Evidence map›Paper›PMID 41676459›Full record

ArticlebioRxiv : the preprint server for biology2026

Farm animal evolution demonstrates hidden molecular basis of human traits.

Noah J Connally, Shamil Sunyaev

Abstract readPreprint
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

2 authors.

Noah J ConnallyDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0003-3818-6739
Shamil SunyaevDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0001-5715-5677

Funding

Statistical methods for studies of rare variantsR01MH101244 · NIMH · HARVARD MEDICAL SCHOOL · PI Benjamin Michael Neale, ALKES L PRICE · 2013 to 2026
$9.4M
The origin, the function and the phenotypic impact of human allelesR35GM127131 · NIGMS · HARVARD MEDICAL SCHOOL · PI SHAMIL SUNYAEV · 2018 to 2026
$8.1M
Predicting the impact of genetic variants, genes and pathways on human DiseaseU01HG012009 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI ALKES L PRICE, Soumya Raychaudhuri · 2021 to 2026
$4.2M
NHGRI NIH HHS U01 HG012009NIGMS NIH HHS R35 GM127131NIMH NIH HHS R01 MH101244
6 · The paper itself

Abstract

Most human variants identified by genome-wide association studies are believed to affect traits by altering gene expression. This belief is supported by considerable circumstantial evidence, but statistical methods are unable to link most trait-associated variants to gene expression-a problem we refer to as "missing regulation." Many explanations have been proposed, including the possibility that natural selection on gene expression limits power. Here, we take a novel approach to the question of missing regulation, beginning with the observation that the majority of trait-associated variants alter gene expression in two non-human species: cattle and pigs. We explain this discrepancy by comparing the species' evolutionary histories. The observed differences in regulatory variants are consistent with selection on human gene regulation and increased genetic drift due to agricultural breeding. The differences are not limited to specific genes and reflect increased ascertainment of regulatory variants that are distal to genes. Additionally, we show that trait-associated gene regulation in cattle and pigs matches observed patterns from complex-trait genetics in humans, and may reflect currently unobserved trait-associated regulation in humans.

Identifiers

PMID41676459
PMCPMC12889581

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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.