Epigenome-wide association data implicates DNA methylation-mediated genetic risk in psoriasis.

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Abstract

Psoriasis is a chronic inflammatory skin disease characterized by epidermal hyperproliferation and altered keratinocyte differentiation and inflammation and is caused by the interplay of genetic and environmental factors. Previous studies have revealed that DNA methylation (DNAm) and genetic makers are closely associated with psoriasis, and strong evidences have shown that DNAm can be controlled by genetic factors, which attracted us to evaluate the relationship among DNAm, genetic makers, and disease status.

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A small cell lung cancer genome reports complex tobacco exposure signatures

Erin Pleasance, Philip J. Stephens, Sarah O’Meara … (2009)

SUMMARY Cancer is driven by mutation. Worldwide, tobacco smoking is the major lifestyle exposure that causes cancer, exerting carcinogenicity through >60 chemicals that bind and mutate DNA. Using massively parallel sequencing technology, we sequenced a small cell lung cancer cell line, NCI-H209, to explore the mutational burden associated with tobacco smoking. 22,910 somatic substitutions were identified, including 132 in coding exons. Multiple mutation signatures testify to the cocktail of carcinogens in tobacco smoke and their proclivities for particular bases and surrounding sequence context. Effects of transcription-coupled repair and a second, more general expression-linked repair pathway were evident. We identified a tandem duplication that duplicates exons 3-8 of CHD7 in-frame, and another two lines carrying PVT1-CHD7 fusion genes, suggesting that CHD7 may be recurrently rearranged in this disease. These findings illustrate the potential for next-generation sequencing to provide unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with cancer.

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Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease

Manuel A. Rivas, Mélissa Beaudoin, Agnès Gardet … (2011)

More than a thousand disease susceptibility loci have been identified via genome-wide association studies (GWAS) of common variants; however, the specific genes and full allelic spectrum of causal variants underlying these findings generally remain to be defined. We utilize pooled next-generation sequencing to study 56 genes in regions associated to Crohn’s Disease in 350 cases and 350 controls. Follow up genotyping of 70 rare and low-frequency protein-altering variants (MAF ~ .001-.05) in nine independent case-control series (16054 CD patients, 12153 UC patients, 17575 healthy controls) identifies four additional independent risk factors in NOD2, two additional protective variants in IL23R, a highly significant association to a novel, protective splice variant in CARD9 (p < 1e-16, OR ~ 0.29), as well as additional associations to coding variants in IL18RAP, CUL2, C1orf106, PTPN22 and MUC19. We extend the results of successful GWAS by providing novel, rare, and likely functional variants that will empower functional experiments and predictive models.

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Disentangling molecular relationships with a causal inference test

Joshua Millstein, Bin Zhang, Jun Zhu … (2009)

Background There has been intense effort over the past couple of decades to identify loci underlying quantitative traits as a key step in the process of elucidating the etiology of complex diseases. Recently there has been some effort to coalesce non-biased high-throughput data, e.g. high density genotyping and genome wide RNA expression, to drive understanding of the molecular basis of disease. However, a stumbling block has been the difficult question of how to leverage this information to identify molecular mechanisms that explain quantitative trait loci (QTL). We have developed a formal statistical hypothesis test, resulting in a p-value, to quantify uncertainty in a causal inference pertaining to a measured factor, e.g. a molecular species, which potentially mediates a known causal association between a locus and a quantitative trait. Results We treat the causal inference as a 'chain' of mathematical conditions that must be satisfied to conclude that the potential mediator is causal for the trait, where the inference is only as good as the weakest link in the chain. P-values are computed for the component conditions, which include tests of linkage and conditional independence. The Intersection-Union Test, in which a series of statistical tests are combined to form an omnibus test, is then employed to generate the overall test result. Using computer simulated mouse crosses, we show that type I error is low under a variety of conditions that include hidden variables and reactive pathways. We show that power under a simple causal model is comparable to other model selection techniques as well as Bayesian network reconstruction methods. We further show empirically that this method compares favorably to Bayesian network reconstruction methods for reconstructing transcriptional regulatory networks in yeast, recovering 7 out of 8 experimentally validated regulators. Conclusion Here we propose a novel statistical framework in which existing notions of causal mediation are formalized into a hypothesis test, thus providing a standard quantitative measure of uncertainty in the form of a p-value. The method is theoretically and computationally accessible and with the provided software may prove a useful tool in disentangling molecular relationships.

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Author and article information

Journal

Journal ID (iso-abbrev): Clin Epigenetics

Title: Clinical epigenetics

Publisher: Springer Nature

ISSN (Electronic): 1868-7083

ISSN (Print): 1868-7075

Publication date (Electronic): 2016

Volume: 8

Affiliations

[1 ] Institute and Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Meishan Road 81, Hefei, 230032 Anhui Province China ; The Key Laboratory of Dermatology, Ministry of Education, Anhui Medical University, Anhui, China ; Collaborative Innovation Center for Complex and Severe Dermatosis, Anhui Medical University, Hefei, 230032 Anhui China.

[2 ] Institute and Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Meishan Road 81, Hefei, 230032 Anhui Province China ; Hebrew SeniorLife Institute for Aging Research and Harvard Medical School, Boston, MA 02131 USA ; Molecular and Integrative Physiological Sciences, Harvard School of Public Health, Boston, MA 02115 USA ; Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA.

[3 ] Department of Dermatology, The Second Affiliated Hospital, Anhui Medical University, Hefei, 230601 Anhui China.

[4 ] Department of Biochemistry, University of New Mexico, Albuquerque, NM 87131 NM USA.

[5 ] Department of Genetics, and Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517 USA.

[6 ] Department of Dermatology, China-Japan Friendship Hospital, Beijing, 100029 China.

[7 ] Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI 48109 USA ; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA ; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109 USA.

[8 ] Hebrew SeniorLife Institute for Aging Research and Harvard Medical School, Boston, MA 02131 USA ; Molecular and Integrative Physiological Sciences, Harvard School of Public Health, Boston, MA 02115 USA ; Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA.

[9 ] Institute and Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Meishan Road 81, Hefei, 230032 Anhui Province China ; The Key Laboratory of Dermatology, Ministry of Education, Anhui Medical University, Anhui, China ; Collaborative Innovation Center for Complex and Severe Dermatosis, Anhui Medical University, Hefei, 230032 Anhui China ; Department of Dermatology, The Second Affiliated Hospital, Anhui Medical University, Hefei, 230601 Anhui China.