Interaction-integrated linear mixed model reveals 3D-genetic basis underlying Autism.

Genetic interactions play critical roles in genotype-phenotype associations. We developed a novel interaction-integrated linear mixed model (ILMM) that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems of searching for combinations. To demonstrate its utility, with 3D genomic interactions (assessed by Hi-C experiments) as a priori, we applied ILMM to whole-genome sequencing data for Autism Spectrum Disorders (ASD) and brain transcriptome data, revealing the 3D-genetic basis of ASD and 3D-expression quantitative loci (3D-eQTLs) for brain tissues. Notably, we reported a potential mechanism involving distal regulation between FOXP2 and DNMT3A, conferring the risk of ASD.

Pentad: a tool for distance-dependent analysis of Hi-C interactions within and between chromatin compartments.

BACKGROUND: Understanding the role of various factors in 3D genome organization is essential to determine their impact on shaping large-scale chromatin units such as euchromatin (A) and heterochromatin (B) compartments. At this level, chromatin compaction is extensively modulated when transcription and epigenetic profiles change upon cell differentiation and response to various external impacts. However, detailed analysis of chromatin contact patterns within and between compartments is complicated because of a lack of suitable computational methods. RESULTS: We developed a tool, Pentad, to perform calculation, visualisation and quantitative analysis of the average chromatin compartment from the Hi-C matrices in cis, trans, and specified genomic distances. As we demonstrated by applying Pentad to publicly available Hi-C datasets, it helps to reliably detect redistribution of contact frequency in the chromatin compartments and assess alterations in the compartment strength. CONCLUSIONS: Pentad is a simple tool for the analysis of changes in chromatin compartmentalization in various biological conditions. Pentad is freely available at https://github.com/magnitov/pentad .

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.

Introduction: Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods: We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results: MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Discussion: Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region.

A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods.

BACKGROUND: Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes. RESULTS: To facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactions. We use BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the TargetFinder and PEP supervised learning methods. We find that while TargetFinder is the best-performing method, it is only modestly better than a baseline distance method for most benchmark datasets when trained and tested with the same cell type and that TargetFinder often does not outperform the distance method when applied across cell types. CONCLUSIONS: Our results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.

Social network Analysis-based classifier (SNAc): A case study on time course gene expression data.

BACKGROUND AND OBJECTIVES: Social Network Analysis is an attractive approach to model and analyze complex networks. In recent years, several bioinformatics related networks have been modeled and analyzed thoroughly using social network analysis. The objective of this study is to build a social network analysis based classifier for time sequential data. METHODS: In this work, we model a genomic time sequential data as a 'social' network of interactions. We define interactions as similarity of patients' measurements. Using this 'genomic social network', we develop a classification model called Social Network Analysis-based Classifier. RESULTS: We conducted some experiments to demonstrate how the developed Social Network Analysis-based Classifier outperforms traditional classifiers by effectively classifying a time sequential genomic dataset. Best achieved accuracy is 64.51% and best f-measure is 78.34%. CONCLUSIONS: Our study emphasized Social Network Analysis-based Classifier Model as a powerful technique for analyzing a time sequential dataset. Eventually, the plan is to develop and evolve the Social Network Analysis-based Classifier model into a general classifier.

Chromatin architecture: A new dimension in the dynamic control of gene expression.

As the most recent evidence of eukaryotic cell complexity, genome architecture has astounded the scientific community and prompted a variety of technical and cognitive challenges. Several technologies have emerged and evidenced the integration of chromatin packaging and topology, epigenetic processes, and transcription for the pertinent regulation of gene expression. In the present addendum we present and discuss some of our recent research, directed toward the holistic comprehension of the processes by which plants respond to environmental and developmental stimuli. We propose that the study of genome topology and genomic interactions is essential for the understanding of the molecular mechanisms behind a phenotype. Even though our knowledge and understanding of genome architecture and hierarchy has improved substantially in the last few years -in Arabidopsis and other eukaryotes -, there is still a long way ahead in this relatively new field of study. For this, it is necessary to take advantage of the high resolution of the emerging available techniques, and perform integrative approaches with which it will be possible to depict the role of chromatin architecture in the regulation of transcription and ultimately, physiological processes.

Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments.

The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along with the methods required for low-level manipulation and processing of those classes. The InteractionSet package exploits existing infrastructure in the open-source Bioconductor project, while in turn being used by Bioconductor packages designed for higher-level analyses. For new packages, use of the functionality in InteractionSet will simplify development, allow access to more features and improve interoperability between packages.