Back

Genetic Analyses of Blood Cell Structure for Biological and Pharmacological Inference

Akbari, P.; Vuckovic, D.; Jiang, T.; Kundu, K.; Kreuzhuber, R.; Bao, E. L.; Mayer, L.; Collins, J. H.; Downes, K.; Georges, M.; Grassi, L.; Guerrero, J. A.; Kaptoge, S.; Knight, J. C.; Meacham, S.; Sambrook, J.; Seyres, D.; Stegle, O.; Verboon, J. M.; Walter, K.; Watkins, N. A.; Danesh, J.; Roberts, D. J.; Di Angelantonio, E.; Sankaran, V. G.; Frontini, M.; Burgess, S.; Kuijpers, T.; Peters, J. E.; Butterworth, A. S.; Ouwehand, W. H.; Soranzo, N.; Astle, W. J.

2020-01-31 genetics
10.1101/2020.01.30.927483 bioRxiv
Show abstract

Thousands of genetic associations with phenotypes of blood cells are known, but few are with phenotypes relevant to cell function. We performed GWAS of 63 flow-cytometry phenotypes, including measures of cell granularity, nucleic acid content, and reactivity, in 39,656 participants in the INTERVAL study, identifying 2,172 variant-trait associations. These include associations mediated by functional cellular structures such as secretory granules, implicated in vascular, thrombotic, inflammatory and neoplastic diseases. By integrating our results with epigenetic data and with signals from molecular abundance/disease GWAS, we infer the hematopoietic origins of population phenotypic variation and identify the transcription factor FOG2 as a regulator of platelet -granularity. We show how flow cytometry genetics can suggest cell types mediating complex disease risk and suggest efficacious drug targets, presenting Daclizumab/Vedolizumab in autoimmune disease as positive controls. Finally, we add to existing evidence supporting IL7/IL7-R as drug targets for multiple sclerosis.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
eLife
● 5828 papers in training set
Top 5%
14.8%
Similar papers in this journal
2
Cell Genomics
○ 172 papers in training set
Top 0.4%
7.1%
Similar papers in this journal
3
Cell
○ 431 papers in training set
Top 0.9%
6.6%
Similar papers in this journal
4
Nature Communications
○ 5641 papers in training set
Top 24%
6.6%
Similar papers in this journal
5
The American Journal of Human Genetics
○ 234 papers in training set
Top 0.9%
5.4%
Similar papers in this journal
6
Nature Genetics
○ 286 papers in training set
Top 1%
5.1%
7
PLOS Genetics
● 862 papers in training set
Top 3%
4.2%
8
Frontiers in Genetics
○ 230 papers in training set
Top 0.7%
4.2%
50% of probability mass above
9
Blood Advances
● 62 papers in training set
Top 0.4%
4.0%
10
EMBO Molecular Medicine
○ 95 papers in training set
Top 0.2%
4.0%
11
Cell Reports
○ 1498 papers in training set
Top 12%
3.2%
12
Human Genetics
○ 28 papers in training set
Top 0.1%
3.2%
13
Blood
● 74 papers in training set
Top 0.7%
2.1%
14
Genome Medicine
○ 183 papers in training set
Top 3%
1.7%
15
Science Advances
● 1243 papers in training set
Top 21%
1.7%
16
Communications Biology
○ 993 papers in training set
Top 17%
1.5%
17
iScience
○ 1154 papers in training set
Top 21%
1.5%
18
Genome Biology
○ 637 papers in training set
Top 7%
1.3%
19
Genome Research
● 468 papers in training set
Top 5%
1.1%
20
Genes & Development
● 90 papers in training set
Top 1%
1.1%
21
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 35%
1.1%
22
GENETICS
◐ 483 papers in training set
Top 4%
1.0%
23
Human Molecular Genetics
◐ 141 papers in training set
Top 3%
1.0%
24
Life Science Alliance
● 285 papers in training set
Top 6%
1.0%
25
BMC Biology
○ 265 papers in training set
Top 6%
0.8%
26
Cell Reports Medicine
○ 153 papers in training set
Top 5%
0.8%
27
Neuron
○ 337 papers in training set
Top 5%
0.6%
28
Journal of Experimental Medicine
● 119 papers in training set
Top 4%
0.6%
29
BMC Genomics
○ 406 papers in training set
Top 9%
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.