Browsing by Subject "Epigenomics"

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Open Access
Genetics and epigenetics of liver cancer
(Elsevier, 2013) Özen, Çiğdem; Yıldız, Gökhan; Dağcan, Alper Tunga; Çevik, Dilek; Örs, Ayşegül; Keleş, Umut; Topel, Hande; Öztürk, Mehmet
Hepatocellular carcinoma (HCC) represents a major form of primary liver cancer in adults. Chronic infections with hepatitis B (HBV) and C (HCV) viruses and alcohol abuse are the major factors leading to HCC. This deadly cancer affects more than 500,000 people worldwide and it is quite resistant to conventional chemo- and radiotherapy. Genetic and epigenetic studies on HCC may help to understand better its mechanisms and provide new tools for early diagnosis and therapy. Recent literature on whole genome analysis of HCC indicated a high number of mutated genes in addition to well-known genes such as TP53, CTNNB1, AXIN1 and CDKN2A, but their frequencies are much lower. Apart from CTNNB1 mutations, most of the other mutations appear to result in loss-of-function. Thus, HCC-associated mutations cannot be easily targeted for therapy. Epigenetic aberrations that appear to occur quite frequently may serve as new targets. Global DNA hypomethylation, promoter methylation, aberrant expression of non-coding RNAs and dysregulated expression of other epigenetic regulatory genes such as EZH2 are the best-known epigenetic abnormalities. Future research in this direction may help to identify novel biomarkers and therapeutic targets for HCC.
Open Access
A statistical framework for mapping risk genes from de novo mutations in whole-genome-sequencing studies
(Cell Press, 2018) Liu, Y.; Liang, Y.; Çiçek, A. Ercüment; Li, Z.; Li, J.; Muhle, R. A.; Krenzer, M.; Mei, Y.; Wang Y.; Knoblauch, N.; Morrison, J.; Zhao, S.; Jiang, Y.; Geller, E.; Ionita-Laza, I.; Wu, J.; Xia, K.; Noonan, J. P.; Sun, Z. S.; He, X.
Analysis of de novo mutations (DNMs) from sequencing data of nuclear families has identified risk genes for many complex diseases, including multiple neurodevelopmental and psychiatric disorders. Most of these efforts have focused on mutations in protein-coding sequences. Evidence from genome-wide association studies (GWASs) strongly suggests that variants important to human diseases often lie in non-coding regions. Extending DNM-based approaches to non-coding sequences is challenging, however, because the functional significance of non-coding mutations is difficult to predict. We propose a statistical framework for analyzing DNMs from whole-genome sequencing (WGS) data. This method, TADA-Annotations (TADA-A), is a major advance of the TADA method we developed earlier for DNM analysis in coding regions. TADA-A is able to incorporate many functional annotations such as conservation and enhancer marks, to learn from data which annotations are informative of pathogenic mutations, and to combine both coding and non-coding mutations at the gene level to detect risk genes. It also supports meta-analysis of multiple DNM studies, while adjusting for study-specific technical effects. We applied TADA-A to WGS data of ∼300 autism-affected family trios across five studies and discovered several autism risk genes. The software is freely available for all research uses.