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Identification of causal noncoding single nucleotide polymorphisms (SNPs) is important for maximizing the knowledge dividend from human genome-wide association studies (GWAS). Recently, diverse machine learning-based methods have been used for functional SNP identification; however, this task remains a fundamental challenge in computational biology. We report CERENKOV3, a machine learning pipeline that leverages clustering-derived and molecular network-derived features to improve prediction accuracy of regulatory SNPs (rSNPs) in the context of post-GWAS analysis. The clustering-derived feature, locus size (number of SNPs in the locus), derives from our locus partitioning procedure and represents the sizes of clusters based on SNP locations. We generated two molecular network-derived features from representation learning on a network representing SNP-gene and gene-gene relations. Based on empirical studies using a ground-truth SNP dataset, CERENKOV3 significantly improves rSNP recognition performance in AUPRC, AUROC, and AVGRANK (a locus-wise rank-based measure of classification accuracy we previously proposed).

We report the development of a homogenous assay for the genotyping of single-nucleotide polymorphisms (SNPs), utilizing a fluorescent dsDNA-binding dye. Termed T_M-shift genotyping, this method combines multiplex allele-specific PCR with sequence differentiation based on the melting temperatures of amplification products. Allele-specific primers differing in length were used with a common reverse primer in a single-tube assay. PCR amplification followed by melt curve analysis was performed with a fluorescent dsDNA-binding dye on a real-time capillary thermocycler. Genotyping was carried out in a single-tube homogeneous assay in 25 minutes. We compared the accuracy and efficiency of this T_M genotyping method with conventional restriction fragment genotyping of a novel single nucleotide polymorphism in the Jagged1 (JAG1) gene. The flexibility, economy and accuracy of this new method for genotyping polymorphisms could make it useful for a variety of research and diagnostic applications.

Eighty percent of DNA outside protein coding regions was shown biochemically functional by the ENCODE project, enabling studies of their interactions. Studies have since explored how convergent downstream mechanisms arise from independent genetic risks of one complex disease. However, the cross-talk and epistasis between intergenic risks associated with distinct complex diseases have not been comprehensively characterized. Our recent integrative genomic analysis unveiled downstream biological effectors of disease-specific polymorphisms buried in intergenic regions, and we then validated their genetic synergy and antagonism in distinct GWAS. We extend this approach to characterize convergent downstream candidate mechanisms of distinct intergenic SNPs across distinct diseases within the same clinical classification. We construct a multipartite network consisting of 467 diseases organized in 15 classes, 2,358 disease-associated SNPs, 6,301 SNPassociated mRNAs by eQTL, and mRNA annotations to 4,538 Gene Ontology mechanisms. Functional similarity between two SNPs (similar SNP pairs) is imputed using a nested information theoretic distance model for which p-values are assigned by conservative scale-free permutation of network edges without replacement (node degrees constant). At FDR≤5%, we prioritized 3,870 intergenic SNP pairs associated, among which 755 are associated with distinct diseases sharing the same disease class, implicating 167 intergenic SNPs, 14 classes, 230 mRNAs, and 134 GO terms. Co-classified SNP pairs were more likely to be prioritized as compared to those of distinct classes confirming a noncoding genetic underpinning to clinical classification (odds ratio ∼3.8; p≤10-25). The prioritized pairs were also enriched in regions bound to the same/interacting transcription factors and/or interacting in long-range chromatin interactions suggestive of epistasis (odds ratio ∼ 2,500; p≤10^-25). This prioritized network implicates complex epistasis between intergenic polymorphisms of co-classified diseases and offers a roadmap for a novel therapeutic paradigm: repositioning medications that target proteins within downstream mechanisms of intergenic disease-associated SNPs. Supplementary information and software: http://lussiergroup.org/publications/disease_class

Last years the study of the influence of genetic factors in the susceptibility to some common diseases has been obtaining satisfactory results. These results contribute to the prevention of these diseases as well as to design personalized treatments. The present work introduces a technique based on 2D representations of the genetic data that can contribute to find these disease associations. A real case application is presented in which we analyze the relation between the allele pair values of 748 Single Nucleotide Polymorphisms (SNPs) and the susceptibility to seven common psychiatrical disorders.