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OmicsPred as a centralised resource for genetic prediction of multi-omic traits

Foguet, C.; Gil, L.; Xu, Y.; Salazar-Magana, S.; Rtichie, S. C.; Persyn, E.; Im, H. K.; Inouye, M.; Lambert, S. A.

2026-05-19 genetic and genomic medicine
10.64898/2026.05.15.26353298 medRxiv
Show abstract

Genetic prediction of multi-omic data has emerged as a cost-effective alternative to direct omics profiling, particularly useful for identifying molecular features associated with disease susceptibility. However, despite its popularity, multi-omic imputation models are fragmented across studies, hindering findability, accessibility, interoperability and re-use. To address this, we developed OmicsPred (https://www.omicspred.org), a centralised platform for the deposition and dissemination of genetic prediction models of multi-omic traits. OmicsPred unifies the most commonly used molecular imputation models (e.g. from PredictDB) and other published studies totalling 3,339,469 prediction models spanning transcriptomic, proteomic, and metabolomic traits (as of May 2026). Each model is accompanied by metadata describing score development and predictive performance, and distributed in formats compatible with popular analytic tools, such as PGS Catalog Calculator and MetaXcan. To demonstrate the utility of the resource for systematic target discovery, we perform a multi-omic phenome-wide association analysis in Million Veterans Program data.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 8%
18.8%
Similar papers in this journal
2
Cell Genomics
○ 172 papers in training set
Top 0.1%
18.8%
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3
Human Genetics and Genomics Advances
○ 84 papers in training set
Top 0.3%
5.6%
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4
Genome Medicine
○ 183 papers in training set
Top 0.7%
4.9%
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5
Bioinformatics
◐ 1204 papers in training set
Top 4%
4.9%
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50% of probability mass above
6
Molecular Systems Biology
○ 162 papers in training set
Top 0.5%
4.1%
7
Nature Genetics
○ 286 papers in training set
Top 2%
3.3%
8
Nature
○ 645 papers in training set
Top 5%
2.5%
9
The American Journal of Human Genetics
○ 234 papers in training set
Top 2%
2.2%
10
Communications Medicine
○ 113 papers in training set
Top 2%
1.8%
11
Nature Aging
○ 60 papers in training set
Top 0.9%
1.8%
12
Scientific Reports
○ 3612 papers in training set
Top 52%
1.7%
13
Genetics in Medicine Open
○ 11 papers in training set
Top 0.1%
1.5%
14
Genetics in Medicine
○ 78 papers in training set
Top 0.6%
1.5%
15
iScience
○ 1154 papers in training set
Top 23%
1.4%
16
Bioinformatics Advances
◐ 203 papers in training set
Top 4%
1.1%
17
Nature Immunology
○ 79 papers in training set
Top 2%
1.1%
18
Nature Medicine
○ 125 papers in training set
Top 2%
1.1%
19
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 3%
1.1%
20
Nature Neuroscience
○ 252 papers in training set
Top 5%
0.9%
21
eBioMedicine
○ 183 papers in training set
Top 6%
0.9%
22
Communications Biology
○ 993 papers in training set
Top 28%
0.9%
23
eLife
● 5828 papers in training set
Top 65%
0.9%
24
Genome Biology
○ 637 papers in training set
Top 9%
0.6%
25
Science Advances
● 1243 papers in training set
Top 32%
0.6%
26
Briefings in Bioinformatics
◐ 354 papers in training set
Top 7%
0.6%
27
BMC Medical Genomics
○ 50 papers in training set
Top 1%
0.6%
28
Patterns
○ 78 papers in training set
Top 3%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.