Unique roles of rare variants in the genetics of complex diseases in humans

TADeus2: a web server facilitating the clinical diagnosis by pathogenicity assessment of structural variations disarranging 3D chromatin structure

In recent years great progress has been made in identification of structural variants (SV) in the human genome. However, the interpretation of SVs, especially located in non-coding DNA, remains challenging. One of the reasons stems in the lack of tools exclusively designed for clinical SVs evaluation acknowledging the 3D chromatin architecture. Therefore, we present TADeus2 a web server dedicated for a quick investigation of chromatin conformation changes, providing a visual framework for the interpretation of SVs affecting topologically associating domains (TADs). This tool provides a convenient visual inspection of SVs, both in a continuous genome view as well as from a rearrangement’s breakpoint perspective. Additionally, TADeus2 allows the user to assess the influence of analyzed SVs within flaking coding/non-coding regions based on the Hi-C matrix. Importantly, the SVs pathogenicity is quantified and ranked using TADA, ClassifyCNV tools and sampling-based P-value. TADeus2 is publicly available at https://tadeus2.mimuw.edu.pl.

Translational relevance of forward genetic screens in animal models for the study of psychiatric disease

Psychiatric disorders represent a significant burden in our societies. Despite the convincing evidence pointing at gene and gene-environment interaction contributions, the role of genetics in the etiology of psychiatric disease is still poorly understood. Forward genetic screens in animal models have helped elucidate causal links. Here we discuss the application of mutagenesis-based forward genetic approaches in common animal model species: two invertebrates, nematodes (Caenorhabditis elegans) and fruit flies (Drosophila sp.); and two vertebrates, zebrafish (Danio rerio) and mice (Mus musculus), in relation to psychiatric disease. We also discuss the use of large scale genomic studies in human populations. Despite the advances using data from human populations, animal models coupled with next-generation sequencing strategies are still needed. Although with its own limitations, zebrafish possess characteristics that make them especially well-suited to forward genetic studies exploring the etiology of psychiatric disorders.

Membrane anchorage-induced (MAGIC) knock-down of non-synonymous point mutations

The promise of personalized medicine for monogenic and complex polygenic diseases depends on the availability of strategies for targeted inhibition of disease-associated polymorphic protein variants. A large majority of disease-causing genetic alterations are non-synonymous single nucleotide genetic variations (nsSNVs). Yet a general strategy for inhibiting the expression of nsSNVs without editing the human genome is currently lacking. Here, we reveal that upon intracellular delivery of lipid conjugated point mutation-specific monoclonal antibodies, a target-specific knockdown of gene expression at both mRNA and protein levels is observed. By harnessing the phenomenon of m embrane a nchorage i ndu c ed (MAGIC) knock-down of epitope-containing protein targets, we reveal a novel approach for inhibiting the expression of amino acid-altering point mutations. This approach opens up a new opportunity for the therapeutic inhibition of undruggable protein variants as well as paves the way for interrogating the nsSNVs in the human genome.

Unbiased metabolome screen leads to personalized medicine strategy for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a rapidly progressive neurodegenerative disease that affects 1/350 individuals in the United Kingdom. The cause of amyotrophic lateral sclerosis is unknown in the majority of cases. Two-sample Mendelian randomization enables causal inference between an exposure, such as the serum concentration of a specific metabolite, and disease risk. We obtained genome-wide association study summary statistics for serum concentrations of 566 metabolites which were population matched with a genome-wide association study of amyotrophic lateral sclerosis. For each metabolite, we performed Mendelian randomization using an inverse variance weighted estimate for significance testing. After stringent Bonferroni multiple testing correction, our unbiased screen revealed three metabolites that were significantly linked to the risk of amyotrophic lateral sclerosis: Estrone-3-sulphate and bradykinin were protective, which is consistent with literature describing a male preponderance of amyotrophic lateral sclerosis and a preventive effect of angiotensin-converting enzyme inhibitors which inhibit the breakdown of bradykinin. Serum isoleucine was positively associated with amyotrophic lateral sclerosis risk. All three metabolites were supported by robust Mendelian randomization measures and sensitivity analyses; estrone-3-sulphate and isoleucine were confirmed in a validation amyotrophic lateral sclerosis genome-wide association study. Estrone-3-sulphate is metabolized to the more active estradiol by the enzyme 17β-hydroxysteroid dehydrogenase 1; further, Mendelian randomization demonstrated a protective effect of estradiol and rare variant analysis showed that missense variants within HSD17B1, the gene encoding 17β-hydroxysteroid dehydrogenase 1, modify risk for amyotrophic lateral sclerosis. Finally, in a zebrafish model of C9ORF72-amyotrophic lateral sclerosis, we present evidence that estradiol is neuroprotective. Isoleucine is metabolized via methylmalonyl-CoA mutase encoded by the gene MMUT in a reaction that consumes vitamin B12. Multivariable Mendelian randomization revealed that the toxic effect of isoleucine is dependent on the depletion of vitamin B12; consistent with this, rare variants which reduce the function of MMUT are protective against amyotrophic lateral sclerosis. We propose that amyotrophic lateral sclerosis patients and family members with high serum isoleucine levels should be offered supplementation with vitamin B12.

Population Structure and Diversity in European Honey Bees (Apismellifera L.)-An Empirical Comparison of Pool and Individual Whole-Genome Sequencing

BACKGROUND

Whole-genome sequencing has become routine for population genetic studies. Sequencing of individuals provides maximal data but is rather expensive and fewer samples can be studied. In contrast, sequencing a pool of samples (pool-seq) can provide sufficient data, while presenting less of an economic challenge. Few studies have compared the two approaches to infer population genetic structure and diversity in real datasets. Here, we apply individual sequencing (ind-seq) and pool-seq to the study of Western honey bees (Apis mellifera).

METHODS

We collected honey bee workers that belonged to 14 populations, including 13 subspecies, totaling 1347 colonies, who were individually (139 individuals) and pool-sequenced (14 pools). We compared allele frequencies, genetic diversity estimates, and population structure as inferred by the two approaches.

RESULTS

Pool-seq and ind-seq revealed near identical population structure and genetic diversities, albeit at different costs. While pool-seq provides genome-wide polymorphism data at considerably lower costs, ind-seq can provide additional information, including the identification of population substructures, hybridization, or individual outliers.

CONCLUSIONS

If costs are not the limiting factor, we recommend using ind-seq, as population genetic structure can be inferred similarly well, with the advantage gained from individual genetic information. Not least, it also significantly reduces the effort required for the collection of numerous samples and their further processing in the laboratory.

Challenge accepted: uncovering the role of rare genetic variants in Alzheimer's disease

The search for rare variants in Alzheimer's disease (AD) is usually deemed a high-risk - high-reward situation. The challenges associated with this endeavor are real. Still, the application of genome-wide technologies to large numbers of cases and controls or to small, well-characterized families has started to be fruitful.Rare variants associated with AD have been shown to increase risk or cause disease, but also to protect against the development of AD. All of these can potentially be targeted for the development of new drugs.Multiple independent studies have now shown associations of rare variants in NOTCH3, TREM2, SORL1, ABCA7, BIN1, CLU, NCK2, AKAP9, UNC5C, PLCG2, and ABI3 with AD and suggested that they may influence disease via multiple mechanisms. These genes have reported functions in the immune system, lipid metabolism, synaptic plasticity, and apoptosis. However, the main pathway emerging from the collective of genes harboring rare variants associated with AD is the Aβ pathway. Associations of rare variants in dozens of other genes have also been proposed, but have not yet been replicated in independent studies. Replication of this type of findings is one of the challenges associated with studying rare variants in complex diseases, such as AD. In this review, we discuss some of these primary challenges as well as possible solutions.Integrative approaches, the availability of large datasets and databases, and the development of new analytical methodologies will continue to produce new genes harboring rare variability impacting AD. In the future, more extensive and more diverse genetic studies, as well as studies of deeply characterized families, will enhance our understanding of disease pathogenesis and put us on the correct path for the development of successful drugs.

Whole exome sequencing reveals potentially pathogenic variants in a small subset of premenopausal women with idiopathic osteoporosis

Osteoporosis in premenopausal women with intact gonadal function and no known secondary cause of bone loss is termed idiopathic osteoporosis (IOP). Women with IOP diagnosed in adulthood have profound bone structural deficits and often report adult and childhood fractures, and family history of osteoporosis. Some have very low bone formation rates (BFR/BS) suggesting osteoblast dysfunction. These features led us to investigate potential genetic etiologies of bone fragility. In 75 IOP women (aged 20-49) with low trauma fractures and/or very low BMD who had undergone transiliac bone biopsies, we performed Whole Exome Sequencing (WES) using our variant analysis pipeline to select candidate rare and novel variants likely to affect known disease genes. We ran rare-variant burden analyses on all genes individually and on phenotypically-relevant gene sets. For particular genes implicated in osteoporosis, we also assessed the frequency of all (including common) variants in subjects versus 6540 non-comorbid female controls. The variant analysis pipeline identified 4 women with 4 heterozygous variants in LRP5 and PLS3 that were considered to contribute to osteoporosis. All 4 women had adult fractures, and 3 women also had multiple fractures, childhood fractures and a family history of osteoporosis. Two women presented during pregnancy/lactation. In an additional 4 subjects, 4 different relevant Variants of Uncertain Significance (VUS) were detected in the genes FKBP10, SLC34A3, and HGD. Of the subjects with VUS, 2 had multiple adult fractures, childhood fractures, and presented during pregnancy/lactation, and 2 had nephrolithiasis. BFR/BS varied among the 8 subjects with identified variants; BFR/BS was quite low in those with variants that are likely to have adverse effects on bone formation. The analysis pipeline did not discover candidate variants in COL1A1, COL1A2, WNT, or ALPL. Although we found several novel and rare variants in LRP5, cases did not have an increased burden of common LRP5 variants compared to controls. Cohort-wide collapsing analysis did not reveal any novel disease genes with genome-wide significance for qualifying variants between controls and our 75 cases. In summary, WES revealed likely pathogenic variants or relevant VUS in 8 (11%) of 75 women with IOP. Notably, the genetic variants identified were consistent with the affected women's diagnostic evaluations that revealed histological evidence of low BFR/BS or biochemical evidence of increased bone resorption and urinary calcium excretion. These results, and the fact that the majority of the women had no identifiable genetic etiology, also suggest that the pathogenesis of and mechanisms leading to osteoporosis in this cohort are heterogeneous. Future research is necessary to identify both new genetic and non-genetic etiologies of early-onset osteoporosis.

Single-Tube qPCR Detection and Quantitation of Hotspot Mutations Down to 0.01% Variant Allele Fraction

Clinically and biologically, rare DNA sequence variants are significant and informative. However, existing common detection technologies are either complex and time-consuming in workflow, or restricted in the limit of detection (LoD), or do not allow for multiplexing. Blocker displacement amplification (BDA) method can stably and effectively detect and enrich multiple rare variants with LoD around 0.1% variant allele fraction (VAF). Nonetheless, the detailed mutation information has to be identified by additional sequencing technologies. Here, we present allele-specific BDA (As-BDA), a method combining BDA with allele-specific TaqMan (As-TaqMan) probes for effective variant enrichment and simultaneous single nucleotide variant or small insertions and deletions (INDELs) profiling. We demonstrated that As-BDA could detect mutations down to 0.01% VAF. Further, As-BDA could detect up to four mutations with low to 0.1% VAF per reaction using only 15 ng DNA input. The median error of As-BDA in VAF determination is approximately 9.1%. Comparison experiments using As-BDA and droplet digital PCR on peripheral blood mononuclear cell clinical samples showed 100% concordance for samples with mutations at ≥ 0.1% VAF. Hence, we have shown that As-BDA can achieve simultaneous enrichment and identification of multiple targeted mutations within the same reaction with high clinical sensitivity and specificity, thus helpful for clinical diagnosis.

Comparison of multiple imputation algorithms and verification using whole-genome sequencing in the CMUH genetic biobank

A genome-wide association study (GWAS) can be conducted to systematically analyze the contributions of genetic factors to a wide variety of complex diseases. Nevertheless, existing GWASs have provided highly ethnic specific data. Accordingly, to provide data specific to Taiwan, we established a large-scale genetic database in a single medical institution at the China Medical University Hospital. With current technological limitations, microarray analysis can detect only a limited number of single-nucleotide polymorphisms (SNPs) with a minor allele frequency of >1%. Nevertheless, imputation represents a useful alternative means of expanding data. In this study, we compared four imputation algorithms in terms of various metrics. We observed that among the compared algorithms, Beagle5.2 achieved the fastest calculation speed, smallest storage space, highest specificity, and highest number of high-quality variants. We obtained 15,277,414 high-quality variants in 175,871 people by using Beagle5.2. In our internal verification process, Beagle5.2 exhibited an accuracy rate of up to 98.75%. We also conducted external verification. Our imputed variants had a 79.91% mapping rate and 90.41% accuracy. These results will be combined with clinical data in future research. We have made the results available for researchers to use in formulating imputation algorithms, in addition to establishing a complete SNP database for GWAS and PRS researchers. We believe that these data can help improve overall medical capabilities, particularly precision medicine, in Taiwan.

Prediction of genetic alteration of phospholipase C isozymes in brain disorders: Studies with deep learning

Genetic mutations leading to the development of various diseases, such as cancer, diabetes, and neurodegenerative disorders, can be attributed to multiple mechanisms and exposure to diverse environments. These disorders further increase gene mutation rates and affect the activity of translated proteins, both phenomena associated with cellular responses. Therefore, maintaining the integrity of genetic and epigenetic information is critical for disease suppression and prevention. With the advent of genome sequencing technologies, large-scale genomic data-based machine learning tools, including deep learning, have been used to predict and identify somatic inactivation or negative dominant expression of target genes in various diseases. Although deep learning studies have recently been highlighted for their ability to distinguish between the genetic information of diseases, conventional wisdom is also necessary to explain the correlation between genotype and phenotype. Herein, we summarize the current understanding of phosphoinositide-specific phospholipase C isozymes (PLCs) and an overview of their associations with genetic variation, as well as their emerging roles in several diseases. We also predicted and discussed new findings of cryptic PLC splice variants by deep learning and the clinical implications of the PLC genetic variations predicted using these tools.

A Novel Quality-Control Procedure to Improve the Accuracy of Rare Variant Calling in SNP Arrays

Background: Single-nucleotide polymorphism (SNP) arrays are an ideal technology for genotyping genetic variants in mass screening. However, using SNP arrays to detect rare variants [with a minor allele frequency (MAF) of <1%] is still a challenge because of noise signals and batch effects. An approach that improves the genotyping quality is needed for clinical applications. Methods: We developed a quality-control procedure for rare variants which integrates different algorithms, filters, and experiments to increase the accuracy of variant calling. Using data from the TWB 2.0 custom Axiom array, we adopted an advanced normalization adjustment to prevent false calls caused by splitting the cluster and a rare het adjustment which decreases false calls in rare variants. The concordance of allelic frequencies from array data was compared to those from sequencing datasets of Taiwanese. Finally, genotyping results were used to detect familial hypercholesterolemia (FH), thrombophilia (TH), and maturity-onset diabetes of the young (MODY) to assess the performance in disease screening. All heterozygous calls were verified by Sanger sequencing or qPCR. The positive predictive value (PPV) of each step was estimated to evaluate the performance of our procedure. Results: We analyzed SNP array data from 43,433 individuals, which interrogated 267,247 rare variants. The advanced normalization and rare het adjustment methods adjusted genotyping calling of 168,134 variants (96.49%). We further removed 3916 probesets which were discordant in MAFs between the SNP array and sequencing data. The PPV for detecting pathogenic variants with 0.01%10,000 are available. The results demonstrated our procedure could perform correct genotype calling of rare variants. It provides a solution of pathogenic variant detection through SNP array. The approach brings tremendous promise for implementing precision medicine in medical practice.

GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers

The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.

Genetic Ancestry Inference and Its Application for the Genetic Mapping of Human Diseases

Admixed populations arise when two or more ancestral populations interbreed. As a result of this admixture, the genome of admixed populations is defined by tracts of variable size inherited from these parental groups and has particular genetic features that provide valuable information about their demographic history. Diverse methods can be used to derive the ancestry apportionment of admixed individuals, and such inferences can be leveraged for the discovery of genetic loci associated with diseases and traits, therefore having important biomedical implications. In this review article, we summarize the most common methods of global and local genetic ancestry estimation and discuss the use of admixture mapping studies in human diseases.

The Role of Neurod Genes in Brain Development, Function, and Disease

Transcriptional regulation is essential for the correct functioning of cells during development and in postnatal life. The basic Helix-loop-Helix (bHLH) superfamily of transcription factors is well conserved throughout evolution and plays critical roles in tissue development and tissue maintenance. A subgroup of this family, called neural lineage bHLH factors, is critical in the development and function of the central nervous system. In this review, we will focus on the function of one subgroup of neural lineage bHLH factors, the Neurod family. The Neurod family has four members: Neurod1, Neurod2, Neurod4, and Neurod6. Available evidence shows that these four factors are key during the development of the cerebral cortex but also in other regions of the central nervous system, such as the cerebellum, the brainstem, and the spinal cord. We will also discuss recent reports that link the dysfunction of these transcription factors to neurological disorders in humans.

Identification of Novel Genomic-Variant Patterns of OR56A5, OR52L1, and CTSD in Retinitis Pigmentosa Patients by Whole-Exome Sequencing

Inherited retinal dystrophies (IRDs) are rare but highly heterogeneous genetic disorders that affect individuals and families worldwide. However, given its wide variability, its analysis of the driver genes for over 50% of the cases remains unexplored. The present study aims to identify novel driver genes, disease-causing variants, and retinitis pigmentosa (RP)-associated pathways. Using family-based whole-exome sequencing (WES) to identify putative RP-causing rare variants, we identified a total of five potentially pathogenic variants located in genes OR56A5, OR52L1, CTSD, PRF1, KBTBD13, and ATP2B4. Of the variants present in all affected individuals, genes OR56A5, OR52L1, CTSD, KBTBD13, and ATP2B4 present as missense mutations, while PRF1 and CTSD present as frameshift variants. Sanger sequencing confirmed the presence of the novel pathogenic variant PRF1 (c.124_128del) that has not been reported previously. More causal-effect or evidence-based studies will be required to elucidate the precise roles of these SNPs in the RP pathogenesis. Taken together, our findings may allow us to explore the risk variants based on the sequencing data and upgrade the existing variant annotation database in Taiwan. It may help detect specific eye diseases such as retinitis pigmentosa in East Asia.

Synthetic genomics for curing genetic diseases

From the beginning of the genome sequencing era, it has become increasingly evident that genetics plays a role in all diseases, of which only a minority are single-gene disorders, the most common target of current gene therapies. However, the majority of people have some kind of health problems resulting from congenital genetic mutations (over 6000 diseases have been associated to genes, https://www.omim.org/statistics/geneMap) and most genetic disorders are rare and only incompletely understood. The vision and techniques applied to the synthesis of genomes may help to address unmet medical needs from a chromosome and genome-scale perspective. In this chapter, we address the potential therapy of genetic diseases from a different outlook, in which we no longer focus on small gene corrections but on higher-order tools for genome manipulation. These will play a crucial role in the next years, as they prelude to a much deeper understanding of the architecture of the human genome and a more accurate modeling of human diseases, offering new therapeutic opportunities.

More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics

While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains nontrivial. Under a fixed budget, scientists face a natural trade-off between quantity and quality: spending resources to sequence a greater number of genomes or spending resources to sequence genomes with increased accuracy. Our goal is to find the optimal allocation of resources between quantity and quality. Optimizing resource allocation promises to reveal as many new variations in the genome as possible. We introduce a Bayesian nonparametric methodology to predict the number of new variants in a follow-up study based on a pilot study. When experimental conditions are kept constant between the pilot and follow-up, we find that our prediction is competitive with the best existing methods. Unlike current methods, though, our new method allows practitioners to change experimental conditions between the pilot and the follow-up. We demonstrate how this distinction allows our method to be used for more realistic predictions and for optimal allocation of a fixed budget between quality and quantity. We validate our method on cancer and human genomics data.

Leveraging Genetics for Hereditary Angioedema: A Road Map to Precision Medicine

Biochemical studies performed during the last decades resulted in the development of various innovative medicinal products for hereditary angioedema (HAE). These therapeutic agents target the production or the function of bradykinin-the main mediator of HAE due to C1-inhibitor (C1-INH) deficiency. However, despite these remarkable achievements, current knowledge cannot provide convincing explanations for the clinical variability of the disease. As a consequence, treatment indications apply for drugs available for C1-INH deficiency. The advent of high-throughput next-generation sequencing technologies may assist in covering the missing part of our understanding of HAE pathogenesis. During the last 3 years alone, several new entities were added to the already described genotypes. The recent discovery of four novel target genes expands our understanding of other causes which may explain recurrent angioedema in individuals and families with normal C1-INH activity. Furthermore, new genetic technologies allowed the recognition of deep intronic variants associated with the disease, and elegant functional studies characterized new variants for the C1-INH gene. Thus, evidence has been provided regarding pathogenetic aspects remaining obscure for many years, such as the defective intracellular transport of mutant C1-INH, and environmental effect on the disease expression. Therefore, it seems that the stage for Precision Medicine era in HAE management is ready. Disease endotypes are expected to be uncovered and specified targets for therapeutic intervention will be detected, promising a more effective, individualized management of the disease.

Possible Association of Polymorphisms in Ubiquitin Specific Peptidase 46 Gene With Post-traumatic Stress Disorder

Introduction: Dynamic proteolysis, through the ubiquitin-proteasome system, has an important role in DNA transcription and cell cycle, and is considered to modulate cell stress response and synaptic plasticity. We investigated whether genetic variants in the ubiquitin carboxyl-terminal hydrolase 46 (USP46) would be associated with post-traumatic stress disorder (PTSD) in people with exposure to combat trauma using a case-control candidate gene association design. Methods: Korean male veterans exposed to the Vietnam War were grouped into those with (n = 128) and without (n = 128) PTSD. Seven tagging SNPs of USP46 were selected, and single-marker and haplotype-based association analyses were performed. All analyses were adjusted for sociodemographic factors and levels of combat exposure severity and alcohol problem. Results: One single-marker (rs2244291) showed nominal evidence of association with PTSD status and with the "re-experiencing" cluster, although the association was not significant after Bonferroni correction. No significant association with the other SNPs or the haplotypes was detected. Conclusion: The present finding suggests preliminarily that genetic vulnerability regarding the ubiquitin-proteasome system may be related to fear memory processes and the development of PTSD symptoms after trauma exposure. Further studies with a larger sample size will be needed to examine the role of the ubiquitin-proteasome system including USP46 in PTSD.