Whole-Exome Sequencing Improves Understanding of Inherited Retinal Dystrophies in Korean Patients

Retinitis pigmentosa (RP) encompasses a diverse range of hereditary, degenerative retinal ailments, presenting notable obstacles to molecular genetic diagnoses due to the intricate array of variants in different genes involved. This study enrolled 21 probands and their families who have been diagnosed with nonsyndromic RP but without a previous molecular diagnosis. We employed whole-exome sequencing (WES) to detect possible harmful gene variations in individuals with unknown-cause RP at the molecular level. WES allowed the identification of ten potential disease-causing variants in eight different genes. In 8 out of the total 21 patients, this method successfully identified the underlying molecular causes, such as putative pathogenic variants in genes including CRB1, KLHL7, PDE6B, RDH12, RP1, RPE65, USH2A, and RHO. A novel variant was identified in one of these genes, specifically PDE6B, providing valuable information on prospective targets for future enhanced gene therapeutic approaches.

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.

Examining the Association of Rare Allelic Variants in Urate Transporters SLC22A11, SLC22A13, and SLC17A1 with Hyperuricemia and Gout

Genetic variations in urate transporters play a significant role in determining human urate levels and have been implicated in developing hyperuricemia or gout. Polymorphism in the key urate transporters, such as ABCG2, URAT1, or GLUT9 was well-documented in the literature. Therefore in this study, our objective was to determine the frequency and effect of rare nonsynonymous allelic variants of SLC22A11, SLC22A13, and SLC17A1 on urate transport. In a cohort of 150 Czech patients with primary hyperuricemia and gout, we examined all coding regions and exon-intron boundaries of SLC22A11, SLC22A13, and SLC17A1 using PCR amplification and Sanger sequencing. For comparison, we used a control group consisting of 115 normouricemic subjects. To examine the effects of the rare allelic nonsynonymous variants on the expression, intracellular processing, and urate transporter protein function, we performed a functional characterization using the HEK293A cell line, immunoblotting, fluorescent microscopy, and site directed mutagenesis for preparing variants in vitro. Variants p.V202M (rs201209258), p.R343L (rs75933978), and p.P519L (rs144573306) were identified in the SLC22A11 gene (OAT4 transporter); variants p.R16H (rs72542450), and p.R102H (rs113229654) in the SLC22A13 gene (OAT10 transporter); and the p.W75C variant in the SLC17A1 gene (NPT1 transporter). All variants minimally affected protein levels and cytoplasmic/plasma membrane localization. The functional in vitro assay revealed that contrary to the native proteins, variants p.P519L in OAT4 (p ≤ 0.05), p.R16H in OAT10 (p ≤ 0.05), and p.W75C in the NPT1 transporter (p ≤ 0.01) significantly limited urate transport activity. Our findings contribute to a better understanding of (1) the risk of urate transporter-related hyperuricemia/gout and (2) uric acid handling in the kidneys.

Whole genome sequencing identifies genetic variants associated with neurogenic inflammation in rosacea

Rosacea is a chronic inflammatory skin disorder with high incidence rate. Although genetic predisposition to rosacea is suggested by existing evidence, the genetic basis remains largely unknown. Here we present the integrated results of whole genome sequencing (WGS) in 3 large rosacea families and whole exome sequencing (WES) in 49 additional validation families. We identify single rare deleterious variants of LRRC4, SH3PXD2A and SLC26A8 in large families, respectively. The relevance of SH3PXD2A, SLC26A8 and LRR family genes in rosacea predisposition is underscored by presence of additional variants in independent families. Gene ontology analysis suggests that these genes encode proteins taking part in neural synaptic processes and cell adhesion. In vitro functional analysis shows that mutations in LRRC4, SH3PXD2A and SLC26A8 induce the production of vasoactive neuropeptides in human neural cells. In a mouse model recapitulating a recurrent Lrrc4 mutation from human patients, we find rosacea-like skin inflammation, underpinned by excessive vasoactive intestinal peptide (VIP) release by peripheral neurons. These findings strongly support familial inheritance and neurogenic inflammation in rosacea development and provide mechanistic insight into the etiopathogenesis of the condition.

The genetics of non-monogenic IBD

Inflammatory bowel disease (IBD), with Crohn's disease and ulcerative colitis as main subtypes, is a prototypical multifactorial disease with both genetic and environmental factors involved. Genetically, IBD covers a wide spectrum from monogenic to polygenic forms. In polygenic disease, many genetic variants each contribute a small amount to disease risk. With the advent of genome-wide association studies (GWAS), it became possible to find these variants and corresponding genes, leading so far to the discovery of ca 240 loci associated with IBD. Together, these however explain only 20-25% of the heritability of IBD, leaving a large portion unaccounted for. This missing heritability might be hidden in common variants with even lower effect than the ones currently found through GWAS, but also in rare variants which can be found through large-scale sequencing studies or potentially in multiplex families. In this review, we will give an overview of the current knowledge about the genetics of non-monogenic IBD and how it differs from the monogenic form(s), and future perspectives. The history of IBD genetic studies from twin studies over linkage studies to GWAS, and finally large-scale sequencing studies and the revisiting of multiplex families will be discussed.

Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes

The preoperative diagnosis of pelvic masses has been elusive to date. Methods for characterization such as CA-125 have had limited specificity. We hypothesize that genomic variation can be used to create prediction models which accurately distinguish high grade serous ovarian cancer (HGSC) from benign tissue.

METHODS

In this retrospective, pilot study, we extracted DNA and RNA from HGSC specimens and from benign fallopian tubes. Then, we performed whole exome sequencing and RNA sequencing, and identified single nucleotide variants (SNV), copy number variants (CNV) and structural variants (SV). We used these variants to create prediction models to distinguish cancer from benign tissue. The models were then validated in independent datasets and with a machine learning platform.

RESULTS

The prediction model with SNV had an AUC of 1.00 (95% CI 1.00-1.00). The models with CNV and SV had AUC of 0.87 and 0.73, respectively. Validated models also had excellent performances.

CONCLUSIONS

Genomic variation of HGSC can be used to create prediction models which accurately discriminate cancer from benign tissue. Further refining of these models (early-stage samples, other tumor types) has the potential to lead to detection of ovarian cancer in blood with cell free DNA, even in early stage.

Whole-Genome Profile of Greek Patients with Teratozοοspermia: Identification of Candidate Variants and Genes

Male infertility is a global health problem that affects a large number of couples worldwide. It can be categorized into specific subtypes, including teratozoospermia. The present study aimed to identify new variants associated with teratozoospermia in the Greek population and to explore the role of genes on which these were identified. For this reason, whole-genome sequencing (WGS) was performed on normozoospermic and teratozoospermic individuals, and after selecting only variants found in teratozoospermic men, these were further prioritized using a wide range of tools, functional and predictive algorithms, etc. An average of 600,000 variants were identified, and of them, 61 were characterized as high impact and 153 as moderate impact. Many of these are mapped in genes previously associated with male infertility, yet others are related for the first time to teratozoospermia. Furthermore, pathway enrichment analysis and Gene ontology (GO) analyses revealed the important role of the extracellular matrix in teratozoospermia. Therefore, the present study confirms the contribution of genes studied in the past to male infertility and sheds light on new molecular mechanisms by providing a list of variants and candidate genes associated with teratozoospermia in the Greek population.

3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk

Background

Genome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell-specific manner. Here, we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D.

Results

Using 3DFAACTS-SNP, we identified from a list of 1228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell type-specific chromosome conformation capture data in 3DFAACTS-SNP identified 266 regulatory regions and 47 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 16 Treg-centric candidate variants and 60 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (> 10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 9376 candidate variants and 4968 candidate target genes, generating a list of potential sites for future T1D or other autoimmune disease research.

Conclusions

We demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function, and illustrate the power of using cell type-specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13072-022-00456-5.

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.

Heritability of Sleep and Its Disorders in Childhood and Adolescence

PURPOSE OF REVIEW

This review summarizes recent literature on the heritability of sleep and sleep disorders in childhood and adolescence. We also identify gaps in the literature and priorities for future research.

RECENT FINDINGS

Findings indicate that age, measurement method, reporter, and timing of sleep measurements can influence heritability estimates. Recent genome-wide association studies (GWAS) have identified differences in the heritability of sleep problems when ancestral differences are considered, but sample sizes are small compared to adult GWAS. Most studies focus on sleep variables in the full range rather than on disorder. Studies using objective measures of sleep typically comprised small samples.

SUMMARY

Current evidence demonstrates a wide range of heritability estimates across sleep phenotypes in childhood and adolescence, but research in larger samples, particularly using objective sleep measures and GWAS, is needed. Further understanding of environmental mechanisms and the interaction between genes and environment is key for future research.

NyuWa Genome resource: A deep whole-genome sequencing-based variation profile and reference panel for the Chinese population

The lack of haplotype reference panels and whole-genome sequencing resources specific to the Chinese population has greatly hindered genetic studies in the world's largest population. Here, we present the NyuWa genome resource, based on deep (26.2×) sequencing of 2,999 Chinese individuals, and construct a NyuWa reference panel of 5,804 haplotypes and 19.3 million variants, which is a high-quality publicly available Chinese population-specific reference panel with thousands of samples. Compared with other panels, the NyuWa reference panel reduces the Han Chinese imputation error rate by a margin ranging from 30% to 51%. Population structure and imputation simulation tests support the applicability of one integrated reference panel for northern and southern Chinese. In addition, a total of 22,504 loss-of-function variants in coding and noncoding genes are identified, including 11,493 novel variants. These results highlight the value of the NyuWa genome resource in facilitating genetic research in Chinese and Asian populations.

Ahmed E, Baldassari S, Achaz G, Elmugadam FA, Abdelgadir WA, Baulac S, Buratti J, Abdalla O, Gamil S, Alzubeir M, Abubaker R, Noé E, Elsayed L, Ahmed AE, Leguern E. Involvement of ADGRV1 Gene in Familial Forms of Genetic Generalized Epilepsy

Background: Genetic generalized epilepsies (GGE) including childhood absence epilepsy (CAE), juvenile absence epilepsy (JAE), juvenile myoclonic epilepsy (JME), and GGE with tonic-clonic seizures alone (GGE-TCS), are common types of epilepsy mostly determined by a polygenic mode of inheritance. Recent studies showed that susceptibility genes for GGE are numerous, and their variants rare, challenging their identification. In this study, we aimed to assess GGE genetic etiology in a Sudanese population. Methods: We performed whole-exome sequencing (WES) on DNA of 40 patients from 20 Sudanese families with GGE searching for candidate susceptibility variants, which were prioritized by CADD software and functional features of the corresponding gene. We assessed their segregation in 138 individuals and performed genotype-phenotype correlations. Results: In a family including three sibs with GGE-TCS, we identified a rare missense variant in ADGRV1 encoding an adhesion G protein-coupled receptor V1, which was already involved in the autosomal recessive Usher type C syndrome. In addition, five other ADGRV1 rare missense variants were identified in four additional families and absent from 119 Sudanese controls. In one of these families, an ADGRV1 variant was found at a homozygous state, in a female more severely affected than her heterozygous brother, suggesting a gene dosage effect. In the five families, GGE phenotype was statistically associated with ADGRV1 variants (0R = 0.9 103). Conclusion: This study highly supports, for the first time, the involvement of ADGRV1 missense variants in familial GGE and that ADGRV1 is a susceptibility gene for CAE/JAE and GGE-TCS phenotypes.

Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences

Late-onset Alzheimer's disease (LOAD) is the most common type of dementia causing irreversible brain damage to the elderly and presents a major public health challenge. Clinical research and genome-wide association studies have suggested a potential contribution of the endocytic pathway to AD, with an emphasis on common loci. However, the contribution of rare variants in this pathway to AD has not been thoroughly investigated. In this study, we focused on the effect of rare variants on AD by first applying a rare-variant gene-set burden analysis using genes in the endocytic pathway on over 3,000 individuals with European ancestry from three large whole-genome sequencing (WGS) studies. We identified significant associations of rare-variant burden within the endocytic pathway with AD, which were successfully replicated in independent datasets. We further demonstrated that this endocytic rare-variant enrichment is associated with neurofibrillary tangles (NFTs) and age-related phenotypes, increasing the risk of obtaining severer brain damage, earlier age-at-onset, and earlier age-of-death. Next, by aggregating rare variants within each gene, we sought to identify single endocytic genes associated with AD and NFTs. Careful examination using NFTs revealed one significantly associated gene, ANKRD13D. To identify functional associations, we integrated bulk RNA-Seq data from over 600 brain tissues and found two endocytic expression genes (eGenes), HLA-A and SLC26A7, that displayed significant influences on their gene expressions. Differential expressions between AD patients and controls of these three identified genes were further examined by incorporating scRNA-Seq data from 48 post-mortem brain samples and demonstrated distinct expression patterns across cell types. Taken together, our results demonstrated strong rare-variant effect in the endocytic pathway on AD risk and progression and functional effect of gene expression alteration in both bulk and single-cell resolution, which may bring more insight and serve as valuable resources for future AD genetic studies, clinical research, and therapeutic targeting.

Prevalence and phenotype associations of complement factor I mutations in geographic atrophy

Rare variants in the complement factor I (CFI) gene, associated with low serum factor I (FI) levels, are strong risk factors for developing the advanced stages of age-related macular degeneration (AMD). No studies have been undertaken on the prevalence of disease-causing CFI mutations in patients with geographic atrophy (GA) secondary to AMD. A multicenter, cross-sectional, noninterventional study was undertaken to identify the prevalence of pathogenic rare CFI gene variants in an unselected cohort of patients with GA and low FI levels. A genotype-phenotype study was performed. Four hundred and sixty-eight patients with GA secondary to AMD were recruited to the study, and 19.4% (n = 91) demonstrated a low serum FI concentration (below 15.6 μg/ml). CFI gene sequencing on these patients resulted in the detection of rare CFI variants in 4.7% (n = 22) of recruited patients. The prevalence of CFI variants in patients with low serum FI levels and GA was 25%. Of the total patients recruited, 3.2% (n = 15) expressed a CFI variant classified as pathogenic or likely pathogenic. The presence of reticular pseudodrusen was detected in all patients with pathogenic CFI gene variants. Patients with pathogenic CFI gene variants and low serum FI levels might be suitable for FI supplementation in therapeutic trials.

Prediction of disease-associated functional variants in noncoding regions through a comprehensive analysis by integrating datasets and features

One of the greatest challenges in human genetics is deciphering the link between functional variants in noncoding sequences and the pathophysiology of complex diseases. To address this issue, many methods have been developed to sort functional single-nucleotide variants (SNVs) for neutral SNVs in noncoding regions. In this study, we integrated well-established features and commonly used datasets and merged them into large-scale datasets based on a random forest model, which yielded promising performance and outperformed some cutting-edge approaches. Our analyses of feature importance and data coverage also provide certain clues for future research in enhancing the prediction of functional noncoding SNVs.

A Bayesian linear mixed model for prediction of complex traits

MOTIVATION

Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance.

RESULTS

We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive.

AVAILABILITYAND IMPLEMENTATION

The R-package is available at https://github.com/yhai943/BLMM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evolutionary demographic models reveal the strength of purifying selection on susceptibility alleles to late-onset diseases

Assessing the role played by purifying selection on a susceptibility allele to late-onset disease (SALOD) is crucial to understanding the puzzling allelic spectrum of a disease, because most alleles are recent and rare. This fact is surprising because it suggests that alleles are under purifying selection while those that are involved in post-menopause mortality are often considered neutral in the genetic literature. The aim of this article is to use an evolutionary demography model to assess the magnitude of selection on SALODs while accounting for epidemiological and sociocultural factors. We develop an age-structured population model allowing for the calculation of SALOD selection coefficients (1) for a large and realistic parameter space for disease onset, (2) in a two-sex model in which men can reproduce in old age and (3) for situations in which child survival depends on maternal, paternal and grandmaternal care. The results show that SALODs are under purifying selection for most known age-at-onset distributions of late-onset genetic diseases. Estimates regarding various genes involved in susceptibility to cancer or Huntington's disease demonstrate that negative selection largely overcomes the effects of drift in most human populations. This is also probably true for neurodegenerative or polycystic kidney diseases, although sociocultural factors modulate the effect of selection in these cases. We conclude that neutrality is probably the exception among alleles that have a deleterious effect in old age and that accounting for sociocultural factors is required to understand the full extent of the force of selection shaping senescence in humans.

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.

Genetics of Alzheimer's disease

Alzheimer’s disease (AD) is the leading cause of neurodegeneration in the elderly and is clinically characterized by slowly progressing cognitive decline, which most commonly affects episodic memory function. This eventually leads to difficulties in activities of daily living. Biomarker studies show that the underlying pathology of AD begins 20 years before clinical symptoms. This results in the need to define specific targets and preclinical stages in order to address the problems of this disease at an earlier point in time. Genetic studies are indispensable for gaining insight into the etiology of neurodegenerative diseases and can play a major role in the early definition of the individual disease risk. This review provides an overview of the currently known genetic features of AD.

Integrative Network Analysis of Predicted miRNA-Targets Regulating Expression of Immune Response Genes in Bovine Coronavirus Infection

Bovine coronavirus (BCoV) infection that causes disease outbreaks among farm animals, resulting in significant economic losses particularly in the cattle industry, has the potential to become zoonotic. miRNAs, which are short non-coding segments of RNA that inhibits the expression of their target genes, have been identified as potential biomarkers and drug targets, though this potential in BCoV remains largely unknown. We hypothesize that certain miRNAs could simultaneously target multiple genes, are significantly conserved across many species, thereby demonstrating the potential to serve as diagnostic or therapeutic tools for bovine coronavirus infection. To this end, we utilized different existing and publicly available computational tools to conduct system analysis predicting important miRNAs that could affect BCoV pathogenesis. Eleven genes including CEBPD, IRF1, TLR9, SRC, and RHOA, significantly indicated in immune-related pathways, were identified to be associated with BCoV, and implicated in other coronaviruses. Of the 70 miRNAs predicted to target the identified genes, four concomitant miRNAs (bta-miR-11975, bta-miR-11976, bta-miR-22-3p, and bta-miR-2325c) were found. Examining the gene interaction network suggests IL-6, IRF1, and TP53 as key drivers. Phylogenetic analysis revealed that miR-22 was completely conserved across all 14 species it was searched against, suggesting a shared and important functional role. Functional annotation and associated pathways of target genes, such as positive regulation of cytokine production, IL-6 signaling pathway, and regulation of leukocyte differentiation, indicate the miRNAs are major participants in multiple aspects of both innate and adaptive immune response. Examination of variants evinced a potentially deleterious SNP in bta-miR-22-3p and an advantageous SNP in bta-miR-2325c. Conclusively, this study provides new insight into miRNAs regulating genes responding to BCoV infection, with bta-miR-22-3p particularly indicated as a potential drug target or diagnostic marker for bovine coronavirus.

How to Assess Founder Effect in Patients with Congenital Factor XIII Deficiency

Congenital factor XIII (FXIII) deficiency is an extremely rare bleeding disorder (RBD) with estimated prevalence of one per 2 million in the general population. The disorder causes different clinical manifestations such as intracranial hemorrhage (ICH), recurrent miscarriage, umbilical cord bleeding, etc. High incidence of the disorder might be due to founder effect. To assess founder effect, haplotype analysis is an important step. For this purpose, suitable and reliable genetic markers such as microsatellites (Hum FXIIIA01 and HumFXIIIA02) and single nucleotide polymorphisms (SNP) are suggested. In the present study we tried to describe evaluation of founder effect in patients with congenital FXIII deficiency via haplotype analysis using suitable genetic markers.

Protein-Protein Interactions Mediated by Intrinsically Disordered Protein Regions Are Enriched in Missense Mutations

Because proteins are fundamental to most biological processes, many genetic diseases can be traced back to single nucleotide variants (SNVs) that cause changes in protein sequences. However, not all SNVs that result in amino acid substitutions cause disease as each residue is under different structural and functional constraints. Influential studies have shown that protein–protein interaction interfaces are enriched in disease-associated SNVs and depleted in SNVs that are common in the general population. These studies focus primarily on folded (globular) protein domains and overlook the prevalent class of protein interactions mediated by intrinsically disordered regions (IDRs). Therefore, we investigated the enrichment patterns of missense mutation-causing SNVs that are associated with disease and cancer, as well as those present in the healthy population, in structures of IDR-mediated interactions with comparisons to classical globular interactions. When comparing the different categories of interaction interfaces, division of the interface regions into solvent-exposed rim residues and buried core residues reveal distinctive enrichment patterns for the various types of missense mutations. Most notably, we demonstrate a strong enrichment at the interface core of interacting IDRs in disease mutations and its depletion in neutral ones, which supports the view that the disruption of IDR interactions is a mechanism underlying many diseases. Intriguingly, we also found an asymmetry across the IDR interaction interface in the enrichment of certain missense mutation types, which may hint at an increased variant tolerance and urges further investigations of IDR interactions.

Rare variant association testing in the non-coding genome

The development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants.

Targeted resequencing reveals rare variants enrichment in multiple sclerosis susceptibility genes

Although genome-wide association studies have identified a number of common variants associated with multiple sclerosis (MS) susceptibility, little is known about the relevance of rare variants. Here, we aimed to explore the role of rare variants in 14 MS risk genes (FCRL1, RGS1, TIMMDC1, HHEX, CXCR5, LTBR, TSFM, GALC, TRAF3, STAT3, TNFSF14, IFI30, CD40, and CYP24A1) by targeted resequencing in an Iberian population of 524 MS cases and 546 healthy controls. Four rare variants-enriched regions within CYP24A1, FCRL1, RGS1, and TRAF3 were identified as significantly associated with MS. Functional studies revealed significantly decreased regulator of G protein signaling 1 (RGS1) gene expression levels in peripheral blood mononuclear cells from MS patients with RGS1 rare variants compared to noncarriers, whereas no significant differences in gene expression were observed for CYP24A1, FCRL1, and TRAF3 between rare variants carriers and noncarriers. Immunophenotyping showed significant decrease in RGS1 expression in peripheral blood B lymphocytes from MS patients with RGS1 rare variants relative to noncarriers. Lastly, peripheral blood mononuclear cell from MS patients carrying RGS1 rare variants showed significantly lower induction of RGS1 gene expression by interferon-β compared to MS patients lacking RGS1 variants. The presence of rare variants in RGS1 reinforce the ideas of high genetic heterogeneity and a role of rare variants in MS pathogenesis.

Transcriptional Regulation of Channelopathies in Genetic and Acquired Epilepsies

Epilepsy is a common neurological disorder characterized by recurrent uncontrolled seizures and has an idiopathic “genetic” etiology or a symptomatic “acquired” component. Genetic studies have revealed that many epilepsy susceptibility genes encode ion channels, including voltage-gated sodium, potassium and calcium channels. The high prevalence of ion channels in epilepsy pathogenesis led to the causative concept of “ion channelopathies,” which can be elicited by specific mutations in the coding or promoter regions of genes in genetic epilepsies. Intriguingly, expression changes of the same ion channel genes by augmentation of specific transcription factors (TFs) early after an insult can underlie acquired epilepsies. In this study, we review how the transcriptional regulation of ion channels in both genetic and acquired epilepsies can be controlled, and compare these epilepsy “ion channelopathies” with other neurodevelopmental disorders.

Rare Variants of Putative Candidate Genes Associated With Sporadic Meniere's Disease in East Asian Population

Objectives: The cause of Meniere's disease (MD) is unclear but likely involves genetic and environmental factors. The aim of this study was to investigate the genetic basis underlying MD by screening putative candidate genes for MD.

Methods: Sixty-eight patients who met the diagnostic criteria for MD of the Barany Society were included. We performed targeted gene sequencing using next generation sequencing (NGS) panel composed of 45 MD-associated genes. We identified the rare variants causing non-synonymous amino acid changes, stop codons, and insertions/deletions in the coding regions, and excluded the common variants with minor allele frequency >0.01 in public databases. The pathogenicity of the identified variants was analyzed by various predictive tools and protein structural modeling.

Results: The average read depth for the targeted regions was 1446.3-fold, and 99.4% of the targeted regions were covered by 20 or more reads, achieving the high quality of the sequencing. After variant filtering, annotation, and interpretation, we identified a total of 15 rare heterozygous variants in 12 (17.6%) sporadic patients. Among them, four variants were detected in familial MD genes (DTNA, FAM136A, DPT), and the remaining 11 in MD-associated genes (PTPN22, NFKB1, CXCL10, TLR2, MTHFR, SLC44A2, NOS3, NOTCH2). Three patients had the variants in two or more genes. All variants were not detected in our healthy controls (n = 100). No significant differences were observed between patients with and without a genetic variant in terms of sex, mean age of onset, bilaterality, the type of MD, and hearing threshold at diagnosis.

Conclusions: Our study identified rare variants of putative candidate genes in some of MD patients. The genes were related to the formation of inner ear structures, the immune-associated process, or systemic hemostasis derangement, suggesting the multiple genetic predispositions in the development of MD.

The Evolving Field of Genetic Epidemiology: From Familial Aggregation to Genomic Sequencing

The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). The field has moved quickly over the past 40 years partly because the technology of genotyping and sequencing has forced it to adapt while adhering to the fundamental principles of genetics. In the last two decades, the available tools for genetic epidemiology have expanded from a genetic focus (considering 1 gene at a time) to a genomic focus (considering the entire genome), and now they must further expand to integrate information from other “-omics” (e.g., epigenomics, transcriptomics as measured by RNA expression) at both the individual and the population levels. Additionally, we can now also evaluate gene and environment interactions across populations to better understand exposure and the heterogeneity in disease risk. The future challenges facing genetic epidemiology are considerable both in scale and techniques, but the importance of the field will not diminish because by design it ties scientific goals with public health applications.

Rare and common variant discovery in complex disease: the IBD case study

Complex diseases such as inflammatory bowel disease (IBD), which consists of ulcerative colitis and Crohn's disease, are a significant medical burden-70 000 new cases of IBD are diagnosed in the United States annually. In this review, we examine the history of genetic variant discovery in complex disease with a focus on IBD. We cover methods that have been applied to microsatellite, common variant, targeted resequencing and whole-exome and -genome data, specifically focusing on the progression of technologies towards rare-variant discovery. The inception of these methods combined with better availability of population level variation data has led to rapid discovery of IBD-causative and/or -associated variants at over 200 loci; over time, these methods have grown exponentially in both power and ascertainment to detect rare variation. We highlight rare-variant discoveries critical to the elucidation of the pathogenesis of IBD, including those in NOD2, IL23R, CARD9, RNF186 and ADCY7. We additionally identify the major areas of rare-variant discovery that will evolve in the coming years. A better understanding of the genetic basis of IBD and other complex diseases will lead to improved diagnosis, prognosis, treatment and surveillance.

Computational analysis of functional SNPs in Alzheimer's disease-associated endocytosis genes

Background

From genome wide association studies on Alzheimer’s disease (AD), it has been shown that many single nucleotide polymorphisms (SNPs) of genes of different pathways affect the disease risk. One of the pathways is endocytosis, and variants in these genes may affect their functions in amyloid precursor protein (APP) trafficking, amyloid-beta (Aβ) production as well as its clearance in the brain. This study uses computational methods to predict the effect of novel SNPs, including untranslated region (UTR) variants, splice site variants, synonymous SNPs (sSNPs) and non-synonymous SNPs (nsSNPs) in three endocytosis genes associated with AD, namely PICALM, SYNJ1 and SH3KBP1.

Materials and Methods

All the variants’ information was retrieved from the Ensembl genome database, and then different variation prediction analyses were performed. UTRScan was used to predict UTR variants while MaxEntScan was used to predict splice site variants. Meta-analysis by PredictSNP2 was used to predict sSNPs. Parallel prediction analyses by five different software packages including SIFT, PolyPhen-2, Mutation Assessor, I-Mutant2.0 and SNPs&GO were used to predict the effects of nsSNPs. The level of evolutionary conservation of deleterious nsSNPs was further analyzed using ConSurf server. Mutant protein structures of deleterious nsSNPs were modelled and refined using SPARKS-X and ModRefiner for structural comparison.

Results

A total of 56 deleterious variants were identified in this study, including 12 UTR variants, 18 splice site variants, eight sSNPs and 18 nsSNPs. Among these 56 deleterious variants, seven variants were also identified in the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Mount Sinai Brain Bank (MSBB) studies.

Discussion

The 56 deleterious variants were predicted to affect the regulation of gene expression, or have functional impacts on these three endocytosis genes and their gene products. The deleterious variants in these genes are expected to affect their cellular function in endocytosis and may be implicated in the pathogenesis of AD as well. The biological consequences of these deleterious variants and their potential impacts on the disease risks could be further validated experimentally and may be useful for gene-disease association study.

Inherited alterations of TGF beta signaling components in Appalachian cervical cancers

PURPOSE

This study examined targeted genomic variants of transforming growth factor beta (TGFB) signaling in Appalachian women. Appalachian women with cervical cancer were compared to healthy Appalachian counterparts to determine whether these polymorphic alleles were over-represented within this high-risk cancer population, and whether lifestyle or environmental factors modified the aggregate genetic risk in these Appalachian women.

METHODS

Appalachian women's survey data and blood samples from the Community Awareness, Resources, and Education (CARE) CARE I and CARE II studies (n = 163 invasive cervical cancer cases, 842 controls) were used to assess gene-environment interactions and cancer risk. Polymorphic allele frequencies and socio-behavioral demographic measurements were compared using t tests and χ2 tests. Multivariable logistic regression was used to evaluate interaction effects between genomic variance and demographic, behavioral, and environmental characteristics.

RESULTS

Several alleles demonstrated significant interaction with smoking (TP53 rs1042522, TGFB1 rs1800469), alcohol consumption (NQO1 rs1800566), and sexual intercourse before the age of 18 (TGFBR1 rs11466445, TGFBR1 rs7034462, TGFBR1 rs11568785). Interestingly, we noted a significant interaction between "Appalachian self-identity" variables and NQO1 rs1800566. Multivariable logistic regression of cancer status in an over-dominant TGFB1 rs1800469/TGFBR1 rs11568785 model demonstrated a 3.03-fold reduction in cervical cancer odds. Similar decreased odds (2.78-fold) were observed in an over-dominant TGFB1 rs1800469/TGFBR1 rs7034462 model in subjects who had no sexual intercourse before age 18.

CONCLUSIONS

This study reports novel associations between common low-penetrance alleles in the TGFB signaling cascade and modified risk of cervical cancer in Appalachian women. Furthermore, our unexpected findings associating Appalachian identity and NQO1 rs1800566 suggests that the complex environmental exposures that contribute to Appalachian self-identity in Appalachian cervical cancer patients represent an emerging avenue of scientific exploration.

GEMPROT: visualization of the impact on the protein of the genetic variants found on each haplotype

SUMMARY

When analyzing sequence data, genetic variants are considered one by one, taking no account of whether or not they are found in the same individual. However, variant combinations might be key players in some diseases as variants that are neutral on their own can become deleterious when associated together. GEMPROT is a new analysis tool that allows, from a phased vcf file, to visualize the consequences of the genetic variants on the protein. At the level of an individual, the program shows the variants on each of the two protein sequences and the Pfam functional protein domains. When data on several individuals are available, GEMPROT lists the haplotypes found in the sample and can compare the haplotype distributions between different sub-groups of individuals. By offering a global visualization of the gene with the genetic variants present, GEMPROT makes it possible to better understand the impact of combinations of genetic variants on the protein sequence.

AVAILABILITY AND IMPLEMENTATION

GEMPROT is freely available at https://github.com/TaniaCuppens/GEMPROT. An on-line version is also available at http://med-laennec.univ-brest.fr/GEMPROT/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rare variant association testing for multicategory phenotype

Genetic association studies have provided new insights into the genetic variability of human complex traits with a focus mainly on continuous or binary traits. Methods have been proposed to take into account disease heterogeneity between subgroups of patients when studying common variants but none was specifically designed for rare variants. Because rare variants are expected to have stronger effects and to be more heterogeneously distributed among cases than common ones, subgroup analyses might be particularly attractive in this context. To address this issue, we propose an extension of burden tests by using a multinomial regression model, which enables association tests between rare variants and multicategory phenotypes. We evaluated the type I error and the power of two burden tests, CAST and WSS, by simulating data under different scenarios. In the case of genetic heterogeneity between case subgroups, we showed an advantage of multinomial regression over logistic regression, which considers all the cases against the controls. We replicated these results on real data from Moyamoya disease where the burden tests performed better when cases were stratified according to age-of-onset. We implemented the functions for association tests in the R package "Ravages" available on Github.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Exploring Impact of Rare Variation in Systemic Lupus Erythematosus by a Genome Wide Imputation Approach

The importance of low frequency and rare variation in complex disease genetics is difficult to estimate in patient populations. Genome-wide association studies are therefore, underpowered to detect rare variation. We have used a combined approach of genome-wide-based imputation with a highly stringent sequence kernel association (SKAT) test and a case-control burden test. We identified 98 candidate genes containing rare variation that in aggregate show association with SLE many of which have recognized immunological function, but also function and expression related to relevant tissues such as the joints, skin, blood or central nervous system. In addition we also find that there is a significant enrichment of genes annotated for disease-causing mutations in the OMIM database, suggesting that in complex diseases such as SLE, such mutations may be involved in subtle or combined phenotypes or could accelerate specific organ abnormalities found in the disease. We here provide an important resource of candidate genes for SLE.

High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes

Type 2 diabetes (T2D) is a complex disorder that is caused by a combination of genetic, epigenetic, and environmental factors. High-throughput approaches have opened a new avenue toward a better understanding of the molecular bases of T2D. A genome-wide association studies (GWASs) identified a group of the most common susceptibility genes for T2D (i.e., TCF7L2, PPARG, KCNJ1, HNF1A, PTPN1, and CDKAL1) and illuminated novel disease-causing pathways. Next-generation sequencing (NGS)-based techniques have shed light on rare-coding genetic variants that account for an appreciable fraction of T2D heritability (KCNQ1 and ADRA2A) and population risk of T2D (SLC16A11, TPCN2, PAM, and CCND2). Moreover, single-cell sequencing of human pancreatic islets identified gene signatures that are exclusive to α-cells (GCG, IRX2, and IGFBP2) and β-cells (INS, ADCYAP1, INS-IGF2, and MAFA). Ongoing epigenome-wide association studies (EWASs) have progressively defined links between epigenetic markers and the transcriptional activity of T2D target genes. Differentially methylated regions were found in TCF7L2, THADA, KCNQ1, TXNIP, SOCS3, SREBF1, and KLF14 loci that are related to T2D. Additionally, chromatin state maps in pancreatic islets were provided and several non-coding RNAs (ncRNA) that are key to T2D pathogenesis were identified (i.e., miR-375). The present review summarizes major progress that has been made in mapping the (epi)genomic landscape of T2D within the last few years.

Prioritization and functional assessment of noncoding variants associated with complex diseases

Unraveling functional noncoding variants associated with complex diseases is still a great challenge. We present a novel algorithm, Prioritization And Functional Assessment (PAFA), that prioritizes and assesses the functionality of genetic variants by introducing population differentiation measures and recalibrating training variants. Comprehensive evaluations demonstrate that PAFA exhibits much higher sensitivity and specificity in prioritizing noncoding risk variants than existing methods. PAFA achieves improved performance in distinguishing both common and rare recurrent variants from non-recurrent variants by integrating multiple annotations and metrics. An integrated platform was developed, providing comprehensive functional annotations for noncoding variants by integrating functional genomic data, which can be accessed at http://159.226.67.237:8080/pafa .

Supporting precision medicine by data mining across multi-disciplines: an integrative approach for generating comprehensive linkages between single nucleotide variants (SNVs) and drug-binding sites

Motivation

Genetic variants in drug targets and metabolizing enzymes often have important functional implications, including altering the efficacy and toxicity of drugs. Identifying single nucleotide variants (SNVs) that contribute to differences in drug response and understanding their underlying mechanisms are fundamental to successful implementation of the precision medicine model. This work reports an effort to collect, classify and analyze SNVs that may affect the optimal response to currently approved drugs.

Results

An integrated approach was taken involving data mining across multiple information resources including databases containing drugs, drug targets, chemical structures, protein-ligand structure complexes, genetic and clinical variations as well as protein sequence alignment tools. We obtained 2640 SNVs of interest, most of which occur rarely in populations (minor allele frequency < 0.01). Clinical significance of only 9.56% of the SNVs is known in ClinVar, although 79.02% are predicted as deleterious. The examples here demonstrate that even if the mapped SNVs predicted as deleterious may not result in significant structural modifications, they can plausibly modify the protein-drug interactions, affecting selectivity and drug-binding affinity. Our analysis identifies potentially deleterious SNVs present on drug-binding residues that are relevant for further studies in the context of precision medicine.

Availability and Implementation

Data are available from Supplementary information file.

Contact

yanli.wang@nih.gov.

Supplementary information

Supplementary Tables S1-S5 are available at Bioinformatics online.

Spontaneous preterm birth: advances toward the discovery of genetic predisposition

Evidence from family and twin-based studies provide strong support for a significant contribution of maternal and fetal genetics to the timing of parturition and spontaneous preterm birth. However, there has been only modest success in the discovery of genes predisposing to preterm birth, despite increasing sophistication of genetic and genomic technology. In contrast, DNA variants associated with other traits/diseases have been identified. For example, there is overwhelming evidence that suggests that the nature and intensity of an inflammatory response in adults and children are under genetic control. Because inflammation is often invoked as an etiologic factor in spontaneous preterm birth, the question of whether spontaneous preterm birth has a genetic predisposition in the case of pathologic inflammation has been of long-standing interest to investigators. Here, we review various genetic approaches used for the discovery of preterm birth genetic variants in the context of inflammation-associated spontaneous preterm birth. Candidate gene studies have sought genetic variants that regulate inflammation in the mother and fetus; however, the promising findings have often not been replicated. Genome-wide association studies, an approach to the identification of chromosomal loci responsible for complex traits, have also not yielded compelling evidence for DNA variants predisposing to preterm birth. A recent genome-wide association study that included a large number of White women (>40,000) revealed that maternal loci contribute to preterm birth. Although none of these loci harbored genes directly related to innate immunity, the results were replicated. Another approach to identify DNA variants predisposing to preterm birth is whole exome sequencing, which examines the DNA sequence of protein-coding regions of the genome. A recent whole exome sequencing study identified rare mutations in genes encoding for proteins involved in the negative regulation (dampening) of the innate immune response (eg, CARD6, CARD8, NLRP10, NLRP12, NOD2, TLR10) and antimicrobial peptide/proteins (eg, DEFB1, MBL2). These findings support the concept that preterm labor, at least in part, has an inflammatory etiology, which can be induced by pathogens (ie, intraamniotic infection) or "danger signals" (alarmins) released during cellular stress or necrosis (ie, sterile intraamniotic inflammation). These findings support the notion that preterm birth has a polygenic basis that involves rare mutations or damaging variants in multiple genes involved in innate immunity and host defense mechanisms against microbes and their noxious products. An overlap among the whole exome sequencing-identified genes and other inflammatory conditions associated with preterm birth, such as periodontal disease and inflammatory bowel disease, was observed, which suggests a shared genetic substrate for these conditions. We propose that whole exome sequencing, as well as whole genome sequencing, is the most promising approach for the identification of functionally significant genetic variants responsible for spontaneous preterm birth, at least in the context of pathologic inflammation. The identification of genes that contribute to preterm birth by whole exome sequencing, or whole genome sequencing, promises to yield valuable population-specific biomarkers to identify the risk for spontaneous preterm birth and potential strategies to mitigate such a risk.

Disorders of erythrocyte hydration

The erythrocyte contains a network of pathways that regulate salt and water content in the face of extracellular and intracellular osmotic perturbations. This allows the erythrocyte to maintain a narrow range of cell hemoglobin concentration, a process critical for normal red blood cell function and survival. Primary disorders that perturb volume homeostasis jeopardize the erythrocyte and may lead to its premature destruction. These disorders are marked by clinical, laboratory, and physiologic heterogeneity. Recent studies have revealed that these disorders are also marked by genetic heterogeneity. They have implicated roles for several proteins, PIEZO1, a mammalian mechanosensory protein; GLUT1, the glucose transporter; SLC4A1, the anion transporter; RhAG, the Rh-associated glycoprotein; KCNN4, the Gardos channel; and ABCB6, an adenosine triphosphate-binding cassette family member, in the maintenance of erythrocyte volume homeostasis. Secondary disorders of erythrocyte hydration include sickle cell disease, thalassemia, hemoglobin CC, and hereditary spherocytosis, where cellular dehydration may be a significant contributor to disease pathology and clinical complications. Understanding the pathways regulating erythrocyte water and solute content may reveal innovative strategies to maintain normal volume in disorders associated with primary or secondary cellular dehydration. These mechanisms will serve as a paradigm for other cells and may reveal new therapeutic targets for disease prevention and treatment beyond the erythrocyte.

Meiotic Interactors of a Mitotic Gene TAO3 Revealed by Functional Analysis of its Rare Variant

Studying the molecular consequences of rare genetic variants has the potential to identify novel and hitherto uncharacterized pathways causally contributing to phenotypic variation. Here, we characterize the functional consequences of a rare coding variant of TAO3, previously reported to contribute significantly to sporulation efficiency variation in Saccharomyces cerevisiae. During mitosis, the common TAO3 allele interacts with CBK1—a conserved NDR kinase. Both TAO3 and CBK1 are components of the RAM signaling network that regulates cell separation and polarization during mitosis. We demonstrate that the role of the rare allele TAO3(4477C) in meiosis is distinct from its role in mitosis by being independent of ACE2—a RAM network target gene. By quantitatively measuring cell morphological dynamics, and expressing the TAO3(4477C) allele conditionally during sporulation, we show that TAO3 has an early role in meiosis. This early role of TAO3 coincides with entry of cells into meiotic division. Time-resolved transcriptome analyses during early sporulation identified regulators of carbon and lipid metabolic pathways as candidate mediators. We show experimentally that, during sporulation, the TAO3(4477C) allele interacts genetically with ERT1 and PIP2, regulators of the tricarboxylic acid cycle and gluconeogenesis metabolic pathways, respectively. We thus uncover a meiotic functional role for TAO3, and identify ERT1 and PIP2 as novel regulators of sporulation efficiency. Our results demonstrate that studying the causal effects of genetic variation on the underlying molecular network has the potential to provide a more extensive understanding of the pathways driving a complex trait.

The Ensembl Variant Effect Predictor

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

The Ensembl Variant Effect Predictor

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

Low frequency of GCH1 and TH mutations in Parkinson's disease

BACKGROUND

The causes of Parkinson's disease (PD) are unknown in the majority of patients. The GCH1 gene encodes GTP-cyclohydrolase I, an important enzyme in dopamine synthesis. Co-occurrence of dopa-responsive dystonia (DRD) and a PD phenotype has been reported in families with GCH1 mutations. Recently, rare coding variants in GCH1 were found to be enriched in PD patients, indicating a role for the enzyme in the neurodegenerative process.

METHODS

To further elucidate the contribution of GCH1 mutations to sporadic PD, we examined its coding exons in a targeted deep sequencing study of 509 PD patients (mean age at onset 56.7 ± 12.0 years) and 230 controls. We further included the tyrosine hydroxylase gene TH, also known to cause DRD. Gene dose assessments were performed to screen for large copy number variants in a subset of 48 patients with early-onset PD.

RESULTS

No putatively pathogenic GCH1 mutations were found. The frequency of rare heterozygous variants in the TH gene was 0.69% (7/1018) in the patient group and 0.22% (1/460) in the control group (p = 0.45).

CONCLUSIONS

Previous studies have found that coding variants in the GCH1 gene may be considered a risk factor for PD. Our study indicates that mutations in GCH1 are rare in late-onset PD. Several patients carried heterozygous variants in the TH gene that may affect protein function. Our study was not designed to determine with certainty if any of these variants play a role as risk factors for late-onset PD.

Feature co-localization landscape of the human genome

Although feature co-localizations could serve as useful guide-posts to genome architecture, a comprehensive and quantitative feature co-localization map of the human genome has been lacking. Herein we show that, in contrast to the conventional bipartite division of genomic sequences into genic and inter-genic regions, pairwise co-localizations of forty-two genomic features in the twenty-two autosomes based on 50-kb to 2,000-kb sequence windows indicate a tripartite zonal architecture comprising Genic zones enriched with gene-related features and Alu-elements; Proximal zones enriched with MIR- and L2-elements, transcription-factor-binding-sites (TFBSs), and conserved-indels (CIDs); and Distal zones enriched with L1-elements. Co-localizations between single-nucleotide-polymorphisms (SNPs) and copy-number-variations (CNVs) reveal a fraction of sequence windows displaying steeply enhanced levels of SNPs, CNVs and recombination rates that point to active adaptive evolution in such pathways as immune response, sensory perceptions, and cognition. The strongest positive co-localization observed between TFBSs and CIDs suggests a regulatory role of CIDs in cooperation with TFBSs. The positive co-localizations of cancer somatic CNVs (CNVT) with all Proximal zone and most Genic zone features, in contrast to the distinctly more restricted co-localizations exhibited by germline CNVs (CNVG), reveal disparate distributions of CNVTs and CNVGs indicative of dissimilarity in their underlying mechanisms.

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background

Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants.

Results

In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data.

Conclusions

Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants.

The Genetic Epidemiological Landscape of Hip and Knee Osteoarthritis: Where Are We Now and Where Are We Going?

Osteoarthritis (OA) is a complex disease that affects the whole joint, with multiple biological and environmental factors contributing to its development. The heritable component for primary OA accounts for ∼50% of susceptibility. So far, candidate gene studies and genome-wide association scans have established 18 OA-associated loci. These findings account for 11% of the heritability, explaining a rather small fraction of the genetic component. To further unravel the genetic architecture of OA, the field needs to facilitate more precise phenotypic definitions, high genome coverage, and large sample metaanalyses, expecting the identification of rare and low frequency variants with potentially higher penetrance, and more accurate methods for calculating phenotype-genotype correlation. Expression analysis, epigenetics, and investigation of interactions can also help clarify the implicated transcriptional regulatory pathways and provide insights into further novel pathogenic OA mechanisms leading to diagnostic biomarker identification and new, more focused therapeutic disease approaches.

GABRB2 Haplotype Association with Heroin Dependence in Chinese Population

Substance dependence is a frequently observed comorbid disorder in schizophrenia, but little is known about genetic factors possibly shared between the two psychotic disorders. GABRB2, a schizophrenia candidate gene coding for GABAA receptor β2 subunit, is examined for possible association with heroin dependence in Han Chinese population. Four single nucleotide polymorphisms (SNPs) in GABRB2, namely rs6556547 (S1), rs1816071 (S3), rs18016072 (S5), and rs187269 (S29), previously associated with schizophrenia, were examined for their association with heroin dependence. Two additional SNPs, rs10051667 (S31) and rs967771 (S32), previously associated with alcohol dependence and bipolar disorder respectively, were also analyzed. The six SNPs were genotyped by direct sequencing of PCR amplicons of target regions for 564 heroin dependent individuals and 498 controls of Han Chinese origin. Interestingly, it was found that recombination between the haplotypes of all-derived-allele (H1; OR = 1.00) and all-ancestral-allele (H2; OR = 0.74) at S5-S29 junction generated two recombinants H3 (OR = 8.51) and H4 (OR = 5.58), both conferring high susceptibility to heroin dependence. Additional recombination between H2 and H3 haplotypes at S1-S3 junction resulted in a risk-conferring haplotype H5 (OR = 1.94x109). In contrast, recombination between H1 and H2 haplotypes at S3-S5 junction rescued the risk-conferring effect of recombination at S5-S29 junction, giving rise to the protective haplotype H6 (OR = 0.68). Risk-conferring effects of S1-S3 and S5-S29 crossovers and protective effects of S3-S5 crossover were seen in both pure heroin dependent and multiple substance dependence subgroups. In conclusion, significant association was found with haplotypes of the S1-S29 segment in GABRB2 for heroin dependence in Han Chinese population. Local recombination was an important determining factor for switching haplotypes between risk-conferring and protective statuses. The present study provide evidence for the schizophrenia candidate gene GABRB2 to play a role in heroin dependence, but replication of these findings is required.

The Use of Non-Variant Sites to Improve the Clinical Assessment of Whole-Genome Sequence Data

Genetic testing, which is now a routine part of clinical practice and disease management protocols, is often based on the assessment of small panels of variants or genes. On the other hand, continuous improvements in the speed and per-base costs of sequencing have now made whole exome sequencing (WES) and whole genome sequencing (WGS) viable strategies for targeted or complete genetic analysis, respectively. Standard WGS/WES data analytical workflows generally rely on calling of sequence variants respect to the reference genome sequence. However, the reference genome sequence contains a large number of sites represented by rare alleles, by known pathogenic alleles and by alleles strongly associated to disease by GWAS. It’s thus critical, for clinical applications of WGS and WES, to interpret whether non-variant sites are homozygous for the reference allele or if the corresponding genotype cannot be reliably called. Here we show that an alternative analytical approach based on the analysis of both variant and non-variant sites from WGS data allows to genotype more than 92% of sites corresponding to known SNPs compared to 6% genotyped by standard variant analysis. These include homozygous reference sites of clinical interest, thus leading to a broad and comprehensive characterization of variation necessary to an accurate evaluation of disease risk. Altogether, our findings indicate that characterization of both variant and non-variant clinically informative sites in the genome is necessary to allow an accurate clinical assessment of a personal genome. Finally, we propose a highly efficient extended VCF (eVCF) file format which allows to store genotype calls for sites of clinical interest while remaining compatible with current variant interpretation software.

Temporal expression profiling identifies pathways mediating effect of causal variant on phenotype

Even with identification of multiple causal genetic variants for common human diseases, understanding the molecular processes mediating the causal variants’ effect on the disease remains a challenge. This understanding is crucial for the development of therapeutic strategies to prevent and treat disease. While static profiling of gene expression is primarily used to get insights into the biological bases of diseases, it makes differentiating the causative from the correlative effects difficult, as the dynamics of the underlying biological processes are not monitored. Using yeast as a model, we studied genome-wide gene expression dynamics in the presence of a causal variant as the sole genetic determinant, and performed allele-specific functional validation to delineate the causal effects of the genetic variant on the phenotype. Here, we characterized the precise genetic effects of a functional MKT1 allelic variant in sporulation efficiency variation. A mathematical model describing meiotic landmark events and conditional activation of MKT1 expression during sporulation specified an early meiotic role of this variant. By analyzing the early meiotic genome-wide transcriptional response, we demonstrate an MKT1-dependent role of novel modulators, namely, RTG1/3, regulators of mitochondrial retrograde signaling, and DAL82, regulator of nitrogen starvation, in additively effecting sporulation efficiency. In the presence of functional MKT1 allele, better respiration during early sporulation was observed, which was dependent on the mitochondrial retrograde regulator, RTG3. Furthermore, our approach showed that MKT1 contributes to sporulation independent of Puf3, an RNA-binding protein that steady-state transcription profiling studies have suggested to mediate MKT1-pleiotropic effects during mitotic growth. These results uncover interesting regulatory links between meiosis and mitochondrial retrograde signaling. In this study, we highlight the advantage of analyzing allele-specific transcriptional dynamics of mediating genes. Applications in higher eukaryotes can be valuable for inferring causal molecular pathways underlying complex dynamic processes, such as development, physiology and disease progression.

The causal path from a genetic variant to a complex phenotype such as disease progression is often not known. Studying gene expression variation is one approach to identify the mediating genes, however, it is difficult to distinguish causative from correlative genes. This becomes a challenge especially when studying developmental and physiological traits, since they involve dynamic processes contributing to the variation and only single static expression profiling is performed. As a proof of concept, we addressed this challenge here in yeast, by studying genome-wide gene expression in the presence of the causative polymorphism of MKT1 as the sole genetic variant, during the time phase when it contributes to sporulation efficiency variation. Our analysis during early sporulation identified mitochondrial retrograde signaling and nitrogen starvation as novel regulators, acting additively to regulate sporulation efficiency. Furthermore, we showed that PUF3, a known interactor of MKT1 had an independent role in sporulation. Our results highlight the role of differential mitochondrial signaling for efficient meiosis, providing insights into the factors regulating infertility. In addition, our study has implications for characterizing the molecular effects of causal genetic variants on dynamic biological processes during development and disease progression.