Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method

Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci

MOTIVATION

CpG sites within the same genomic region often share similar methylation patterns and tend to be co-regulated by multiple genetic variants that may interact with one another.

RESULTS

We propose a multi-trait methylation random field (multi-MRF) method to evaluate the joint association between a set of CpG sites and a set of genetic variants. The proposed method has several advantages. First, it is a multi-trait method that allows flexible correlation structures between neighboring CpG sites (e.g. distance-based correlation). Second, it is also a multi-locus method that integrates the effect of multiple common and rare genetic variants. Third, it models the methylation traits with a beta distribution to characterize their bimodal and interval properties. Through simulations, we demonstrated that the proposed method had improved power over some existing methods under various disease scenarios. We further illustrated the proposed method via an application to a study of congenital heart defects (CHDs) with 83 cardiac tissue samples. Our results suggested that gene BACE2, a methylation quantitative trait locus (QTL) candidate, colocalized with expression QTLs in artery tibial and harbored genetic variants with nominal significant associations in two genome-wide association studies of CHD.

AVAILABILITY AND IMPLEMENTATION

https://github.com/chenlyu2656/Multi-MRF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Detecting methylation quantitative trait loci using a methylation random field method

DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

Interpreting Sequence Variation in PDAC-Predisposing Genes Using a Multi-Tier Annotation Approach Performed at the Gene, Patient, and Cohort Level

We investigated germline variation in pancreatic ductal adenocarcinoma (PDAC) predisposition genes in 535 patients, using a custom-built panel and a new complementary bioinformatic approach. Our panel assessed genes belonging to DNA repair, cell cycle checkpoints, migration, and preneoplastic pancreatic conditions. Our bioinformatics approach integrated annotations of variants by using data derived from both germline and somatic references. This integrated approach with expanded evidence enabled us to consider patterns even among private mutations, supporting a functional role for certain alleles, which we believe enhances individualized medicine beyond classic gene-centric approaches. Concurrent evaluation of three levels of evidence, at the gene, sample, and cohort level, has not been previously done. Overall, we identified in PDAC patient germline samples, 12% with mutations previously observed in pancreatic cancers, 23% with mutations previously discovered by sequencing other human tumors, and 46% with mutations with germline associations to cancer. Non-polymorphic protein-coding pathogenic variants were found in 18.4% of patient samples. Moreover, among patients with metastatic PDAC, 16% carried at least one pathogenic variant, and this subgroup was found to have an improved overall survival (22.0 months versus 9.8; p=0.008) despite a higher pre-treatment CA19-9 level (p=0.02). Genetic alterations in DNA damage repair genes were associated with longer overall survival among patients who underwent resection surgery (92 months vs. 46; p=0.06). ATM alterations were associated with more frequent metastatic stage (p = 0.04) while patients with BRCA1 or BRCA2 alterations had improved overall survival (79 months vs. 39; p=0.05). We found that mutations in genes associated with chronic pancreatitis were more common in non-white patients (p<0.001) and associated with longer overall survival (52 months vs. 26; p=0.004), indicating the need for greater study of the relationship among these factors. More than 90% of patients were found to have variants of uncertain significance, which is higher than previously reported. Furthermore, we generated 3D models for selected mutant proteins, which suggested distinct mechanisms underlying their dysfunction, likely caused by genetic alterations. Notably, this type of information is not predictable from sequence alone, underscoring the value of structural bioinformatics to improve genomic interpretation. In conclusion, the variation in PDAC predisposition genes appears to be more extensive than anticipated. This information adds to the growing body of literature on the genomic landscape of PDAC and brings us closer to a more widespread use of precision medicine for this challenging disease.

SAMD14 promoter methylation is strongly associated with gene expression and poor prognosis in gastric cancer

BACKGROUND

Gastric cancer (GC) is the fifth most common malignancy worldwide and the third leading cause of cancer-related mortality. In recent years, SAMD14 has been studied in various malignant cancers; however, little is known about the exact mechanisms of SAMD14 involvement in carcinogenesis and malignant progression.

METHODS

60 paired GC-normal gastric tissues were evaluated for their SAMD14 mRNA expression in relation to SAMD14 gene promoter methylation. GC patient survival was assessed by Kaplan-Meier analyses and a Cox's proportional hazard model was employed for multivariate analyses.

RESULTS

SAMD14 expression was significantly inversely correlated with the Borrmann type (P = 0.017), lymph node metastasis (P = 0.006) and tumor-node-metastasis (TNM) stage (P = 0.033). Methylation-specific PCR (MSP) revealed hyper-methylation of the SAMD14 promoter in 56.7% (34/60) of the primary GC tissues tested and in 10% (6/60) of matched non-malignant tissues. The SAMD14 promoter methylation status was also related to pathological differentiation, Borrmann type, TNM stage and lymph node metastasis. The results showed SAMD14 expression was significantly downregulated in Borrmann type, lymph node metastasis and TNM stage, which showed significantly higher methylation. SAMD14 promoter hyper-methylation was significantly associated with a poor prognosis and could serve as an independent marker for survival using multivariate Cox regression analysis.

CONCLUSIONS

Our results indicated that promoter methylation was a key mechanism contributing to the downregulation of SAMD14 in GC. SAMD14 may be an epigenetically silenced tumor suppressor gene, and hyper-methylation of the SAMD14 promoter may serve as a biomarker to predict the clinical outcome of GC.