Background: Studies of related individuals have consistently
demonstrated notable familial aggregation of cancer. We aim to estimate
the heritability and genetic correlation attributable to the additive
effects of common single-nucleotide polymorphisms (SNPs) for cancer at
13 anatomical sites.
Methods: Between 2007 and 2014, the US National Cancer Institute has
generated data from genome-wide association studies (GWAS) for 49 492
cancer case patients and 34 131 control patients. We apply novel mixed
model methodology (GCTA) to this GWAS data to estimate the heritability
of individual cancers, as well as the proportion of heritability
attributable to cigarette smoking in smoking-related cancers, and the
genetic correlation between pairs of cancers.
Results: GWAS heritability was statistically significant at nearly all
sites, with the estimates of array-based heritability, h(l)(2), on the
liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the
combined heritability of multiple smoking characteristics, we calculate
that at least 24% (95% confidence interval [CI] = 14% to 37%) and
7% (95% CI = 4% to 11%) of the heritability for lung and bladder
cancer, respectively, can be attributed to genetic determinants of
smoking. Most pairs of cancers studied did not show evidence of strong
genetic correlation. We found only four pairs of cancers with marginally
statistically significant correlations, specifically kidney and testes
(rho = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and
pediatric osteosarcoma (rho = 0.53, SE = 0.21), DLBCL and chronic
lymphocytic leukemia (CLL) (rho = 0.51, SE = 0.18), and bladder and lung
(rho = 0.35, SE = 0.14). Correlation analysis also indicates that the
genetic architecture of lung cancer differs between a smoking population
of European ancestry and a nonsmoking Asian population, allowing for the
possibility that the genetic etiology for the same disease can vary by
population and environmental exposures.
Conclusion: Our results provide important insights into the genetic
architecture of cancers and suggest new avenues for investigation