Investigating the genetic relationship between Alzheimer’s disease and cancer using GWAS summary statistics
Growing evidence from both epidemiology and basic science suggest an inverse association between Alzheimer’s disease (AD) and cancer. We examined the genetic relationship between AD and various cancer types using GWAS summary statistics from the IGAP and GAME-ON consortia. Sample size ranged from 9931 to 54,162; SNPs were imputed to the 1000 Genomes European panel. Our results based on cross-trait LD Score regression showed a significant positive genetic correlation between AD and five cancers combined (colon, breast, prostate, ovarian, lung; rg = 0.17, P = 0.04), and specifically with breast cancer (ER-negative and overall; rg = 0.21 and 0.18, P = 0.035 and 0.034) and lung cancer (adenocarcinoma, squamous cell carcinoma and overall; rg = 0.31, 0.38 and 0.30, P = 0.029, 0.016, and 0.006). Estimating the genetic correlation in specific functional categories revealed mixed positive and negative signals, notably stronger at annotations associated with increased enhancer activity. This suggests a role of gene expression regulators in the shared genetic etiology between AD and cancer, and that some shared variants modulate disease risk concordantly while others have effects in opposite directions. Due to power issues, we did not detect cross-phenotype associations at individual SNPs. This genetic overlap is not likely driven by a handful of major loci. Our study is the first to examine the co-heritability of AD and cancer leveraging large-scale GWAS results. The functional categories highlighted in this study need further investigation to illustrate the details of the genetic sharing and to bridge between different levels of associations.
- Ibanez K, Boullosa C, Tabares-Seisdedos R, Baudot A, Valencia A (2014) Molecular evidence for the inverse comorbidity between central nervous system disorders and cancers detected by transcriptomic meta-analyses. PLoS Genet 10:e1004173. doi:10.1371/journal.pgen.1004173 CrossRefPubMedPubMedCentralGoogle Scholar
- Lee SH, Yang J, Goddard ME, Visscher PM, Wray NR (2012) Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28:2540–2542. doi:10.1093/bioinformatics/bts474 CrossRefPubMedPubMedCentralGoogle Scholar