Genetic Disease Research Research Articles

Enhancing RNA-seq bias mitigation with the Gaussian self-benchmarking framework: towards unbiased sequencing data

BackgroundRNA sequencing is a vital technique for analyzing RNA behavior in cells, but it often suffers from various biases that distort the data. Traditional methods to address these biases are typically empirical and handle them individually, limiting their effectiveness. Our study introduces the Gaussian Self-Benchmarking (GSB) framework, a novel approach that leverages the natural distribution patterns of guanine (G) and cytosine (C) content in RNA to mitigate multiple biases simultaneously. This method is grounded in a theoretical model, organizing k-mers based on their GC content and applying a Gaussian model for alignment to ensure empirical sequencing data closely match their theoretical distribution.ResultsThe GSB framework demonstrated superior performance in mitigating sequencing biases compared to existing methods. Testing with synthetic RNA constructs and real human samples showed that the GSB approach not only addresses individual biases more effectively but also manages co-existing biases jointly. The framework’s reliance on accurately pre-determined parameters like mean and standard deviation of GC content distribution allows for a more precise representation of RNA samples. This results in improved accuracy and reliability of RNA sequencing data, enhancing our understanding of RNA behavior in health and disease.ConclusionsThe GSB framework presents a significant advancement in RNA sequencing analysis by providing a well-validated, multi-bias mitigation strategy. It functions independently from previously identified dataset flaws and sets a new standard for unbiased RNA sequencing results. This development enhances the reliability of RNA studies, broadening the potential for scientific breakthroughs in medicine and biology, particularly in genetic disease research and the development of targeted treatments.

Advancements in Research on Genetic Kidney Diseases Using Human-Induced Pluripotent Stem Cell-Derived Kidney Organoids.

Genetic or hereditary kidney disease stands as a pivotal cause of chronic kidney disease (CKD). The proliferation and widespread utilization of DNA testing in clinical settings have notably eased the diagnosis of genetic kidney diseases, which were once elusive but are now increasingly identified in cases previously deemed CKD of unknown etiology. However, despite these diagnostic strides, research into disease pathogenesis and novel drug development faces significant hurdles, chiefly due to the dearth of appropriate animal models and the challenges posed by limited patient cohorts in clinical studies. Conversely, the advent and utilization of human-induced pluripotent stem cells (hiPSCs) offer a promising avenue for genetic kidney disease research. Particularly, the development of hiPSC-derived kidney organoid systems presents a novel platform for investigating various forms of genetic kidney diseases. Moreover, the integration of the CRISPR/Cas9 technique into this system holds immense potential for efficient research on genetic kidney diseases. This review aims to explore the applications of in vitro kidney organoids generated from hiPSCs in the study of diverse genetic kidney diseases. Additionally, it will delve into the limitations of this research platform and outline future perspectives for advancing research in this crucial area.

Long-read sequencing identifies an SVA_D retrotransposon insertion deep within the intron of ATP7A as a novel cause of occipital horn syndrome

BackgroundSINE-VNTR-Alu (SVA) retrotransposons move from one genomic location to another in a ‘copy-and-paste’ manner. They continue to move actively and cause monogenic diseases through various mechanisms. Currently, disease-causing SVA retrotransposons...

Gene Editing Technologies: CRISPR/Cas9 and Beyond for Genetic Disease Therapy and Research

Gene editing technologies have revolutionized the field of genetic disease therapy and research. Among these, CRISPR/Cas9 stands out as a versatile tool for precisely targeting and modifying specific sequences within the genome. This paper provides an overview of CRISPR/Cas9 and other emerging gene editing technologies, discussing their potential applications in treating genetic diseases and advancing scientific research. Additionally, ethical considerations and challenges associated with gene editing are explored, along with future directions for this rapidly evolving field. Gene editing technologies have emerged as powerful tools in the field of genetic disease therapy and research, offering unprecedented precision and versatility in manipulating the genome. Among these technologies, CRISPR/Cas9 has garnered significant attention for its simplicity, efficiency, and accuracy in targeted gene modification.

ChIP-Atlas 3.0: a data-mining suite to explore chromosome architecture together with large-scale regulome data.

ChIP-Atlas (https://chip-atlas.org/) presents a suite of data-mining tools for analyzing epigenomic landscapes, powered by the comprehensive integration of over 376000 public ChIP-seq, ATAC-seq, DNase-seqand Bisulfite-seq experiments from six representative model organisms. To unravel the intricacies of chromatin architecture that mediates the regulome-initiated generation of transcriptional and phenotypic diversity within cells, we report ChIP-Atlas 3.0 that enhances clarity by incorporating additional tracks for genomic and epigenomic features within a newly consolidated 'annotation track' section. The tracks include chromosomal conformation (Hi-C and eQTL datasets), transcriptional regulatory elements (ChromHMM and FANTOM5 enhancers), and genomic variants associated with diseases and phenotypes (GWAS SNPs and ClinVar variants). These annotation tracks are easily accessible alongside other experimental tracks, facilitating better elucidation of chromatin architecture underlying the diversification of transcriptional and phenotypic traits. Furthermore, 'Diff Analysis,' a new online tool, compares the query epigenome data to identify differentially bound, accessible, and methylated regions using ChIP-seq, ATAC-seq and DNase-seq, and Bisulfite-seq datasets, respectively. The integration of annotation tracks and the Diff Analysis tool, coupled with continuous data expansion, renders ChIP-Atlas 3.0 a robust resource for mining the landscape of transcriptional regulatory mechanisms, thereby offering valuable perspectives, particularly for genetic disease research and drug discovery.

VARista: a free web platform for streamlined whole-genome variant analysis across T2T, hg38, and hg19.

With the increasing importance of genomic data in understanding genetic diseases, there is an essential need for efficient and user-friendly tools that simplify variant analysis. Although multiple tools exist, many present barriers such as steep learning curves, limited reference genome compatibility, or costs. We developed VARista, a free web-based tool, to address these challenges and provide a streamlined solution for researchers, particularly those focusing on rare monogenic diseases. VARista offers a user-centric interface that eliminates much of the technical complexity typically associated with variant analysis. The tool directly supports VCF files generated using reference genomes hg19, hg38, and the emerging T2T, with seamless remapping capabilities between them. Features such as gene summaries and links, tissue and cell-specific gene expression data for both adults and fetuses, as well as automated PCR design and integration with tools such as SpliceAI and AlphaMissense, enable users to focus on the biology and the case itself. As we demonstrate, VARista proved effective in narrowing down potential disease-causing variants, prioritizing them effectively, and providing meaningful biological context, facilitating rapid decision-making. VARista stands out as a freely available and comprehensive tool that consolidates various aspects of variant analysis into a single platform that embraces the forefront of genomic advancements. Its design inherently supports a shift in focus from technicalities to critical thinking, thereby promoting better-informed decisions in genetic disease research. Given its unique capabilities and user-centric design, VARista has the potential to become an essential asset for the genomic research community. https://VARista.link.

The rare genetic disease research landscape in India

The rare genetic disease research landscape in India

Assessing the efficacy of target adaptive sampling long-read sequencing through hereditary cancer patient genomes

Innovations in sequencing technology have led to the discovery of novel mutations that cause inherited diseases. However, many patients with suspected genetic diseases remain undiagnosed. Long-read sequencing technologies are expected to significantly improve the diagnostic rate by overcoming the limitations of short-read sequencing. In addition, Oxford Nanopore Technologies (ONT) offers adaptive sampling and computationally driven target enrichment technology. This enables more affordable intensive analysis of target gene regions compared to standard non-selective long-read sequencing. In this study, we developed an efficient computational workflow for target adaptive sampling long-read sequencing (TAS-LRS) and evaluated it through application to 33 genomes collected from suspected hereditary cancer patients. Our workflow can identify single nucleotide variants with nearly the same accuracy as the short-read platform and elucidate complex forms of structural variations. We also newly identified several SINE-R/VNTR/Alu (SVA) elements affecting the APC gene in two patients with familial adenomatous polyposis, as well as their sites of origin. In addition, we demonstrated that off-target reads from adaptive sampling, which is typically discarded, can be effectively used to accurately genotype common single-nucleotide polymorphisms (SNPs) across the entire genome, enabling the calculation of a polygenic risk score. Furthermore, we identified allele-specific MLH1 promoter hypermethylation in a Lynch syndrome patient. In summary, our workflow with TAS-LRS can simultaneously capture monogenic risk variants including complex structural variations, polygenic background as well as epigenetic alterations, and will be an efficient platform for genetic disease research and diagnosis.

Recent progress in using Drosophila as a platform for human genetic disease research

Recent progress in using <i>Drosophila</i> as a platform for human genetic disease research

A knowledge graph approach to predict and interpret disease-causing gene interactions

BackgroundUnderstanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease research. This trend is reflected by the growing amount of clinical research on oligogenic diseases, where disease manifestations are influenced by combinations of variants on a few specific genes. Although statistical machine-learning methods have been developed to identify relevant genetic variant or gene combinations associated with oligogenic diseases, they rely on abstract features and black-box models, posing challenges to interpretability for medical experts and impeding their ability to comprehend and validate predictions. In this work, we present a novel, interpretable predictive approach based on a knowledge graph that not only provides accurate predictions of disease-causing gene interactions but also offers explanations for these results.ResultsWe introduce BOCK, a knowledge graph constructed to explore disease-causing genetic interactions, integrating curated information on oligogenic diseases from clinical cases with relevant biomedical networks and ontologies. Using this graph, we developed a novel predictive framework based on heterogenous paths connecting gene pairs. This method trains an interpretable decision set model that not only accurately predicts pathogenic gene interactions, but also unveils the patterns associated with these diseases. A unique aspect of our approach is its ability to offer, along with each positive prediction, explanations in the form of subgraphs, revealing the specific entities and relationships that led to each pathogenic prediction.ConclusionOur method, built with interpretability in mind, leverages heterogenous path information in knowledge graphs to predict pathogenic gene interactions and generate meaningful explanations. This not only broadens our understanding of the molecular mechanisms underlying oligogenic diseases, but also presents a novel application of knowledge graphs in creating more transparent and insightful predictors for genetic research.

The Illusion of Identity: At-Home Ancestry Testing, Forensics, and the Accuracy Problem

Race, as a social construct, plays a significantly large role in the scientific field of genetics. This review analyzes several studies to reach a more comprehensive understanding of race’s historical influence in genetics, the modern implications of this relationship, and the validity of race as a scientific category in genetics. While the scientific validity of race has largely been disproved, it continues to be used by scientists and geneticists, specifically in genetic disease research. Aside from the obstacles stemming from race’s lack of legitimacy in science, its social implications are also a liability for scientific advancements because of how race has historically marginalized people groups. Moreover, the monetization of recent discoveries in genetics, including at-home ancestry test kits and molecular photofitting in forensics, are a cause for concern due to their lack of scientific accuracy. The process of racial categorization of test subjects is not as accurate as companies advertise, and this lack of transparency can lead to false conclusions about personal identity based on misinformation of at-home ancestry test results which rely upon a limited data set. In addition, this possibility of error in the DNA testing process can lead to the criminalization of innocent suspects. Therefore, awareness about both the primary motives of these companies, as well as the social implications of interpreting genetic test results, is critical to understanding the broader effects of race in genetics in society.

Gathering the Stakeholder's Perspective: Experiences and Opportunities in Rare Genetic Disease Research.

Research participant feedback is rarely collected; therefore, investigators have limited understanding regarding stakeholders' (affected individuals/caregivers) motivation to participate. Members of the Genes to Mental Health Network (G2MH) surveyed stakeholders affected by copy number variants (CNVs) regarding perceived incentives for study participation, opinions concerning research priorities, and the necessity for future funding. Respondents were also asked about feelings of preparedness, research burden, and satisfaction with research study participation. Modified validated surveys were used to assess stakeholders´ views across three domains: (1) Research Study Enrollment, Retainment, Withdrawal, and Future Participation; (2) Overall Research Experience, Burden, and Preparedness; (3) Research Priorities and Obstacles. Top box score analyses were performed. A total of 704 stakeholders´ responded from 29 countries representing 55 CNVs. The top reasons for initial participation in the research included reasons related to education and altruism. The top reasons for leaving a research study included treatment risks and side effects. The importance of sharing research findings and laboratory results with stakeholders was underscored by participants. Most stakeholders reported positive research experiences. This study provides important insight into how individuals and families affected with a rare CNV feel toward research participation and their overall experience in rare disease research. There are clear targets for areas of improvement for study teams, although many stakeholders reported positive research experiences. Key findings from this international survey may help advance collaborative research and improve the experience of participants, investigators, and other stakeholders moving forward.

Calculating variant penetrance from family history of disease and average family size in population-scale data

BackgroundGenetic penetrance is the probability of a phenotype when harbouring a particular pathogenic variant. Accurate penetrance estimates are important across biomedical fields including genetic counselling, disease research, and gene therapy. However, existing approaches for penetrance estimation require, for instance, large family pedigrees or availability of large databases of people affected and not affected by a disease.MethodsWe present a method for penetrance estimation in autosomal dominant phenotypes. It examines the distribution of a variant among people affected (cases) and unaffected (controls) by a phenotype within population-scale data and can be operated using cases only by considering family disease history. It is validated through simulation studies and candidate variant-disease case studies.ResultsOur method yields penetrance estimates which align with those obtained via existing approaches in the Parkinson’s disease LRRK2 gene and pulmonary arterial hypertension BMPR2 gene case studies. In the amyotrophic lateral sclerosis case studies, examining penetrance for variants in the SOD1 and C9orf72 genes, we make novel penetrance estimates which correspond closely to understanding of the disease.ConclusionsThe present approach broadens the spectrum of traits for which reliable penetrance estimates can be obtained. It has substantial utility for facilitating the characterisation of disease risks associated with rare variants with an autosomal dominant inheritance pattern. The yielded estimates avoid any kinship-specific effects and can circumvent ascertainment biases common when sampling rare variants among control populations.

Genetic Diversity of Viral Populations Associated with Ananas Germplasm and Improvement of Virus Diagnostic Protocols.

Pineapple (Ananas comosus L. [Merr.]) accessions from the U.S. Tropical Plant Genetic Resources and Disease Research (TPGRDR) in Hilo, Hawaii were subjected to RNA-sequencing to study the occurrence of viral populations associated with this vegetatively propagated crop. Analysis of high-throughput sequencing data obtained from 24 germplasm accessions and public domain transcriptome shotgun assembly (TSA) data identified two novel sadwaviruses, putatively named "pineapple secovirus C" (PSV-C) and "pineapple secovirus D" (PSV-D). They shared low amino acid sequence identity (from 34.8 to 41.3%) compared with their homologs in the Pro-pol region of the previously reported PSV-A and PSV-B. The complete genome (7485 bp) corresponding to a previously reported partial sequence of the badnavirus, pineapple bacilliform ER virus (PBERV), was retrieved from one of the datasets. Overall, we discovered a total of 69 viral sequences representing ten members within the Ampelovirus, Sadwavirus, and Badnavirus genera. Genetic diversity and recombination events were found in members of the pineapple mealybug wilt-associated virus (PMWaV) complex as well as PSVs. PMWaV-1, -3, and -6 presented recombination events across the quintuple gene block, while no recombination events were found for PMWaV-2. High recombination frequency of the RNA1 and RNA2 molecules from PSV-A and PSV-B were congruent with the diversity found by phylogenetic analyses. Here, we also report the development and improvement of RT-PCR diagnostic protocols for the specific identification and detection of viruses infecting pineapple based on the diverse viral populations characterized in this study. Given the high occurrence of recombination events, diversity, and discovery of viruses found in Ananas germplasm, the reported and validated RT-PCR assays represent an important advance for surveillance of viral infections of pineapple.

Experimental method for haplotype phasing across the entire length of chromosome 21 in trisomy 21 cells using a chromosome elimination technique

Modern sequencing technologies produce a single consensus sequence without distinguishing between homologous chromosomes. Haplotype phasing solves this limitation by identifying alleles on the maternal and paternal chromosomes. This information is critical for understanding gene expression models in genetic disease research. Furthermore, the haplotype phasing of three homologous chromosomes in trisomy cells is more complicated than that in disomy cells. In this study, we attempted the accurate and complete haplotype phasing of chromosome 21 in trisomy 21 cells. To separate homologs, we established three corrected disomy cell lines (ΔPaternal chromosome, ΔMaternal chromosome 1, and ΔMaternal chromosome 2) from trisomy 21 induced pluripotent stem cells by eliminating one chromosome 21 utilizing the Cre-loxP system. These cells were then whole-genome sequenced by a next-generation sequencer. By simply comparing the base information of the whole-genome sequence data at the same position between each corrected disomy cell line, we determined the base on the eliminated chromosome and performed phasing. We phased 51,596 single nucleotide polymorphisms (SNPs) on chromosome 21, randomly selected seven SNPs spanning the entire length of the chromosome, and confirmed that there was no contradiction by direct sequencing.

Recent advances and application of whole genome amplification in molecular diagnosis and medicine.

Whole genome amplification (WGA) is a technology for non‐selective amplification of the whole genome sequence, first appearing in 1992. Its primary purpose is to amplify and reflect the whole genome of trace tissues and single cells without sequence bias and to provide sufficient DNA template for subsequent multigene and multilocus analysis, along with comprehensive genome research. WGA provides a method to obtain a large amount of genetic information from a small amount of DNA and provides a valuable tool for preserving limited samples in molecular biology. WGA technology is especially suitable for forensic identification and genetic disease research, along with new technologies such as next‐generation sequencing (NGS). In addition, WGA is also widely used in single‐cell sequencing. Due to the small amount of DNA in a single cell, it is often unable to meet the amount of samples needed for sequencing, so WGA is generally used to achieve the amplification of trace samples. This paper reviews WGA methods based on different principles, summarizes both amplification principle and amplification quality, and discusses the application prospects and challenges of WGA technology in molecular diagnosis and medicine.

Limitations of lymphoblastoid cell lines for establishing genetic reference datasets in the immunoglobulin loci.

Lymphoblastoid cell lines (LCLs) have been critical to establishing genetic resources for biomedical science. They have been used extensively to study human genetic diversity, genome function, and inform the development of tools and methodologies for augmenting disease genetics research. While the validity of variant callsets from LCLs has been demonstrated for most of the genome, previous work has shown that DNA extracted from LCLs is modified by V(D)J recombination within the immunoglobulin (IG) loci, regions that harbor antibody genes critical to immune system function. However, the impacts of V(D)J on short read sequencing data generated from LCLs has not been extensively investigated. In this study, we used LCL-derived short read sequencing data from the 1000 Genomes Project (n = 2,504) to identify signatures of V(D)J recombination. Our analyses revealed sample-level impacts of V(D)J recombination that varied depending on the degree of inferred monoclonality. We showed that V(D)J associated somatic deletions impacted genotyping accuracy, leading to adulterated population-level estimates of allele frequency and linkage disequilibrium. These findings illuminate limitations of using LCLs and short read data for building genetic resources in the IG loci, with implications for interpreting previous disease association studies in these regions.

Integrative identification of the pathogenic role of a novel G6PD missense mutation c.697G>C.

BackgroundGlucose-6-phosphate dehydrogenase (G6PD) deficiency is a hereditary disease caused by pathogenic mutations of G6PD. While most of the pathogenic variants of G6PD have been annotated, hemolysis of unknown etiology but analogous to that in G6PD deficiency persists, implying the existence of undocumented pathogenic variants. In our previous study, we reported four novel G6PD variants in China, for which the pathogenicity remains to be verified.MethodsThe variants were verified by exogenous expression in HEK-293 cells, and their functions were predicted by PolyPhen-2 and SIFT. The CRISPR/Cas9 system was exploited to edit the G6PD c.697G>C variant in HEK-293 cells and K562 cells. The expression of G6PD was detected by quantitative PCR (qPCR) and western blotting. The cell growth capacity was detected by the CCK-8 assay and crystal violet staining. The G6PD enzyme activity was reflected by the G6P/6PG ratio test. The apoptosis of cells was detected by Annexin V-APC/7-AAD staining. The secondary and crystallographic structures were denoted according to the literature and PyMOL software. The G6PD protein was purified from lysis of transformed Escherichia coli (E. coli) cell with Ni-charged Resin Column. The enzymatic activity was detected at different temperatures.ResultsThe G6PD activity of exogenous G6PD c.697G>C in HEK-293 cells was significantly lower than that of wild type (WT) G6PD, a finding that was consistent with the observation in clinical samples. The functional predictions conducted by different algorithms indicated the damage role of the G6PD c.697G>C variant in its enzymatic activity. We recapitulated the G6PD c.697G>C variant both in HEK-293 cells and K562 cells by adapting the CRISPR/Cas9 strategy. Using distinct cell lines expressing the G6PD c.697G>C variant endogenously, we confirmed the deteriorative role of the G6PD c.697G>C variant in its enzymatic activity. Regarding the secondary and crystallographic structure, we found a mutated amino acid approaching the structural NADP+ binding site. Finally, we demonstrated the c.697G>C variant compromised the thermal stability of G6PD protein.ConclusionsOur data delineated the pathogenic role of G6PD c.697G>C variant for G6PD deficiency, implying the wide usage of CRISPR/Cas9 for genetic disease research.

Computational Detection of Known Pathogenic Gene Fusions in a Normal Tissue Database and Implications for Genetic Disease Research.

Several recent studies have demonstrated the utility of RNA-Seq in the diagnosis of rare inherited disease. Diagnostic rates 35% higher than those previously achievable with DNA-Seq alone have been attained. These studies have primarily profiled gene expression and splicing defects, however, some have also shown that fusion transcripts are diagnostic or phenotypically relevant in patients with constitutional disorders. Fusion transcripts have traditionally been studied as oncogenic phenomena, with relevance only to cancer testing. Consequently, fusion detection algorithms were biased toward the detection of well-known oncogenic fusions, hindering their application to rare Mendelian genetic disease studies. A recent methodology published by the authors successfully tailored a traditional algorithm to the detection of pathogenic fusion events in inherited disease. A key mechanism of decreasing false positive or biologically benign events was comparison to a database of events detected in normal tissues. This approach is akin to population frequency-based filtering of genetic variants. It is predicated on the idea that pathogenic fusion transcripts are absent from normal tissue. We report on an analysis of RNA-Seq data from the genotype-tissue expression (GTEx) project in which known pathogenic fusions are computationally detected at low levels in normal tissues unassociated with the disease phenotype. Examples include archetypal cancer fusion transcripts, as well as fusions responsible for rare inherited disease. We consider potential explanations for the detectability of such transcripts and discuss the bearing such results have on the future profiling of genetic disease patients for pathogenic gene fusions.

Establishing a Multi-Country Sickle Cell Disease Registry in Africa: Ethical Considerations.

Sickle cell disease (SCD) is one of the most prevalent genetic conditions in sub-Saharan Africa. It is a chronic, lifelong disease often characterized by severe pain. However, SCD has received little investment terms of health research, though there is currently a growing pool of SCD data from health and research facilities in different countries. To facilitate research on SCD in Africa, the SickleInAfrica consortium has established a SickleInAfrica registry. The registry will store a systematic collection of longitudinal data from persons with SCD across sub-Saharan Africa, and currently, participants are being enrolled in Ghana, Nigeria, and Tanzania. In establishing this registry, the SickleInAfrica consortium decided to actively identify and anticipate possible ethical issues that may arise in the development and management of the registry. This was motivated, in part, by the near absence of well documented ethical issues for registry research in Africa, more-so for registries enrolling participants across multiple countries and for a genetic condition. The consortium aims to establish standards for the equitable use of data stored in the registry. This paper presents a comprehensive report on the ethical considerations that came up in setting up a genetic disease registry across multiple African countries and how they were addressed by the SickleInAfrica consortium. Major issues included: active involvement of patients in the initiation and management of the registry; questions of assent and re-consent; the importance of ensuring that fears of exploitation are not replicated in African–African research collaborations; and the importance of public engagement in the management of registries. Drawing on this experience, SickleInAfrica plans to set up an ethics helpdesk for genetic disease registries and research in Africa.