0000-0003-2025-2507
Chunlian ZhangCalico Life Sciences LLC, South San Francisco, California, United States of America.
Johannes RieglerCalico Life Sciences LLC, South San Francisco, California, United States of America.ORCID https://orcid.org/0000-0002-4713-7392
Astrid GillichCalico Life Sciences LLC, South San Francisco, California, United States of America.
J Graham RubyCalico Life Sciences LLC, South San Francisco, California, United States of America.ORCID https://orcid.org/0000-0002-4798-5420

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diversity Outbred (DO) mice are a powerful model system for mapping complex traits due to their high genetic diversity and mapping resolution. However, while there are extensive tools available for standard genetic analysis in DO mice, fewer techniques have been implemented to facilitate integrated, cross-study analysis. Here, we implement Haseman-Elston regression to estimate genetic correlations among 7,233 phenotypes measured across eleven independent DO mouse studies. We used this network of genetic correlations to cluster phenotypes according to shared genetics, which enhanced the power to detect quantitative trait loci (QTL). This approach empowered the detection of 884 QTL for 383 meta-phenotypes, explaining an average of 40.36% of the total genetic variance per mega-analysis. We leveraged this network for insights into specific areas of biology, including lifespan, frailty, immune composition, histological and functional lung phenotypes, and histological phenotypes of the aorta. We found the genetics of lifespan to share limited correlation with the genetics of frailty but stronger correlation with the genetics of immune cell composition. Additionally, mega-analyses driven by genetic correlations identified candidate genes (e.g., Cdkn2b) associated with degraded extracellular matrix in the aorta. Finally, an ensemble of genetic analyses implicated pulmonary neuroendocrine cell signaling and/or differentiation as a key driver of multiple lung pathophenotypes.

Indexed as

AgingQuantitative Trait LociAnimalsChromosome MappingGenetic Association StudiesGenetic VariationLongevityMicePhenotype

Identifiers

PMID41758904
PMCPMC12948109

What OpenQuestion holds

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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.