Variant Functional Annotations Research Articles

FAVOR-GPT: a generative natural language interface to whole genome variant functional annotations.

Functional Annotation of genomic Variants Online Resources (FAVOR) offers multi-faceted, whole genome variant functional annotations, which is essential for Whole Genome and Exome Sequencing (WGS/WES) analysis and the functional prioritization of disease-associated variants. A versatile chatbot designed to facilitate informative interpretation and interactive, user-centric summary of the whole genome variant functional annotation data in the FAVOR database is needed. We have developed FAVOR-GPT, a generative natural language interface powered by integrating large language models (LLMs) and FAVOR. It is developed based on the Retrieval Augmented Generation (RAG) approach, and complements the original FAVOR portal, enhancing usability for users, especially those without specialized expertise. FAVOR-GPT simplifies raw annotations by providing interpretable explanations and result summaries in response to the user's prompt. It shows high accuracy when cross-referencing with the FAVOR database, underscoring the robustness of the retrieval framework. Researchers can access FAVOR-GPT at FAVOR's main website (https://favor.genohub.org).

Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

FAVOR: functional annotation of variants online resource and annotator for variation across the human genome.

Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the genotype data of large biobank-scale WGS studies. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive multi-faceted variant functional annotation online portal that summarizesand visualizes findings of all possible nine billion single nucleotide variants (SNVs) across the genome. It allows for rapid variant-, gene-and region-level queries of variant functional annotations. FAVOR integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, we provide a scalable annotation tool, FAVORannotator, to functionally annotate large-scale WGS studies and efficiently store the genotype and their variant functional annotation data in a single file using the annotated Genomic Data Structure (aGDS) format, making downstream analysis more convenient. FAVOR and FAVORannotator are available at https://favor.genohub.org.

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

Integrating variant functional annotation scores have varied abilities to improve power of genome-wide association studies

Functional annotations have the potential to increase power of genome-wide association studies (GWAS) by prioritizing variants according to their biological function, but this potential has not been well studied. We comprehensively evaluated all 1132 traits in the UK Biobank whose SNP-heritability estimates were given “medium” or “high” labels by Neale’s lab. For each trait, we integrated GWAS summary statistics of close to 8 million common variants (minor allele frequency >1%) with either their 75 individual functional scores or their meta-scores, using three different data-integration methods. Overall, the number of new genome-wide significant findings after data-integration increases as a trait SNP-heritability estimate increases. However, there is a trade-off between new findings and loss of baseline GWAS findings, resulting in similar total numbers of significant findings between using GWAS alone and integrating GWAS with functional scores, across all 1132 traits analyzed and all three data-integration methods considered. Our findings suggest that, even with the current biobank-level sample size, more informative functional scores and/or new data-integration methods are needed to further improve the power of GWAS of common variants. For example, studying variants in coding sequence and obtaining cell-type-specific scores are potential future directions.

Patient-specific induced pluripotent stem cells as "disease-in-a-dish" models for inherited cardiomyopathies and channelopathies - 15 years of research.

Among inherited cardiac conditions, a special place is kept by cardiomyopathies (CMPs) and channelopathies (CNPs), which pose a substantial healthcare burden due to the complexity of the therapeutic management and cause early mortality. Like other inherited cardiac conditions, genetic CMPs and CNPs exhibit incomplete penetrance and variable expressivity even within carriers of the same pathogenic deoxyribonucleic acid variant, challenging our understanding of the underlying pathogenic mechanisms. Until recently, the lack of accurate physiological preclinical models hindered the investigation of fundamental cellular and molecular mechanisms. The advent of induced pluripotent stem cell (iPSC) technology, along with advances in gene editing, offered unprecedented opportunities to explore hereditary CMPs and CNPs. Hallmark features of iPSCs include the ability to differentiate into unlimited numbers of cells from any of the three germ layers, genetic identity with the subject from whom they were derived, and ease of gene editing, all of which were used to generate “disease-in-a-dish” models of monogenic cardiac conditions. Functionally, iPSC-derived cardiomyocytes that faithfully recapitulate the patient-specific phenotype, allowed the study of disease mechanisms in an individual-/allele-specific manner, as well as the customization of therapeutic regimen. This review provides a synopsis of the most important iPSC-based models of CMPs and CNPs and the potential use for modeling disease mechanisms, personalized therapy and deoxyribonucleic acid variant functional annotation.

Potential Impact of DPYD Variation on Fluoropyrimidine Drug Response in sub-Saharan African Populations.

Cancer is a critical health burden in Africa, and mortality rates are rising rapidly. Treatments are expensive and often cause adverse drug reactions (ADRs). Fluoropyrimidine treatments can lead to severe toxicity events which have been linked to variants within the dihydropyrimidine dehydrogenase (DPYD) gene. There are clinical guidelines to improve safety outcomes of treatment, but these are primarily based on variants assessed in non-African populations. Whole genome sequencing data from the 1000 Genomes Project and the African Genome Variation Project were mined to assess variation in DPYD in eight sub-Saharan African populations. Variant functional annotation was performed with a series of bioinformatics tools to assess potential likelihood of deleterious impact. There were 29 DPYD coding variants identified in the datasets assessed, of which 25 are rare, and some of which are known to be deleterious. One African-specific variant (rs115232898-C), is common in sub-Saharan Africans (1–4%) and known to reduce the function of the dihydropyrimidine dehydrogenase enzyme (DPD), having been linked to cases of severe toxicity. This variant, once validated in clinical trials, should be considered for inclusion in clinical guidelines for use in sub-Saharan African populations. The rs2297595-C variant is less well-characterized in terms of effect, but shows significant allele frequency differences between sub-Saharan African populations (0.5–11.5%; p = 1.5 × 10−4), and is more common in East African populations. This study highlights the relevance of African-data informed guidelines for fluorouracil drug safety in sub-Saharan Africans, and the need for region-specific data to ensure that Africans may benefit optimally from a precision medicine approach.

Whole-Exome Sequencing and hiPSC Cardiomyocyte Models Identify MYRIP, TRAPPC11, and SLC27A6 of Potential Importance to Left Ventricular Hypertrophy in an African Ancestry Population.

Background: Indices of left ventricular (LV) structure and geometry represent useful intermediate phenotypes related to LV hypertrophy (LVH), a predictor of cardiovascular (CV) disease (CVD) outcomes.Methods and Results: We conducted an exome-wide association study of LV mass (LVM) adjusted to height2.7, LV internal diastolic dimension (LVIDD), and relative wall thickness (RWT) among 1,364 participants of African ancestry (AAs) in the Hypertension Genetic Epidemiology Network (HyperGEN). Both single-variant and gene-based sequence kernel association tests were performed to examine whether common and rare coding variants contribute to variation in echocardiographic traits in AAs. We then used a data-driven procedure to prioritize and select genes for functional validation using a human induced pluripotent stem cell cardiomyocyte (hiPSC-CM) model. Three genes [myosin VIIA and Rab interacting protein (MYRIP), trafficking protein particle complex 11 (TRAPPC11), and solute carrier family 27 member 6 (SLC27A6)] were prioritized based on statistical significance, variant functional annotations, gene expression in the hiPSC-CM model, and prior biological evidence and were subsequently knocked down in the hiPSC-CM model. Expression profiling of hypertrophic gene markers in the knockdowns suggested a decrease in hypertrophic expression profiles. MYRIP knockdowns showed a significant decrease in atrial natriuretic factor (NPPA) and brain natriuretic peptide (NPPB) expression. Knockdowns of the heart long chain fatty acid (FA) transporter SLC27A6 resulted in downregulated caveolin 3 (CAV3) expression, which has been linked to hypertrophic phenotypes in animal models. Finally, TRAPPC11 knockdown was linked to deficient calcium handling.Conclusions: The three genes are biologically plausible candidates that provide new insight to hypertrophic pathways.

Exome Chip Analyses and Genetic Risk for IgA Nephropathy among Han Chinese.

IgA nephropathy is the most common form of primary GN worldwide. The evidence of geographic and ethnic differences, as well as familial aggregation of the disease, supports a strong genetic contribution to IgA nephropathy. Evidence for genetic factors in IgA nephropathy comes also from genome-wide association patient-control studies. However, few studies have systematically evaluated the contribution of coding variation in IgA nephropathy. We performed a two-stage exome chip-based association study in 13,242 samples, including 3363 patients with IgA nephropathy and 9879 healthy controls of Han Chinese ancestry. Common variant functional annotation, gene-based low-frequency variants analysis, differential mRNA expression, and gene network integration were also explored. We identified three non-HLA gene regions (FBXL21, CCR6, and STAT3) and one HLA gene region (GABBR1) with suggestive significance (Pmeta <5×10-5) in single-variant associations. These novel non-HLA variants were annotated as expression-associated single-nucleotide polymorphisms and were located in enhancer regions enriched in histone marks H3K4me1 in primary B cells. Gene-based low-frequency variants analysis suggests CFB as another potential susceptibility gene. Further combined expression and network integration suggested that the five novel susceptibility genes, TGFBI, CCR6, STAT3, GABBR1, and CFB, were involved in IgA nephropathy. Five novel gene regions with suggestive significance for IgA nephropathy were identified and shed new light for further mechanism investigation.

KRGDB: the large-scale variant database of 1722 Koreans based on whole genome sequencing.

Since 2012, the Center for Genome Science of the Korea National Institute of Health (KNIH) has been sequencing complete genomes of 1722 Korean individuals. As a result, more than 32 million variant sites have been identified, and a large proportion of the variant sites have been detected for the first time. In this article, we describe the Korean Reference Genome Database (KRGDB) and its genome browser. The current version of our database contains both single nucleotide and short insertion/deletion variants. The DNA samples were obtained from four different origins and sequenced in different sequencing depths (10× coverage of 63 individuals, 20× coverage of 194 individuals, combined 10× and 20× coverage of 135 individuals, 30× coverage of 230 individuals and 30× coverage of 1100 individuals). The major features of the KRGDB are that it contains information on the Korean genomic variant frequency, frequency difference between the Korean and other populations and the variant functional annotation (such as regulatory elements in ENCODE regions and coding variant functions) of the variant sites. Additionally, we performed the genome-wide association study (GWAS) between Korean genome variant sites for the 30×230 individuals and three major common diseases (diabetes, hypertension and metabolic syndrome). The association results are displayed on our browser. The KRGDB uses the MySQL database and Apache-Tomcat web server adopted with Java Server Page (JSP) and is freely available at http://coda.nih.go.kr/coda/KRGDB/index.jsp.Availability: http://coda.nih.go.kr/coda/KRGDB/index.jsp

Complement Genome Annotation Lift Over Using a Weighted Sequence Alignment Strategy.

With the broad application of high-throughput sequencing, more whole-genome resequencing data and de novo assemblies of natural populations are becoming available. For a particular species, in general, only the reference genome is well established and annotated. Computational tools based on sequence alignment have been developed to investigate the gene models of individuals belonging to the same or closely related species. During this process, inconsistent alignment often obscures genome annotation lift over and leads to improper functional impact prediction for a genomic variant, especially in plant species. Here, we proposed the zebraic striped dynamic programming algorithm, which provides different weights to genetic features to refine genome annotation lift over. Testing of our zebraic striped dynamic programming algorithm on both plant and animal genomic data showed complementation to standard sequence approach for highly diverse individuals. Using the lift over genome annotation as anchors, a base-pair resolution genome-wide sequence alignment and variant calling pipeline for de novo assembly has been implemented in the GEAN software. GEAN could be used to compare haplotype diversity, refine the genetic variant functional annotation, annotate de novo assembly genome sequence, detect homologous syntenic blocks, improve the quantification of gene expression levels using RNA-seq data, and unify genomic variants for population genetic analysis. We expect that GEAN will be a standard tool for the coming of age of de novo assembly population genetics.

GWAS4D: multidimensional analysis of context-specific regulatory variant for human complex diseases and traits.

Genome-wide association studies have generated over thousands of susceptibility loci for many human complex traits, and yet for most of these associations the true causal variants remain unknown. Tissue/cell type-specific prediction and prioritization of non-coding regulatory variants will facilitate the identification of causal variants and underlying pathogenic mechanisms for particular complex diseases and traits. By leveraging recent large-scale functional genomics/epigenomics data, we develop an intuitive web server, GWAS4D (http://mulinlab.tmu.edu.cn/gwas4d or http://mulinlab.org/gwas4d), that systematically evaluates GWAS signals and identifies context-specific regulatory variants. The updated web server includes six major features: (i) updates the regulatory variant prioritization method with our new algorithm; (ii) incorporates 127 tissue/cell type-specific epigenomes data; (iii) integrates motifs of 1480 transcriptional regulators from 13 public resources; (iv) uniformly processes Hi-C data and generates significant interactions at 5 kb resolution across 60 tissues/cell types; (v) adds comprehensive non-coding variant functional annotations; (vi) equips a highly interactive visualization function for SNP-target interaction. Using a GWAS fine-mapped set for 161 coronary artery disease risk loci, we demonstrate that GWAS4D is able to efficiently prioritize disease-causal regulatory variants.

Single-Exome sequencing identified a novel RP2 mutation in a child with X-linked retinitis pigmentosa

ObjectiveTo present an efficient and successful application of a single-exome sequencing study in a family clinically diagnosed with X-linked retinitis pigmentosa. DesignExome sequencing study based on clinical examination data. ParticipantsAn 8-year-old proband and his family. MethodsThe proband and his family members underwent comprehensive ophthalmologic examinations. Exome sequencing was undertaken in the proband using Agilent SureSelect Human All Exon Kit and Illumina HiSeq 2000 platform. Bioinformatic analysis used Illumina pipeline with Burrows-Wheeler Aligner-Genome Analysis Toolkit (BWA-GATK), followed by ANNOVAR to perform variant functional annotation. All variants passing filter criteria were validated by Sanger sequencing to confirm familial segregation. ResultsAnalysis of exome sequence data identified a novel frameshift mutation in RP2 gene resulting in a premature stop codon (c.665delC, p.Pro222fsTer237). Sanger sequencing revealed this mutation co-segregated with the disease phenotype in the child’s family. ConclusionsWe identified a novel causative mutation in RP2 from a single proband’s exome sequence data analysis. This study highlights the effectiveness of the whole-exome sequencing in the genetic diagnosis of X-linked retinitis pigmentosa, over the conventional sequencing methods. Even using a single exome, exome sequencing technology would be able to pinpoint pathogenic variant(s) for X-linked retinitis pigmentosa, when properly applied with aid of adequate variant filtering strategy.

Comparison of GENCODE and RefSeq gene annotation and the impact of reference geneset on variant effect prediction.

BackgroundA vast amount of DNA variation is being identified by increasingly large-scale exome and genome sequencing projects. To be useful, variants require accurate functional annotation and a wide range of tools are available to this end. McCarthy et al recently demonstrated the large differences in prediction of loss-of-function (LoF) variation when RefSeq and Ensembl transcripts are used for annotation, highlighting the importance of the reference transcripts on which variant functional annotation is based.ResultsWe describe a detailed analysis of the similarities and differences between the gene and transcript annotation in the GENCODE and RefSeq genesets. We demonstrate that the GENCODE Comprehensive set is richer in alternative splicing, novel CDSs, novel exons and has higher genomic coverage than RefSeq, while the GENCODE Basic set is very similar to RefSeq. Using RNAseq data we show that exons and introns unique to one geneset are expressed at a similar level to those common to both. We present evidence that the differences in gene annotation lead to large differences in variant annotation where GENCODE and RefSeq are used as reference transcripts, although this is predominantly confined to non-coding transcripts and UTR sequence, with at most ~30% of LoF variants annotated discordantly. We also describe an investigation of dominant transcript expression, showing that it both supports the utility of the GENCODE Basic set in providing a smaller set of more highly expressed transcripts and provides a useful, biologically-relevant filter for further reducing the complexity of the transcriptome.ConclusionsThe reference transcripts selected for variant functional annotation do have a large effect on the outcome. The GENCODE Comprehensive transcripts contain more exons, have greater genomic coverage and capture many more variants than RefSeq in both genome and exome datasets, while the GENCODE Basic set shows a higher degree of concordance with RefSeq and has fewer unique features. We propose that the GENCODE Comprehensive set has great utility for the discovery of new variants with functional potential, while the GENCODE Basic set is more suitable for applications demanding less complex interpretation of functional variants.