Sequencing studies in human genetics: design and interpretation

Rare variant association studies: Significance, methods, and applications in chronic pain studies

Rare genetic variants, characterized by their low frequency in a population, have emerged as essential components in the study of complex disease genetics. The biology of rare variants underscores their significance, as they can exert profound effects on phenotypic variation and disease susceptibility. Recent advancements in sequencing technologies have yielded the availability of large-scale sequencing data such as the UK Biobank whole-exome sequencing (WES) cohort empowered researchers to conduct rare variant association studies (RVASs). This review paper discusses the significance of rare variants, available methodologies, and applications. We provide an overview of RVASs, emphasizing their relevance in unraveling the genetic architecture of complex diseases with special focus on chronic pain and Arthritis. Additionally, we discuss the strengths and limitations of various rare variant association testing methods, outlining a typical pipeline for conducting rare variant association. This pipeline encompasses crucial steps such as quality control of WES data, rare variant annotation, and association testing. It serves as a comprehensive guide for researchers in the field of chronic pain diseases interested in rare variant association studies in large-scale sequencing datasets like the UK Biobank WES cohort. Lastly, we discuss how the identified variants can be further investigated through detailed experimental studies in animal models to elucidate their functional impact and underlying mechanisms.

An overview of recent technological developments in bovine genomics

Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.

Clinical Implementation of Rare and Novel DPYD Variants for Personalizing Fluoropyrimidine Treatment: Challenges and Opportunities

Fluoropyrimidines (FLs) [5-Fluorouracil, Capecitabine] are used in the treatment of several solid tumors. Dihydropyrimidine dehydrogenase (DPD) is the rate-limiting enzyme for FL detoxification, and its deficiency could lead to severe, life-threatening or fatal toxicity after FL administration. Testing with a pharmacogenetic panel of four deleterious variants in the dihydropyrimidine dehydrogenase gene (DPYD) (DPYD*2A, DPYD*13, c.2846A > T, c.1129-5923C > G) prior to FL treatment, is recommended by scientific consortia (e.g., CPIC, DPWG) and drug regulatory agencies (e.g., EMA). However, this panel identifies < 20% of patients at risk of severe FL-related toxicity. Cumulative recent evidence highlights the potential clinical value of rare (minor allele frequency < 1%) and novel DPYD genetic variants for identifying an additional fraction of DPD-deficient patients at increased risk of severe FL-related toxicity. In this review, we aimed to comprehensively describe the available evidence regarding the potential clinical predictive role of novel and rare DPYD variants as toxicity markers in FL-treated patients, and to discuss the challenges and opportunities in tailoring FL treatment based upon clinical application of such markers. Although we must overcome existing barriers to the clinical implementation, the available data support that comprehensive assessment of the DPYD sequence, including rare and novel genetic variants, may significantly enhance the pre-emptive identification of at-risk patients, compared to the current targeted approach.

Predicted genetic burden and frequency of phenotype-associated variants in the horse

Disease-causing variants have been identified for less than 20% of suspected equine genetic diseases. Whole genome sequencing (WGS) allows rapid identification of rare disease causal variants. However, interpreting the clinical variant consequence is confounded by the number of predicted deleterious variants that healthy individuals carry (predicted genetic burden). Estimation of the predicted genetic burden and baseline frequencies of known deleterious or phenotype associated variants within and across the major horse breeds have not been performed. We used WGS of 605 horses across 48 breeds to identify 32,818,945 variants, demonstrate a high predicted genetic burden (median 730 variants/horse, interquartile range: 613-829), show breed differences in predicted genetic burden across 12 target breeds, and estimate the high frequencies of some previously reported disease variants. This large-scale variant catalog for a major and highly athletic domestic animal species will enhance its ability to serve as a model for human phenotypes and improves our ability to discover the bases for important equine phenotypes.

Computational Tools to Assist in Analyzing Effects of the SERPINA1 Gene Variation on Alpha-1 Antitrypsin (AAT)

In the rapidly advancing field of bioinformatics, the development and application of computational tools to predict the effects of single nucleotide variants (SNVs) are shedding light on the molecular mechanisms underlying disorders. Also, they hold promise for guiding therapeutic interventions and personalized medicine strategies in the future. A comprehensive understanding of the impact of SNVs in the SERPINA1 gene on alpha-1 antitrypsin (AAT) protein structure and function requires integrating bioinformatic approaches. Here, we provide a guide for clinicians to navigate through the field of computational analyses which can be applied to describe a novel genetic variant. Predicting the clinical significance of SERPINA1 variation allows clinicians to tailor treatment options for individuals with alpha-1 antitrypsin deficiency (AATD) and related conditions, ultimately improving the patient's outcome and quality of life. This paper explores the various bioinformatic methodologies and cutting-edge approaches dedicated to the assessment of molecular variants of genes and their product proteins using SERPINA1 and AAT as an example.

A 25-year odyssey of genomic technology advances and structural variant discovery

This perspective focuses on advances in genome technology over the last 25 years and their impact on germline variant discovery within the field of human genetics. The field has witnessed tremendous technological advances from microarrays to short-read sequencing and now long-read sequencing. Each technology has provided genome-wide access to different classes of human genetic variation. We are now on the verge of comprehensive variant detection of all forms of variation for the first time with a single assay. We predict that this transition will further transform our understanding of human health and biology and, more importantly, provide novel insights into the dynamic mutational processes shaping our genomes.

Whole exome sequencing approach for identification of the molecular etiology in pediatric patients with hematuria

BACKGROUND

Hematuria is a common condition in clinical practice of pediatric patients. It is related to a wide spectrum of disorders and has high heterogeneity both clinically and genetically, which contributes to challenges of diagnosis and lead many pediatric patients with hematuria not to receive accurate diagnosis and early management.

METHODS

In this single center study, 42 children with hematuria were included in Tianjin Children's Hospital between 2019 and 2020. We analyzed the clinical information and performed WES (Whole exome sequencing) for all cases. Then the classification of identified variants was performed according to the American College of Medical Genetics and Genomics (ACMG) guidelines for interpreting sequence variants. For the fragment deletion, qPCR was performed to validate and confirm the inherited pattern.

RESULTS

For the 42 patients, 16 cases had gross hematuria and 26 had microscopic hematuria. Molecular genetic causes were uncovered in 9 (21.4%) children, including 7 with Alport syndrome (AS), one with polycystic nephropathy and one with lipoprotein glomerulopathy. The genetic causes for other patients were not related with hematuria.

CONCLUSIONS

WES is a rapid and effective way to evaluate patients with hematuria. The analysis of genotype-phenotype correlations of patients with AS indicated that severe variants were associated with early kidney failure. Secondary findings were not rare in Chinese children, thus the clinician should pay more attention to the clinical interpretation of sequencing results and properly interaction with patients and their family.

Potentials of single nucleotide polymorphisms and genetic diversity studies at HSP90AB1 gene in Nigerian White Fulani, Muturu, and N'Dama cattle breeds

The study was aimed at genetic characterization of Nigerian breeds of Muturu, N'Dama, and White Fulani cattle breeds at heat shock protein 90AB1 locus. Also, the goal of the study was to detect the presence of single nucleotide polymorphisms (SNPs) at HSP90AB1 locus and consequently recommend them as bio-markers for thermo-tolerance potentials in Nigerian cattle breeds when exposed to assaults of thermal conditions/heat shock of tropical environment. Based on the previously published potentials of this candidate gene to lower assaults of thermal conditions/heat shock such as heat stress, the detected SNPs of HSP90AB1 within the population of the Nigerian cattle in this study will be recommended for population-based screening with a view to genetically improving those zebu cattle breeds that are more vulnerable to heat shock and assaults of thermal conditions. Total number of 200 blood samples were randomly collected from White Fulani (84 samples), Muturu (73 samples), and N'Dama (43 samples) breeds of cattle. Out of these, 20 DNA samples were randomly selected from each of the three cattle breeds and were used for DNA extraction and downstream analyses to further confirm findings of previous study, hence the goal of our study. DNA was extracted from the blood samples using the Zymo-bead DNA extraction kit and DNA sequencing of our samples was performed. A total number of 9 SNPs (within exons 5-6 coding regions) and 11 SNPs (within exons 12-13 coding regions) were detected at HSP90AB1 locus using the codon code aligner software. ARLEQUIN 2.0001 software was used to estimate the basic population genetic statistics while the DnaSP version 5.10.01 was used to estimate the genetic diversity indices. This study detected new SNPs (polymorphic sites) at HSP90AB1 locus within the DNAs of Nigerian White Fulani (WF), Muturu (MU), and N'Dama (ND) breeds of cattle. Within exons 5-6 coding regions, the N'Dama (ND) cattle breed had the highest for number of SNPs (5) and genetic diversity indices while White Fulani (WF) and Muturu (MU) had the least (2) number of SNPs each. Within exons 12-13 coding regions, WF had the highest numbers of SNPs (7) and genetic diversity indices while MU had the least number of SNPs (1) and genetic diversity indices. Some of the detected SNPs at HSP90AB1 locus were shared among the three breeds, suggesting that these three Nigerian cattle breeds showed shared ancestral alleles and lineage. Our study further revealed that HSP90AB1 is highly polymorphic/variable and diverse among the three Nigerian cattle breeds examined. Based on the previously documented thermo-tolerance potentials of members of HSP90 sub-family including the findings of our study, we hypothesize therefore that the presence of SNPs of HSP90AB1 within the DNAs of these three breeds of Nigerian cattle (WF, ND, and MU) may confer them thermo-tolerance potentials for thermal assault conditions and heat shock of the tropics at HSP90AB1 locus. Therefore, the detected SNPs can be recommended as bio-markers to improve the thermo-tolerance potentials of Nigerian breeds of zebu cattle raised under the challenges of heat shock for better adaptation and survival.

Clinical and genetic analyses of a Swedish patient series diagnosed with ataxia

Hereditary ataxia is a heterogeneous group of complex neurological disorders. Next-generation sequencing methods have become a great help in clinical diagnostics, but it may remain challenging to determine if a genetic variant is the cause of the patient's disease. We compiled a consecutive single-center series of 87 patients from 76 families with progressive ataxia of known or unknown etiology. We investigated them clinically and genetically using whole exome or whole genome sequencing. Test methods were selected depending on family history, clinical phenotype, and availability. Genetic results were interpreted based on the American College of Medical Genetics criteria. For high-suspicion variants of uncertain significance, renewed bioinformatical and clinical evaluation was performed to assess the level of pathogenicity. Thirty (39.5%) of the 76 families had received a genetic diagnosis at the end of our study. We present the predominant etiologies of hereditary ataxia in a Swedish patient series. In two families, we established a clinical diagnosis, although the genetic variant was classified as "of uncertain significance" only, and in an additional three families, results are pending. We found a pathogenic variant in one family, but we suspect that it does not explain the complete clinical picture. We conclude that correctly interpreting genetic variants in complex neurogenetic diseases requires genetics and clinical expertise. The neurologist's careful phenotyping remains essential to confirm or reject a diagnosis, also by reassessing clinical findings after a candidate genetic variant is suggested. Collaboration between neurology and clinical genetics and combining clinical and research approaches optimizes diagnostic yield.

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

Rare genetic variant burden in DPYD predicts severe fluoropyrimidine-related toxicity risk

Preemptive targeted pharmacogenetic testing of candidate variations in DPYD is currently being used to limit toxicity associated with fluoropyrimidines. The use of innovative next generation sequencing (NGS) approaches could unveil additional rare (minor allele frequency <1%) genetic risk variants. However, their predictive value and management in clinical practice are still controversial, at least partly due to the challenges associated with functional analyses of rare variants. The aim of this study was to define the predictive power of rare DPYD variants burden on the risk of severe fluoropyrimidine-related toxicity.

The DPYD coding sequence and untranslated regions were analyzed by NGS in 120 patients developing grade 3–5 (NCI-CTC vs3.0) fluoropyrimidine-related toxicity and 104 matched controls (no-toxicity). The functional impact of rare variants was assessed using two different in silico predictive tools (i.e., Predict2SNP and ADME Prediction Framework) and structural modeling. Plasma concentrations of uracil (U) and dihydrouracil (UH2) were quantified in carriers of the novel variants.

Here, we demonstrate that the burden of rare variants was significantly higher in patients with toxicity compared to controls (p = 0.007, Mann-Whitney test). Carriers of at least one rare missense DPYD variant had a 16-fold increased risk in the first cycle and an 11-fold increased risk during the entire course of chemotherapy of developing a severe adverse event compared to controls (p = 0.013 and p = 0.0250, respectively by multinomial regression model). Quantification of plasmatic U/UH2 metabolites and in silico visualization of the encoded protein were consistent with the predicted functional effect for the novel variations.

Analysis and consideration of rare variants by DPYD-sequencing could improve prevention of severe toxicity of fluoropyrimidines and improve patients’ quality of life.

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DPYD genotype-guided dosing reduces fluoropyrimidine (FP) toxicity risk.

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Rare DPYD variants associate with severe FP toxicities.

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Carriers of rare DPYD variants have 11-fold increased risk of toxicity.

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DPYD sequencing and in silico functional prediction could prevent FP toxicity events.

A tree-based gene-environment interaction analysis with rare features

Gene-environment (G-E) interaction analysis plays a critical role in understanding and modeling complex diseases. Compared to main-effect-only analysis, it is more seriously challenged by higher dimensionality, weaker signals, and the unique "main effects, interactions" variable selection hierarchy. In joint G-E interaction analysis under which a large number of G factors are analysed in a single model, effort tailored to rare features (e.g., SNPs with low minor allele frequencies) has been limited. Existing investigations on rare features have been mostly focused on marginal analysis, where various data aggregation techniques have been developed, and hypothesis testings have been conducted to identify significant aggregated features. However, such techniques cannot be extended to joint G-E interaction analysis. In this study, building on a very recent tree-based data aggregation technique, which has been developed for main-effect-only analysis, we develop a new G-E interaction analysis approach tailored to rare features. The adopted data aggregation technique allows for more efficient information borrowing from neighboring rare features. Similar to some existing state-of-the-art ones, the proposed approach adopts penalization for variable selection, regularized estimation, and respect of the variable selection hierarchy. Simulation shows that it has more accurate identification of important interactions and main effects than several competing alternatives. In the analysis of NFBC1966 study, the proposed approach leads to findings different from the alternatives and with satisfactory prediction and stability performance.

Predicting drug response and toxicity in metastatic colorectal cancer: the role of germline markers

INTRODUCTION

Despite the introduction of targeted agents leading to therapeutic advances, clinical management of patients with metastatic colorectal cancer (mCRC) is still challenged by significant interindividual variability in treatment outcomes, both in terms of toxicity and therapy efficacy. The study of germline genetic variants could help to personalize and optimize therapeutic approaches in mCRC.

AREAS COVERED

A systematic review of pharmacogenetic studies in mCRC patients published on PubMed between 2011 and 2021, evaluating the role of germline variants as predictive markers of toxicity and efficacy of drugs currently approved for treatment of mCRC, was perfomed.

EXPERT OPINION

Despite the large amount of pharmacogenetic data published to date, only a few genetic markers (i.e., DPYD and UGT1A1 variants) reached the clinical practice, mainly to prevent the toxic effects of chemotherapy. The large heterogeneity of available studies represents the major limitation in comparing results and identifying potential markers for clinical use, the role of which remains exploratory in most cases. However, the available published findings are an important starting point for future investigations. They highlighted new promising pharmacogenetic markers within the network of inflammatory and immune response signaling. In addition, the emerging role of previously overlooked rare variants has been pointed out.

The Clinical Variant Analysis Tool: Analyzing the evidence supporting reported genomic variation in clinical practice

PURPOSE

Genomic test results, regardless of laboratory variant classification, require clinical practitioners to judge the applicability of a variant for medical decisions. Teaching and standardizing clinical interpretation of genomic variation calls for a methodology or tool.

METHODS

To generate such a tool, we distilled the Clinical Genome Resource framework of causality and the American College of Medical Genetics/Association of Molecular Pathology and Quest Diagnostic Laboratory scoring of variant deleteriousness into the Clinical Variant Analysis Tool (CVAT). Applying this to 289 clinical exome reports, we compared the performance of junior practitioners with that of experienced medical geneticists and assessed the utility of reported variants.

RESULTS

CVAT enabled performance comparable to that of experienced medical geneticists. In total, 124 of 289 (42.9%) exome reports and 146 of 382 (38.2%) reported variants supported a diagnosis. Overall, 10.5% (1 pathogenic [P] or likely pathogenic [LP] variant and 39 variants of uncertain significance [VUS]) of variants were reported in genes without established disease association; 20.2% (23 P/LP and 54 VUS) were in genes without sufficient phenotypic concordance; 7.3% (15 P/LP and 13 VUS) conflicted with the known molecular disease mechanism; and 24% (91 VUS) had insufficient evidence for deleteriousness.

CONCLUSION

Implementation of CVAT standardized clinical interpretation of genomic variation and emphasized the need for collaborative and transparent reporting of genomic variation.

Mutation in the CCAL1 locus accounts for bidirectional process of human subchondral bone turnover and cartilage mineralization

OBJECTIVES

To study the mechanism by which the readthrough mutation in TNFRSF11B, encoding osteoprotegerin (OPG) with additional 19 amino acids at its C-terminus (OPG-XL), causes the characteristic bidirectional phenotype of subchondral bone turnover accompanied by cartilage mineralization in chondrocalcinosis patients.

METHODS

OPG-XL was studied by human induced pluripotent stem cells expressing OPG-XL and two isogenic CRISPR/Cas9-corrected controls in cartilage and bone organoids. Osteoclastogenesis was studied with monocytes from OPG-XL carriers and matched healthy controls followed by gene expression characterization. Dual energy X-ray absorptiometry scans and MRI analyses were used to characterize the phenotype of carriers and non-carriers of the mutation.

RESULTS

Human OPG-XL carriers relative to sex- and age-matched controls showed, after an initial delay, large active osteoclasts with high number of nuclei. By employing hiPSCs expressing OPG-XL and isogenic CRISPR/Cas9-corrected controls to established cartilage and bone organoids, we demonstrated that expression of OPG-XL resulted in excessive fibrosis in cartilage and high mineralization in bone accompanied by marked downregulation of MGP, encoding matrix Gla protein, and upregulation of DIO2, encoding type 2 deiodinase, gene expression, respectively.

CONCLUSIONS

The readthrough mutation at CCAL1 locus in TNFRSF11B identifies an unknown role for OPG-XL in subchondral bone turnover and cartilage mineralization in humans via DIO2 and MGP functions. Previously, OPG-XL was shown to affect binding between RANKL and heparan sulphate (HS) resulting in loss of immobilized OPG-XL. Therefore, effects may be triggered by deficiency in the immobilization of OPG-XL Since the characteristic bidirectional pathophysiology of articular cartilage calcification accompanied by low subchondral bone mineralization is also a hallmark of OA pathophysiology, our results are likely extrapolated to common arthropathies.

Novel prostate cancer susceptibility gene SP6 predisposes patients to aggressive disease

Prostate cancer (PrCa) is one of the most common cancers in men, but little is known about factors affecting its clinical outcomes. Genome-wide association studies have identified more than 170 germline susceptibility loci, but most of them are not associated with aggressive disease. We performed a genome-wide analysis of 185,478 SNPs in Finnish samples (2738 cases, 2400 controls) from the international Collaborative Oncological Gene-Environment Study (iCOGS) to find underlying PrCa risk variants. We identified a total of 21 common, low-penetrance susceptibility loci, including 10 novel variants independently associated with PrCa risk. Novel risk loci were located in the 8q24 (CASC8 rs16902147, OR 1.86, padj = 3.53 × 10-8 and rs58809953, OR 1.71, padj = 4.00 × 10-6; intergenic rs79012498, OR 1.81, padj = 4.26 × 10-8), 17q21 (SP6 rs2074187, OR 1.66, padj = 3.75 × 10-5), 11q13 (rs12795301, OR 1.42, padj = 2.89 × 10-5) and 8p21 (rs995432, OR 1.38, padj = 3.00 × 10-11) regions. Here, we describe SP6, a transcription factor gene, as a new, potentially high-risk gene for PrCa. The intronic variant rs2074187 in SP6 was associated not only with overall susceptibility to PrCa (OR 1.66) but also with a higher odds ratio for aggressive PrCa (OR 1.89) and lower odds for non-aggressive PrCa (OR 1.43). Furthermore, the new intergenic variant rs79012498 at 8q24 conferred risk for aggressive PrCa. Our findings highlighted the power of a population-stratified approach to identify novel, clinically actionable germline PrCa risk loci and strongly suggested SP6 as a new PrCa candidate gene that may be involved in the pathogenesis of PrCa.

High-impact FN1 mutation decreases chondrogenic potential and affects cartilage deposition via decreased binding to collagen type II

Osteoarthritis is the most prevalent joint disease worldwide, yet progress in development of effective disease-modifying treatments is slow because of lack of insight into the underlying disease pathways. Therefore, we aimed to identify the causal pathogenic mutation in an early-onset osteoarthritis family, followed by functional studies in human induced pluripotent stem cells (hiPSCs) in an in vitro organoid cartilage model. We demonstrated that the identified causal missense mutation in the gelatin-binding domain of the extracellular matrix protein fibronectin resulted in significant decreased binding capacity to collagen type II. Further analyses of formed hiPSC-derived neo-cartilage tissue highlighted that mutated fibronectin affected chondrogenic capacity and propensity to a procatabolic osteoarthritic state. Together, we demonstrate that binding of fibronectin to collagen type II is crucial for fibronectin downstream gene expression of chondrocytes. We advocate that effective treatment development should focus on restoring or maintaining proper binding between fibronectin and collagen type II.

Pharmacogenomics: A road ahead for precision medicine in psychiatry

Psychiatric genomics is providing insights into the nature of psychiatric conditions that in time should identify new drug targets and improve patient care. Less attention has been paid to psychiatric pharmacogenomics research, despite its potential to deliver more rapid change in clinical practice and patient outcomes. The pharmacogenomics of treatment response encapsulates both pharmacokinetic ("what the body does to a drug") and pharmacodynamic ("what the drug does to the body") effects. Despite early optimism and substantial research in both these areas, they have to date made little impact on clinical management in psychiatry. A number of bottlenecks have hampered progress, including a lack of large-scale replication studies, inconsistencies in defining valid treatment outcomes across experiments, a failure to routinely incorporate adverse drug reactions and serum metabolite monitoring in study designs, and inadequate investment in the longitudinal data collections required to demonstrate clinical utility. Nonetheless, advances in genomics and health informatics present distinct opportunities for psychiatric pharmacogenomics to enter a new and productive phase of research discovery and translation.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Detecting rare haplotype association with two correlated phenotypes of binary and continuous types

Multiple correlated traits/phenotypes are often collected in genetic association studies and they may share a common genetic mechanism. Joint analysis of correlated phenotypes has well-known advantages over one-at-a-time analysis including gain in power and better understanding of genetic etiology. However, when the phenotypes are of discordant types such as binary and continuous, the joint modeling is more challenging. Another research area of current interest is discovery of rare genetic variants. Currently there is no method available for detecting association of rare (or common) haplotypes with multiple discordant phenotypes jointly. Our goal is to fill this gap specifically for two discordant phenotypes. We consider a rare haplotype association method for a binary phenotype, logistic Bayesian LASSO (univariate LBL) and its extension for two correlated binary phenotypes (bivariate LBL-2B). Under this framework, we propose a haplotype association test with binary and continuous phenotypes jointly (bivariate LBL-BC). Specifically, we use a latent variable to induce correlation between the two phenotypes. We carry out extensive simulations to investigate bivariate LBL-BC and compare it with univariate LBL and bivariate LBL-2B. In most settings, bivariate LBL-BC performs the best. In only two situations, bivariate LBL-BC has similar performance-when the two phenotypes are (1) weakly or not correlated and the target haplotype affects the binary phenotype only and (2) strongly positively correlated and the target haplotype affects both phenotypes in positive direction. Finally, we apply the method to a data set on lung cancer and nicotine dependence and detect several haplotypes including a rare one.

Genetic Variants Associated with Therapy-Related Cardiomyopathy among Childhood Cancer Survivors of African Ancestry

Cardiomyopathy occurs at significantly higher rates in survivors of childhood cancer than the general population, but few studies have evaluated racial or ethnic disparities, and none have assessed potential genetic factors contributing to this outcome. In this study, childhood cancer survivors of African ancestry exposed to cardiotoxic therapies (anthracyclines and/or heart radiotherapy; n = 246) were compared with cardiotoxic-exposed survivors of European ancestry (n = 1,645) in the St. Jude Lifetime Cohort. Genetic variants were examined using whole-genome sequencing data among survivors of African ancestry, first based on ejection fraction (EF) as a continuous outcome, followed by clinical history of cardiomyopathy. Survivors of African ancestry showed 1.53- and 2.47-fold risks of CTCAE grade 2-4 and grade 3-4 cardiomyopathy than survivors of European ancestry. A novel locus at 1p13.2 showed significant association with EF (rs6689879*C: EF reduction = 4.2%; P = 2.8 × 10^-8) in 246 survivors of African ancestry, which was successfully replicated in 1,645 survivors of European ancestry but with attenuated magnitude (EF reduction = 0.4%; P = 0.042). In survivors of African ancestry, rs6689879*C showed a 5.43-fold risk of cardiomyopathy and 1.31-fold risk in those of European ancestry. Among survivors of African ancestry with rs6689879*C and CTCAE grade 2-4 cardiomyopathy, the PHTF1 promoter region was hypomethylated. Similar results were observed in survivors of European ancestry, albeit with reduced magnitudes of hypomethylation among those with rs6689879*C and CTCAE grade 2-4 cardiomyopathy. PHTF1 was upregulated in human-induced pluripotent stem cell-derived cardiomyocytes from patients with doxorubicin-induced cardiomyopathy. These findings have potential implications for long-term cardiac surveillance and up-front cancer care for patients of African ancestry. SIGNIFICANCE: Childhood cancer survivors of African ancestry are at higher risk of cardiomyopathy than those of European ancestry, and a novel locus at 1p13.2 is associated with therapy-related cardiomyopathy specifically in African-American survivors.See related commentary by Brown and Richard, p. 2272.

dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs

Whole exome sequencing has been increasingly used in human disease studies. Prioritization based on appropriate functional annotations has been used as an indispensable step to select candidate variants. Here we present the latest updates to dbNSFP (version 4.1), a database designed to facilitate this step by providing deleteriousness prediction and functional annotation for all potential nonsynonymous and splice-site SNVs (a total of 84,013,093) in the human genome. The current version compiled 36 deleteriousness prediction scores, including 12 transcript-specific scores, and other variant and gene-level functional annotations. The database is available at http://database.liulab.science/dbNSFP with a downloadable version and a web-service.

Refinement of coding SNPs in the human aryl hydrocarbon receptor gene using ISNPranker: An integrative-SNP ranking web-tool

Different bioinformatic methods apply various approaches to predict how much the effect of a SNP could be deleterious and therefore their results may differ significantly. However, variation studies often need to consider an integrated prediction result to analyze the effect of SNPs. To address this problem, we used an algorithm to map ordinal predictions to a numeral space and averaging them, and based on it we developed the ISNPranker web-tool (http://isnpranker.semilab.ir/). It takes heterogonous outputs of different predictors and generates integrated numerical predictions and ranks SNPs based on them. Afterward, we used ISNPranker to identify the most deleterious coding SNPs (cSNPs) of the human aryl hydrocarbon receptor (AHR) gene. AHR is a ligand-activated transcription factor that governs many molecular and cellular mechanisms and cSNPs may affect its structure, interactions, and function. Forty validated cSNPs of AHR were initially analyzed using 16 publicly available SNP analyzers and the results were introduced to the ISNPranker and integrated predictions were obtained. The cSNPs were ranked in 34 levels of danger and rs200257782 in the ARNT dimerization domain (ADD_121-289) of AHR was identified as the most deleterious cSNP. The rs148360742, which affect ADD_40-79 and Hsp90 binding domain (HBD_27-79) was in the second rank and the third and fourth ranks were occupied by ADD_121-289-located variations rs571123681 and rs141667112 respectively. In conclusion, we introduced ISNPranker, which is a web-tool for integrative ranking of SNPs, and we showed that AHR structure and function may be highly sensitive to the cSNPs in the ARNT dimerization domain.

Rare genetic causes of complex kidney and urological diseases

Although often considered a single-entity, chronic kidney disease (CKD) comprises many pathophysiologically distinct disorders that result in persistently abnormal kidney structure and/or function, and encompass both monogenic and polygenic aetiologies. Rare inherited forms of CKD frequently span diverse phenotypes, reflecting genetic phenomena including pleiotropy, incomplete penetrance and variable expressivity. Use of chromosomal microarray and massively parallel sequencing technologies has revealed that genomic disorders and monogenic aetiologies contribute meaningfully to seemingly complex forms of CKD across different clinically defined subgroups and are characterized by high genetic and phenotypic heterogeneity. Investigations of prevalent genomic disorders in CKD have integrated genetic, bioinformatic and functional studies to pinpoint the genetic drivers underlying their renal and extra-renal manifestations, revealing both monogenic and polygenic mechanisms. Similarly, massively parallel sequencing-based analyses have identified gene- and allele-level variation that contribute to the clinically diverse phenotypes observed for many monogenic forms of nephropathy. Genome-wide sequencing studies suggest that dual genetic diagnoses are found in at least 5% of patients in whom a genetic cause of disease is identified, highlighting the fact that complex phenotypes can also arise from multilocus variation. A multifaceted approach that incorporates genetic and phenotypic data from large, diverse cohorts will help to elucidate the complex relationships between genotype and phenotype for different forms of CKD, supporting personalized medicine for individuals with kidney disease.

Advancing Charcot-Marie-Tooth disease diagnostics, through the UK 100,000 Genomes Project

Whole genome sequencing (WGS) is regarded by many as the pinnacle of contemporary molecular genetic testing, and has only been possible because of the rapid development and roll-out of next-generation sequencing technologies. It provides a phenotype-agnostic analysis of the genome and has important advantages compared to other techniques including a consistent coverage across the coding and non-coding genome, the application of high resolution homozygosity mapping and the ability to detect and highlight structural variation.

Realising this potential and with a bid to sequence 100,000 genomes, the UK rolled out the 100,000 Genomes Project as a proof of concept of integrating genomics in the national health service. Participants with cancer and rare diseases enrolled in the project whose infrastructure comprises of a central national biorepository and 13 regional genomic medicine centres where clinicians, geneticists and other scientists work as part of a multidisciplinary team. Amongst participants are also patients with genetically unclassified Charcot-Marie-Tooth disease who have benefited substantially from improved diagnostic rates and many more stand to benefit as the analysis of genomic data is ongoing.

WGS is an important tool as we head towards more personalised medicine and in our quest to improve public health and treat and where possible prevent disease.

'Artificial spermatid'-mediated genome editing†

For years, extensive efforts have been made to use mammalian sperm as the mediator to generate genetically modified animals; however, the strategy of sperm-mediated gene transfer (SMGT) is unable to produce stable and diversified modifications in descendants. Recently, haploid embryonic stem cells (haESCs) have been successfully derived from haploid embryos carrying the genome of highly specialized gametes, and can stably maintain haploidy (through periodic cell sorting based on DNA quantity) and both self-renewal and pluripotency in long-term cell culture. In particular, haESCs derived from androgenetic haploid blastocysts (AG-haESCs), carrying only the sperm genome, can support the generation of live mice (semi-cloned, SC mice) through oocyte injection. Remarkably, after removal of the imprinted control regions H19-DMR (differentially methylated region of DNA) and IG-DMR in AG-haESCs, the double knockout (DKO)-AG-haESCs can stably produce SC animals with high efficiency, and so can serve as a sperm equivalent. Importantly, DKO-AG-haESCs can be used for multiple rounds of gene modifications in vitro, followed by efficient generation of live and fertile mice with the expected genetic traits. Thus, DKO-AG-haESCs (referred to as 'artificial spermatids') combed with CRISPR-Cas technology can be used as the genetically tractable fertilization agent, to efficiently create genetically modified offspring, and is a versatile genetic tool for in vivo analyses of gene function.

A weighted empirical Bayes risk prediction model using multiple traits

With rapid advances in high-throughput sequencing technology, millions of single-nucleotide variants (SNVs) can be simultaneously genotyped in a sequencing study. These SNVs residing in functional genomic regions such as exons may play a crucial role in biological process of the body. In particular, non-synonymous SNVs are closely related to the protein sequence and its function, which are important in understanding the biological mechanism of sequence evolution. Although statistically challenging, models incorporating such SNV annotation information can improve the estimation of genetic effects, and multiple responses may further strengthen the signals of these variants on the assessment of disease risk. In this work, we develop a new weighted empirical Bayes method to integrate SNV annotation information in a multi-trait design. The performance of this proposed model is evaluated in simulation as well as a real sequencing data; thus, the proposed method shows improved prediction accuracy compared to other approaches.

Genetic testing for kidney disease of unknown etiology

In many cases of chronic kidney disease, the cause of disease remains unknown despite a thorough nephrologic workup. Genetic testing has revolutionized many areas of medicine and promises to empower diagnosis and targeted management of such cases of kidney disease of unknown etiology. Recent studies using genetic testing have demonstrated that Mendelian etiologies account for approximately 20% of cases of kidney disease of unknown etiology. Although genetic testing has significant benefits, including tailoring of therapy, informing targeted workup, detecting extrarenal disease, counseling patients and families, and redirecting care, it also has important limitations and risks that must be considered.

Inference attacks against differentially private query results from genomic datasets including dependent tuples

MOTIVATION

The rapid decrease in the sequencing technology costs leads to a revolution in medical research and clinical care. Today, researchers have access to large genomic datasets to study associations between variants and complex traits. However, availability of such genomic datasets also results in new privacy concerns about personal information of the participants in genomic studies. Differential privacy (DP) is one of the rigorous privacy concepts, which received widespread interest for sharing summary statistics from genomic datasets while protecting the privacy of participants against inference attacks. However, DP has a known drawback as it does not consider the correlation between dataset tuples. Therefore, privacy guarantees of DP-based mechanisms may degrade if the dataset includes dependent tuples, which is a common situation for genomic datasets due to the inherent correlations between genomes of family members.

RESULTS

In this article, using two real-life genomic datasets, we show that exploiting the correlation between the dataset participants results in significant information leak from differentially private results of complex queries. We formulate this as an attribute inference attack and show the privacy loss in minor allele frequency (MAF) and chi-square queries. Our results show that using the results of differentially private MAF queries and utilizing the dependency between tuples, an adversary can reveal up to 50% more sensitive information about the genome of a target (compared to original privacy guarantees of standard DP-based mechanisms), while differentially privacy chi-square queries can reveal up to 40% more sensitive information. Furthermore, we show that the adversary can use the inferred genomic data obtained from the attribute inference attack to infer the membership of a target in another genomic dataset (e.g. associated with a sensitive trait). Using a log-likelihood-ratio test, our results also show that the inference power of the adversary can be significantly high in such an attack even using inferred (and hence partially incorrect) genomes.

AVAILABILITY AND IMPLEMENTATION

https://github.com/nourmadhoun/Inference-Attacks-Differential-Privacy.

Variations of type IV collagen-encoding genes in patients with histological diagnosis of focal segmental glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS), an important cause of end-stage kidney disease (ESKD), covers a spectrum of clinicopathological syndromes sharing a common glomerular lesion, based on an injury of podocytes caused by diverse insults to glomeruli. Although it is well expressed in many reports that the term FSGS is not useful and applicable to a single disease, particularly in genetic studies, FSGS continues to be used as a single clinical diagnosis. Distinguishing genetic forms of FSGS is important for the treatment and overall prognosis because secondary forms of FSGS, produced by rare pathogenic variations in podocyte genes, are not good candidates for immunosuppressive treatment. Over the past decade, several next generation sequencing (NGS) methods have been used to investigate the patients with steroid resistance nephrotic syndrome (SRNS) or FSGS. Pathogenic variants in COL4A3, COL4A4, or COL4A5 genes have been frequently identified in patients with histologic diagnosis of FSGS. The contribution of these mostly heterozygous genetic variations in FSGS pathogenesis and the clinical course of patients with these variations have not been well characterized. This review emphasizes the importance of appropriate approach in selection and diagnosis of cases and interpretation of the genetic data in these studies and suggests a detailed review of existing clinical variant databases using newly available population genetic data.

Transcript expression-aware annotation improves rare variant interpretation

The acceleration of DNA sequencing in samples from patients and population studies has resulted in extensive catalogues of human genetic variation, but the interpretation of rare genetic variants remains problematic. A notable example of this challenge is the existence of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals. Here, by manual curation of putative loss-of-function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD)1, we show that one explanation for this paradox involves alternative splicing of mRNA, which allows exons of a gene to be expressed at varying levels across different cell types. Currently, no existing annotation tool systematically incorporates information about exon expression into the interpretation of variants. We develop a transcript-level annotation metric known as the 'proportion expressed across transcripts', which quantifies isoform expression for variants. We calculate this metric using 11,706 tissue samples from the Genotype Tissue Expression (GTEx) project2 and show that it can differentiate between weakly and highly evolutionarily conserved exons, a proxy for functional importance. We demonstrate that expression-based annotation selectively filters 22.8% of falsely annotated pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of high-confidence pathogenic variants in the same genes. Finally, we apply our expression filter to the analysis of de novo variants in patients with autism spectrum disorder and intellectual disability or developmental disorders to show that pLoF variants in weakly expressed regions have similar effect sizes to those of synonymous variants, whereas pLoF variants in highly expressed exons are most strongly enriched among cases. Our annotation is fast, flexible and generalizable, making it possible for any variant file to be annotated with any isoform expression dataset, and will be valuable for the genetic diagnosis of rare diseases, the analysis of rare variant burden in complex disorders, and the curation and prioritization of variants in recall-by-genotype studies.

Genetic Variation and Response to Neurocritical Illness: a Powerful Approach to Identify Novel Pathophysiological Mechanisms and Therapeutic Targets

Disease-specific therapeutic options for critically ill neurological patients are limited. The identification of new preventive, therapeutic, and rehabilitation strategies is of the utmost importance in the field of neurocritical care research. Population genetics offers powerful tools to identify and prioritize biological pathways to be targeted by novel interventions. New treatments with supportive genetic evidence have twice the chances of obtaining final FDA approval compared to those without this support. Large collaborations, public access to data, reproducible science, and innovative analytical methods have exponentially increased the pace of discoveries related to neurocritical care genetics.

Analysis of gene variants in the GASH/Sal model of epilepsy

Epilepsy is a complex neurological disorder characterized by sudden and recurrent seizures, which are caused by various factors, including genetic abnormalities. Several animal models of epilepsy mimic the different symptoms of this disorder. In particular, the genetic audiogenic seizure hamster from Salamanca (GASH/Sal) animals exhibit sound-induced seizures similar to the generalized tonic seizures observed in epileptic patients. However, the genetic alterations underlying the audiogenic seizure susceptibility of the GASH/Sal model remain unknown. In addition, gene variations in the GASH/Sal might have a close resemblance with those described in humans with epilepsy, which is a prerequisite for any new preclinical studies that target genetic abnormalities. Here, we performed whole exome sequencing (WES) in GASH/Sal animals and their corresponding controls to identify and characterize the mutational landscape of the GASH/Sal strain. After filtering the results, moderate- and high-impact variants were validated by Sanger sequencing, assessing the possible impact of the mutations by “in silico” reconstruction of the encoded proteins and analyzing their corresponding biological pathways. Lastly, we quantified gene expression levels by RT-qPCR. In the GASH/Sal model, WES showed the presence of 342 variations, in which 21 were classified as high-impact mutations. After a full bioinformatics analysis to highlight the high quality and reliable variants, the presence of 3 high-impact and 15 moderate-impact variants were identified. Gene expression analysis of the high-impact variants of Asb14 (ankyrin repeat and SOCS Box Containing 14), Msh3 (MutS Homolog 3) and Arhgef38 (Rho Guanine Nucleotide Exchange Factor 38) genes showed a higher expression in the GASH/Sal than in control hamsters. In silico analysis of the functional consequences indicated that those mutations in the three encoded proteins would have severe functional alterations. By functional analysis of the variants, we detected 44 significantly enriched pathways, including the glutamatergic synapse pathway. The data show three high-impact mutations with a major impact on the function of the proteins encoded by these genes, although no mutation in these three genes has been associated with some type of epilepsy until now. Furthermore, GASH/Sal animals also showed gene variants associated with different types of epilepsy that has been extensively documented, as well as mutations in other genes that encode proteins with functions related to neuronal excitability, which could be implied in the phenotype of the GASH/Sal. Our findings provide valuable genetic and biological pathway data associated to the genetic burden of the audiogenic seizure susceptibility and reinforce the need to validate the role of each key mutation in the phenotype of the GASH/Sal model.

Inherited Renal Tubulopathies-Challenges and Controversies

Electrolyte homeostasis is maintained by the kidney through a complex transport function mostly performed by specialized proteins distributed along the renal tubules. Pathogenic variants in the genes encoding these proteins impair this function and have consequences on the whole organism. Establishing a genetic diagnosis in patients with renal tubular dysfunction is a challenging task given the genetic and phenotypic heterogeneity, functional characteristics of the genes involved and the number of yet unknown causes. Part of these difficulties can be overcome by gathering large patient cohorts and applying high-throughput sequencing techniques combined with experimental work to prove functional impact. This approach has led to the identification of a number of genes but also generated controversies about proper interpretation of variants. In this article, we will highlight these challenges and controversies.

The diagnosis of inborn errors of metabolism by an integrative "multi-omics" approach: A perspective encompassing genomics, transcriptomics, and proteomics

Given the rapidly decreasing cost and increasing speed and accessibility of massively parallel technologies, the integration of comprehensive genomic, transcriptomic, and proteomic data into a "multi-omics" diagnostic pipeline is within reach. Even though genomic analysis has the capability to reveal all possible perturbations in our genetic code, analysis typically reaches a diagnosis in just 35% of cases, with a diagnostic gap arising due to limitations in prioritization and interpretation of detected variants. Here we review the utility of complementing genetic data with transcriptomic data and give a perspective for the introduction of proteomics into the diagnostic pipeline. Together these methodologies enable comprehensive capture of the functional consequence of variants, unobtainable by the analysis of each methodology in isolation. This facilitates functional annotation and reprioritization of candidate genes and variants-a promising approach to shed light on the underlying molecular cause of a patient's disease, increasing diagnostic rate, and allowing actionability in clinical practice.

Genetics of Skeletal Disorders

Bone and mineral diseases encompass a variety of conditions that involve altered skeletal homeostasis and are frequently associated with changes in circulating calcium, phosphate, or vitamin D metabolites. These disorders often have a genetic etiology and comprise monogenic disorders caused by a single-gene mutation, which may be germline or somatic, or an oligogenic or polygenic condition involving multiple genetic variants. Single-gene mutations causing Mendelian diseases are usually highly penetrant, whereas the gene variants contributing to oligogenic or polygenic disorders are each associated with smaller effects with additional contributions from environmental factors. The detection of monogenic disorders is clinically important and facilitates timely assessment and management of the patient and their affected relatives. The diagnosis of monogenic metabolic bone disorders requires detailed clinical assessment of the wide variety of symptoms and signs associated with these diseases. Thus, clinicians should undertake a systematic approach commencing with careful history taking and physical examination, followed by appropriate laboratory and skeletal imaging investigations. Finally, clinicians should be familiar with the range of molecular genetic tests available to ensure their appropriate use and interpretation. These considerations are reviewed in this chapter.

Rare variants contribute disproportionately to quantitative trait variation in yeast

How variants with different frequencies contribute to trait variation is a central question in genetics. We use a unique model system to disentangle the contributions of common and rare variants to quantitative traits. We generated ~14,000 progeny from crosses among 16 diverse yeast strains and identified thousands of quantitative trait loci (QTLs) for 38 traits. We combined our results with sequencing data for 1011 yeast isolates to show that rare variants make a disproportionate contribution to trait variation. Evolutionary analyses revealed that this contribution is driven by rare variants that arose recently, and that negative selection has shaped the relationship between variant frequency and effect size. We leveraged the structure of the crosses to resolve hundreds of QTLs to single genes. These results refine our understanding of trait variation at the population level and suggest that studies of rare variants are a fertile ground for discovery of genetic effects.

Regulatory variants: from detection to predicting impact

Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin-chromatin and chromatin-protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution. We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.

Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes

In genetic association studies, joint modeling of related traits/phenotypes can utilize the correlation between them and thereby provide more power and uncover additional information about genetic etiology. Moreover, detecting rare genetic variants are of current scientific interest as a key to missing heritability. Logistic Bayesian LASSO (LBL) has been proposed recently to detect rare haplotype variants using case-control data, that is, a single binary phenotype. As there is currently no haplotype association method that can handle multiple binary phenotypes, we extend LBL to fill this gap. We develop a bivariate model by using a latent variable to induce correlation between the two outcomes. We carry out extensive simulations to investigate the bivariate LBL and compare with the univariate LBL. The bivariate LBL performs better or similar to the univariate LBL in most settings. It has the highest gain in power when a haplotype is associated with both traits and it affects at least one trait in a direction opposite to the direction of the correlation between the traits. We analyze two data sets-Genetic Analysis Workshop 19 sequence data on systolic and diastolic blood pressures and a genome-wide association data set on lung cancer and smoking and detect several associated rare haplotypes.

Whole Genome Analyses of Chinese Population and De Novo Assembly of A Northern Han Genome

To unravel the genetic mechanisms of disease and physiological traits, it requires comprehensive sequencing analysis of large sample size in Chinese populations. Here, we report the primary results of the Chinese Academy of Sciences Precision Medicine Initiative (CASPMI) project launched by the Chinese Academy of Sciences, including the de novo assembly of a northern Han reference genome (NH1.0) and whole genome analyses of 597 healthy people coming from most areas in China. Given the two existing reference genomes for Han Chinese (YH and HX1) were both from the south, we constructed NH1.0, a new reference genome from a northern individual, by combining the sequencing strategies of PacBio, 10× Genomics, and Bionano mapping. Using this integrated approach, we obtained an N50 scaffold size of 46.63 Mb for the NH1.0 genome and performed a comparative genome analysis of NH1.0 with YH and HX1. In order to generate a genomic variation map of Chinese populations, we performed the whole-genome sequencing of 597 participants and identified 24.85 million (M) single nucleotide variants (SNVs), 3.85 M small indels, and 106,382 structural variations. In the association analysis with collected phenotypes, we found that the T allele of rs1549293 in KAT8 significantly correlated with the waist circumference in northern Han males. Moreover, significant genetic diversity in MTHFR, TCN2, FADS1, and FADS2, which associate with circulating folate, vitamin B12, or lipid metabolism, was observed between northerners and southerners. Especially, for the homocysteine-increasing allele of rs1801133 (MTHFR 677T), we hypothesize that there exists a "comfort" zone for a high frequency of 677T between latitudes of 35-45 degree North. Taken together, our results provide a high-quality northern Han reference genome and novel population-specific data sets of genetic variants for use in the personalized and precision medicine.

Epilepsy genetics: clinical impacts and biological insights

Genomics now has an increasingly important role in neurology clinics. Regarding the epilepsies, innovations centred around technology, analytics, and collaboration have led to remarkable progress in gene discovery and have revealed the diverse array of genetic mechanisms and neurobiological pathways that contribute to these disorders. The new genomic era can present a challenge to clinicians, who now find themselves asked to interpret and apply genetic data to their daily management of patients with epilepsy. Navigation of this new era will require genetic literacy and familiarity with research advances in epilepsy genetics. Genetic epilepsy diagnoses now directly affect clinical care, and their importance will only increase as new targeted treatments continue to emerge. At the same time, new genetic insights challenge us to move from a deterministic view of genetic changes to a more nuanced appreciation of genetic risk within complex neurobiological systems that give rise to epilepsy.

The clinical and genetic characteristics of permanent neonatal diabetes (PNDM) in the state of Qatar

Background

Neonatal diabetes mellitus (NDM) is a rare condition that occurs within the first six months of life. Permanent NDM (PNDM) is caused by mutations in specific genes that are known for their expression at early and/or late stages of pancreatic beta‐ cell development, and are either involved in beta‐cell survival, insulin processing, regulation, and release. The native population in Qatar continues to practice consanguineous marriages that lead to a high level of homozygosity. To our knowledge, there is no previous report on the genomics of NDM among the Qatari population. The aims of the current study are to identify patients with NDM diagnosed between 2001 and 2016, and examine their clinical and genetic characteristics.

Methods

To calculate the incidence of PNDM, all patients with PNDM diagnosed between 2001 and 2016 were compared to the total number of live births over the 16‐year‐period. Whole Genome Sequencing (WGS) was used to investigate the genetic etiology in the PNDM cohort.

Results

PNDM was diagnosed in nine (n = 9) patients with an estimated incidence rate of 1:22,938 live births among the indigenous Qatari. Seven different mutations in six genes (PTF1A, GCK, SLC2A2, EIF2AK3, INS, and HNF1B) were identified. In the majority of cases, the genetic etiology was part of a previously identified autosomal recessive disorder. Two novel de novo mutations were identified in INS and HNF1B.

Conclusion

Qatar has the second highest reported incidence of PNDM worldwide. A majority of PNDM cases present as rare familial autosomal recessive disorders. Pancreas associated transcription factor 1a (PTF1A) enhancer deletions are the most common cause of PNDM in Qatar, with only a few previous cases reported in the literature.

Gene therapy for primary immunodeficiency

Gene therapy is now being trialled as a therapeutic option for an expanding number of conditions, based primarily on the successful treatment over the past two decades of patients with specific primary immunodeficiencies (PIDs) including severe combined immunodeficiency and Wiskott–Aldrich syndrome and metabolic conditions such as leukodystrophy. The field has evolved from the use of gammaretroviral vectors to more sophisticated lentiviral platforms that offer an improved biosafety profile alongside greater efficiency for hematopoietic stem cells gene transfer. Here we review more recent developments including licensing of gene therapies, use of gene corrected autologous T cells as an alternative strategy for some PIDs and the potential of targeted gene correction using various gene editing platforms. Given the promising results of recent clinical trials, it is likely that autologous gene therapies will become standard of care for a number of devastating diseases in the coming decade.

HGVA: the Human Genome Variation Archive

High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic data for key reference projects in a clean, fast and integrated fashion. HGVA provides an efficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libraries in Python, Java and JavaScript for fast programmatic access to its knowledge base. HGVA calculates population frequencies for these projects and enriches their data with variant annotation provided by CellBase, a rich and fast annotation solution. HGVA serves as a proof-of-concept of the genome analysis developments being carried out by the University of Cambridge together with UK's 100 000 genomes project and the National Institute for Health Research BioResource Rare-Diseases, in particular, deploying open-source for Computational Biology (OpenCB) software platform for storing and analyzing massive genomic datasets.

Genetic approaches to metabolic bone diseases

Metabolic bone diseases comprise a diverse group of disorders characterized by alterations in skeletal homeostasis, and are often associated with abnormal circulating concentrations of calcium, phosphate or vitamin D metabolites. These diseases commonly have a genetic basis and represent either a monogenic disorder due to a germline or somatic single gene mutation, or an oligogenic or polygenic disorder that involves variants in more than one gene. Germline single gene mutations causing Mendelian diseases typically have a high penetrance, whereas the genetic variations causing oligogenic or polygenic disorders are each associated with smaller effects with additional contributions from environmental factors. Recognition of familial monogenic disorders is of clinical importance to facilitate timely investigations and management of the patient and any affected relatives. The diagnosis of monogenic metabolic bone disease requires careful clinical evaluation of the large diversity of symptoms and signs associated with these disorders. Thus, the clinician must pursue a systematic approach beginning with a detailed history and physical examination, followed by appropriate laboratory and skeletal imaging evaluations. Finally, the clinician must understand the increasing number and complexity of molecular genetic tests available to ensure their appropriate use and interpretation.