Abstract
Our aim was to identify genes that influence the inverse association of coffee with the risk of developing Parkinson's disease (PD). We used genome-wide genotype data and lifetime caffeinated-coffee-consumption data on 1,458 persons with PD and 931 without PD from the NeuroGenetics Research Consortium (NGRC), and we performed a genome-wide association and interaction study (GWAIS), testing each SNP's main-effect plus its interaction with coffee, adjusting for sex, age, and two principal components. We then stratified subjects as heavy or light coffee-drinkers and performed genome-wide association study (GWAS) in each group. We replicated the most significant SNP. Finally, we imputed the NGRC dataset, increasing genomic coverage to examine the region of interest in detail. The primary analyses (GWAIS, GWAS, Replication) were performed using genotyped data. In GWAIS, the most significant signal came from rs4998386 and the neighboring SNPs in GRIN2A. GRIN2A encodes an NMDA-glutamate-receptor subunit and regulates excitatory neurotransmission in the brain. Achieving P2df = 10−6, GRIN2A surpassed all known PD susceptibility genes in significance in the GWAIS. In stratified GWAS, the GRIN2A signal was present in heavy coffee-drinkers (OR = 0.43; P = 6×10−7) but not in light coffee-drinkers. The a priori Replication hypothesis that “Among heavy coffee-drinkers, rs4998386_T carriers have lower PD risk than rs4998386_CC carriers” was confirmed: ORReplication = 0.59, PReplication = 10−3; ORPooled = 0.51, PPooled = 7×10−8. Compared to light coffee-drinkers with rs4998386_CC genotype, heavy coffee-drinkers with rs4998386_CC genotype had 18% lower risk (P = 3×10−3), whereas heavy coffee-drinkers with rs4998386_TC genotype had 59% lower risk (P = 6×10−13). Imputation revealed a block of SNPs that achieved P2df<5×10−8 in GWAIS, and OR = 0.41, P = 3×10−8 in heavy coffee-drinkers. This study is proof of concept that inclusion of environmental factors can help identify genes that are missed in GWAS. Both adenosine antagonists (caffeine-like) and glutamate antagonists (GRIN2A-related) are being tested in clinical trials for treatment of PD. GRIN2A may be a useful pharmacogenetic marker for subdividing individuals in clinical trials to determine which medications might work best for which patients.
Highlights
Common disorders are thought to have both genetic and environmental components
Parkinson’s disease (PD), like most common disorders, involves interactions between genetic make-up and environmental exposures that are unique to each individual
In a genome-wide search, we discovered that variations in the glutamate-receptor gene GRIN2A modulate the risk of developing PD in heavy coffee drinkers
Summary
Common disorders are thought to have both genetic and environmental components. Genome-wide association studies (GWAS) have successfully identified numerous susceptibility loci for many common disorders ranging from behavioral traits such as addiction and substance abuse to infectious and immune-related disorders, age-related neurodegenerative disorders like Alzheimer’s, Parkinson’s and macular degeneration, metabolic disorders, psychiatric disorders, and many more (for the list and results of over 800 published GWAS see http://www.genome.gov/gwastudies). Sequencing the genome and novel analytical methods will help identify the rare variants. Another hiding place for the missing heritability is in interactions. Genes that impact disease through interactions with other genes or environmental factors are not detected by GWAS if their main effects are small. GWAS can only identify genes that exhibit significant main effects; genes that require the interacting factor to be included in the study to show their association with disease are missed. Inclusion of key environmental factors in genome-wide studies is anticipated to be an important step for deciphering the genetic structure of common multifactorial disorders. Interaction studies require at least four times the sample size that standard GWAS would require to detect an effect of similar magnitude (reviewed in [2]). There are fewer datasets with both DNA and environmental exposure data than those with DNA alone, and their sample sizes are often smaller
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