Hope for GWAS: relevant risk genes uncovered from GWAS statistical noise

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.

Polymorphisms within base and nucleotide excision repair pathways and risk of differentiated thyroid carcinoma

The thyrocytes are exposed to high levels of oxidative stress which could induce DNA damages. Base excision repair (BER) is one of the principal mechanisms of defense against oxidative DNA damage, however recent evidences suggest that also nucleotide excision repair (NER) could be involved. The aim of present work was to identify novel differentiated thyroid cancer (DTC) risk variants in BER and NER genes. For this purpose, the most strongly associated SNPs within NER and BER genes found in our previous GWAS on DTC were selected and replicated in an independent series of samples for a new case-control study. Although a positive signal was detected at the nominal level of 0.05 for rs7689099 (encoding for an aminoacid change proline to arginine at codon 117 within NEIL3), none of the considered SNPs (i.e. rs7990340 and rs690860 within RFC3, rs3744767 and rs1131636 within RPA1, rs16962916 and rs3136166 in ERCC4, and rs17739370 and rs7689099 in NEIL3) was associated with the risk of DTC when the correction of multiple testing was applied. In conclusion, a role of NER and BER pathways was evoked in the susceptibility to DTC. However, this seemed to be limited to few polymorphic genes and the overall effect size appeared weak.