Back

Systematic common and rare variant association testing in 392,030 whole genomes in All of Us

Lu, W.; Carroll, R. J.; Solomonson, M.; Guez, J.; He, M. K.; Marten, D. J.; Martinez-Carrosco, A.; Wang, Y.; Dowd, C. S.; Kanai, M.; Gorissen, B. L.; Kouame, A. J. S.; Brogan, J.; Waxse, B. J.; Samarakoon, R.; Cook, J. A.; Qian, J.; Zhou, Y.; Choi, K. W.; Basford, M.; Lyons, M.; Linder, J. E.; Stewart, S.; Gupta, N.; Schultz, P.; Goldstein, D.; Llanwarne, C.; Goldstein, J. I.; Higham, E. G. C.; King, D. C.; Palmer, D. S.; Elenbaas, J. S.; Rohlicek, G. K.; He, Q.; Goodrich, J. K.; The All of Us Research ProgramGenomics Investigators, ; Smoller, J. W.; Lichtenstein, L.; Gabriel, S. B.; Martin,

2026-05-12 genetic and genomic medicine
10.64898/2026.05.08.26350964 medRxiv
Show abstract

Large-scale genome-wide association studies (GWAS) and rare variant association studies (RVAS) from population biobanks provide valuable resources for gene discovery in complex human traits. We present an analysis of the All of Us Research Program v8 release, which includes whole genome sequencing data and harmonized phenotypic information of 392,030 participants after quality control, enabling a unified investigation of rare and common variants across a spectrum of human traits and diseases. We build an extensive phenome- and genome-wide ("All by All") computational framework to perform GWAS and RVAS on 3,602 phenotypes and identify 49,863 approximately independent, high-quality single-variant and gene-level associations. Meta-analyses of All of Us and UK Biobank, with sample sizes as large as 786,871 participants, further enhance statistical power and find 193 pLoF gene-phenotype associations that are not significant in either cohort alone, including 22 associations not highlighted by previous studies. We also present a public interactive browser that integrates association results for common and rare variants to facilitate interpretation and rapid querying of summary statistics, along with supporting documentation, and a Featured Workspace in the All of Us Researcher Workbench. Our framework will apply to iterative data releases as All of Us grows, empowering researchers worldwide to uncover insights into the functional effects of genetic components on complex traits and diseases.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Nature Genetics
240 papers in training set
Top 0.1%
22.2%
2
The American Journal of Human Genetics
206 papers in training set
Top 0.4%
10.3%
3
Genome Medicine
154 papers in training set
Top 0.5%
10.0%
4
Nature Communications
4913 papers in training set
Top 19%
10.0%
50% of probability mass above
5
Nature
575 papers in training set
Top 4%
7.1%
6
Cell Genomics
162 papers in training set
Top 1%
3.9%
7
Nature Human Behaviour
85 papers in training set
Top 1.0%
3.5%
8
Genome Biology
555 papers in training set
Top 3%
2.7%
9
Nucleic Acids Research
1128 papers in training set
Top 8%
2.6%
10
Science
429 papers in training set
Top 13%
1.9%
11
Nature Medicine
117 papers in training set
Top 2%
1.7%
12
PLOS Genetics
756 papers in training set
Top 9%
1.7%
13
Scientific Reports
3102 papers in training set
Top 62%
1.5%
14
Briefings in Bioinformatics
326 papers in training set
Top 5%
1.3%
15
Human Genetics and Genomics Advances
70 papers in training set
Top 0.4%
1.3%
16
Nature Neuroscience
216 papers in training set
Top 5%
1.2%
17
Bioinformatics
1061 papers in training set
Top 8%
1.2%
18
Frontiers in Genetics
197 papers in training set
Top 7%
0.9%
19
Cell
370 papers in training set
Top 16%
0.9%
20
Genetics in Medicine
69 papers in training set
Top 1.0%
0.8%
21
European Journal of Human Genetics
49 papers in training set
Top 1%
0.8%
22
Cell Systems
167 papers in training set
Top 12%
0.7%
23
PLOS ONE
4510 papers in training set
Top 68%
0.7%
24
Nature Methods
336 papers in training set
Top 7%
0.6%