A genome-wide association study of blood cell morphology identifies cellular proteins implicated in disease aetiology.
Parsa AkbariDragana VuckovicLuca StefanucciTao JiangKousik KunduRoman KreuzhuberErik L BaoJanine H CollinsKate DownesLuigi GrassiJose Antonio GuerreroStephen KaptogeJulian C KnightStuart MeachamJennifer SambrookDenis SeyresOliver StegleJeffrey M VerboonKlaudia WalterNicholas A WatkinsJohn DaneshDavid J RobertsEmanuele Di AngelantonioVijay G SankaranMattia FrontiniStephen BurgessTaco KuijpersJames E PetersAdam S ButterworthWillem Hendrik OuwehandNicole SoranzoWilliam John AstlePublished in: Nature communications (2023)
Blood cells contain functionally important intracellular structures, such as granules, critical to immunity and thrombosis. Quantitative variation in these structures has not been subjected previously to large-scale genetic analysis. We perform genome-wide association studies of 63 flow-cytometry derived cellular phenotypes-including cell-type specific measures of granularity, nucleic acid content and reactivity-in 41,515 participants in the INTERVAL study. We identify 2172 distinct variant-trait associations, including associations near genes coding for proteins in organelles implicated in inflammatory and thrombotic diseases. By integrating with epigenetic data we show that many intracellular structures are likely to be determined in immature precursor cells. By integrating with proteomic data we identify the transcription factor FOG2 as an early regulator of platelet formation and α-granularity. Finally, we show that colocalisation of our associations with disease risk signals can suggest aetiological cell-types-variants in IL2RA and ITGA4 respectively mirror the known effects of daclizumab in multiple sclerosis and vedolizumab in inflammatory bowel disease.
Keyphrases
- induced apoptosis
- transcription factor
- multiple sclerosis
- flow cytometry
- high resolution
- genome wide
- nucleic acid
- cell cycle arrest
- single cell
- genome wide association study
- genome wide association
- cell therapy
- electronic health record
- dna methylation
- rheumatoid arthritis
- oxidative stress
- big data
- endoplasmic reticulum stress
- stem cells
- gene expression
- cell death
- pulmonary embolism
- machine learning
- cell proliferation
- reactive oxygen species
- systemic sclerosis
- ulcerative colitis
- systemic lupus erythematosus
- disease activity
- white matter
- deep learning
- idiopathic pulmonary fibrosis
- interstitial lung disease
- patients with inflammatory bowel disease
- pi k akt
- genome wide analysis