Hunting for inherited risk genes
To do that, the scientists first used genome-wide association studies, or GWAS, which has been used for years to identify cancer risk genes. GWAS have already netted 140 independent risk variants for colorectal study.
But the scientists didn’t just use this tried-and-true method to hunt down colorectal cancer genes.
“We’re trying to understand the mechanism,” said Hsu, lead biostatistician on this and a slew of other colorectal cancer projects in collaboration within the Peters Studies lab. “We start from the DNA and ask, how does genetic variance associated with colorectal cancer risk come about? Through what particular genes? What gene expression? What particular methylation? We’re also looking at biological pathways or mechanisms.”
For their new comprehensive analysis, the scientists brought together almost all currently available GWAS data, then used new statistical methods developed by Hsu to fold in studies that look at the associations between health and the way genetic instructions are “read,” or expressed, in cells. These are called transcriptome- and methylome-wide association studies.
The transcriptome looks at RNA transcripts, or the DNA’s various marching orders, and is the first step in gene expression. The methylome recognizes genome-wide DNA methylation patterns; methylation refers to small molecules called methyl groups that are attached to DNA in a way that regulates patterns of gene expression and can lead to disease.
By amassing all of this information, Peters, Hsu and others were able to investigate the genes and the mechanisms underlying both established and new colorectal cancer risk loci (loci are the specific physical locations of a gene or variant, like the street address on the chromosome).
“To go from DNA to disease risk — from germline DNA to disease — is a complicated biological process,” Hsu said. “What sets this study apart is that we’re looking at the intermediate states — gene expression or methylation — trying to understand how the genetic variants impact the gene expression and further impact the disease risk.”
The investigators also identified credible effector genes — genes that activate a cellular process — and the tissues in which they act, furthering science’s understanding of how colorectal cancer gets started in the first place.
Thomas is a research associate tasked with creating colorectal cancer risk prediction models using machine learning and statistical approaches. She said incorporating the different genomic approaches made for a “very good study.”
“If you look at the results, we identified many genes functionally leading to colorectal cancer,” she said. “It offered us a biological glimpse inside these cancers.”
Peters, who holds the Fred Hutch 40th Anniversary Endowed Chair, looks forward to using the information to cut back on cancer cases.
“Most people don’t have a positive family history of colorectal cancer,” she said. “But this allows us to identify those at particularly high risk, who are not aware of their risk, and who could benefit from earlier, more frequent screenings or from dietary interventions. Some of these are modifiable risk factors.”