Analysis of Heritability and Genetic Architecture of Pancreatic Cancer: A PanC4 Study

Abstract

Background: Pancreatic cancer is the fourth-leading cause of cancer death in both men and women in the United States. The currently identified common susceptibility loci account for a small fraction of estimated heritability. We sought to estimate overall heritability of pancreatic cancer and partition the heritability by variant frequencies and functional annotations.

Methods: Analysis using the genome-based restricted maximum likelihood method (GREML) was conducted on Pancreatic Cancer Case-Control Consortium (PanC4) genome-wide association study (GWAS) data from 3,568 pancreatic cancer cases and 3,363 controls of European Ancestry.

Results: Applying linkage disequilibrium- and minor allele frequency-stratified GREML (GREML-LDMS) method to imputed GWAS data, we estimated the overall heritability of pancreatic cancer to be 21.2% (SE = 4.8%). Across the functional groups (intronic, intergenic, coding, and regulatory variants), intronic variants account for most of the estimated heritability (12.4%). Previously identified GWAS loci explained 4.1% of the total phenotypic variation of pancreatic cancer. Mutations in hereditary pancreatic cancer susceptibility genes are present in 4% to 10% of patients with pancreatic cancer, yet our GREML-LDMS results suggested these regions explain only 0.4% of total phenotypic variance for pancreatic cancer.

Conclusions: Although higher than previous studies, our estimated 21.2% overall heritability may still be downwardly biased due to the inherent limitation that the contribution of rare variants in genes with a substantive overall impact on disease are not captured when applying these commonly used methods to imputed GWAS data.

Impact: Our work demonstrated the importance of rare and common variants in pancreatic cancer risk.

Figure 1.. Minor allele frequency (MAF) and functional annotation of PanC4 imputed variants.

A, MAF distribution of imputed variants passed all quality control filters showed that majority of these variants had a MAF < 0.05. B, Imputed variants were annotated into six functional groups by ANNOVA, among which intergenic (52.7%) and intronic (37.2%) variants were the two largest groups.

Figure 2.. Estimated variance explained by imputed variants on individual chromosome stratified by MAF and LD.

Variants on each chromosome were stratified into 2 MAF categories and 2 LD groups. The estimated variance associated with individual chromosome was aggregated from the variance explained by four MAF-LD groups. This analysis ranks chromosome 9, 7, 16, 8, 5, 2 and 1 as top contributors to the estimated heritability.

Figure 3.. Estimated variance explained by imputed variants stratified by MAF.

Variants were stratified into six minor allele frequency (MAF) categories: < 0.01, 0.01–0.10, 0.10–0.20, 0.20–0.30, 0.30–0.40 and ≥0.40. Across the MAF categories, rare variants with MAF < 0.01 accounts for the most variance, followed by variants with MAF ranged from 0.01 to 0.10