Abstract
Background
Increasing evidence suggests the association between caffeine and the brain and nervous system. However, there is limited research on the genetic associations between coffee consumption subtypes and brain proteome, plasma proteomes, and peripheral metabolites.
Methods
First, proteome-wide association study (PWAS) of coffee consumption subtypes was performed by integrating two independent genome-wide association study (GWAS) datasets (91,462–502,650 subjects) with two reference human brain proteomes (ROS/MAP and Banner), by using the FUSION pipeline. Second, transcriptome-wide association study (TWAS) analysis of coffee consumption subtypes was conducted by integrating the two gene expression weight references (RNAseq and splicing) of brain RNA-seq and the two GWAS datasets (91,462–502,650 subjects) of coffee consumption subtypes. Finally, we used the LD Score Regression (LDSC) analysis to evaluate the genetic correlations of coffee consumption subtypes with plasma proteomes and peripheral metabolites.
Results
For the traits related to coffee consumption, we identified 3 common PWAS proteins, such as MADD (P PWAS−Banner−dis=0.0114, P PWAS−ROS/MAP−rep =0.0489). In addition, 11 common TWAS genes were found in two cohorts, such as ARPC2 (P TWAS−splicing−dis =2063×10− 12, P TWAS−splicing−dis =1.25×10− 10, P TWAS−splicing−dis =1.24e-08, P TWAS−splicing−rep =3.25×10− 9 and P TWAS−splicing−rep =3.42×10− 13). Importantly, we have identified 8 common genes between PWAS and TWAS, such as ALDH2 (P PWAS−banner−rep =1.22×10− 22, PTWAS− splicing−dis = 4.54×10− 92). For the LDSC analysis of human plasma proteome, we identified 11 plasma proteins, such as CHL1 (P dis = 0.0151, P rep =0.0438). For the LDSC analysis of blood metabolites, 5 metabolites have been found, such as myo-inositol (P dis = 0.0073, P dis = 0.0152, P dis =0.0414, P rep =0.0216).
Conclusions
We identified several brain proteins and genes associated with coffee consumption subtypes. In addition, we also detected several candidate plasma proteins and metabolites related to these subtypes.
Genomic analysis of diet composition finds novel loci and associations with health and lifestyle
