High-resolution genetic mapping of putative causal interactions between regions of open chromatin

Complexities of Understanding Function from CKD-Associated DNA Variants

Genome-wide association studies (GWASs) have facilitated the unbiased discovery of hundreds of genomic loci associated with CKD and kidney function. The vast majority of disease-associated DNA variants are noncoding. Those that are causal in CKD pathogenesis likely modulate transcription of target genes in a cell type-specific manner. To gain novel biological insights into mechanisms driving the development of CKD, the causal variants (which are usually not the most significant variant reported in a GWAS), their target genes, and causal cell types need to be identified. This functional validation requires a large number of new data sets, complex bioinformatics analyses, and experimental cellular and in vivo studies. Here, we review the basic principles and some of the current approaches being leveraged to assign functional significance to a genotype-phenotype association.

Systems Genetics for Mechanistic Discovery in Heart Diseases

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

TypeTE: a tool to genotype mobile element insertions from whole genome resequencing data

Alu retrotransposons account for more than 10% of the human genome, and insertions of these elements create structural variants segregating in human populations. Such polymorphic Alus are powerful markers to understand population structure, and they represent variants that can greatly impact genome function, including gene expression. Accurate genotyping of Alus and other mobile elements has been challenging. Indeed, we found that Alu genotypes previously called for the 1000 Genomes Project are sometimes erroneous, which poses significant problems for phasing these insertions with other variants that comprise the haplotype. To ameliorate this issue, we introduce a new pipeline - TypeTE - which genotypes Alu insertions from whole-genome sequencing data. Starting from a list of polymorphic Alus, TypeTE identifies the hallmarks (poly-A tail and target site duplication) and orientation of Alu insertions using local re-assembly to reconstruct presence and absence alleles. Genotype likelihoods are then computed after re-mapping sequencing reads to the reconstructed alleles. Using a high-quality set of PCR-based genotyping of >200 loci, we show that TypeTE improves genotype accuracy from 83% to 92% in the 1000 Genomes dataset. TypeTE can be readily adapted to other retrotransposon families and brings a valuable toolbox addition for population genomics.

Promoter-anchored chromatin interactions predicted from genetic analysis of epigenomic data

Promoter-anchored chromatin interactions (PAIs) play a pivotal role in transcriptional regulation. Current high-throughput technologies for detecting PAIs, such as promoter capture Hi-C, are not scalable to large cohorts. Here, we present an analytical approach that uses summary-level data from cohort-based DNA methylation (DNAm) quantitative trait locus (mQTL) studies to predict PAIs. Using mQTL data from human peripheral blood ([Formula: see text]), we predict 34,797 PAIs which show strong overlap with the chromatin contacts identified by previous experimental assays. The promoter-interacting DNAm sites are enriched in enhancers or near expression QTLs. Genes whose promoters are involved in PAIs are more actively expressed, and gene pairs with promoter-promoter interactions are enriched for co-expression. Integration of the predicted PAIs with GWAS data highlight interactions among 601 DNAm sites associated with 15 complex traits. This study demonstrates the use of mQTL data to predict PAIs and provides insights into the role of PAIs in complex trait variation.

Contribution of unfixed transposable element insertions to human regulatory variation

Thousands of unfixed transposable element (TE) insertions segregate in the human population, but little is known about their impact on genome function. Recently, a few studies associated unfixed TE insertions to mRNA levels of adjacent genes, but the biological significance of these associations, their replicability across cell types and the mechanisms by which they may regulate genes remain largely unknown. Here, we performed a TE-expression QTL analysis of 444 lymphoblastoid cell lines (LCL) and 289 induced pluripotent stem cells using a newly developed set of genotypes for 2743 polymorphic TE insertions. We identified 211 and 176 TE-eQTL acting in cis in each respective cell type. Approximately 18% were shared across cell types with strongly correlated effects. Furthermore, analysis of chromatin accessibility QTL in a subset of the LCL suggests that unfixed TEs often modulate the activity of enhancers and other distal regulatory DNA elements, which tend to lose accessibility when a TE inserts within them. We also document a case of an unfixed TE likely influencing gene expression at the post-transcriptional level. Our study points to broad and diverse cis-regulatory effects of unfixed TEs in the human population and underscores their plausible contribution to phenotypic variation. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.

A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine

Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.

A practical view of fine-mapping and gene prioritization in the post-genome-wide association era

Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.

EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model

Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms. However, existing computational methods mainly focus on the prediction of interactions between enhancers and promoters, leaving enhancer-enhancer (E-E) interactions not well explored. In this work, we develop a novel deep learning approach, named Enhancer-enhancer contacts prediction (EnContact), to predict E-E contacts using genomic sequences as input. We statistically demonstrated the predicting ability of EnContact using training sets and testing sets derived from HiChIP data of seven cell lines. We also show that our model significantly outperforms other baseline methods. Besides, our model identifies finer-mapping E-E interactions from region-based chromatin contacts, where each region contains several enhancers. In addition, we identify a class of hub enhancers using the predicted E-E interactions and find that hub enhancers tend to be active across cell lines. We summarize that our EnContact model is capable of predicting E-E interactions using features automatically learned from genomic sequences.

Mapping causal pathways from genetics to neuropsychiatric disorders using genome-wide imaging genetics: Current status and future directions

Imaging genetics aims to identify genetic variants associated with the structure and function of the human brain. Recently, collaborative consortia have been successful in this goal, identifying and replicating common genetic variants influencing gross human brain structure as measured through magnetic resonance imaging. In this review, we contextualize imaging genetic associations as one important link in understanding the causal chain from genetic variant to increased risk for neuropsychiatric disorders. We provide examples in other fields of how identifying genetic variant associations to disease and multiple phenotypes along the causal chain has revealed a mechanistic understanding of disease risk, with implications for how imaging genetics can be similarly applied. We discuss current findings in the imaging genetics research domain, including that common genetic variants can have a slightly larger effect on brain structure than on risk for disorders like schizophrenia, indicating a somewhat simpler genetic architecture. Also, gross brain structure measurements share a genetic basis with some, but not all, neuropsychiatric disorders, invalidating the previously held belief that they are broad endophenotypes, yet pinpointing brain regions likely involved in the pathology of specific disorders. Finally, we suggest that in order to build a more detailed mechanistic understanding of the effects of genetic variants on the brain, future directions in imaging genetics research will require observations of cellular and synaptic structure in specific brain regions beyond the resolution of magnetic resonance imaging. We expect that integrating genetic associations at biological levels from synapse to sulcus will reveal specific causal pathways impacting risk for neuropsychiatric disorders.

Genetic effects on promoter usage are highly context-specific and contribute to complex traits

Genetic variants regulating RNA splicing and transcript usage have been implicated in both common and rare diseases. Although transcript usage quantitative trait loci (tuQTLs) have been mapped across multiple cell types and contexts, it is challenging to distinguish between the main molecular mechanisms controlling transcript usage: promoter choice, splicing and 3' end choice. Here, we analysed RNA-seq data from human macrophages exposed to three inflammatory and one metabolic stimulus. In addition to conventional gene-level and transcript-level analyses, we also directly quantified promoter usage, splicing and 3' end usage. We found that promoters, splicing and 3' ends were predominantly controlled by independent genetic variants enriched in distinct genomic features. Promoter usage QTLs were also 50% more likely to be context-specific than other tuQTLs and constituted 25% of the transcript-level colocalisations with complex traits. Thus, promoter usage might be an underappreciated molecular mechanism mediating complex trait associations in a context-specific manner.