Evidence map›Paper›PMID 42094400›Full record

ArticlebioRxiv : the preprint server for biology2026

Highly efficient genotype compression leveraging genealogical relatedness.

Amber Shen, Xinran Wang, Nicholas Mancuso, Luke J O'Connor

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Amber ShenDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Xinran WangCenter for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Nicholas MancusoCenter for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Luke J O'ConnorDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Funding

Statistical Methods for Integrative Genomics in CancerP01CA196569 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI David V Conti · 2016 to 2026
$25.5M
Characterizing the evolutionary architecture of complex disease within and across diverse populationsR01HG012133 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MANCUSO, NICHOLAS · 2021 to 2025
$3.6M
The functional and phenotypic effects of protein coding genetic variationR35GM155278 · NIGMS · HARVARD MEDICAL SCHOOL · PI Luke Jen O'Connor · 2024 to 2026
$1.3M
NCI NIH HHS P01 CA196569NHGRI NIH HHS R01 HG012133NIGMS NIH HHS R35 GM155278
6 · The paper itself

Abstract

Large genetic datasets are terabytes in size, presenting a computational challenge that will intensify as sequencing efforts scale. We present a lossless compression algorithm,

Identifiers

PMID42094400
PMCPMC13142413

What Socratic holds

Textmetadata
LicenceCC BY
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.