Candidate Disease Genes Research Articles

ILDgenDB: integrated genetic knowledge resource for interstitial lung diseases (ILDs).

Interstitial lung diseases (ILDs) are a diverse group of ∼200 acute and chronic pulmonary disorders that are characterized by variable amounts of inflammation, fibrosis and architectural distortion with substantial morbidity and mortality. Inaccurate and delayed diagnoses increase the risk, especially in developing countries. Studies have indicated the significant roles of genetic elements in ILDs pathogenesis. Therefore, the first genetic knowledge resource, ILDgenDB, has been developed with an objective to provide ILDs genetic data and their integrated analyses for the better understanding of disease pathogenesis and identification of diagnostics-based biomarkers. This resource contains literature-curated disease candidate genes (DCGs) enriched with various regulatory elements that have been generated using an integrated bioinformatics workflow of databases searches, literature-mining and DCGs–microRNA (miRNAs)–single nucleotide polymorphisms (SNPs) association analyses. To provide statistical significance to disease-gene association, ILD-specificity index and hypergeomatric test scores were also incorporated. Association analyses of miRNAs, SNPs and pathways responsible for the pathogenesis of different sub-classes of ILDs were also incorporated. Manually verified 299 DCGs and their significant associations with 1932 SNPs, 2966 miRNAs and 9170 miR-polymorphisms were also provided. Furthermore, 216 literature-mined and proposed biomarkers were identified. The ILDgenDB resource provides user-friendly browsing and extensive query-based information retrieval systems. Additionally, this resource also facilitates graphical view of predicted DCGs–SNPs/miRNAs and literature associated DCGs–ILDs interactions for each ILD to facilitate efficient data interpretation. Outcomes of analyses suggested the significant involvement of immune system and defense mechanisms in ILDs pathogenesis. This resource may potentially facilitate genetic-based disease monitoring and diagnosis.Database URL: http://14.139.240.55/ildgendb/index.php

Development of a comprehensive noninvasive prenatal test.

Our aim was to develop and apply a comprehensive noninvasive prenatal test (NIPT) by using high-coverage targeted next-generation sequencing to estimate fetal fraction, determine fetal sex, and detect trisomy and monogenic disease without parental genotype information. We analyzed 45 pregnancies, 40 mock samples, and eight mother-child pairs to generate 35 simulated datasets. Fetal fraction (FF) was estimated based on analysis of the single nucleotide polymorphism (SNP) allele fraction distribution. A Z-score was calculated for trisomy of chromosome 21 (T21), and fetal sex detection. Monogenic disease detection was performed through variant analysis. Model validation was performed using the simulated datasets. The novel model to estimate FF was robust and accurate (r2= 0.994, p-value < 2.2e-16). For samples with FF > 0.04, T21 detection had 100% sensitivity (95% CI: 63.06 to 100%) and 98.53% specificity (95% CI: 92.08 to 99.96%). Fetal sex was determined with 100% accuracy. We later performed a proof of concept for monogenic disease diagnosis of 5/7 skeletal dysplasia cases. In conclusion, it is feasible to perform a comprehensive NIPT by using only data from high coverage targeted sequencing, which, in addition to detecting trisomies, also make it possible to identify pathogenic variants of the candidate genes for monogenic diseases.

Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of networks remains poorly understood. We launched the "Disease Module Identification DREAM Challenge", an open competition to comprehensively assess module identification methods across diverse gene, protein and signaling networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). While a number of approaches were successful in terms of discovering complementary trait-associated modules, consensus predictions derived from the challenge submissions performed best. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

PGA: post-GWAS analysis for disease gene identification.

Although the genome-wide association study (GWAS) is a powerful method to identify disease-associated variants, it does not directly address the biological mechanisms underlying such genetic association signals. Here, we present PGA, a Perl- and Java-based program for post-GWAS analysis that predicts likely disease genes given a list of GWAS-reported variants. Designed with a command line interface, PGA incorporates genomic and eQTL data in identifying disease gene candidates and uses gene network and ontology data to score them based upon the strength of their relationship to the disease in question. http://zdzlab.einstein.yu.edu/1/pga.html. zhengdong.zhang@einstein.yu.edu. Supplementary data are available at Bioinformatics online.

The association between an endothelial nitric oxide synthase gene polymorphism and coronary heart disease in young people and the underlying mechanism.

With the development of molecular biological technology, the association between genes and diseases has drawn increasing attention of researchers; the endothelial nitric oxide synthase (eNOS) gene has been reported to be a candidate gene for cardiovascular disease (CHD). The present study aimed to investigate the association between a polymorphism of eNOS and the risk of CHD in young people (≤40years old), in addition to the underlying mechanism. A total of 234cases of CHD in young individuals were collected as the CHD group and 228cases of healthy individuals as the control group. Peripheral blood was collected and the genotype of the eNOS G894T polymorphism was identified by polymerase chain reaction-restriction fragment length polymorphism, the gene frequency was calculated and the distributions of genotype and allele frequency between the two groups were compared. Bioinformatics tools were employed to analyze the differences in the local protein structures of the eNOS G894T polymorphism and the biological mechanism was preliminary discussed. The results demonstrated that there were significant differences in the distribution of genotype frequency and allele frequency of the eNOS G894T gene polymorphism between the CHD group and control group (P<0.05). The risk of CHD in GT and TT genotypes were higher compared with the GG genotype (P<0.05). The G894T polymorphism led to Glu298Asp mutation of encoded protein, which is within the active site of eNOS, and partial structures of the protein were converted from random coil to α‑helix. In conclusion, the eNOS G894T gene polymorphism was associated with the occurrence and development of CHD in young people. The potential mechanism is that the G894T polymorphism leads to altered protein structure, which affects the function of eNOS in generating nitric oxide and cardiovascular diastole. The results of the present study suggested a potential target gene for the prevention and treatment of CHD in young people (≤40years old).

A link prediction method for heterogeneous networks based on BP neural network

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease–gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene–disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

How zebrafish research has helped in understanding thyroid diseases.

Next-generation sequencing technologies have revolutionized the identification of disease-causing genes, accelerating the discovery of new mutations and new candidate genes for thyroid diseases. To face this flow of novel genetic information, it is important to have suitable animal models to study the mechanisms regulating thyroid development and thyroid hormone availability and activity. Zebrafish ( Danio rerio), with its rapid external embryonic development, has been extensively used in developmental biology. To date, almost all of the components of the zebrafish thyroid axis have been characterized and are structurally and functionally comparable with those of higher vertebrates. The availability of transgenic fluorescent zebrafish lines allows the real-time analysis of thyroid organogenesis and its alterations. Transient morpholino-knockdown is a solution to silence the expression of a gene of interest and promptly obtain insights on its contribution during the development of the zebrafish thyroid axis. The recently available tools for targeted stable gene knockout have further increased the value of zebrafish to the study of thyroid disease. All of the reported zebrafish models can also be used to screen small compounds and to test new drugs and may allow the establishment of experimental proof of concept to plan subsequent clinical trials.

In vivo and ex vivo analyses of amyloid toxicity in the Tc1 mouse model of Down syndrome.

Rationale:The prevalence of Alzheimer’s disease is increased in people with Down syndrome. The pathology appears much earlier than in the general population, suggesting a predisposition to develop Alzheimer’s disease. Down syndrome results from trisomy of human chromosome 21, leading to overexpression of possible Alzheimer’s disease candidate genes, such as amyloid precursor protein gene. To better understand how the Down syndrome context results in increased vulnerability to Alzheimer’s disease, we analysed amyloid-β [25-35] peptide toxicity in the Tc1 mouse model of Down syndrome, in which ~75% of protein coding genes are functionally trisomic but, importantly, not amyloid precursor protein.Results:Intracerebroventricular injection of oligomeric amyloid-β [25-35] peptide in three-month-old wildtype mice induced learning deficits, oxidative stress, synaptic marker alterations, activation of glycogen synthase kinase-3β, inhibition of protein kinase B (AKT), and apoptotic pathways as compared to scrambled peptide-treated wildtype mice. Scrambled peptide-treated Tc1 mice presented high levels of toxicity markers as compared to wildtype mice. Amyloid-β [25-35] peptide injection in Tc1 mice induced significant learning deficits and enhanced glycogen synthase kinase-3β activity in the cortex and expression of apoptotic markers in the hippocampus and cortex. Interestingly, several markers, including oxidative stress, synaptic markers, glycogen synthase kinase-3β activity in the hippocampus and AKT activity in the hippocampus and cortex, were unaffected by amyloid-β [25-35] peptide injection in Tc1 mice.Conclusions:Tc1 mice present several toxicity markers similar to those observed in amyloid-β [25-35] peptide-treated wildtype mice, suggesting that developmental modifications in these mice modify their response to amyloid peptide. However, amyloid toxicity led to severe memory deficits in this Down syndrome mouse model.

ABCA1 rs2230805 and rs2230806 common gene variants are associated with Alzheimer's disease.

The ATP-binding cassette, sub-family A, member 1 gene (ABCA1) is a relevant positional and functional candidate gene for Alzheimer's disease (AD). A case-control association study of genetic variations covering the ABCA1 locus was performed in relation to AD risk in a Hungarian sample. Five single nucleotide polymorphisms (rs2422493: C-477T, rs2740483: G-17C, rs2230805: G474A/L158L, rs2230806: G656A/R219K and rs2066718: G2311A/V771M) were genotyped in 431 AD patients and 302 cognitively healthy, elderly controls. In single marker analysis, significant associations were found in the case of rs2230805 and rs2230806 polymorphisms: the minor A allele containing genotypes for both polymorphisms were more frequent in the control compared to the AD group. Haplotype analysis revealed that rs2230805, rs2230806 and rs2066718 polymorphisms created a linkage disequilibrium (LD) block with a strong LD between rs2230805 and rs2230806 polymorphisms. In the haplotype risk association tests, A-A-G haplotype of the rs2230805-rs2230806-rs2066718 polymorphisms was found to be nominally significantly more frequent in the control group. After correcting p values for multiple testing, only the effects of the rs2230805 and rs2230806 polymorphisms remained significant in the recessive model suggesting a modest protective effect of their minor alleles in AD, which should be interpreted with considerable caution, until further studies elucidate their role in AD pathology.

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay.

Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other neurological diseases at an increasing speed. However, little is known about the molecular mechanisms that underlie these disorders. The genetic, molecular, and behavioral toolbox of Drosophila melanogaster makes this model organism particularly useful to characterize new disease genes and mechanisms in a high-throughput manner. Nevertheless, high-throughput screens require efficient and reliable assays that, ideally, are cost-effective and allow for the automatized quantification of traits relevant to these disorders. The island assay is a cost-effective and easily set-up method to evaluate Drosophila locomotor behavior. In this assay, flies are thrown onto a platform from a fixed height. This induces an innate motor response that enables the flies to escape from the platform within seconds. At present, quantitative analyses of filmed island assays are done manually, which is a laborious undertaking, particularly when performing large screens.This manuscript describes the "Drosophila Island Assay" and "Island Assay Analysis" algorithms for high-throughput, automated data processing and quantification of island assay data. In the setup, a simple webcam connected to a laptop collects an image series of the platform while the assay is performed. The "Drosophila Island Assay" algorithm developed for the open-source software Fiji processes these image series and quantifies, for each experimental condition, the number of flies on the platform over time. The "Island Assay Analysis" script, compatible with the free software R, was developed to automatically process the obtained data and to calculate whether treatments/genotypes are statistically different. This greatly improves the efficiency of the island assay and makes it a powerful readout for basic locomotion and flight behavior. It can thus be applied to large screens investigating fly locomotor ability, Drosophila models of movement disorders, and drug efficacy.

High-throughput Analysis of Locomotor Behavior in the &lt;em&gt;Drosophila &lt;/em&gt;Island Assay

Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other neurological diseases at an increasing speed. However, little is known about the molecular mechanisms that underlie these disorders. The genetic, molecular, and behavioral toolbox of Drosophila melanogaster makes this model organism particularly useful to characterize new disease genes and mechanisms in a high-throughput manner. Nevertheless, high-throughput screens require efficient and reliable assays that, ideally, are cost-effective and allow for the automatized quantification of traits relevant to these disorders. The island assay is a cost-effective and easily set-up method to evaluate Drosophila locomotor behavior. In this assay, flies are thrown onto a platform from a fixed height. This induces an innate motor response that enables the flies to escape from the platform within seconds. At present, quantitative analyses of filmed island assays are done manually, which is a laborious undertaking, particularly when performing large screens. This manuscript describes the "Drosophila Island Assay" and "Island Assay Analysis" algorithms for high-throughput, automated data processing and quantification of island assay data. In the setup, a simple webcam connected to a laptop collects an image series of the platform while the assay is performed. The "Drosophila Island Assay" algorithm developed for the open-source software Fiji processes these image series and quantifies, for each experimental condition, the number of flies on the platform over time. The "Island Assay Analysis" script, compatible with the free software R, was developed to automatically process the obtained data and to calculate whether treatments/genotypes are statistically different. This greatly improves the efficiency of the island assay and makes it a powerful readout for basic locomotion and flight behavior. It can thus be applied to large screens investigating fly locomotor ability, Drosophila models of movement disorders, and drug efficacy.

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks.

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.

Identification of novel candidate disease genes from de novo exonic copy number variants

BackgroundExon-targeted microarrays can detect small (<1000 bp) intragenic copy number variants (CNVs), including those that affect only a single exon. This genome-wide high-sensitivity approach increases the molecular diagnosis for conditions with known disease-associated genes, enables better genotype–phenotype correlations, and facilitates variant allele detection allowing novel disease gene discovery.MethodsWe retrospectively analyzed data from 63,127 patients referred for clinical chromosomal microarray analysis (CMA) at Baylor Genetics laboratories, including 46,755 individuals tested using exon-targeted arrays, from 2007 to 2017. Small CNVs harboring a single gene or two to five non-disease-associated genes were identified; the genes involved were evaluated for a potential disease association.ResultsIn this clinical population, among rare CNVs involving any single gene reported in 7200 patients (11%), we identified 145 de novo autosomal CNVs (117 losses and 28 intragenic gains), 257 X-linked deletion CNVs in males, and 1049 inherited autosomal CNVs (878 losses and 171 intragenic gains); 111 known disease genes were potentially disrupted by de novo autosomal or X-linked (in males) single-gene CNVs. Ninety-one genes, either recently proposed as candidate disease genes or not yet associated with diseases, were disrupted by 147 single-gene CNVs, including 37 de novo deletions and ten de novo intragenic duplications on autosomes and 100 X-linked CNVs in males. Clinical features in individuals with de novo or X-linked CNVs encompassing at most five genes (224 bp to 1.6 Mb in size) were compared to those in individuals with larger-sized deletions (up to 5 Mb in size) in the internal CMA database or loss-of-function single nucleotide variants (SNVs) detected by clinical or research whole-exome sequencing (WES). This enabled the identification of recently published genes (BPTF, NONO, PSMD12, TANGO2, and TRIP12), novel candidate disease genes (ARGLU1 and STK3), and further confirmation of disease association for two recently proposed disease genes (MEIS2 and PTCHD1). Notably, exon-targeted CMA detected several pathogenic single-exon CNVs missed by clinical WES analyses.ConclusionsTogether, these data document the efficacy of exon-targeted CMA for detection of genic and exonic CNVs, complementing and extending WES in clinical diagnostics, and the potential for discovery of novel disease genes by genome-wide assay.

ISyTE 2.0: a database for expression-based gene discovery in the eye.

Although successful in identifying new cataract-linked genes, the previous version of the database iSyTE (integrated Systems Tool for Eye gene discovery) was based on expression information on just three mouse lens stages and was functionally limited to visualization by only UCSC-Genome Browser tracks. To increase its efficacy, here we provide an enhanced iSyTE version 2.0 (URL: http://research.bioinformatics.udel.edu/iSyTE) based on well-curated, comprehensive genome-level lens expression data as a one-stop portal for the effective visualization and analysis of candidate genes in lens development and disease. iSyTE 2.0 includes all publicly available lens Affymetrix and Illumina microarray datasets representing a broad range of embryonic and postnatal stages from wild-type and specific gene-perturbation mouse mutants with eye defects. Further, we developed a new user-friendly web interface for direct access and cogent visualization of the curated expression data, which supports convenient searches and a range of downstream analyses. The utility of these new iSyTE 2.0 features is illustrated through examples of established genes associated with lens development and pathobiology, which serve as tutorials for its application by the end-user. iSyTE 2.0 will facilitate the prioritization of eye development and disease-linked candidate genes in studies involving transcriptomics or next-generation sequencing data, linkage analysis and GWAS approaches.

Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information.

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.

Chromosome contacts in activated T cells identify autoimmune disease candidate genes

BackgroundAutoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4+ T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes.ResultsWithin 4 h, activation of CD4+ T cells invokes changes in histone modifications and enhancer RNA transcription that correspond to altered expression of the interacting genes identified by promoter capture Hi-C. By integrating promoter capture Hi-C data with genetic associations for five autoimmune diseases, we prioritised 245 candidate genes with a median distance from peak signal to prioritised gene of 153 kb. Just under half (108/245) prioritised genes related to activation-sensitive interactions. This included IL2RA, where allele-specific expression analyses were consistent with its interaction-mediated regulation, illustrating the utility of the approach.ConclusionsOur systematic experimental framework offers an alternative approach to candidate causal gene identification for variants with cell state-specific functional effects, with achievable sample sizes.

Constructing an integrated gene similarity network for the identification of disease genes

BackgroundDiscovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale.ResultsWe proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer’s disease and predict some novel disease genes that supported by literature.ConclusionsRWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/.

A GJA9 frameshift variant is associated with polyneuropathy in Leonberger dogs

BackgroundMany inherited polyneuropathies (PN) observed in dogs have clinical similarities to the genetically heterogeneous group of Charcot-Marie-Tooth (CMT) peripheral neuropathies in humans. The canine disorders collectively show a variable expression of progressive clinical signs and ages of onset, and different breed prevalences. Previously in the Leonberger breed, a variant highly associated with a juvenile-onset PN was identified in the canine orthologue of a CMT-associated gene. As this deletion in ARHGEF10 (termed LPN1) does not explain all cases, PN in this breed may encompass variants in several genes with similar clinical and histopathological features.ResultsA genome-wide comparison of 173 k SNP genotypes of 176 cases, excluding dogs homozygous for the ARHGEF10 variant, and 138 controls, was carried out to detect further PN-associated variants. A single suggestive significant association signal on CFA15 was found. The genome of a PN-affected Leonberger homozygous for the associated haplotype was sequenced and variants in the 7.7 Mb sized critical interval were identified. These variants were filtered against a database of variants observed in 202 genomes of various dog breeds and 3 wolves, and 6 private variants in protein-coding genes, all in complete linkage disequilibrium, plus 92 non-coding variants were revealed. Five of the coding variants were predicted to have low or moderate effect on the encoded protein, whereas a 2 bp deletion in GJA9 results in a frameshift of high impact. GJA9 encodes connexin 59, a connexin gap junction family protein, and belongs to a group of CMT-associated genes that have emerged as important components of peripheral myelinated nerve fibers. The association between the GJA9 variant and PN was confirmed in an independent cohort of 296 cases and 312 controls. Population studies showed a dominant mode of inheritance, an average age of onset of approximately 6 years, and incomplete penetrance.ConclusionsThis GJA9 variant represents a highly probable candidate variant for another form of PN in Leonberger dogs, which we have designated LPN2, and a new candidate gene for CMT disease. To date, approximately every third PN-diagnosed Leonberger dog can be explained by the ARHGEF10 or GJA9 variants, and we assume that additional genetic heterogeneity in this condition exists in the breed.

Whole-Exome Sequencing Identifies Novel Variants for Tooth Agenesis

Tooth agenesis is a common craniofacial abnormality in humans and represents failure to develop 1 or more permanent teeth. Tooth agenesis is complex, and variations in about a dozen genes have been reported as contributing to the etiology. Here, we combined whole-exome sequencing, array-based genotyping, and linkage analysis to identify putative pathogenic variants in candidate disease genes for tooth agenesis in 10 multiplex Turkish families. Novel homozygous and heterozygous variants in LRP6, DKK1, LAMA3, and COL17A1 genes, as well as known variants in WNT10A, were identified as likely pathogenic in isolated tooth agenesis. Novel variants in KREMEN1 were identified as likely pathogenic in 2 families with suspected syndromic tooth agenesis. Variants in more than 1 gene were identified segregating with tooth agenesis in 2 families, suggesting oligogenic inheritance. Structural modeling of missense variants suggests deleterious effects to the encoded proteins. Functional analysis of an indel variant (c.3607+3_6del) in LRP6 suggested that the predicted resulting mRNA is subject to nonsense-mediated decay. Our results support a major role for WNT pathways genes in the etiology of tooth agenesis while revealing new candidate genes. Moreover, oligogenic cosegregation was suggestive for complex inheritance and potentially complex gene product interactions during development, contributing to improved understanding of the genetic etiology of familial tooth agenesis.

Cellular and molecular mechanisms coordinating pancreas development.

The pancreas is an endoderm-derived glandular organ that participates in the regulation of systemic glucose metabolism and food digestion through the function of its endocrine and exocrine compartments, respectively. While intensive research has explored the signaling pathways and transcriptional programs that govern pancreas development, much remains to be discovered regarding the cellular processes that orchestrate pancreas morphogenesis. Here, we discuss the developmental mechanisms and principles that are known to underlie pancreas development, from induction and lineage formation to morphogenesis and organogenesis. Elucidating such principles will help to identify novel candidate disease genes and unravel the pathogenesis of pancreas-related diseases, such as diabetes, pancreatitis and cancer.