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The histone demethylase KDM6A has recently elicited significant attention because its mutations are associated with a rare congenital disorder (Kabuki syndrome) and various types of human cancers. However, distinguishing KDM6A mutations that are deleterious to the enzyme and their underlying mechanisms of dysfunction remain to be fully understood. Here, we report the results from a multi-tiered approach evaluating the impact of 197 KDM6A somatic mutations using information derived from combining conventional genomics data with computational biophysics. This comprehensive approach incorporates multiple scores derived from alterations in protein sequence, structure, and molecular dynamics. Using this method, we classify the KDM6A mutations into 136 damaging variants (69.0%), 32 tolerated variants (16.2%), and 29 variants of uncertain significance (VUS, 14.7%), which is a significant improvement from the previous classification based on the conventional tools (over 40% VUS). We further classify the damaging variants into 15 structural variants (SV), 88 dynamic variants (DV), and 33 structural and dynamic variants (SDV). Comparison with variant scoring methods used in current clinical diagnosis guidelines demonstrates that our approach provides a more comprehensive evaluation of damaging potential and reveals mechanisms of dysfunction. Thus, these results should be taken into consideration for clinical assessment of the damaging potential of each mutation, as they provide hypotheses for experimental validation and critical information for the development of mutant-specific drugs to fight diseases caused by KDM6A dysfunctions.

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Single nucleotide polymorphisms (SNPs) help to understand the phenotypic variations in humans. Genome-wide association studies (GWAS) have identified SNPs located in the tumor protein 63 (TP63) locus to be associated with the genetic susceptibility of cancers. However, there is a lack of in-depth characterization of the structural and functional impacts of the SNPs located at the TP63 gene. The current study was designed for the comprehensive characterization of the coding and non-coding SNPs in the human TP63 gene for their functional and structural significance. The functional and structural effects of the SNPs were investigated using a wide variety of computational tools and approaches, including molecular dynamics (MD) simulation. The deleterious impact of eight nonsynonymous SNPs (nsSNPs) affecting protein stability, structure, and functions was measured by using 13 bioinformatics tools. These eight nsSNPs are in highly conserved positions in protein and were predicted to decrease protein stability and have a deleterious impact on the TP63 protein function. Molecular docking analysis showed five nsSNPs to reduce the binding affinity of TP63 protein to DNA with significant results for three SNPs (R319H, G349E, and C347F). Further, MD simulations revealed the possible disruption of TP63 and DNA binding, hampering the essential protein function. PolymiRTS study found five non-coding SNPs in miRNA binding sites, and the GTEx portal recognized five eQTLs SNPs in single tissue of the lung, heart (LV), and cerebral hemisphere (brain). Characterized nsSNPs and non-coding SNPs will help researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

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The HapMap Project is a major international research effort to construct a resource to facilitate the discovery of relationships between human genetic variations and health and disease. The Ser19Stop single nucleotide polymorphism (SNP) of human phytanoyl-CoA hydroxylase-interacting protein-like (PHYHIPL) gene was detected in HapMap project and registered in the dbSNP. PHYHIPL gene expression is altered in global ischemia and glioblastoma multiforme. However, the function of PHYHIPL is unknown. We generated PHYHIPL Ser19Stop knock-in mice and found that PHYHIPL impacts the morphology of cerebellar Purkinje cells (PCs), the innervation of climbing fibers to PCs, the inhibitory inputs to PCs from molecular layer interneurons, and motor learning ability. Thus, the Ser19Stop SNP of the PHYHIPL gene may be associated with cerebellum-related diseases.

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BACKGROUND: Perturbed posttranslational modification (PTM) landscapes commonly cause pathological phenotypes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors allowing the identification of spontaneous cancer-driving mutations, while Uniprot and dbSNP manage genetic disease-associated variants in the human population. PhosphoSitePlus (PSP) is the most comprehensive resource for studying experimentally observed PTM sites and the only repository with daily updates on functional annotations for many of these sites. To elucidate altered PTM landscapes on a large scale, we integrated disease-associated mutations from TCGA, Uniprot, and dbSNP with PTM sites from PhosphoSitePlus. We characterized each dataset individually, compared somatic with germline mutations, and analyzed PTM sites intersecting directly with disease variants. To assess the impact of mutations in the flanking regions of phosphosites, we developed DeltaScansite, a pipeline that compares Scansite predictions on wild type versus mutated sequences. Disease mutations are also visualized in PhosphoSitePlus. RESULTS: Characterization of somatic variants revealed oncoprotein-like mutation profiles of U2AF1, PGM5, and several other proteins, showing alteration patterns similar to germline mutations. The union of all datasets uncovered previously unknown losses and gains of PTM events in diseases unevenly distributed across different PTM types. Focusing on phosphorylation, our DeltaScansite workflow predicted perturbed signaling networks consistent with calculations by the machine learning method MIMP. CONCLUSIONS: We discovered oncoprotein-like profiles in TCGA and mutations that presumably modify protein function by impacting PTM sites directly or by rewiring upstream regulation. The resulting datasets are enriched with functional annotations from PhosphoSitePlus and present a unique resource for potential biomarkers or disease drivers.

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The STR sequence template file published in 2016 as part of the considerations from the DNA Commission of the International Society for Forensic Genetics on minimal STR sequence nomenclature requirements, has been comprehensively revised and audited using the latest GRCh38 genome assembly. The list of forensic STRs characterized was expanded by including supplementary autosomal, X- and Y-chromosome microsatellites in less common use for routine DNA profiling, but some likely to be adopted in future massively parallel sequencing (MPS) STR panels. We outline several aspects of sequence alignment and annotation that required care and attention to detail when comparing sequences to GRCh37 and GRCh38 assemblies, as well as the necessary matching of MPS-based allele descriptions to previously established repeat region structures described in initial sequencing studies of the less well known forensic STRs. The revised sequence guide is now available in a dynamically updated FTP format from the STRidER website with a date-stamped change log to allow users to explore their own MPS data with the most up-to-date forensic STR sequence information compiled in a simple guide.

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BACKGROUND: In the search for novel causal mutations, public and/or private variant databases are nearly always used to facilitate the search as they result in a massive reduction of putative variants in one step. Practically, variant filtering is often done by either using all variants from the variant database (called the absence-approach, i.e. it is assumed that disease-causing variants do not reside in variant databases) or by using the subset of variants with an allelic frequency > 1% (called the 1%-approach). We investigate the validity of these two approaches in terms of false negatives (the true disease-causing variant does not pass all filters) and false positives (a harmless mutation passes all filters and is erroneously retained in the list of putative disease-causing variants) and compare it with an novel approach which we named the quantile-based approach. This approach applies variable instead of static frequency thresholds and the calculation of these thresholds is based on prior knowledge of disease prevalence, inheritance models, database size and database characteristics. RESULTS: Based on real-life data, we demonstrate that the quantile-based approach outperforms the absence-approach in terms of false negatives. At the same time, this quantile-based approach deals more appropriately with the variable allele frequencies of disease-causing alleles in variant databases relative to the 1%-approach and as such allows a better control of the number of false positives. We also introduce an alternative application for variant database usage and the quantile-based approach. If disease-causing variants in variant databases deviate substantially from theoretical expectancies calculated with the quantile-based approach, their association between genotype and phenotype had to be reconsidered in 12 out of 13 cases. CONCLUSIONS: We developed a novel method and demonstrated that this so-called quantile-based approach is a highly suitable method for variant filtering. In addition, the quantile-based approach can also be used for variant flagging. For user friendliness, lookup tables and easy-to-use R calculators are provided.

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The goal of systems genetics is to understand the impact of genetic variation across all levels of biological organization, from mRNAs, proteins, and metabolites, to higher-order physiological and behavioral traits. This approach requires the accumulation and integration of many types of data, and also requires the use of many types of statistical tools to extract relevant patterns of covariation and causal relations as a function of genetics, environment, stage, and treatment. In this protocol we explain how to use the GeneNetwork web service, a powerful and free online resource for systems genetics. We provide workflows and methods to navigate massive multiscalar data sets and we explain how to use an extensive systems genetics toolkit for analysis and synthesis. Finally, we provide two detailed case studies that take advantage of human and mouse cohorts to evaluate linkage between gene variants, addiction, and aging.

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Genetic variability in humans can explain many differences in disease risk factors. Polymorphism-related studies focus mainly on the single nucleotide polymorphisms (SNPs) of coding regions of the genes. SNPs on DNA binding motifs of the promoter region have been less explored. On a recent study of SNPs in patients with non-Hodgkin lymphomas we faced the problem of SNP selection from promoter regions and developed a practical methodology for clinical studies. The process consists in identifying SNPs in the coding and promoter regions of the antigen-processing system using the 'dbSNP' database. With the 'HapMap' program, we select SNPs with frequencies >20% in Caucasian populations. For coding regions, we sought biologically and clinically relevant SNPs described in the literature. For the promoter regions, we determined their chromosomal location on 'QiagenSABioscience' site database. The nucleotide sequence of ancestral and variant alleles is available in the 'dbSNP'. These sequences were used in 'Promoter TESS' to determine binding differences of transcription factors. Each sequence may have affinity to different TFs. Thus, SNP selection on the promoter regions was based in the differences on TF binding pattern between the old and the new allele. The potential clinical relevance of the new TFs was also evaluated before the final selection. With this approach, we found that almost half of the relevant SNP fall within the promoter region. In conclusion, we were able to develop a methodology of oriented selection of promoter regions of human genes, comparing the TF with affinity to the ancestral allele with the TF to a variant allele. We selected those SNPs that change the TF's affinity to a pattern with functional significance.

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The reference single nucleotide polymorphism (rs) ID in dbSNP (http://www.ncbi.nlm.nih.gov/SNP/) is a key resource identifier, which is widely used in human genetics and genomics studies. However, its application is often complicated by the varied IDs of different versions. Here, we developed a user-friendly tool, SNPTracker, for comprehensively tracking and unifying the rs IDs and genomic coordinates of massive sequence variants at a time. It worked perfectly, and had much higher accuracy and capacity than two alternative utilities in our proof-of-principle examples. SNPTracker will greatly facilitate genetic data exchange and integration in the postgenome-wide association study era.

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For development and evaluation of methods for predicting the effects of variations, benchmark datasets are needed. Some previously developed datasets are available for this purpose, but newer and larger benchmark sets for benign variants have largely been missing. VariSNP datasets are selected from dbSNP. These subsets were filtered against disease-related variants in the ClinVar, UniProtKB/Swiss-Prot, and PhenCode databases, to identify neutral or nonpathogenic cases. All variant descriptions include mapping to reference sequences on chromosomal, genomic, coding DNA, and protein levels. The datasets will be updated with automated scripts on a regular basis and are freely available at http://structure.bmc.lu.se/VariSNP.

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The increase in availability of resequencing data is greatly accelerating SNP discovery and has facilitated the development of SNP genotyping assays. This, in turn, is increasing interest in annotation of individual SNPs. Currently, these data are only available through curation, or comparison to a reference genome. Many species lack a reference genome, but are still important genetic models or are significant species in agricultural production or natural ecosystems. For these species, it is possible to annotate SNPs through comparison with cDNA, or data from well-annotated genes in public repositories. We present SNPMeta, a tool which gathers information about SNPs by comparison with sequences present in GenBank databases. SNPMeta is able to annotate SNPs from contextual sequence in SNP assay designs, and SNPs discovered through genotyping by sequencing (GBS) approaches. However, SNPs discovered through GBS occur throughout the genome, rather than only in gene space, and therefore do not annotate at high rates. SNPMeta can therefore be used to annotate SNPs in nonmodel species or species that lack a reference genome. Annotations generated by SNPMeta are highly concordant with annotations that would be obtained from a reference genome.

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Alagille syndrome is a multisystem disorder with an autosomic dominant pattern of inheritance that affects the liver, heart, eyes, kidneys, skeletal system and presents characteristic facial features. Mutations of the JAG1 gene have been identified in 20-89% of the patients with Alagille syndrome, this gene encodes for a ligand that activates the Notch signaling pathway. In the present study we analyzed 9 Mexican patients with Alagille syndrome who presented the clinical criteria for the classical presentation of the disease. By using the denaturing high performance liquid chromatography mutation analysis we were able to identify different mutations in 7 of the patients (77.77%), importantly, we found 5 novel mutations in JAG1 gene. The allelic frequency distribution of 13 polymorphisms in Mexican population is also reported. The overall results demonstrated an expanding mutational spectrum of JAG1 gene in the Mexican population.

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INTRODUCTION: Single-nucleotide polymorphisms (SNPs) are biomarkers for exploring the genetic basis of many complex human diseases. The prediction of SNPs is promising in modern genetic analysis but it is still a great challenge to identify the functional SNPs in a disease-related gene. The computational approach has overcome this challenge and an increase in the successful rate of genetic association studies and reduced cost of genotyping have been achieved. The objective of this study is to identify deleterious non-synonymous SNPs (nsSNPs) associated with the COL1A1 gene. MATERIAL AND METHODS: The SNPs were retrieved from the Single Nucleotide Polymorphism Database (dbSNP). Using I-Mutant, protein stability change was calculated. The potentially functional nsSNPs and their effect on proteins were predicted by PolyPhen and SIFT respectively. FASTSNP was used for estimation of risk score. RESULTS: Our analysis revealed 247 SNPs as non-synonymous, out of which 5 nsSNPs were found to be least stable by I-Mutant 2.0 with a DDG value of > -1.0. Four nsSNPs, namely rs17853657, rs17857117, rs57377812 and rs1059454, showed a highly deleterious tolerance index score of 0.00 with a change in their physicochemical properties by the SIFT server. Seven nsSNPs, namely rs1059454, rs8179178, rs17853657, rs17857117, rs72656340, rs72656344 and rs72656351, were found to be probably damaging with a PSIC score difference between 2.0 and 3.5 by the PolyPhen server. Three nsSNPs, namely rs1059454, rs17853657 and rs17857117, were found to be highly polymorphic with a risk score of 3-4 with a possible effect of non-conservative change and splicing regulation by FASTSNP. CONCLUSIONS: Three nsSNPs, namely rs1059454, rs17853657 and rs17857117, are potential functional polymorphisms that are likely to have a functional impact on the COL1A1 gene.

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CONTEXT: Osteoporosis is a polygenic, multifactorial disease that is characterized by demineralization of bone, and thus presented with decreasing bone mineral mass. Vitamin D receptor (VDR) gene polymorphisms in the 3'-end region (as determined by the enzymes BsmI and ApaI) have been inconsistently associated with bone mineral mass. Another important VDR start codon polymorphism (as determined by the enzyme FokI) has been found to be related to adult bone mineral density (BMD) in pre-and post-menopausal American women. AIMS: This study aims to investigate the prevalence of the FokI VDR gene polymorphism in Jordanian perimenopausal women and study its relationship with bone mineral density. MATERIALS AND METHODS: DNA was isolated from 90 controls (Mean age = 50.41 ± 1.29 y), and 120 patients with symptomatic vertebral fractures (Mean age = 49.14 ± 3.19 y). Restriction Fragment Length Polymorphism (RFLP) analysis of FokI was performed on DNA samples. STATISTICAL ANALYSIS: Data was analyzed using SPSS v19 and Microsoft Excel 2007. RESULTS: The results showed that in controls, the FF (-0.70 ± 0.51) genotype is associated with high lumbar spine BMD Z-score as compared to Ff (-1.25 ± 0.26) and ff (-1.66 ± 0.47) genotypes (P = 0.0095). In patients, the ff genotype was associated with lower lumbar spine BMD in T-score (-2.31 ± 0.17) and Z-score (-1.56 ± 0.09) genotypes (P = 0.031). No significant association was seen in the femoral neck BMD. CONCLUSION: FokI polymorphism may be associated with low BMD in our studied population; however, further studies including other polymorphisms and large sample number are needed.

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Sepsis is a leading cause of death around the world, and 73-83% of all sepsis cases requiring attention in intensive care units are linked to intra-abdominal infection (IAI) or pneumonia. The activation of innate immunity is central to the manifestation of sepsis, and toll-like receptor (TLR) 4 plays an important role in this activation process. The 299G and 399I alleles of TLR4 have been linked with an increased risk of Gram-negative bacteria (GNB) infections and septic shock in some populations. This case-control study evaluated the prevalence of D299G/T399I polymorphisms in Mexican patients with IAI and/or pneumonia and in healthy controls. Genotyping revealed that 1 in 44 patients (2.3%; CI 95%: 0.05-12.0%) and 4 in 126 controls (3.2%; CI 95%: 0.9-7.9%) were heterozygous for both the D299G and T399l polymorphisms (OR: 0.71, CI 95%: 0.01-7.44, p = NS), confirming the co-segregation of these alleles in this population. Furthermore, the patients with a GNB infection and severe sepsis were not carriers of the risk alleles. In summary, this report shows that the frequency of the D299G and T399I polymorphisms in Mexican-Mestizos is lower than anticipated in comparison with other ethnic groups, emphasizing the variable distribution of TLR4 polymorphisms among different populations. Consequently, this study was not able to detect associations between TLR4 polymorphisms and sepsis in this population.

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CD36 is a fatty acid translocase in striated muscle cells and cardiomyocytes. Some study suggested that alterations in CD36 gene may be associated with coronary artery disease (CAD) risk. The aim of the current study was to compare the frequency of CD36 variants in region encoding lipid-binding domain in Caucasian patients with early-onset CAD, no-CAD adult controls and neonates. The study group comprised 100 patients with early onset CAD. The genetic control groups were 306 infants and 40 no-CAD adults aged over 70years. Exons 4, 5 and 6 including fragments of flanking introns were studied using the denaturing high-performance liquid chromatography technique and direct sequencing. Changes detected in analyzed fragment of CD36: IVS3-6 T/C (rs3173798), IVS4-10 G/A (rs3211892), C311T (Thr104Ile, not described so far) in exon 5, G550A (Asp184Asn, rs138897347), C572T (Pro191Leu, rs143150225), G573A (Pro191Pro, rs5956) and A591T (Thr197Thr, rs141680676) in exon 6. No significant differences in the CD36 genotype, allele and haplotype frequencies were found between the three groups. Only borderline differences (p=0.066) were found between early onset CAD patients and newborns in the frequencies of 591T allele (2.00% vs 0.50%) and CGCGCGT haplotype (2.00% vs 0.50%) with both IVS3-6C and 591T variant alleles. In conclusion, CD36 variants: rs3173798, rs3211892, rs138897347, rs5956, rs143150225 rs141680676 and C311T do not seem to be involved in the risk of early-onset CAD in Caucasian population.

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Hearing impairment is characterized by great genetic heterogeneity. We report the identification, by whole exome sequencing, of two different nonsense mutations (c.1558C>T; p.Gln520 and c.2773C>T; p.Arg925) in the otogelin-like gene (OTOGL), in a child affected by mild to moderate isolated deafness. Parental genotypes allowed us to conclude that these mutations are present in the compound heterozygous state in the patient. In addition, our clinical data establish that the tectorial membrane and/or the outer hair cells are defective in this form of deafness.

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Response to environmental chemicals can vary widely among individuals and between population groups. In human health risk assessment, data on susceptibility can be utilized by deriving risk levels based on a study of a susceptible population and/or an uncertainty factor may be applied to account for the lack of information about susceptibility. Defining genetic susceptibility in response to environmental chemicals across human populations is an area of interest in the NAS' new paradigm of toxicity pathway-based risk assessment. Data from high-throughput/high content (HT/HC), including -omics (e.g., genomics, transcriptomics, proteomics, metabolomics) technologies, have been integral to the identification and characterization of drug target and disease loci, and have been successfully utilized to inform the mechanism of action for numerous environmental chemicals. Large-scale population genotyping studies may help to characterize levels of variability across human populations at identified target loci implicated in response to environmental chemicals. By combining mechanistic data for a given environmental chemical with next generation sequencing data that provides human population variation information, one can begin to characterize differential susceptibility due to genetic variability to environmental chemicals within and across genetically heterogeneous human populations. The integration of such data sources will be informative to human health risk assessment.

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