Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers

Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive and pathway-based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single-nucleotide polymorphism (SNP)-set-based association tests, adaptive sum of powered score (aSPU) and data-adaptive pathway-based (aSPUpath) tests, increase the power in genome-wide association studies (GWASs) with a single disease trait in a case–control study. We extend aSPU and aSPUpath to multi-traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels. $p$-values from different parameters assuming varying genetic architecture are combined to yield data-adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data-adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large-scale real-world International Cancer Genome Consortium whole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.