FIRE: functional inference of genetic variants that regulate gene expression

Exploring non-coding genetic variability in ACE2: Functional annotation and in vitro validation of regulatory variants

The surge in human whole-genome sequencing data has facilitated the study of non-coding region variations, yet understanding their biological significance remains a challenge. We used a computational workflow to assess the regulatory potential of non-coding variants, with a particular focus on the Angiotensin Converting Enzyme 2 (ACE2) gene. This gene is crucial in physiological processes and serves as the entry point for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 19 (COVID-19). In our analysis, using data from the gnomAD population database and functional annotation, we identified 17 significant Single Nucleotide Variants (SNVs) in ACE2, particularly in its enhancers, promoters, and 3' untranslated regions (UTRs). We found preliminary evidence supporting the regulatory impact of some of these variants on ACE2 expression. Our detailed examination of two SNVs, rs147718775 and rs140394675, in the ACE2 promoter revealed that these co-occurring SNVs, when mutated, significantly enhance promoter activity, suggesting a possible increase in specific ACE2 isoform expression. This method proves effective in identifying and interpreting impactful non-coding variants, aiding in further studies and enhancing understanding of molecular bases of monogenic and complex traits.

GUANinE v1.0: Benchmark Datasets for Genomic AI Sequence-to-Function Models

Computational genomics increasingly relies on machine learning methods for genome interpretation, and the recent adoption of neural sequence-to-function models highlights the need for rigorous model specification and controlled evaluation, problems familiar to other fields of AI. Research strategies that have greatly benefited other fields - including benchmarking, auditing, and algorithmic fairness - are also needed to advance the field of genomic AI and to facilitate model development. Here we propose a genomic AI benchmark, GUANinE, for evaluating model generalization across a number of distinct genomic tasks. Compared to existing task formulations in computational genomics, GUANinE is large-scale, de-noised, and suitable for evaluating pretrained models. GUANinE v1.0 primarily focuses on functional genomics tasks such as functional element annotation and gene expression prediction, and it also draws upon connections to evolutionary biology through sequence conservation tasks. The current GUANinE tasks provide insight into the performance of existing genomic AI models and non-neural baselines, with opportunities to be refined, revisited, and broadened as the field matures. Finally, the GUANinE benchmark allows us to evaluate new self-supervised T5 models and explore the tradeoffs between tokenization and model performance, while showcasing the potential for self-supervision to complement existing pretraining procedures.

Topologically associating domains define the impact of de novo promoter variants on autism spectrum disorder risk

Whole-genome sequencing (WGS) studies of autism spectrum disorder (ASD) have demonstrated the roles of rare promoter de novo variants (DNVs). However, most promoter DNVs in ASD are not located immediately upstream of known ASD genes. In this study analyzing WGS data of 5,044 ASD probands, 4,095 unaffected siblings, and their parents, we show that promoter DNVs within topologically associating domains (TADs) containing ASD genes are significantly and specifically associated with ASD. An analysis considering TADs as functional units identified specific TADs enriched for promoter DNVs in ASD and indicated that common variants in these regions also confer ASD heritability. Experimental validation using human induced pluripotent stem cells (iPSCs) showed that likely deleterious promoter DNVs in ASD can influence multiple genes within the same TAD, resulting in overall dysregulation of ASD-associated genes. These results highlight the importance of TADs and gene-regulatory mechanisms in better understanding the genetic architecture of ASD.

NCAD v1.0: a database for non-coding variant annotation and interpretation

The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.

VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome

VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.

Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481-0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984-0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837-0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766-0.5188). We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.

The Impact of Stability Considerations on Genetic Fine-Mapping

Fine-mapping methods, which aim to identify genetic variants responsible for complex traits following genetic association studies, typically assume that sufficient adjustments for confounding within the association study cohort have been made, e.g., through regressing out the top principal components (i.e., residualization). Despite its widespread use, however, residualization may not completely remove all sources of confounding. Here, we propose a complementary stability-guided approach that does not rely on residualization, which identifies consistently fine-mapped variants across different genetic backgrounds or environments. We demonstrate the utility of this approach by applying it to fine-map eQTLs in the GEUVADIS data. Using 378 different functional annotations of the human genome, including recent deep learning-based annotations (e.g., Enformer), we compare enrichments of these annotations among variants for which the stability and traditional residualization-based fine-mapping approaches agree against those for which they disagree, and find that the stability approach enhances the power of traditional fine-mapping methods in identifying variants with functional impact. Finally, in cases where the two approaches report distinct variants, our approach identifies variants comparably enriched for functional annotations. Our findings suggest that the stability principle, as a conceptually simple device, complements existing approaches to fine-mapping, reinforcing recent advocacy of evaluating cross-population and cross-environment portability of biological findings. To support visualization and interpretation of our results, we provide a Shiny app, available at: https://alan-aw.shinyapps.io/stability_v0/.

GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) a meta-analysis for Alzheimer's disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.

Multi-omics approach dissects cis-regulatory mechanisms underlying North Carolina macular dystrophy, a retinal enhanceropathy

North Carolina macular dystrophy (NCMD) is a rare autosomal-dominant disease affecting macular development. The disease is caused by non-coding single-nucleotide variants (SNVs) in two hotspot regions near PRDM13 and by duplications in two distinct chromosomal loci, overlapping DNase I hypersensitive sites near either PRDM13 or IRX1. To unravel the mechanisms by which these variants cause disease, we first established a genome-wide multi-omics retinal database, RegRet. Integration of UMI-4C profiles we generated on adult human retina then allowed fine-mapping of the interactions of the PRDM13 and IRX1 promoters and the identification of eighteen candidate cis-regulatory elements (cCREs), the activity of which was investigated by luciferase and Xenopus enhancer assays. Next, luciferase assays showed that the non-coding SNVs located in the two hotspot regions of PRDM13 affect cCRE activity, including two NCMD-associated non-coding SNVs that we identified herein. Interestingly, the cCRE containing one of these SNVs was shown to interact with the PRDM13 promoter, demonstrated in vivo activity in Xenopus, and is active at the developmental stage when progenitor cells of the central retina exit mitosis, suggesting that this region is a PRDM13 enhancer. Finally, mining of single-cell transcriptional data of embryonic and adult retina revealed the highest expression of PRDM13 and IRX1 when amacrine cells start to synapse with retinal ganglion cells, supporting the hypothesis that altered PRDM13 or IRX1 expression impairs interactions between these cells during retinogenesis. Overall, this study provides insight into the cis-regulatory mechanisms of NCMD and supports that this condition is a retinal enhanceropathy.

TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning

MOTIVATION

Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk.

RESULTS

With thousands of tissue- and cell-type-specific epigenomic features, we propose TVAR. This multi-label learning-based deep neural network predicts the functionality of non-coding variants in the genome based on eQTLs across 49 human tissues in the GTEx project. TVAR learns the relationships between high-dimensional epigenomics and eQTLs across tissues, taking the correlation among tissues into account to understand shared and tissue-specific eQTL effects. As a result, TVAR outputs tissue-specific annotations, with an average AUROC of 0.77 across these tissues. We evaluate TVAR's performance on four complex diseases (coronary artery disease, breast cancer, Type 2 diabetes and Schizophrenia), using TVAR's tissue-specific annotations, and observe its superior performance in predicting functional variants for both common and rare variants, compared with five existing state-of-the-art tools. We further evaluate TVAR's G-score, a scoring scheme across all tissues, on ClinVar, fine-mapped GWAS loci, Massive Parallel Reporter Assay (MPRA) validated variants and observe the consistently better performance of TVAR compared with other competing tools.

AVAILABILITY AND IMPLEMENTATION

The TVAR source code and its scores on the ClinVar catalog, fine mapped GWAS Loci, high confidence eQTLs from GTEx dataset, and MPRA validated functional variants are available at https://github.com/haiyang1986/TVAR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Natural and Experimental Rewiring of Gene Regulatory Regions

The successful development and ongoing functioning of complex organisms depend on the faithful execution of the genetic code. A critical step in this process is the correct spatial and temporal expression of genes. The highly orchestrated transcription of genes is controlled primarily by cis-regulatory elements: promoters, enhancers, and insulators. The medical importance of this key biological process can be seen by the frequency with which mutations and inherited variants that alter cis-regulatory elements lead to monogenic and complex diseases and cancer. Here, we provide an overview of the methods available to characterize and perturb gene regulatory circuits. We then highlight mechanisms through which regulatory rewiring contributes to disease, and conclude with a perspective on how our understanding of gene regulation can be used to improve human health.

"Demystifying non-coding GWAS variants: an overview of computational tools and methods."

Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and their mechanism of action. In this review, we give an overview of current tools and methods for the analysis of non-coding GWAS variants in disease. We provide a workflow that allows for the accumulation of in silico evidence to generate novel hypotheses on mechanisms underlying disease and prioritize targets for follow-up study using non-coding GWAS variants. Lastly, we discuss the need for comprehensive benchmarks and novel tools for the analysis of non-coding variants.

Universal annotation of the human genome through integration of over a thousand epigenomic datasets

BACKGROUND

Genome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative "stacked modeling" approach was previously suggested, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges.

RESULTS

Using a version of ChromHMM enhanced for large-scale applications, we apply the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types, with the learned model denoted as the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations, which we use in characterizing each state. Compared to per-cell-type annotations, the full-stack annotations directly differentiate constitutive from cell type-specific activity and is more predictive of locations of external genomic annotations.

CONCLUSIONS

The full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing per-cell-type annotations for studying the non-coding human genome.

OUP accepted manuscript

Non-coding variants have long been recognized as important contributors to common disease risks, but with the expansion of clinical whole genome sequencing, examples of rare, high-impact non-coding variants are also accumulating. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging. Here, we propose a new framework for the prioritization of non-coding regulatory variants that integrates information about regulatory regions with prediction scores and HPO-based prioritization. Firstly, we created a comprehensive collection of annotations for regulatory regions including a database of 2.4 million regulatory elements (GREEN-DB) annotated with controlled gene(s), tissue(s) and associated phenotype(s) where available. Secondly, we calculated a variation constraint metric and showed that constrained regulatory regions associate with disease-associated genes and essential genes from mouse knock-outs. Thirdly, we compared 19 non-coding impact prediction scores providing suggestions for variant prioritization. Finally, we developed a VCF annotation tool (GREEN-VARAN) that can integrate all these elements to annotate variants for their potential regulatory impact. In our evaluation, we show that GREEN-DB can capture previously published disease-associated non-coding variants as well as identify additional candidate disease genes in trio analyses.

Whole-genome sequencing identifies functional noncoding variation in SEMA3C that cosegregates with dyslexia in a multigenerational family

Dyslexia is a common heritable developmental disorder involving impaired reading abilities. Its genetic underpinnings are thought to be complex and heterogeneous, involving common and rare genetic variation. Multigenerational families segregating apparent monogenic forms of language-related disorders can provide useful entrypoints into biological pathways. In the present study, we performed a genome-wide linkage scan in a three-generational family in which dyslexia affects 14 of its 30 members and seems to be transmitted with an autosomal dominant pattern of inheritance. We identified a locus on chromosome 7q21.11 which cosegregated with dyslexia status, with the exception of two cases of phenocopy (LOD = 2.83). Whole-genome sequencing of key individuals enabled the assessment of coding and noncoding variation in the family. Two rare single-nucleotide variants (rs144517871 and rs143835534) within the first intron of the SEMA3C gene cosegregated with the 7q21.11 risk haplotype. In silico characterization of these two variants predicted effects on gene regulation, which we functionally validated for rs144517871 in human cell lines using luciferase reporter assays. SEMA3C encodes a secreted protein that acts as a guidance cue in several processes, including cortical neuronal migration and cellular polarization. We hypothesize that these intronic variants could have a cis-regulatory effect on SEMA3C expression, making a contribution to dyslexia susceptibility in this family.

Multiomics dissection of molecular regulatory mechanisms underlying autoimmune-associated noncoding SNPs

More than 90% of autoimmune-associated variants are located in noncoding regions, leading to challenges in deciphering the underlying causal roles of functional variants and genes and biological mechanisms. Therefore, to reduce the gap between traditional genetic findings and mechanistic understanding of disease etiologies and clinical drug development, it is important to translate systematically the regulatory mechanisms underlying noncoding variants. Here, we prioritized functional noncoding SNPs with regulatory gene targets associated with 19 autoimmune diseases by incorporating hundreds of immune cell-specific multiomics data. The prioritized SNPs are associated with transcription factor (TF) binding, histone modification, or chromatin accessibility, indicating their allele-specific regulatory roles. Their target genes are significantly enriched in immunologically related pathways and other known immunologically related functions. We found that 90.1% of target genes are regulated by distal SNPs involving several TFs (e.g., the DNA-binding protein CCCTC-binding factor [CTCF]), suggesting the importance of long-range chromatin interaction in autoimmune diseases. Moreover, we predicted potential drug targets for autoimmune diseases, including 2 genes (NFKB1 and SH2B3) with known drug indications on other diseases, highlighting their potential drug repurposing opportunities. Taken together, these findings may provide useful information for future experimental follow-up and drug applications on autoimmune diseases.

Predicting target genes of non-coding regulatory variants with IRT

SUMMARY

Interpreting genetic variants of unknown significance (VUS) is essential in clinical applications of genome sequencing for diagnosis and personalized care. Non-coding variants remain particularly difficult to interpret, despite making up a large majority of trait associations identified in genome-wide association studies (GWAS) analyses. Predicting the regulatory effects of non-coding variants on candidate genes is a key step in evaluating their clinical significance. Here, we develop a machine-learning algorithm, Inference of Connected expression quantitative trait loci (eQTLs) (IRT), to predict the regulatory targets of non-coding variants identified in studies of eQTLs. We assemble datasets using eQTL results from the Genotype-Tissue Expression (GTEx) project and learn to separate positive and negative pairs based on annotations characterizing the variant, gene and the intermediate sequence. IRT achieves an area under the receiver operating characteristic curve (ROC-AUC) of 0.799 using random cross-validation, and 0.700 for a more stringent position-based cross-validation. Further evaluation on rare variants and experimentally validated regulatory variants shows a significant enrichment in IRT identifying the true target genes versus negative controls. In gene-ranking experiments, IRT achieves a top-1 accuracy of 50% and top-3 accuracy of 90%. Salient features, including GC-content, histone modifications and Hi-C interactions are further analyzed and visualized to illustrate their influences on predictions. IRT can be applied to any VUS of interest and each candidate nearby gene to output a score reflecting the likelihood of regulatory effect on the expression level. These scores can be used to prioritize variants and genes to assist in patient diagnosis and GWAS follow-up studies.

AVAILABILITY AND IMPLEMENTATION

Codes and data used in this work are available at https://github.com/miaecle/eQTL_Trees.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants

Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants on specific locus or at base-wise resolution. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.

Systematic discovery of conservation states for single-nucleotide annotation of the human genome

Comparative genomics sequence data is an important source of information for interpreting genomes. Genome-wide annotations based on this data have largely focused on univariate scores or binary elements of evolutionary constraint. Here we present a complementary whole genome annotation approach, ConsHMM, which applies a multivariate hidden Markov model to learn de novo 'conservation states' based on the combinatorial and spatial patterns of which species align to and match a reference genome in a multiple species DNA sequence alignment. We applied ConsHMM to a 100-way vertebrate sequence alignment to annotate the human genome at single nucleotide resolution into 100 conservation states. These states have distinct enrichments for other genomic information including gene annotations, chromatin states, repeat families, and bases prioritized by various variant prioritization scores. Constrained elements have distinct heritability partitioning enrichments depending on their conservation state assignment. ConsHMM conservation states are a resource for analyzing genomes and genetic variants.

Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes

Characterizing how variation at the level of individual nucleotides contributes to traits and diseases has been an area of growing interest since the completion of sequencing the first human genome. Our understanding of how a single nucleotide polymorphism (SNP) leads to a pathogenic phenotype on a genome-wide scale is a fruitful endeavor for anyone interested in developing diagnostic tests, therapeutics, or simply wanting to understand the etiology of a disease or trait. To this end, many datasets and algorithms have been developed as resources/tools to annotate SNPs. One of the most common practices is to annotate coding SNPs that affect the protein sequence. Synonymous variants are often grouped as one type of variant, however there are in fact many tools available to dissect their effects on gene expression. More recently, large consortiums like ENCODE and GTEx have made it possible to annotate non-coding regions. Although annotating variants is a common technique among human geneticists, the constant advances in tools and biology surrounding SNPs requires an updated summary of what is known and the trajectory of the field. This review will discuss the history behind SNP annotation, commonly used tools, and newer strategies for SNP annotation. Additionally, we will comment on the caveats that distinguish approaches from one another, along with gaps in the current state of knowledge, and potential future directions. We do not intend for this to be a comprehensive review for any specific area of SNP annotation, but rather it will be an excellent resource for those unfamiliar with computational tools used to functionally characterize SNPs. In summary, this review will help illustrate how each SNP annotation method impacts the way in which the genetic and molecular etiology of a disease is explored in-silico.

Systematics for types and effects of DNA variations

BACKGROUND

Numerous different types of variations can occur in DNA and have diverse effects and consequences. The Variation Ontology (VariO) was developed for systematic descriptions of variations and their effects at DNA, RNA and protein levels.

RESULTS

VariO use and terms for DNA variations are described in depth. VariO provides systematic names for variation types and detailed descriptions for changes in DNA function, structure and properties. The principles of VariO are presented along with examples from published articles or databases, most often in relation to human diseases. VariO terms describe local DNA changes, chromosome number and structure variants, chromatin alterations, as well as genomic changes, whether of genetic or non-genetic origin.

CONCLUSIONS

DNA variation systematics facilitates unambiguous descriptions of variations and their effects and further reuse and integration of data from different sources by both human and computers.

From genome-wide associations to candidate causal variants by statistical fine-mapping

Advancing from statistical associations of complex traits with genetic markers to understanding the functional genetic variants that influence traits is often a complex process. Fine-mapping can select and prioritize genetic variants for further study, yet the multitude of analytical strategies and study designs makes it challenging to choose an optimal approach. We review the strengths and weaknesses of different fine-mapping approaches, emphasizing the main factors that affect performance. Topics include interpreting results from genome-wide association studies (GWAS), the role of linkage disequilibrium, statistical fine-mapping approaches, trans-ethnic studies, genomic annotation and data integration, and other analysis and design issues.