Functional disease architectures reveal unique biological role of transposable elements

SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease

We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.

The Role of Transposable Elements in Sexual Development

Up to 50% of most mammalian genomes are made up of transposable elements (TEs) that have the potential to mobilize around the genome. Despite this prevalence, research on TEs is only beginning to gain traction within the field of neuroscience. While TEs have long been regarded as “junk” or parasitic DNA, it has become evident that they are adaptive DNA and RNA regulatory elements. In addition to their vital role in normal development, TEs can also interact with steroid receptors, which are key elements to sexual development. In this review, we provide an overview of the involvement of TEs in processes related to sexual development- from TE activity in the germline to TE accumulation in sex chromosomes. Moreover, we highlight sex differences in TE activity and their regulation of genes related to sexual development. Finally, we speculate on the epigenetic mechanisms that may govern TEs’ role in sexual development. In this context, we emphasize the need to further the understanding of sexual development through the lens of TEs including in a variety of organs at different developmental stages, their molecular networks, and evolution.

The "missing heritability"-Problem in psychiatry: Is the interaction of genetics, epigenetics and transposable elements a potential solution?

Psychiatric disorders exhibit an enormous burden on the health care systems worldwide accounting for around one-third of years lost due to disability among adults. Their etiology is largely unknown and diagnostic classification is based on symptomatology and course of illness and not on objective biomarkers. Most psychiatric disorders are moderately to highly heritable. However, it is still unknown what mechanisms may explain the discrepancy between heritability estimates and the present data from genetic analysis. In addition to genetic differences also epigenetic modifications are considered as potentially relevant in the transfer of susceptibility to psychiatric diseases. Though, whether or not epigenetic alterations can be inherited for many generations is highly controversial. In the present article, we will critically summarize both the genetic findings and the results from epigenetic analyses, including also those of noncoding RNAs. We will argue that one possible solution to the "missing heritability" problem in psychiatry is a potential role of retrotransposons, the exploration of which is presently only in its beginnings.

Topologically associating domain boundaries that are stable across diverse cell types are evolutionarily constrained and enriched for heritability

Topologically associating domains (TADs) are fundamental units of three-dimensional (3D) nuclear organization. The regions bordering TADs-TAD boundaries-contribute to the regulation of gene expression by restricting interactions of cis-regulatory sequences to their target genes. TAD and TAD-boundary disruption have been implicated in rare-disease pathogenesis; however, we have a limited framework for integrating TADs and their variation across cell types into the interpretation of common-trait-associated variants. Here, we investigate an attribute of 3D genome architecture-the stability of TAD boundaries across cell types-and demonstrate its relevance to understanding how genetic variation in TADs contributes to complex disease. By synthesizing TAD maps across 37 diverse cell types with 41 genome-wide association studies (GWASs), we investigate the differences in disease association and evolutionary pressure on variation in TADs versus TAD boundaries. We demonstrate that genetic variation in TAD boundaries contributes more to complex-trait heritability, especially for immunologic, hematologic, and metabolic traits. We also show that TAD boundaries are more evolutionarily constrained than TADs. Next, stratifying boundaries by their stability across cell types, we find substantial variation. Compared to boundaries unique to a specific cell type, boundaries stable across cell types are further enriched for complex-trait heritability, evolutionary constraint, CTCF binding, and housekeeping genes. Thus, considering TAD boundary stability across cell types provides valuable context for understanding the genome's functional landscape and enabling variant interpretation that takes 3D structure into account.

Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease

Despite considerable progress on pathogenicity scores prioritizing variants for Mendelian disease, little is known about the utility of these scores for common disease. Here, we assess the informativeness of Mendelian disease-derived pathogenicity scores for common disease and improve upon existing scores. We first apply stratified linkage disequilibrium (LD) score regression to evaluate published pathogenicity scores across 41 common diseases and complex traits (average N = 320K). Several of the resulting annotations are informative for common disease, even after conditioning on a broad set of functional annotations. We then improve upon published pathogenicity scores by developing AnnotBoost, a machine learning framework to impute and denoise pathogenicity scores using a broad set of functional annotations. AnnotBoost substantially increases the informativeness for common disease of both previously uninformative and previously informative pathogenicity scores, implying that Mendelian and common disease variants share similar properties. The boosted scores also produce improvements in heritability model fit and in classifying disease-associated, fine-mapped SNPs. Our boosted scores may improve fine-mapping and candidate gene discovery for common disease.

Evaluating the informativeness of deep learning annotations for human complex diseases

Deep learning models have shown great promise in predicting regulatory effects from DNA sequence, but their informativeness for human complex diseases is not fully understood. Here, we evaluate genome-wide SNP annotations from two previous deep learning models, DeepSEA and Basenji, by applying stratified LD score regression to 41 diseases and traits (average N = 320K), conditioning on a broad set of coding, conserved and regulatory annotations. We aggregated annotations across all (respectively blood or brain) tissues/cell-types in meta-analyses across all (respectively 11 blood or 8 brain) traits. The annotations were highly enriched for disease heritability, but produced only limited conditionally significant results: non-tissue-specific and brain-specific Basenji-H3K4me3 for all traits and brain traits respectively. We conclude that deep learning models have yet to achieve their full potential to provide considerable unique information for complex disease, and that their conditional informativeness for disease cannot be inferred from their accuracy in predicting regulatory annotations.

Deep learning models have shown great promise in predicting regulatory effects from DNA sequence. Here the authors evaluate sequence-based epigenomic deep learning models and conclude that these models are not yet ready to inform our knowledge of human disease.

Genome-wide analysis of polymerase III-transcribed Alu elements suggests cell-type-specific enhancer function

Alu elements are one of the most successful families of transposons in the human genome. A portion of Alu elements is transcribed by RNA Pol III, whereas the remaining ones are part of Pol II transcripts. Because Alu elements are highly repetitive, it has been difficult to identify the Pol III–transcribed elements and quantify their expression levels. In this study, we generated high-resolution, long-genomic-span RAMPAGE data in 155 biosamples all with matching RNA-seq data and built an atlas of 17,249 Pol III–transcribed Alu elements. We further performed an integrative analysis on the ChIP-seq data of 10 histone marks and hundreds of transcription factors, whole-genome bisulfite sequencing data, ChIA-PET data, and functional data in several biosamples, and our results revealed that although the human-specific Alu elements are transcriptionally repressed, the older, expressed Alu elements may be exapted by the human host to function as cell-type–specific enhancers for their nearby protein-coding genes.