A novel deep intronic variant in LAMA2 identified by RNA sequencing

LAMA2-related muscular dystrophy is caused by pathogenic variants of the alpha2 subunit of Laminin. This common form of muscular dystrophy is characterized by elevated CK >1000IU/L, dystrophic changes on muscle biopsy, complete or partial absence of merosin staining, and both central and peripheral nervous system involvement. Advancements in genomic testing using NGS and wider application of RNA sequencing has expanded our knowledge of novel non-coding pathogenic variants in LAMA2. RNA sequencing is an increasingly utilized technique to directly analyze the transcriptome, through creation of a complementary DNA (cDNA) from the transcript within a tissue sample. Here we describe a homozygous deep intronic variant that produces a novel splice junction in LAMA2 identified by RNA sequencing analysis in a patient with a clinical phenotype in keeping with LAMA2-related muscular dystrophy. Furthermore, in this case merosin staining was retained suggestive of a functional deficit.

Evolutionary Insights into the Relationship of Frogs, Salamanders, and Caecilians and Their Adaptive Traits, with an Emphasis on Salamander Regeneration and Longevity

The extant amphibians have developed uncanny abilities to adapt to their environment. I compared the genes of amphibians to those of other vertebrates to investigate the genetic changes underlying their unique traits, especially salamanders' regeneration and longevity. Using the well-supported Batrachia tree, I found that salamander genomes have undergone accelerated adaptive evolution, especially for development-related genes. The group-based comparison showed that several genes are under positive selection, rapid evolution, and unexpected parallel evolution with traits shared by distantly related species, such as the tail-regenerative lizard and the longer-lived naked mole rat. The genes, such as EEF1E1, PAFAH1B1, and OGFR, may be involved in salamander regeneration, as they are involved in the apoptotic process, blastema formation, and cell proliferation, respectively. The genes PCNA and SIRT1 may be involved in extending lifespan, as they are involved in DNA repair and histone modification, respectively. Some genes, such as PCNA and OGFR, have dual roles in regeneration and aging, which suggests that these two processes are interconnected. My experiment validated the time course differential expression pattern of SERPINI1 and OGFR, two genes that have evolved in parallel in salamanders and lizards during the regeneration process of salamander limbs. In addition, I found several candidate genes responsible for frogs' frequent vocalization and caecilians' degenerative vision. This study provides much-needed insights into the processes of regeneration and aging, and the discovery of the critical genes paves the way for further functional analysis, which could open up new avenues for exploiting the genetic potential of humans and improving human well-being.

Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility

Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF.

Sequence capture: Obsolete or irreplaceable? A thorough validation across phylogenetic distances and its applicability to hybrids and allopolyploids

As whole-genome sequencing has become pervasive, some have suggested that reduced genomic representation approaches, for example, sequence capture, are becoming obsolete. In the present study, we argue that these techniques still provide excellent tools in terms of price and quality of data as well as in their ability to provide markers with specific features, as required, for example, in phylogenomics. A potential drawback of the wide-scale application of reduced representation approaches could be their drop in efficiency with increasing phylogenetic distance from the reference species. While some studies have focused on the degree and performance of reduced representation techniques in such situations, to our knowledge, none of them evaluated their applicability to inter-specific hybrids and polyploids. This highlights a significant gap in current knowledge since there is increasing evidence for the frequent occurrence of natural hybrids and polyploids, as well as for the major importance of both phenomena in evolution. The main aim of the present study was to carry out a thorough validation of SEQcap applicability to (1) a set of non-model taxa with a wide range of phylogenetic relatedness and (2) inter-specific hybrids of various ploidies and genomic compositions. Considering the latter point, we especially focused on mechanisms causing allelic bias and consequent allelic dropout, as these could have confounding effects with respect to the evolutionary genomic dynamics of hybrids, especially in asexuals, which virtually reproduce as a frozen F1 generation.

GDF15 and ACE2 stratify COVID-19 patients according to severity while ACE2 mutations increase infection susceptibility

Coronavirus disease 19 (COVID-19) is a persistent global pandemic with a very heterogeneous disease presentation ranging from a mild disease to dismal prognosis. Early detection of sensitivity and severity of COVID-19 is essential for the development of new treatments. In the present study, we measured the levels of circulating growth differentiation factor 15 (GDF15) and angiotensin-converting enzyme 2 (ACE2) in plasma of severity-stratified COVID-19 patients and uninfected control patients and characterized the in vitro effects and cohort frequency of ACE2 SNPs. Our results show that while circulating GDF15 and ACE2 stratify COVID-19 patients according to disease severity, ACE2 missense SNPs constitute a risk factor linked to infection susceptibility.

Omic Technologies in HIV: Searching Transcriptional Signatures Involved in Long-Term Non-Progressor and HIV Controller Phenotypes

This article reviews the main discoveries achieved by transcriptomic approaches on HIV controller (HIC) and long-term non-progressor (LTNP) individuals, who are able to suppress HIV replication and maintain high CD4+ T cell levels, respectively, in the absence of antiretroviral therapy. Different studies using high throughput techniques have elucidated multifactorial causes implied in natural control of HIV infection. Genes related to IFN response, calcium metabolism, ribosome biogenesis, among others, are commonly differentially expressed in LTNP/HIC individuals. Additionally, pathways related with activation, survival, proliferation, apoptosis and inflammation, can be deregulated in these individuals. Likewise, recent transcriptomic studies include high-throughput sequencing in specific immune cell subpopulations, finding additional gene expression patterns associated to viral control and/or non-progression in immune cell subsets. Herein, we provide an overview of the main differentially expressed genes and biological routes commonly observed on immune cells involved in HIV infection from HIC and LTNP individuals, analyzing also different technical aspects that could affect the data analysis and the future perspectives and gaps to be addressed in this field.

Clinical and molecular features of sacrum chordoma in Chinese patients

Background

Chordoma is a rare malignant bone tumor with high recurrence and metastasis rates. Little is known about the mutational process of this incurable disease. The aim of our research was to explore the potential driver genes and signal pathways in the pathogenesis of chordoma and provide a new idea for the study of molecular biological therapy of chordoma.

Methods

We performed whole-exome-sequencing (WES) on 8 sacrum chordoma tissue samples (matched to peripheral blood samples that had been drawn from patients before surgery) to identify genetic alterations in Chinese patients. We analyzed the sequencing data from known driver genes, pathway enrichment analysis and significantly mutated genes (SMGs) after quality control of sequencing, comparison of reference genomes, analysis of mutations and identification of somatic mutations. Immunohistochemistry staining, Sanger sequencing and GeneChip were used to verify the related genes obtained from the analysis of sequencing data.

Results

The driver genes Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha (PIK3CA), Phosphoinositide-3-Kinase Regulatory Subunit 1 (PIK3R1), and Phosphatase And Tensin Homolog (PTEN) were enriched in the Phosphatidylinositol 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) signaling pathway and could be potential therapeutic targets for the treatment of sacrum chordoma. The significantly mutated gene Claudin 9 (CLDN9) may play a critical role in the development and progression of sacrum chordoma.

Conclusions

Collectively, our results identified the genetic signature of sacrum chordoma and could be used to develop a potential promising therapeutic strategy for the treatment of sacrum chordoma in Chinese patients.

RNA-seq driven expression and enrichment analysis to investigate CVD genes with associated phenotypes among high-risk heart failure patients

Background

Heart failure (HF) is one of the most common complications of cardiovascular diseases (CVDs) and among the leading causes of death in the US. Many other CVDs can lead to increased mortality as well. Investigating the genetic epidemiology and susceptibility to CVDs is a central focus of cardiology and biomedical life sciences. Several studies have explored expression of key CVD genes specially in HF, yet new targets and biomarkers for early diagnosis are still missing to support personalized treatment. Lack of gender-specific cardiac biomarker thresholds in men and women may be the reason for CVD underdiagnosis in women, and potentially increased morbidity and mortality as a result, or conversely, an overdiagnosis in men. In this context, it is important to analyze the expression and enrichment of genes with associated phenotypes and disease-causing variants among high-risk CVD populations.

Methods

We performed RNA sequencing focusing on key CVD genes with a great number of genetic associations to HF. Peripheral blood samples were collected from a broad age range of adult male and female CVD patients. These patients were clinically diagnosed with CVDs and CMS/HCC HF, as well as including cardiomyopathy, hypertension, obesity, diabetes, asthma, high cholesterol, hernia, chronic kidney, joint pain, dizziness and giddiness, osteopenia of multiple sites, chest pain, osteoarthritis, and other diseases.

Results

We report RNA-seq driven case–control study to analyze patterns of expression in genes and differentiating the pathways, which differ between healthy and diseased patients. Our in-depth gene expression and enrichment analysis of RNA-seq data from patients with mostly HF and other CVDs on differentially expressed genes and CVD annotated genes revealed 4,885 differentially expressed genes (DEGs) and regulation of 41 genes known for HF and 23 genes related to other CVDs, with 15 DEGs as significantly expressed including four genes already known (FLNA, CST3, LGALS3, and HBA1) for HF and CVDs with the enrichment of many pathways. Furthermore, gender and ethnic group specific analysis showed shared and unique genes between the genders, and among different races. Broadening the scope of the results in clinical settings, we have linked the CVD genes with ICD codes.

Conclusions

Many pathways were found to be enriched, and gender-specific analysis showed shared and unique genes between the genders. Additional testing of these genes may lead to the development of new clinical tools to improve diagnosis and prognosis of CVD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00367-8.

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

Application of NGS Technology in Understanding the Pathology of Autoimmune Diseases

NGS technologies have transformed clinical diagnostics and broadly used from neonatal emergencies to adult conditions where the diagnosis cannot be made based on clinical symptoms. Autoimmune diseases reveal complicate molecular background and traditional methods could not fully capture them. Certainly, NGS technologies meet the needs of modern exploratory research, diagnostic and pharmacotherapy. Therefore, the main purpose of this review was to briefly present the application of NGS technology used in recent years in the understanding of autoimmune diseases paying particular attention to autoimmune connective tissue diseases. The main issues are presented in four parts: (a) panels, whole-genome and -exome sequencing (WGS and WES) in diagnostic, (b) Human leukocyte antigens (HLA) as a diagnostic tool, (c) RNAseq, (d) microRNA and (f) microbiome. Although all these areas of research are extensive, it seems that epigenetic impact on the development of systemic autoimmune diseases will set trends for future studies on this area.

Limitations of Detecting Genetic Variants from the RNA Sequencing Data in Tissue and Fine-Needle Aspiration Samples

Background: Genetic profiling of resected tumor or biopsy samples is increasingly used for cancer diagnosis and therapy selection for thyroid and other cancer types. Although mutations occur in cell DNA and are typically detected using DNA sequencing, recent attempts focused on detecting pathogenic variants from RNA. The aim of this study was to determine the completeness of capturing mutations using RNA sequencing (RNA-Seq) in thyroid tissue and fine-needle aspiration (FNA) samples. Methods: To compare the detection rate of mutations between DNA sequencing and RNA-Seq, 35 tissue samples were analyzed in parallel by whole-exome DNA sequencing (WES) and whole-transcriptome RNA-Seq at two study sites. Then, DNA and RNA from 44 thyroid FNA samples and 47 tissue samples were studied using both targeted DNA sequencing and RNA-Seq. Results: Of 162 genetic variants identified by WES of DNA in 35 tissue samples, 77 (48%) were captured by RNA-Seq, with a detection rate of 49% at site 1 and 46% at site 2 and no difference between thyroid and nonthyroid samples. Targeted DNA sequencing of 91 thyroid tissue and FNA samples detected 118 pathogenic variants, of which 57 (48%) were identified by RNA-Seq. For DNA variants present at >10% allelic frequency (AF), the detection rate of RNA-Seq was 62%, and for those at low (5-10%) AF, the detection rate of RNA-Seq was 7% (p < 0.0001). For common oncogenes (BRAF and RAS), 94% of mutations present at >10% AF and 11% of mutations present at 5-10% AF were captured by RNA-Seq. As expected, none of TERT promoter mutations were identified by RNA-Seq. The rate of mutation detection by RNA-Seq was lower in FNA samples than in tissue samples (32% vs. 49%, p = 0.02). Conclusions: In this study, RNA-Seq analysis detected only 46-49% of pathogenic variants identifiable by sequencing of tumor DNA. Detection of mutations by RNA-Seq was more successful for mutations present at a high allelic frequency. Mutations were more often missed by RNA-Seq when present at low frequency or when tested on FNA samples. All TERT mutations were missed by RNA-Seq. These data suggest that RNA-Seq does not detect a significant proportion of clinically relevant mutations and should be used with caution in clinical practice for detecting DNA mutations.

Structural variant identification and characterization

Structural variant (SV) differences between human genomes can cause germline and mosaic disease as well as inter-individual variation. De-regulation of accurate DNA repair and genomic surveillance mechanisms results in a large number of SVs in cancer. Analysis of the DNA sequences at SV breakpoints can help identify pathways of mutagenesis and regions of the genome that are more susceptible to rearrangement. Large-scale SV analyses have been enabled by high-throughput genome-level sequencing on humans in the past decade. These studies have shed light on the mechanisms and prevalence of complex genomic rearrangements. Recent advancements in both sequencing and other mapping technologies as well as calling algorithms for detection of genomic rearrangements have helped propel SV detection into population-scale studies, and have begun to elucidate previously inaccessible regions of the genome. Here, we discuss the genomic organization of simple and complex SVs, the molecular mechanisms of their formation, and various ways to detect them. We also introduce methods for characterizing SVs and their consequences on human genomes.

Identification of De Novo JAK2 and MAPK7 Mutations Related to Autism Spectrum Disorder Using Whole-Exome Sequencing in a Chinese Child and Adolescent Trio-Based Sample

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with high phenotypic and genetic heterogeneity. Whole-exome sequencing studies have shown that de novo single-nucleotide variations (SNVs) play an important role in sporadic ASD. The present study aimed to search for de novo SNVs using whole-exome sequencing in 59 unrelated Chinese ASD sporadic trios, and found 24 genes (including five reported ASD candidate genes CACNA1D, ACHE, YY1, TTN, and FBXO11) with de novo harmful SNVs. Five genes (CACNA1D, JAK2, ACHE, MAPK7, and PRKAG2) classified as “medium-confidence” genes were found to be related to ASD using the Phenolyzer gene analysis tool, which predicts the correlation between the candidate genes and the ASD phenotype. De novo SNVs in JAK2, MAPK7, and PRKAG2 were first found in ASD. Both JAK2 and MAPK7 were involved in the regulation of the MAPK signaling pathway. Gene co-expression and inter-gene interaction networks were constructed and gene expression data in different brain regions were further extracted, revealing that JAK2 and MAPK7 genes were associated with certain previously reported ASD genes and played an important role in early brain development. The findings of this study suggest that the aforementioned five reported ASD genes and JAK2 and MAPK7 may be related to ASD susceptibility. Further investigations of expression studies in cellular and animal models are needed to explore the mechanism underlying the involvement of JAK2 and MAPK7 in ASD.

Hybridisation-based target enrichment of phenology genes to dissect the genetic basis of yield and adaptation in barley

Barley (Hordeum vulgare L.) is a major cereal grain widely used for livestock feed, brewing malts and human food. Grain yield is the most important breeding target for genetic improvement and largely depends on optimal timing of flowering. Little is known about the allelic diversity of genes that underlie flowering time in domesticated barley, the genetic changes that have occurred during breeding, and their impact on yield and adaptation. Here, we report a comprehensive genomic assessment of a worldwide collection of 895 barley accessions based on the targeted resequencing of phenology genes. A versatile target-capture method was used to detect genome-wide polymorphisms in a panel of 174 flowering time-related genes, chosen based on prior knowledge from barley, rice and Arabidopsis thaliana. Association studies identified novel polymorphisms that accounted for observed phenotypic variation in phenology and grain yield, and explained improvements in adaptation as a result of historical breeding of Australian barley cultivars. We found that 50% of genetic variants associated with grain yield, and 67% of the plant height variation was also associated with phenology. The precise identification of favourable alleles provides a genomic basis to improve barley yield traits and to enhance adaptation for specific production areas.

Targeted enrichment by solution-based hybrid capture to identify genetic sequence variants in barley

In barley and other cereal crops, phenological diversity drives adaptation to different cultivation areas. Improvement of barley yield and quality traits requires adaptation to specific production areas with introgression of favorable alleles dependent upon precise identification of the underlying genes. Combining targeted sequence capture systems with next-generation sequencing provides an efficient approach to explore target genetic regions at high resolution, and allows rapid discovery of thousands of genetic polymorphisms. Here, we apply a versatile target-capture method to detect genome-wide polymorphisms in 174 flowering time-related genes, chosen based on prior knowledge from barley, rice, and Arabidopsis thaliana. Sequences were generated across a phenologically diverse panel of 895 barley varieties, resulting a high mean depth coverage of ~25x allowing reliable discovery and calling of insertion-deletion (InDel) and single nucleotide polymorphisms (SNPs). Sequences of InDel and SNPs from the targeted enrichment were utilized to develop 67 Kompetitive Allele Specific PCR (KASP) markers for validation. This work provides researchers and breeders a comprehensive molecular toolkit for the selection of phenology-related traits in barley.

Design Type(s)	genetic polymorphism analysis objective • strain comparison design
Measurement Type(s)	genetic variation
Technology Type(s)	DNA sequencing
Factor Type(s)	cultivar
Sample Characteristic(s)	Hordeum vulgare subsp. vulgare • leaf

Machine-accessible metadata file describing the reported data (ISA-Tab format)

Towards population genomics in non-model species with large genomes: a case study of the marine zooplankton Calanus finmarchicus

Advances in next-generation sequencing technologies and the development of genome-reduced representation protocols have opened the way to genome-wide population studies in non-model species. However, species with large genomes remain challenging, hampering the development of genomic resources for a number of taxa including marine arthropods. Here, we developed a genome-reduced representation method for the ecologically important marine copepod Calanus finmarchicus (haploid genome size of 6.34 Gbp). We optimized a capture enrichment-based protocol based on 2656 single-copy genes, yielding a total of 154 087 high-quality SNPs in C. finmarchicus including 62 372 in common among the three locations tested. The set of capture probes was also successfully applied to the congeneric C. glacialis. Preliminary analyses of these markers revealed similar levels of genetic diversity between the two Calanus species, while populations of C. glacialis showed stronger genetic structure compared to C. finmarchicus. Using this powerful set of markers, we did not detect any evidence of hybridization between C. finmarchicus and C. glacialis. Finally, we propose a shortened version of our protocol, offering a promising solution for population genomics studies in non-model species with large genomes.

Fundamentals of RNA Analysis on Biobanked Specimens

Compared to DNA, analysis of RNA is one step closer on the central dogma of biology to assessing cellular function. This makes it an extremely valuable target for research and clinical testing in nearly all areas of molecular biology. Most RNA molecules are ephemeral by nature. They exist as temporary intermediates, ostensibly enabling data transfer between the genome and the organism. Their ribose backbone renders them sensitive to simple degradation over time and they are the target molecule for numerous and abundant ribonucleases which have evolved to chop them to pieces with extreme efficiency. At the biochemical level, this means that they degrade rapidly in most physiological and laboratory conditions and are thus challenging to study. When considering specimen banking, it is critical to keep this reality in mind, as some commonly used banking modalities will not adequately preserve the relevant RNA molecules in a measureable state.In this chapter, we explore the broad range of RNA testing methodologies in current use, with particular focus on how specimen preparation impacts analysis. Following an overview in the introduction, Subheading 2 covers the major specimen types amenable to RNA analysis in the context of biobanking. Subheading 3 discusses the applications of various RNA analysis modalities to research and clinical testing.

Targeted resequencing of coding DNA sequences for SNP discovery in nonmodel species

Targeted capture coupled with high-throughput sequencing can be used to gain information about nuclear sequence variation at hundreds to thousands of loci. Divergent reference capture makes use of molecular data of one species to enrich target loci in other (related) species. This is particularly valuable for nonmodel organisms, for which often no a priori knowledge exists regarding these loci. Here, we have used targeted capture to obtain data for 809 nuclear coding DNA sequences (CDS) in a nonmodel organism, the Eurasian lynx Lynx lynx, using baits designed with the help of the published genome of a related model organism (the domestic cat Felis catus). Using this approach, we were able to survey intraspecific variation at hundreds of nuclear loci in L. lynx across the species' European range. A large set of biallelic candidate SNPs was then evaluated using a high-throughput SNP genotyping platform (Fluidigm), which we then reduced to a final 96 SNP-panel based on assay performance and reliability; validation was carried out with 100 additional Eurasian lynx samples not included in the SNP discovery phase. The 96 SNP-panel developed from CDS performed very successfully in the identification of individuals and in population genetic structure inference (including the assignment of individuals to their source population). In keeping with recent studies, our results show that genic SNPs can be valuable for genetic monitoring of wildlife species.

Capture Hybridization of Long-Range DNA Fragments for High-Throughput Sequencing

Capture hybridization coupled with high-throughput sequencing (HTS) has become one of the most popular approaches to address some scientific problems not only for fundamental evolution but also for ecology and human disease in recent years. However, the technical problem of limited probe capture ability affects its widespread application. Here, we propose to capture hybridize long-range DNA fragments for HTS (termed LR-LCH). We provide a case of three amphibian samples to examine LR-LCH with 2 kb libraries and comparison of standard capture hybridization with 480 bp libraries. Capture sensitivity increased from an average 13.57% of standard capture hybridization to an average 19.80% of LR-LCH; capture efficiency also increased from an average 72.56% of standard capture hybridization to an average 97.71% of LR-LCH. These indicate that longer fragments in the library generally contain both relatively variable regions and relatively conservative regions. The divergent parts of target DNA are enriched along with conservative parts of DNA sequence that effectively captured during hybridization. We present a protocol that allows users to overcome the low capture sensitivity problem for high divergent regions.

Panomics for Precision Medicine

Medicine is poised to undergo a digital transformation. High-throughput platforms are creating terabytes of genomic, transcriptomic, proteomic, and metabolomic data. The challenge is to interpret these data in a meaningful manner - to uncover relationships that are not readily apparent between molecular profiles and states of health or disease. This will require the development of novel data pipelines and computational tools. The combined analysis of multi-dimensional data is referred to as 'panomics'. The ultimate hope of integrative panomics is that it will lead to the discovery and application of novel markers and targeted therapeutics that drive forward a new era of 'precision medicine' where inter-individual variation is accounted for in the treatment of patients.

A Path to Implement Precision Child Health Cardiovascular Medicine

Congenital heart defects (CHDs) affect approximately 1% of live births and are a major source of childhood morbidity and mortality even in countries with advanced healthcare systems. Along with phenotypic heterogeneity, the underlying etiology of CHDs is multifactorial, involving genetic, epigenetic, and/or environmental contributors. Clear dissection of the underlying mechanism is a powerful step to establish individualized therapies. However, the majority of CHDs are yet to be clearly diagnosed for the underlying genetic and environmental factors, and even less with effective therapies. Although the survival rate for CHDs is steadily improving, there is still a significant unmet need for refining diagnostic precision and establishing targeted therapies to optimize life quality and to minimize future complications. In particular, proper identification of disease associated genetic variants in humans has been challenging, and this greatly impedes our ability to delineate gene–environment interactions that contribute to the pathogenesis of CHDs. Implementing a systematic multileveled approach can establish a continuum from phenotypic characterization in the clinic to molecular dissection using combined next-generation sequencing platforms and validation studies in suitable models at the bench. Key elements necessary to advance the field are: first, proper delineation of the phenotypic spectrum of CHDs; second, defining the molecular genotype/phenotype by combining whole-exome sequencing and transcriptome analysis; third, integration of phenotypic, genotypic, and molecular datasets to identify molecular network contributing to CHDs; fourth, generation of relevant disease models and multileveled experimental investigations. In order to achieve all these goals, access to high-quality biological specimens from well-defined patient cohorts is a crucial step. Therefore, establishing a CHD BioCore is an essential infrastructure and a critical step on the path toward precision child health cardiovascular medicine.

Recent Advances in Autism Spectrum Disorders: Applications of Whole Exome Sequencing Technology

Autism spectrum disorders (ASD) is characterized by three core symptoms with impaired reciprocal social interaction and communication, a pattern of repetitive behavior and/or restricted interests in early childhood. The prevalence is higher in male children than in female children. As a complex neurodevelopmental disorder, the phenotype and severity of autism are extremely heterogeneous with differences from one patient to another. Genetics has a key role in the etiology of autism. Environmental factors are also interacting with the genetic profile and cause abnormal changes in neuronal development, brain growth, and functional connectivity. The term of exome represents less than 1% of the human genome, but contains 85% of known disease-causing variants. Whole-exome sequencing (WES) is an application of the next generation sequencing technology to determine the variations of all coding regions, or exons of known genes. For this reason, WES has been extensively used for clinical studies in the recent years. WES has achieved great success in the past years for identifying Mendelian disease genes. This review evaluates the potential of current findings in ASD for application in next generation sequencing technology, particularly WES. WES and whole-genome sequencing (WGS) approaches may lead to the discovery of underlying genetic factors for ASD and may thereby identify novel therapeutic targets for this disorder.

Understanding and utilizing crop genome diversity via high-resolution genotyping

High-resolution genome analysis technologies provide an unprecedented level of insight into structural diversity across crop genomes. Low-cost discovery of sequence variation has become accessible for all crops since the development of next-generation DNA sequencing technologies, using diverse methods ranging from genome-scale resequencing or skim sequencing, reduced-representation genotyping-by-sequencing, transcriptome sequencing or sequence capture approaches. High-density, high-throughput genotyping arrays generated using the resulting sequence data are today available for the assessment of genomewide single nucleotide polymorphisms in all major crop species. Besides their application in genetic mapping or genomewide association studies for dissection of complex agronomic traits, high-density genotyping arrays are highly suitable for genomic selection strategies. They also enable description of crop diversity at an unprecedented chromosome-scale resolution. Application of population genetics parameters to genomewide diversity data sets enables dissection of linkage disequilibrium to characterize loci underlying selective sweeps. High-throughput genotyping platforms simultaneously open the way for targeted diversity enrichment, allowing rejuvenation of low-diversity chromosome regions in strongly selected breeding pools to potentially reverse the influence of linkage drag. Numerous recent examples are presented which demonstrate the power of next-generation genomics for high-resolution analysis of crop diversity on a subgenomic and chromosomal scale. Such studies give deep insight into the history of crop evolution and selection, while simultaneously identifying novel diversity to improve yield and heterosis.

Targeted capture in evolutionary and ecological genomics

The rapid expansion of next-generation sequencing has yielded a powerful array of tools to address fundamental biological questions at a scale that was inconceivable just a few years ago. Various genome-partitioning strategies to sequence select subsets of the genome have emerged as powerful alternatives to whole-genome sequencing in ecological and evolutionary genomic studies. High-throughput targeted capture is one such strategy that involves the parallel enrichment of preselected genomic regions of interest. The growing use of targeted capture demonstrates its potential power to address a range of research questions, yet these approaches have yet to expand broadly across laboratories focused on evolutionary and ecological genomics. In part, the use of targeted capture has been hindered by the logistics of capture design and implementation in species without established reference genomes. Here we aim to (i) increase the accessibility of targeted capture to researchers working in nonmodel taxa by discussing capture methods that circumvent the need of a reference genome, (ii) highlight the evolutionary and ecological applications where this approach is emerging as a powerful sequencing strategy and (iii) discuss the future of targeted capture and other genome-partitioning approaches in the light of the increasing accessibility of whole-genome sequencing. Given the practical advantages and increasing feasibility of high-throughput targeted capture, we anticipate an ongoing expansion of capture-based approaches in evolutionary and ecological research, synergistic with an expansion of whole-genome sequencing.

RNAseq analysis for the diagnosis of muscular dystrophy

The precise genetic cause remains elusive in nearly 50% of patients with presumed neurogenetic disease, representing a significant barrier for clinical care. This is despite significant advances in clinical genetic diagnostics, including the application of whole‐exome sequencing and next‐generation sequencing‐based gene panels. In this study, we identify a deep intronic mutation in the DMD gene in a patient with muscular dystrophy using both conventional and RNAseq‐based transcriptome analyses. The implications of our data are that noncoding mutations likely comprise an important source of unresolved genetic disease and that RNAseq is a powerful platform for detecting such mutations.

Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud

Massively parallel RNA sequencing (RNA-seq) has rapidly become the assay of choice for interrogating RNA transcript abundance and diversity. This article provides a detailed introduction to fundamental RNA-seq molecular biology and informatics concepts. We make available open-access RNA-seq tutorials that cover cloud computing, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality-control strategies, expression, differential expression, and alternative splicing analysis methods. These tutorials and additional training resources are accompanied by complete analysis pipelines and test datasets made available without encumbrance at www.rnaseq.wiki.

Inconsistency and features of single nucleotide variants detected in whole exome sequencing versus transcriptome sequencing: A case study in lung cancer

Whole exome sequencing (WES) and RNA sequencing (RNA-Seq) are two main platforms used for next-generation sequencing (NGS). While WES is primarily for DNA variant discovery and RNA-Seq is mainly for measurement of gene expression, both can be used for detection of genetic variants, especially single nucleotide variants (SNVs). How consistently variants can be detected from WES and RNA-Seq has not been systematically evaluated. In this study, we examined the technical and biological inconsistencies in SNV detection using WES and RNA-Seq data from 27 pairs of tumor and matched normal samples. We analyzed SNVs in three categories: WES unique - those only detected in WES, RNA-Seq unique - those only detected in RNA-Seq, and shared - those detected in both. We found a small overlap (average ∼14%) between the SNVs called in WES and RNA-Seq. The WES unique SNVs were mainly due to low coverage, low expression, or their location on the non-transcribed strand in RNA-Seq data, while the RNA-Seq unique SNVs were primarily due to their location out of the WES-capture boundary regions (accounting ∼71%), as well as low coverage of the regions, low coverage of the mutant alleles or RNA-editing. The shared SNVs had high locus-specific coverage in both WES and RNA-Seq and high gene expression levels. Additionally, WES unique and RNA-Seq unique SNVs showed different nucleotide substitution patterns, e.g., ∼55% of RNA-Seq unique variants were A:T→G:C, a hallmark of RNA editing. This study provides an important evaluation on the inconsistencies of somatic SNVs called in WES and RNA-Seq data.

RADIA: RNA and DNA integrated analysis for somatic mutation detection

The detection of somatic single nucleotide variants is a crucial component to the characterization of the cancer genome. Mutation calling algorithms thus far have focused on comparing the normal and tumor genomes from the same individual. In recent years, it has become routine for projects like The Cancer Genome Atlas (TCGA) to also sequence the tumor RNA. Here we present RADIA (RNA and DNA Integrated Analysis), a novel computational method combining the patient-matched normal and tumor DNA with the tumor RNA to detect somatic mutations. The inclusion of the RNA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating an individual's DNA and RNA, we are able to detect mutations that would otherwise be missed by traditional algorithms that examine only the DNA. We demonstrate high sensitivity (84%) and very high precision (98% and 99%) for RADIA in patient data from endometrial carcinoma and lung adenocarcinoma from TCGA. Mutations with both high DNA and RNA read support have the highest validation rate of over 99%. We also introduce a simulation package that spikes in artificial mutations to patient data, rather than simulating sequencing data from a reference genome. We evaluate sensitivity on the simulation data and demonstrate our ability to rescue back mutations at low DNA allelic frequencies by including the RNA. Finally, we highlight mutations in important cancer genes that were rescued due to the incorporation of the RNA.

Integrated RNA and DNA sequencing improves mutation detection in low purity tumors

Identifying somatic mutations is critical for cancer genome characterization and for prioritizing patient treatment. DNA whole exome sequencing (DNA-WES) is currently the most popular technology; however, this yields low sensitivity in low purity tumors. RNA sequencing (RNA-seq) covers the expressed exome with depth proportional to expression. We hypothesized that integrating DNA-WES and RNA-seq would enable superior mutation detection versus DNA-WES alone. We developed a first-of-its-kind method, called UNCeqR, that detects somatic mutations by integrating patient-matched RNA-seq and DNA-WES. In simulation, the integrated DNA and RNA model outperformed the DNA-WES only model. Validation by patient-matched whole genome sequencing demonstrated superior performance of the integrated model over DNA-WES only models, including a published method and published mutation profiles. Genome-wide mutational analysis of breast and lung cancer cohorts (n = 871) revealed remarkable tumor genomics properties. Low purity tumors experienced the largest gains in mutation detection by integrating RNA-seq and DNA-WES. RNA provided greater mutation signal than DNA in expressed mutations. Compared to earlier studies on this cohort, UNCeqR increased mutation rates of driver and therapeutically targeted genes (e.g. PIK3CA, ERBB2 and FGFR2). In summary, integrating RNA-seq with DNA-WES increases mutation detection performance, especially for low purity tumors.

Exome sequencing greatly expedites the progressive research of Mendelian diseases

The advent of whole-exome sequencing (WES) has facilitated the discovery of rare structure and functional genetic variants. Combining exome sequencing with linkage studies is one of the most efficient strategies in searching disease genes for Mendelian diseases. WES has achieved great success in the past three years for Mendelian disease genetics and has identified over 150 new Mendelian disease genes. We illustrate the workflow of exome capture and sequencing to highlight the advantages of WES. We also indicate the progress and limitations of WES that can potentially result in failure to identify disease-causing mutations in part of patients. With an affordable cost, WES is expected to become the most commonly used tool for Mendelian disease gene identification. The variants detected cumulatively from previous WES studies will be widely used in future clinical services.

Systems pharmacology identifies drug targets for Stargardt disease-associated retinal degeneration

A systems pharmacological approach that capitalizes on the characterization of intracellular signaling networks can transform our understanding of human diseases and lead to therapy development. Here, we applied this strategy to identify pharmacological targets for the treatment of Stargardt disease, a severe juvenile form of macular degeneration. Diverse GPCRs have previously been implicated in neuronal cell survival, and crosstalk between GPCR signaling pathways represents an unexplored avenue for pharmacological intervention. We focused on this receptor family for potential therapeutic interventions in macular disease. Complete transcriptomes of mouse and human samples were analyzed to assess the expression of GPCRs in the retina. Focusing on adrenergic (AR) and serotonin (5-HT) receptors, we found that adrenoceptor α 2C (Adra2c) and serotonin receptor 2a (Htr2a) were the most highly expressed. Using a mouse model of Stargardt disease, we found that pharmacological interventions that targeted both GPCR signaling pathways and adenylate cyclases (ACs) improved photoreceptor cell survival, preserved photoreceptor function, and attenuated the accumulation of pathological fluorescent deposits in the retina. These findings demonstrate a strategy for the identification of new drug candidates and FDA-approved drugs for the treatment of monogenic and complex diseases.

Next generation sequencing and rare genetic variants: from human population studies to medical genetics

The allelic frequency spectrum emerging from several Next Generation Sequencing (NGS) projects is revealing important details about evolutionary and demographic forces that shaped the human genome. Herein, we discuss some of the achievements of the use of low-frequency and rare variants from NGS studies. The majority of variants that affect protein-coding regions are recent and rare. Often, the novel rare variants are enriched for deleterious alleles and are population-specific, making them suitable for the study of disease susceptibility. To investigate this kind of variation and its effects in association studies, very large sample sizes will be necessary to achieve sufficient statistical power. Moreover, as these variants are typically population-specific, the replication of disease associations across populations could be very difficult due to population stratification. Therefore, the design of experiments focusing on the identification of rare variants and their effects should be carefully planned. Although several successes have already been achieved through NGS for genetic epidemiology, pharmacogenetic and clinical purposes, with improvements of the sequencing technology and decreased costs, further advances are expected in the near future.