Identifying Novel Susceptibility Genes for Colorectal Cancer Risk From a Transcriptome-Wide Association Study of 125,478 Subjects

Abstract

See Covering the Cover synopsis on page 976. BACKGROUND AND AIMS: Susceptibility genes and the underlying mechanisms for the majority of risk loci identified by genome-wide association studies (GWAS) for colorectal cancer (CRC) risk remain largely unknown. We conducted a transcriptome-wide association study (TWAS) to identify putative susceptibility genes. METHODS: Gene-expression prediction models were built using transcriptome and genetic data from the 284 normal transverse colon tissues of European descendants from the Genotype-Tissue Expression (GTEx), and model performance was evaluated using data from The Cancer Genome Atlas (n ¼ 355). We applied the gene-expression prediction models and GWAS data to evaluate associations of genetically predicted gene-expression with CRC risk in 58,131 CRC cases and 67,347 controls of European ancestry. Dualluciferase reporter assays and knockdown experiments in CRC cells and tumor xenografts were conducted. RESULTS: We identified 25 genes associated with CRC risk at a Bonferronicorrected threshold of P < 9.1 Â 10 -6 , including genes in 4 novel loci, PYGL (14q22.1), RPL28 (19q13.42), CAPN12 (19q13.2), MYH7B (20q11.22), and MAP1L3CA (20q11.22). In 9 known GWAS-identified loci, we uncovered 9 genes that have not been reported previously, whereas 4 genes remained statistically significant after adjusting for the lead risk variant of the locus. Through colocalization analysis in GWAS loci, we additionally identified 12 putative susceptibility genes that were supported by TWAS analysis at P < .01. We showed that risk allele of the lead risk variant rs1741640 affected the promoter activity of CABLES2. Knockdown experiments confirmed that CABLES2 plays a vital role in colorectal carcinogenesis. CONCLUSIONS: Our study reveals new putative susceptibility genes and provides new insight into the biological mechanisms underlying CRC development.

Multi-tissue neocortical transcriptome-wide association study implicates 8 genes across 6 genomic loci in Alzheimer’s disease

“…We trained predictive models to impute gene-expression components attributable to cis-variation across multiple neocortical tissues using a well-powered training set compared to the field of TWAS studies [35,36]. This is the first pan-cortical analysis and is broadly abstractable throughout the neocortex, providing a valuable resource to investigate a multitude neurological conditions and disorders.…”

Presence of Comorbidities Associated with Severe Coronavirus Infection in Patients with Inflammatory Bowel Disease

“…This research utilized the Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease (SECURE-IBD) database. The creation of this database has been previously described [ 18 ]. In brief, healthcare providers reported demographic information of patients with IBD with confirmed COVID-19, as well as IBD treatments and outcomes of COVID-19 infection.…”

“…Reporters were asked “Does the patient have any of the following comorbidities (check all that apply)?” and provided with a list of comorbidities to select from, as well as the opportunity to enter any other comorbidities afflicting the patient. Reporters are requested to submit cases after one week of symptoms and sufficient time has passed to observe the clinical course of the illness through resolution or death [ 18 ].…”

“…Our group has previously published research utilizing an international database of patients with IBD who were infected with COVID. A key finding was that, in patients with IBD, increasing age and number of comorbidities were significantly associated with the severity of COVID-19 [ 18 ]. However, the relative effect of specific comorbidities in patients with IBD has not yet been established.…”

Identification of a ferroptosis-related gene signature predictive model in colon cancer

“…Therefore, a reliable prognosis assessment model is eagerly awaited to predict the prognosis of patients with CC and to optimize clinical treatment strategies. Furthermore, with the development of next-generation sequencing technology, the perception of the cancer molecular network and transcriptomic analysis can provide better technical support for prediction of CC prognosis [5,6].…”