Characterization of single-nucleotide polymorphisms in coding regions of human genes

A functional SNP in miR-625-5p binding site of AKT2 3'UTR is associated with noise-induced hearing loss susceptibility in the Chinese population

The purposes of the current study were to investigate the association of a few of single nucleotide polymorphisms (SNPs) within the AKT2 gene and noise-induced hearing loss (NIHL) susceptibility and explore the potential mechanism underlying NIHL. Three SNPs (rs2304186, rs41275750, and rs76524493) were genotyped in a Chinese population which consists of 690 NIHL patients and 650 normal hearing controls. Bioinformatic analysis was conducted to predict the potential miRNA-binding site of SNPs. Plasmid construction, cell transfection, and dual-luciferase reporter assay were performed to investigate the potential molecular mechanism of SNPs involving in NIHL. The results revealed that rs2304186 GT genotype (OR = 1.41; 95% CI = 1.09-1.83) and TT genotype (OR = 1.51; 95% CI = 1.08-2.10) imparted increased risk of NIHL, and the increased risk could also be found in a dominant model (OR = 1.44; 95% CI = 1.12-1.84). The stratification analysis showed that rs2304186 GT/TT conferred a higher risk for NIHL, especially in subgroups of male, age (35-45 and > 45 years), noise exposure time (> 16 years), and noise exposure level (≤ 85 and ≥ 92 dB), when GG genotype as a reference. Furthermore, the haplotype TCCTACT (rs2304186-rs41275750-rs76524493) was found to be significantly associated with a high risk of NIHL (OR = 1.19; 95% CI = 1.02-1.40). Functional experiments showed that rs2304186 G allele combined with hsa-miR-625-5p mimics could significantly decrease the luciferase activity compared with T allele, indicating that rs2304186 altered the binding affinity of hsa-miR-625-5p to SNP rs2304186 mutation region, thus directly targeting AKT2 gene. In conclusion, our study provides evidence for the first time that SNP rs2304186 of AKT2 3'UTR might affect NIHL susceptibility by altering the binding affinity of has-miR-625-5p to mutation region in an allele-specific manner and it may act as a potential biomarker of NIHL susceptibility.

The effect of zinc supplementation on brain derived neurotrophic factor: A meta-analysis

BACKGROUND

Zinc in one of the most abundant trace minerals in human body which is involved in numerous biological pathways and has variety of roles in the nervous system. It has been assumed that zinc exerts its role in nervous system through increasing brain derived neurotrophic factor (BDNF) concentrations.

OBJECTIVES

Present meta-analysis was aimed to review the effect of zinc supplementation on serum concentrations of BDNF.

METHODS AND MATERIALS

Four electronic databases (Pubmed, Scopus, Web of Science, Embase) were searched for identifying studies that examined BDNF levels prior and after zinc supplementation up to May 2020. According to the Cochrane guideline, a meta-analysis was performed to pool the effect size estimate (Hedges' test) of serum BDNF across studies. Risk of publication bias was assessed using a funnel plot and Egger's test.

RESULTS

Five studies were eligible and 238 participants were included. These studies enrolled subjects with premenstrual syndrome, diabetic retinopathy, major depression disorder, overweight/obese and obese with mild to moderate depressive disorders. Zinc supplementation failed to increase blood BDNF concentrations with effect size of 0.30 (95 % CI: -0.08, 0.67, P = 0.119). Funnel plot did not suggest publication bias.

CONCLUSION

Zinc supplementation may not significantly increase BDNF levels. However, the small number of included articles and significant heterogeneity between them can increase the risk of a false negative result; therefore, the results should be interpreted with caution.

Polymorphism in miRNA target sites of CEP-63 and CEP-152 ring complex influences expression of CEP genes and favors tumorigenesis in glioma

Purpose: The present study was designed to screen the genetic polymorphisms and expression profiling of CEP-152 and CEP-63 genes in brain tumor patients. Methods: The amplification refractory mutation system PCR technique (ARMS-PCR) was used for mutation analysis using 300 blood samples of brain tumor patients and 300 overtly healthy controls. For expression analysis, 150 brain tumor tissue samples along with adjacent uninvolved/normal tissues (controls) were collected. Results: A significantly higher frequency of the mutant genotype of the CEP-152 single nucleotide polymorphism (rs2169757) and CEP-63 single nucleotide polymorphisms (rs9809619 and rs13060247) was observed in patients versus overtly healthy controls. The authors' results showed highly significant deregulation of CEP-152 (p < 0.0001) and CEP-63 (p < 0.0001) in glioma/meningioma tumor tissues versus adjacent normal tissue. Conclusion: The present study showed that variations in CEP-152 and CEP-63 genes were associated with an increased risk of brain tumor.

In silico analysis of deleterious SNPs of human MTUS1 gene and their impacts on subsequent protein structure and function

The mitochondrial tumor suppressor 1 (MTUS1) gene acts as a crucial tumor suppressor by inhibiting growth and proliferation of eukaryotic cells including tumor cell lines. Down regulation of MTUS1 gene has been implicated in a wide range of cancers as well as various human diseases. Alteration through nsSNPs can potentially damage the structure and/or function of the protein. As characterization of functional SNPs in such disease linked genes is a major challenge, it is feasible to analyze putative functional SNPs prior to performing larger population studies. Hence, in this in silico study we differentiated the potentially harmful nsSNPs of the MTUS1 gene from the neutral ones by using various sequence and structure based bioinformatic tools. In a total of 215 nsSNPs, 9 were found to be most likely to exert deleterious effect using 7 prediction tools. From which, 5nsSNPs (S1259L, E960K, P503T, L1084V and L1143Q) were selected as potentially damaging due to their presence in the highly conserved region and ability to decrease protein stability. In fact, 2 nsSNPs (S1259L and E960K) among these 5 were found to be individually associated with two distinctive cancers named Stomach adenocarcinoma and Uterine corpus endometrial carcinoma. As this is the first comprehensive study analyzing the functional nsSNPs of MTUS1, the results of the current study would certainly be helpful in future prospects concerning large population-based studies as well as drug discovery, especially developing individualized medicine.

Mechanisms of TP53 Pathway Inactivation in Embryonic and Somatic Cells-Relevance for Understanding (Germ Cell) Tumorigenesis

The P53 pathway is the most important cellular pathway to maintain genomic and cellular integrity, both in embryonic and non-embryonic cells. Stress signals induce its activation, initiating autophagy or cell cycle arrest to enable DNA repair. The persistence of these signals causes either senescence or apoptosis. Over 50% of all solid tumors harbor mutations in TP53 that inactivate the pathway. The remaining cancers are suggested to harbor mutations in genes that regulate the P53 pathway such as its inhibitors Mouse Double Minute 2 and 4 (MDM2 and MDM4, respectively). Many reviews have already been dedicated to P53, MDM2, and MDM4, while this review additionally focuses on the other factors that can deregulate P53 signaling. We discuss that P14^ARF (ARF) functions as a negative regulator of MDM2, explaining the frequent loss of ARF detected in cancers. The long non-coding RNA Antisense Non-coding RNA in the INK4 Locus (ANRIL) is encoded on the same locus as ARF, inhibiting ARF expression, thus contributing to the process of tumorigenesis. Mutations in tripartite motif (TRIM) proteins deregulate P53 signaling through their ubiquitin ligase activity. Several microRNAs (miRNAs) inactivate the P53 pathway through inhibition of translation. CCCTC-binding factor (CTCF) maintains an open chromatin structure at the TP53 locus, explaining its inactivation of CTCF during tumorigenesis. P21, a downstream effector of P53, has been found to be deregulated in different tumor types. This review provides a comprehensive overview of these factors that are known to deregulate the P53 pathway in both somatic and embryonic cells, as well as their malignant counterparts (i.e., somatic and germ cell tumors). It provides insights into which aspects still need to be unraveled to grasp their contribution to tumorigenesis, putatively leading to novel targets for effective cancer therapies.

Selection of a Noninvasive Source of Human DNA Envisaging Genotyping Assays in Epidemiological Studies: Urine or Saliva?

Genetic epidemiology requires an appropriate approach to measure genetic variation within the population. The aim of this study was to evaluate the characteristics and genotyping results of DNA extracted from 2 human DNA sources, selected for their rapid and noninvasive sampling, and the use of simple and standardized protocols that are essential for large-scale epidemiologic studies. Saliva and urine samples were collected at the same day from 20 subjects aged 9-10 yr. Genomic DNA was extracted using commercial kits. Quantitative and qualitative evaluation was done by assessing the yield, the purity, and integrity of the extracted DNA. As a proof-of-concept, genotyping was performed targeting CC16 A38G and uteroglobin-related protein 1 (UGRP1)-112G/A. Saliva was found to provide the highest yield and concentration of total DNA extracted. Salivary DNA showed higher purity and a significantly less degraded state compared to urinary DNA. Consequently, the salivary DNA gave better genotyping results than urinary DNA. Therefore, if the choice exists, saliva is the preferred noninvasive matrix for genotyping purposes in large-scale genetic epidemiologic studies. Only in particular cases using urine could nevertheless be considered useful, although specific limitations need to be taken into account.

Genetic variation in the Mauritian cynomolgus macaque population reflects variation in the human population

The cynomolgus macaque is an important species for preclinical research, however the extent of genetic variation in this population and its similarity to the human population is not well understood. Exome sequencing was conducted for 101 cynomolgus macaques to characterize genetic variation. The variant distribution frequency was 7.81 variants per kilobase across the sequenced regions, with a total of 2,770,009 single nucleotide variants identified from 2,996,041 loci. A large portion (85.6%) had minor allele frequencies greater than 5%. Enriched pathways for genes with high genetic diversity (≥10 variants per kilobase) were those involving signaling peptides and immune response. Compared to human, the variant distribution frequency and nucleotide diversity in the macaque exome was approximately 4 times greater; however the ratio of non-synonymous to synonymous variants was similar (0.735 and 0.831, respectively). Understanding genetic variability in cynomolgus macaques will enable better interpretation and human translation of phenotypic variability in this species.

Influence of Disease-Causing Mutations on Protein Structural Networks

The interactions between residues in a protein tertiary structure can be studied effectively using the approach of protein structure network (PSN). A PSN is a node-edge representation of the structure with nodes representing residues and interactions between residues represented by edges. In this study, we have employed weighted PSNs to understand the influence of disease-causing mutations on proteins of known 3D structures. We have used manually curated information on disease mutations from UniProtKB/Swiss-Prot and their corresponding protein structures of wildtype and disease variant from the protein data bank. The PSNs of the wildtype and disease-causing mutant are compared to analyse variation of global and local dissimilarity in the overall network and at specific sites. We study how a mutation at a given site can affect the structural network at a distant site which may be involved in the function of the protein. We have discussed specific examples of the disease cases where the protein structure undergoes limited structural divergence in their backbone but have large dissimilarity in their all atom networks and vice versa, wherein large conformational alterations are observed while retaining overall network. We analyse the effect of variation of network parameters that characterize alteration of function or stability.

Regarding the F-word: The effects of data filtering on inferred genotype-environment associations

Genotype-environment association (GEA) methods have become part of the standard landscape genomics toolkit, yet, we know little about how to best filter genotype-by-sequencing data to provide robust inferences for environmental adaptation. In many cases, default filtering thresholds for minor allele frequency and missing data are applied regardless of sample size, having unknown impacts on the results, negatively affecting management strategies. Here, we investigate the effects of filtering on GEA results and the potential implications for assessment of adaptation to environment. We use empirical and simulated data sets derived from two widespread tree species to assess the effects of filtering on GEA outputs. Critically, we find that the level of filtering of missing data and minor allele frequency affect the identification of true positives. Even slight adjustments to these thresholds can change the rate of true positive detection. Using conservative thresholds for missing data and minor allele frequency substantially reduces the size of the data set, lessening the power to detect adaptive variants (i.e., simulated true positives) with strong and weak strengths of selection. Regardless, strength of selection was a good predictor for GEA detection, but even some SNPs under strong selection went undetected. False positive rates varied depending on the species and GEA method, and filtering significantly impacted the predictions of adaptive capacity in downstream analyses. We make several recommendations regarding filtering for GEA methods. Ultimately, there is no filtering panacea, but some choices are better than others, depending on the study system, availability of genomic resources, and desired objectives.

Transcriptome analysis and association mapping reveal the genetic regulatory network response to cadmium stress in Populus tomentosa

Long non-coding RNAs (lncRNAs) play essential roles in plant abiotic stress responses, but the response of lncRNA-mediated genetic networks to cadmium (Cd) treatment remain elusive in trees, the promising candidates for phytoremediation of Cd contamination. We identified 172 Cd-responsive lncRNAs and 295 differentially expressed target genes in the leaves of Cd-treated Populus tomentosa. Functional annotation revealed that these lncRNAs were involved in various processes, including photosynthesis, hormone regulation, and phenylalanine metabolism. Association studies identified 78 significant associations, representing 14 Cd-responsive lncRNAs and 28 target genes for photosynthetic and leaf physiological traits. Epistasis uncovered 83 pairwise interactions among these traits, revealing Cd-responsive lncRNA-mediated genetic networks for photosynthesis and leaf physiology in P. tomentosa. We focused on the roles of two Cd-responsive lncRNA-gene pairs, MSTRG.22608.1-PtoMYB73 and MSTRG.5634.1-PtoMYB27, in Cd tolerance of Populus, and detected insertions/deletions within lncRNAs as polymorphisms driving target gene expression. Genotype analysis of lncRNAs and heterologous overexpression of PtoMYB73 and PtoMYB27 in Arabidopsis indicated their effects on enhancing Cd tolerance, photosynthetic rate, and leaf growth, and the potential interaction mechanisms of PtoMYB73 with abiotic stresses. Our study identifies the genetic basis for the response of Populus to Cd treatment, facilitating genetic improvement of Cd tolerance in trees.

HapSolo: an optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding

BACKGROUND

Despite marked recent improvements in long-read sequencing technology, the assembly of diploid genomes remains a difficult task. A major obstacle is distinguishing between alternative contigs that represent highly heterozygous regions. If primary and secondary contigs are not properly identified, the primary assembly will overrepresent both the size and complexity of the genome, which complicates downstream analysis such as scaffolding.

RESULTS

Here we illustrate a new method, which we call HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies. We illustrate the performance of HapSolo on genome data from three species: the Chardonnay grape (Vitis vinifera), with a genome of 490 Mb, a mosquito (Anopheles funestus; 200 Mb) and the Thorny Skate (Amblyraja radiata; 2650 Mb).

CONCLUSIONS

HapSolo rapidly identified candidate assemblies that yield improvements in assembly metrics, including decreased genome size and improved N50 scores. Contig N50 scores improved by 35%, 9% and 9% for Chardonnay, mosquito and the thorny skate, respectively, relative to unreduced primary assemblies. The benefits of HapSolo were amplified by down-stream analyses, which we illustrated by scaffolding with Hi-C data. We found, for example, that prior to the application of HapSolo, only 52% of the Chardonnay genome was captured in the largest 19 scaffolds, corresponding to the number of chromosomes. After the application of HapSolo, this value increased to ~ 84%. The improvements for the mosquito's largest three scaffolds, representing the number of chromosomes, were from 61 to 86%, and the improvement was even more pronounced for thorny skate. We compared the scaffolding results to assemblies that were based on PurgeDups for identifying secondary contigs, with generally superior results for HapSolo.

The signaling pathway and polymorphisms of Mrgprs

Mas-related G protein-coupled receptors (Mrgprs) are a family of receptors implicated in a diverse array of human diseases. Since their discovery in 2001, great progress has been made in determining their relation to human disease. Vital for Mrgprs therapeutic efforts across all disease disciplines is a thorough understanding of Mrgprs signal transduction pathways and polymorphisms, as these offer insights into new drug candidates, existing discrepancies in drug response, and differences in disease susceptibility. In this review, we discuss the current state of knowledge regarding Mrgprs signaling pathways and polymorphisms.

The evolutionary genetics of personality

Genetic influences on personality differences are ubiquitous, but their nature is not well understood. A theoretical framework might help, and can be provided by evolutionary genetics. We assess three evolutionary genetic mechanisms that could explain genetic variance in personality differences: selective neutrality, mutation‐selection balance, and balancing selection. Based on evolutionary genetic theory and empirical results from behaviour genetics and personality psychology, we conclude that selective neutrality is largely irrelevant, that mutation‐selection balance seems best at explaining genetic variance in intelligence, and that balancing selection by environmental heterogeneity seems best at explaining genetic variance in personality traits. We propose a general model of heritable personality differences that conceptualises intelligence as fitness components and personality traits as individual reaction norms of genotypes across environments, with different fitness consequences in different environmental niches. We also discuss the place of mental health in the model. This evolutionary genetic framework highlights the role of gene‐environment interactions in the study of personality, yields new insight into the person‐situation‐debate and the structure of personality, and has practical implications for both quantitative and molecular genetic studies of personality. Copyright © 2007 John Wiley & Sons, Ltd.

Variation under domestication in animal models: the case of the Mexican axolotl

BACKGROUND

Species adaptation to laboratory conditions is a special case of domestication that has modified model organisms phenotypically and genetically. The characterisation of these changes is crucial to understand how this variation can affect the outcome of biological experiments. Yet despite the wide use of laboratory animals in biological research, knowledge of the genetic diversity within and between different strains and populations of some animal models is still scarce. This is particularly the case of the Mexican axolotl, which has been bred in captivity since 1864.

RESULTS

Using gene expression data from nine different projects, nucleotide sequence variants were characterised, and distinctive genetic background of the experimental specimens was uncovered. This study provides a catalogue of thousands of nucleotide variants along predicted protein-coding genes, while identifying genome-wide differences between pigment phenotypes in laboratory populations.

CONCLUSIONS

Awareness of the genetic variation could guide a better experimental design while helping to develop molecular tools for monitoring genetic diversity and studying gene functions in laboratory axolotls. Overall, this study highlights the cross-taxa utility that transcriptomic data might have to assess the genetic variation of the experimental specimens, which might help to shorten the journey towards reproducible research.

Lighting up single-nucleotide variation in situ in single cells and tissues

The ability to 'see' genetic information directly in single cells can provide invaluable insights into complex biological systems. In this review, we discuss recent advances of in situ imaging technologies for visualizing the subtlest sequence alteration, single-nucleotide variation (SNV), at single-cell level. The mechanism of recently developed methods for SNV discrimination are summarized in detail. With recent developments, single-cell SNV imaging methods have opened a new door for studying the heterogenous and stochastic genetic information in individual cells. Furthermore, SNV imaging can be used on morphologically preserved tissue, which can provide information on histological context for gene expression profiling in basic research and genetic diagnosis. Moreover, the ability to visualize SNVs in situ can be further developed into in situ sequencing technology. We expect this review to inspire more research work into in situ SNV imaging technologies for investigating cellular phenotypes and gene regulation at single-nucleotide resolution, and developing new clinical and biomedical applications.

Generalized Structured Component Analysis in candidate gene association studies: applications and limitations

Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.

Identification of SHANK2 Pathogenic Variants in a Chinese Uygur Population with Schizophrenia

Genomic studies on schizophrenia (SCZ) have revealed several candidate genes involved in excitatory synapse function and plasticity associated with its etiology. SHANK2 is a postsynaptic scaffolding protein, which anchors a protein complex connecting NMDAR, AMPAR, and mGluR receptors at excitatory neuronal synapses. Mutations in the SHANK2 gene have been reported to be associated with human autism spectrum disorders (ASDs) and SCZ. To identify variants in the SHANK2 gene and determine the association of SHANK2 with SCZ in the Chinese Uygur population, we conducted targeted sequencing of whole exon regions and exon-intron boundaries of SHANK2 in 1574 SCZ patients and 1481 healthy controls. A total of 149 variants were identified, including six common variants and 143 rare variants. For common variants, rs62622853 and rs3924047 showed allelic significance with SCZ before correction, but the association was eliminated after Bonferroni correction. Seven rare nonsynonymous variants, p.Arg739Trp, p.Pro807Leu, p.Ile854Phe, p.Thr1322Ser, p.Leu1434Arg, p.Val1486Ile, and p.Thr1674Met, occurred only in the patients but not in any of the healthy controls. In silico analysis predicted that p.Arg739Trp, p.Leu1434Arg, and p.Val1486Ile variants are likely to be damaging. The present study suggests that individuals with two novel rare nonsynonymous variants (p.Arg739Trp, p.Leu1434Arg) and p.Val1486Ile variants of SHANK2 might increase the susceptibility to developing SCZ disorder.

Prospective avenues for human population genomics and disease mapping in southern Africa

Population substructure within human populations is globally evident and a well-known confounding factor in many genetic studies. In contrast, admixture mapping exploits population stratification to detect genotype-phenotype correlations in admixed populations. Southern Africa has untapped potential for disease mapping of ancestry-specific disease risk alleles due to the distinct genetic diversity in its populations compared to other populations worldwide. This diversity contributes to a number of phenotypes, including ancestry-specific disease risk and response to pathogens. Although the 1000 Genomes Project significantly improved our understanding of genetic variation globally, southern African populations are still severely underrepresented in biomedical and human genetic studies due to insufficient large-scale publicly available data. In addition to a lack of genetic data in public repositories, existing software, algorithms and resources used for imputation and phasing of genotypic data (amongst others) are largely ineffective for populations with a complex genetic architecture such as that seen in southern Africa. This review article, therefore, aims to summarise the current limitations of conducting genetic studies on populations with a complex genetic architecture to identify potential areas for further research and development.

New insights on brain-derived neurotrophic factor epigenetics: from depression to memory extinction

Advances in characterizing molecular profiles provide valuable insights and opportunities for deciphering the neuropathology of depression. Although abnormal brain-derived neurotrophic factor (BDNF) expression in depression has gained much support from preclinical and clinical research, how it mediates behavioral alterations in the depressed state remains largely obscure. Environmental factors contribute significantly to the onset of depression and produce robust epigenetic changes. Epigenetic regulation of BDNF, as one of the most characterized gene loci in epigenetics, has recently emerged as a target in research on memory and psychiatric disorders. Specifically, epigenetic alterations of BDNF exons are heavily involved in mediating memory functions and antidepressant effects. In this review, we discuss key research on stress-induced depression from both preclinical and clinical studies, which revealed that differential epigenetic regulation of specific BDNF exons is associated with depression pathophysiology. Considering that BDNF has a central role in depression, we argue that memory extinction, an adaptive response to fear exposure, is dependent on BDNF modulation and holds promise as a prospective target for alleviating or treating depression and anxiety disorders.

Detecting DNA and RNA and Differentiating Single-Nucleotide Variations via Field-Effect Transistors

We detect short oligonucleotides and distinguish between sequences that differ by a single base, using label-free, electronic field-effect transistors (FETs). Our sensing platform utilizes ultrathin-film indium oxide FETs chemically functionalized with single-stranded DNA (ssDNA). The ssDNA-functionalized semiconducting channels in FETs detect fully complementary DNA sequences and differentiate these sequences from those having different types and locations of single base-pair mismatches. Changes in charge associated with surface-bound ssDNA vs double-stranded DNA (dsDNA) alter FET channel conductance to enable detection due to differences in DNA duplex stability. We illustrate the capability of ssDNA-FETs to detect complementary RNA sequences and to distinguish from RNA sequences with single nucleotide variations. The development and implementation of electronic biosensors that rapidly and sensitively detect and differentiate oligonucleotides present new opportunities in the fields of disease diagnostics and precision medicine.

Identification of SNPs and InDels associated with berry size in table grapes integrating genetic and transcriptomic approaches

BACKGROUND

Berry size is considered as one of the main selection criteria in table grapes breeding programs, due to the consumer preferences. However, berry size is a complex quantitive trait under polygenic control, and its genetic determination of berry weight is not yet fully understood. The aim of this work was to perform marker discovery using a transcriptomic approach, in order to identify and characterize SNP and InDel markers associated with berry size in table grapes. We used an integrative analysis based on RNA-Seq, SNP/InDel search and validation on table grape segregants and varieties with different genetic backgrounds.

RESULTS

Thirty SNPs and eight InDels were identified using a transcriptomic approach (RNA-Seq). These markers were selected from SNP/InDel found among segregants from a Ruby x Sultanina population with contrasting phenotypes for berry size. The set of 38 SNP and InDel markers was distributed in eight chromosomes. Genotype-phenotype association analyses were performed using a set of 13 RxS segregants and 41 table grapes varieties with different genetic backgrounds during three seasons. The results showed several degrees of association of these markers with berry size (10.2 to 30.7%) as other berry-related traits such as length and width. The co-localization of SNP and /or InDel markers and previously reported QTLs and candidate genes associated with berry size were analysed.

CONCLUSIONS

We identified a set of informative and transferable SNP and InDel markers associated with berry size. Our results suggest the suitability of SNPs and InDels as candidate markers for berry weight in seedless table grape breeding. The identification of genomic regions associated with berry weight in chromosomes 8, 15 and 17 was achieved with supporting evidence derived from a transcriptome experiment focused on SNP/InDel search, as well as from a QTL-linkage mapping approach. New regions possibly associated with berry weight in chromosomes 3, 6, 9 and 14 were identified.

Transforming growth factor beta 1 (TGFβ1) polymorphisms and unexplained infertility: A genetic association study

The prevalence of infertility is increasing and worrisome. About 10 to 30% of infertility is classified as idiopathic or unexplained infertility (UI).TGF-β is multifunctional and immunoregulatry cytokine which regulates both implantation and adhesion of trophoblasts to the extracellular matrix during pregnancy. The aim of the current study was to investigate the association between two polymorphisms rs1800470 (C29T) and rs1800471 (G74C) of the TGF-β1 gene in Iranian patients with unexplained infertility. A total of 250 UI patients and 484 healthy individuals with no history of infertility were included in the study. The amplification and sequencing of target DNA fragments were done using PCR and automated sequencing methods, respectively. The effects of these polymorphisms on both TGF-β1 structure and function of mRNA and protein were analyzed using new in-silico tools. The frequency distribution of the alleles, genotypes, and haplotypes of both rs1800470 and rs1800471 polymorphisms had a statistically significant difference between subjects and controls. CC genotype of TGF-β1 rs1800470 (29C→T) increase the risk of UI in male UI patients. Moreover, C alleles of TGF-β1 rs1800471 was associated with increased risk of UI in female UI patients. Couples, subgroup analysis revealed a significant association between TGF-β1 polymorphisms (rs1800470, rs1800471) and the risk of UI in male, female, and all UI patients. The frequency of TG and CG haplotypes were statistically different in both UI and healthy subjects group (P < 0.05). RS1800471 polymorphisms changed the secondary structure of TGF-β1 mRNA and resulted in the removal of one mRNA arm and creation of two new arms. Taken together, the results of the current study suggest that TGF-β1 functional polymorphisms may play an important role in the susceptibility to UI in Iranian population. According to in silico analysis, polymorphisms in TGF-β1 can reduce mRNA half-life and, therefore, reduced TGF-β1 expression. .

Assessments of fine-scale spatial patterns of SNPs in an old-growth beech forest

The spatial patterns of non-neutral genetic variations at fine spatial scales and their possible associations with microenvironments have not been well-documented for tree populations. Based on 25-32 SNP markers, we examine whether non-neutral SNPs and their associations with microenvironments can be detected in FcMYB1603, a gene homologous to that encoding a protein induced by drought stress in Arabidopsis thaliana for the 166 adult trees in a 1-ha plot in a mature population of Fagus crenata. In the 83 individuals of a younger cohort of below canopy trees, the nonsynonymous SNP at locus FcMYB1603_684 exhibited a spatial signature representing a departure from the expected spatial patterns of neutral genetic variation. Evaluations of non-neutrality for this locus were robust against the potential risks of false positives due to the low number of SNP loci, a low criterion set for minor allele frequency, and any edge effect on the trees' spatial structure. An older cohort exhibited no signal of the existence of non-neutral genetic variation, suggesting that temporal fluctuation in the microenvironmental conditions on the forest floor may have exposed different cohorts to different magnitudes of selection pressure. Although genotypes of the locus showed a spatial association with a microenvironmental variable potentially related to soil moisture, the present study was subject to a limitation due to the generally low polymorphism of nonsynonymous loci within the single plot, which suggests that it will be important to replicate the study design in order to carry out research on fine-scale non-neutral genetic variations.

Signatures of positive selection underlying beef production traits in Korean cattle breeds

The difference in the breeding programs and population history may have diversely shaped the genomes of Korean native cattle breeds. In the absence of phenotypic data, comparisons of breeds that have been subjected to different selective pressures can aid to identify genomic regions and genes controlling qualitative and complex traits. In this study to decipher genetic variation and identify evidence of divergent selection, 3 Korean cattle breeds were genotyped using the recently developed high-density GeneSeek Genomic Profiler F250 (GGP-F250) array. The three Korean cattle breeds clustered according to their coat color phenotypes and breeding programs. The Heugu breed reliably showed smaller effective population size at all generations considered. Across the autosomal chromosomes, 113 and 83 annotated genes were identified from Hanwoo-Chikso and Hanwoo-Heugu comparisons, respectively of which 16 genes were shared between the two pairwise comparisons. The most important signals of selection were detected on bovine chromosomes 14 (24.39–25.13 Mb) and 18 (13.34–15.07 Mb), containing genes related to body size, and coat color (XKR4, LYN, PLAG1, SDR16C5, TMEM68, CDH15, MC1R, and GALNS). Some of the candidate genes are also associated with meat quality traits (ACSF3, EIF2B1, BANP, APCDD1, and GALM) and harbor quantitative trait locus (QTL) for beef production traits. Further functional analysis revealed that the candidate genes (DBI, ACSF3, HINT2, GBA2, AGPAT5, SCAP, ELP6, APOB, and RBL1) were involved in gene ontology (GO) terms relevant to meat quality including fatty acid oxidation, biosynthesis, and lipid storage. Candidate genes previously known to affect beef production and quality traits could be used in the beef cattle selection strategies.

In silico analysis on the functional and structural impact of Rad50 mutations involved in DNA strand break repair

BACKGROUND

DNA double strand break repair is important to preserve the fidelity of our genetic makeup after DNA damage. Rad50 is one of the components in MRN complex important for DNA repair mechanism. Rad50 mutations can lead to microcephaly, mental retardation and growth retardation in human. However, Rad50 mutations in human and other organisms have never been gathered and heuristically compared for their deleterious effects. It is important to assess the conserved region in Rad50 and its homolog to identify vital mutations that can affect functions of the protein.

METHOD

In this study, Rad50 mutations were retrieved from SNPeffect 4.0 database and literature. Each of the mutations was analyzed using various bioinformatic analyses such as PredictSNP, MutPred, SNPeffect 4.0, I-Mutant and MuPro to identify its impact on molecular mechanism, biological function and protein stability, respectively.

RESULTS

We identified 103 mostly occurred mutations in the Rad50 protein domains and motifs, which only 42 mutations were classified as most deleterious. These mutations are mainly situated at the specific motifs such as Walker A, Q-loop, Walker B, D-loop and signature motif of the Rad50 protein. Some of these mutations were predicted to negatively affect several important functional sites that play important roles in DNA repair mechanism and cell cycle signaling pathway, highlighting Rad50 crucial role in this process. Interestingly, mutations located at non-conserved regions were predicted to have neutral/non-damaging effects, in contrast with previous experimental studies that showed deleterious effects. This suggests that software used in this study may have limitations in predicting mutations in non-conserved regions, implying further improvement in their algorithm is needed. In conclusion, this study reveals the priority of acid substitution associated with the genetic disorders. This finding highlights the vital roles of certain residues such as K42E, C681A/S, CC684R/S, S1202R, E1232Q and D1238N/A located in Rad50 conserved regions, which can be considered for a more targeted future studies.

MALDI-TOF-MS-based high throughput genotyping of mutations associated with body measurement traits in cattle

Exploration of genes in relation to body measurement traits through large-scaled mutation identification is highly conductive for the genomics-assisted breeding of superior productivity cattle. In this investigation, 31 objective mutations were genotyped synchronously in 384 yellow cattle of 8 breeds through the application of optimized MALDI-TOF-MS and multiplex PCR techniques. High genotyping rate was obtained as well as greatly decreased cost which was below one thirtieth of the routine analysis. Results from genotyping revealed 23 mutations as valid mutations in the studied cattle population with gene heterozygosity and effective allele number varying from 0.0052 to 0.4998 and 1.0052 to 1.9991, respectively. Among the 23 effective mutations, 12 was classified as moderate polymorphism (0.25 < PIC < 0.5) while the other 11 belonged to low polymorphism (PIC < 0.25), 7 mutations did not obey the HW equilibrium (p < 0.05) and linkage mainly appeared between mutations of UCP2 and PTHR1 genes. Furthermore, 8 body measurement traits in the 384 cattle were recorded to validate their association with tag mutations, and significant correlations were found in 12 mutations of 9 genes including PTHR1, CDK6, IHH, HHIP, GHRL, COL1A1, INS, GDF5 and UCP2, of which, PTHR1 was proved to be the most potential contributor to bone modeling in cattle. Results highlight the potential application value of 12 novel mutations in enhancing cattle production traits as well as the high genotyping rate achieved by MALDI-TOF-MS coupled with multiplex PCR technique.

BDNF Val66Met polymorphism is associated with aggressive behavior in schizophrenia

Brain-derived neurotrophic factor (BDNF) gene variants may potentially influence behaviour. In order to test this hypothesis, we investigated the relationship between BDNF Val66Met polymorphism and aggressive behaviour in a population of schizophrenic patients. Our results showed that increased number of BDNF Met alleles was associated with increased aggressive behaviour.

Lack of association between interleukin-22 gene polymorphisms and cancer risk: a case-control study and a meta-analysis

BACKGROUND

Interleukin-22 (IL22) has been implicated in inflammation and tumorigenesis. The association between IL22 gene polymorphisms and cancer risk has been widely explored. However, the limited sample sizes of previous studies may produce inadequate statistical power and conflicting results, which calls for further investigations. In this study, we recruited a total of 1490 cancer patients (480 liver cancer patients, 550 lung cancer patients, and 460 gastric cancer patients) and 800 normal controls to explore the associations between IL22 gene polymorphisms (rs1179251, rs2227485, rs2227511, and rs2227473) and cancer risk.

METHOD

The genotyping was performed with polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and Sanger sequencing.

RESULTS

Our results showed that none of the four IL22 gene polymorphisms was associated with the risk of liver, lung or gastric cancer in Hubei Han Chinese population. To improve the statistical strength, a meta-analysis was further conducted. The results further confirmed our present findings and showed that rs1179251, rs2227485, and rs2227473 were not associated with cancer risk in total or stratified analysis.

CONCLUSION

Consequently, the rs1179251, rs2227485, rs2227511, and rs2227473 polymorphisms may not be associated with cancer risk. However, further investigations using larger samples in different ethnic populations are required.

MicroRNA binding site polymorphism in inflammatory genes associated with colorectal cancer: literature review and bioinformatics analysis

Inflammation, among environmental risk factors, is one of the most important contributors to colorectal cancer (CRC) development. In this way, studies revealed that the incidence of CRC in inflammatory bowel disease patients is up to 60% higher than the general population. MicroRNAs (miRNAs), small noncoding RNA molecules, have attracted excessive attention due to their fundamental role in various aspects of cellular biology, such as inflammation by binding to the 3'-untranslated regions (3'-UTR) of pro and anti-inflammatory genes. Based on multiple previous studies, SNPs at 3'-UTR can affect miRNA recognition elements by changing the thermodynamic features and secondary structure. This effect can be categorized, based on the number of changes, into four groups, including break, decrease, create, and enhance. In this paper, we will focus on functional variants in miRNA binding sites in inflammatory genes, which can modulate the risk of CRC by both investigating previous studies, regarding miRSNPs in inflammatory genes associated with CRC and recruiting in silico prediction algorithms to report putative miRSNPs in 176 inflammatory genes. In our analysis, we achieved 110 miRSNPs in 3'-UTR of 67 genes that seem good targets for future researches.

Ethylene Biosynthesis Inhibition Combined with Cyanide Degradation Confer Resistance to Quinclorac in Echinochloa crus-galli var. mitis

Echinochloa crus-galli var. mitis has rarely been reported for herbicide resistance, and no case of quinclorac resistance has been reported so far. Synthetic auxin-type herbicide quinclorac is used extensively to control rice weeds worldwide. A long history of using quinclorac in Chinese rice fields escalated the resistance in E. crus-galli var. mitis against this herbicide. Bioassays in Petri plates and pots exhibited four biotypes that evolved into resistance to quinclorac ranking as JS01-R > AH01-R > JS02-R > JX01-R from three provinces of China. Ethylene production in these biotypes was negatively correlated with resistance level and positively correlated with growth inhibition. Determination of the related ethylene response pathway exhibited resistance in biotypes that recorded a decline in 1-aminocyclopropane-1-carboxylic acid (ACC) content, ACC synthase oxidase activities, and less inducible ACS and ACO genes expressions than the susceptible biotype, suggesting that there was a positive correlation between quinclorac resistance and ethylene biosynthesis inhibition. Cyanides produced during the ethylene biosynthesis pathway mainly degraded by the activity of β-cyanoalanine synthase (β-CAS). Resistant biotypes exhibited higher β-CAS activity than the susceptible ones. Nucleotide changes were found in the EcCAS gene of resistant biotypes as compared to sensitive ones that caused three amino acid substitutions (Asn-105-Lys, Gln-195-Glu, and Gly-298-Val), resulting in alteration of enzyme structure, increased binding residues in the active site with its cofactor, and decreased binding free energy; hence, its activity was higher in resistant biotypes. Moreover, these mutations increased the structural stability of the enzyme. In view of the positive correlation between ethylene biosynthesis inhibition and cyanide degradation with resistance level, it is concluded that the alteration in ethylene response pathway or at least variation in ACC synthase and ACC oxidase enzyme activities—due to less relative expression of ACS and ACO genes and enhanced β-CAS activity, as well as mutation and increased relative expression of EcCAS gene—can be considered as a probable mechanism of quinclorac resistance in E. crus-galli var. mitis.

In silico approach to the analysis of SNPs in the human APAF1 gene

The apoptotic protease activating factor 1 (APAF1) gene encodes a cytoplasmic protein that initiates apoptosis and is a crucial factor in the mitochondria-dependent death pathway. APAF1 is implicated in many pathways such as apoptosis, neurodegenerative diseases, and cancer. The purpose of this study was to predict deleterious/damaging SNPs in the APAF1 gene viain silicoanalysis. To this end, APAF1 missense SNPs were obtained from the NCBI dbSNP database. In silico analysis of the missense SNPs was carried out by using publicly available online software tools. The stabilization and three-dimensional modeling of mutant proteins were also determined by using the I-Mutant 2.0 and Project HOPE webservers, respectively. In total, 772 missense SNPs were found in the APAF1 gene from the NCBI dbSNP database, 18 SNPs of which were demonstrated to be deleterious or damaging. Of those, 13 SNPs had a decreasing effect on protein stability, while the other 5 SNPs had an increasing effect. Based on the modeling results, some dissimilarities of mutant type amino acids from wild-type amino acids such as size, charge, and hydrophobicity were revealed. The SNPs predicted to be deleterious in this study might be used in the selection of target SNPs for genotyping in disease association studies. Therefore, we could suggest that the present study could pave the way for future experimental studies.

Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors

Transcription factors (TFs) are the major components of human gene regulation. In particular, they bind onto specific DNA sequences and regulate neighborhood genes in different tissues at different developmental stages. Non-synonymous single nucleotide polymorphisms on its protein-coding sequences could result in undesired consequences in human. Therefore, it is necessary to develop methods for predicting any abnormality among those non-synonymous single nucleotide polymorphisms. To address it, we have developed and compared different strategies to predict deleterious non-synonymous single nucleotide polymorphisms (also known as missense mutations) on the protein-coding sequences of human TFs. Taking advantage of evolutionary conservation signals, we have developed and compared different classifiers with different feature sets as computed from different evolutionarily related sequence collections. The results indicate that the classic ensemble algorithm, Adaboost with decision stumps, with orthologous sequence collection, has performed the best (namely, TFmedic). We have further compared TFmedic with other state-of-the-arts methods (i.e., PolyPhen-2 and SIFT) on PolyPhen-2's own datasets, demonstrating that TFmedic can outperform the others. As applications, we have further applied TFmedic to all possible missense mutations on all human transcription factors; the proteome-wide results reveal interesting insights, consistent with the existing physiochemical knowledge. A case study with the actual 3D structure is conducted, revealing how TFmedic can be contributed to protein-DNA binding complex studies.

Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA

Mucopolysaccharidosis (MPS) IIIA, also known as Sanfilippo syndrome type A, is a severe, progressive disease that affects the central nervous system (CNS). MPS IIIA is inherited in an autosomal recessive manner and is caused by a deficiency in the lysosomal enzyme sulfamidase, which is required for the degradation of heparan sulfate. The sulfamidase is produced by the N-sulphoglucosamine sulphohydrolase (SGSH) gene. In MPS IIIA patients, the excess of lysosomal storage of heparan sulfate often leads to mental retardation, hyperactive behavior, and connective tissue impairments, which occur due to various known missense mutations in the SGSH, leading to protein dysfunction. In this study, we focused on three mutations (R74C, S66W, and R245H) based on in silico pathogenic, conservation, and stability prediction tool studies. The three mutations were further subjected to molecular dynamic simulation (MDS) analysis using GROMACS simulation software to observe the structural changes they induced, and all the mutants exhibited maximum deviation patterns compared with the native protein. Conformational changes were observed in the mutants based on various geometrical parameters, such as conformational stability, fluctuation, and compactness, followed by hydrogen bonding, physicochemical properties, principal component analysis (PCA), and salt bridge analyses, which further validated the underlying cause of the protein instability. Additionally, secondary structure and surrounding amino acid analyses further confirmed the above results indicating the loss of protein function in the mutants compared with the native protein. The present results reveal the effects of three mutations on the enzymatic activity of sulfamidase, providing a molecular explanation for the cause of the disease. Thus, this study allows for a better understanding of the effect of SGSH mutations through the use of various computational approaches in terms of both structure and functions and provides a platform for the development of therapeutic drugs and potential disease treatments.

A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

Leucine Rich Repeat Proteins: Sequences, Mutations, Structures and Diseases

Mutations in the genes encoding Leucine Rich Repeat (LRR) containing proteins are associated with over sixty human diseases; these include high myopia, mitochondrial encephalomyopathy, and Crohn's disease. These mutations occur frequently within the LRR domains and within the regions that shield the hydrophobic core of the LRR domain. The amino acid sequences of fifty-five LRR proteins have been published. They include Nod-Like Receptors (NLRs) such as NLRP1, NLRP3, NLRP14, and Nod-2, Small Leucine Rich Repeat Proteoglycans (SLRPs) such as keratocan, lumican, fibromodulin, PRELP, biglycan, and nyctalopin, and F-box/LRR-repeat proteins such as FBXL2, FBXL4, and FBXL12. For example, 363 missense mutations have been identified. Replacement of arginine, proline, or cysteine by another amino acid, or the reverse, is frequently observed. The diverse effects of the mutations are discussed based on the known structures of LRR proteins. These mutations influence protein folding, aggregation, oligomerization, stability, protein-ligand interactions, disulfide bond formation, and glycosylation. Most of the mutations cause loss of function and a few, gain of function.

Generalized Structured Component Analysis in candidate gene association studies: applications and limitations

Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.

A complete physical mapping of the vitamin D receptor gene for dental implant loss: A pilot study

OBJECTIVES

The aim of this pilot case-control study was to investigate the association of clinical variables and genetic polymorphisms in the vitamin D receptor gene (VDR) with dental implant loss.

MATERIAL AND METHODS

This study was carried out with 244 individuals with mean age 51.90 ± 11.28 (81 cases and 163 controls matched by age, sex, and smoking habit). Also, the clusterization phenomenon was investigated stratifying the sample into two groups: (a) 34 patients with multiple losses (presenting two or more lost implants) and (b) 210 without multiple losses (up to one implant loss). Sociodemographic, clinical, and periodontal parameters were analyzed. The tagSNPs in the VDR gene were analyzed by real-time PCR. Univariate and multivariate analyses were performed (p < .05).

RESULTS

Edentulism, number of implants installed, and Gingival, Plaque, and Calculus Indexes were associated with implant loss in the univariate analysis. After the multivariate analysis, the allele G of rs3782905 in the recessive model, together with number of installed implants and Gingival Index, was associated with implant failure.

CONCLUSION

It is suggested that the allele G of rs3782905 in the recessive model may be a new genetic risk marker for dental implant loss in patients who lost two or more dental implants. In addition, number of implants installed and Gingival Index were also associated. Replication is mandatory to confirm these findings, due to the modest sample size of this work.

Polymorphisms in major histocompatibility complex genes and its associations with milk quality in Murrah buffaloes

Animal breeding programs have used molecular genetic tools as an auxiliary method to identify and select animals with superior genetic merit for milk production and milk quality traits as well as disease resistance. Genes of the major histocompatibility complex (MHC) are important molecular markers for disease resistance that could be applied for genetic selection. The aim of this study was to identify single nucleotide polymorphisms (SNPs) and haplotypes in DRB2, DRB3, DMA, and DMB genes in Murrah breed and to analyze the association between molecular markers and milk, fat, protein and mozzarella production, fat and protein percentage, and somatic cell count. Two hundred DNA samples from Murrah buffaloes were used. The target regions of candidate genes were amplified by polymerase chain reaction (PCR) followed by sequencing and identification of polymorphisms. Allele and genotype frequencies, as well as linkage disequilibrium between SNPs, were calculated. Genotypes were used in association analyses with milk production and quality traits. Except for the DMA gene, identified as monomorphic, the other genes presented several polymorphisms. The DMB, DRB2, and DRB3 genes presented two, six, and seven SNPs, respectively. Fifty-seven haplotype blocks were constructed from 15 SNPs identified, which was used in association analyses. All the studied traits had at least one associated haplotype. In conclusion, it is suggested that the haplotypes found herein can be associated with important traits related to milk production and quality.

Locating potentially lethal genes using the abnormal distributions of genotypes

Genes are the basic functional units of heredity. Differences in genes can lead to various congenital physical conditions. One kind of these differences is caused by genetic variations named single nucleotide polymorphisms (SNPs). An SNP is a variation in a single nucleotide that occurs at a specific position in the genome. Some SNPs can affect splice sites and protein structures and cause gene abnormalities. SNPs on paired chromosomes may lead to fatal diseases so that a fertilized embryo cannot develop into a normal fetus or the people born with these abnormalities die in childhood. The distributions of genotypes on these SNP sites are different from those on other sites. Based on this idea, we present a novel statistical method to detect the abnormal distributions of genotypes and locate the potentially lethal genes. The test was performed on HapMap data and 74 suspicious SNPs were found. Ten SNP maps “reviewed” genes in the NCBI database. Among them, 5 genes were related to fatal childhood diseases or embryonic development, 1 gene can cause spermatogenic failure, and the other 4 genes were associated with many genetic diseases. The results validated our method. The method is very simple and is guaranteed by a statistical test. It is an inexpensive way to discover potentially lethal genes and the mutation sites. The mined genes deserve further study.

A screen for deeply conserved non-coding GWAS SNPs uncovers a MIR-9-2 functional mutation associated to retinal vasculature defects in human

Thousands of human disease-associated single nucleotide polymorphisms (SNPs) lie in the non-coding genome, but only a handful have been demonstrated to affect gene expression and human biology. We computationally identified risk-associated SNPs in deeply conserved non-exonic elements (CNEs) potentially contributing to 45 human diseases. We further demonstrated that human CNE1/rs17421627 associated with retinal vasculature defects showed transcriptional activity in the zebrafish retina, while introducing the risk-associated allele completely abolished CNE1 enhancer activity. Furthermore, deletion of CNE1 led to retinal vasculature defects and to a specific downregulation of microRNA-9, rather than MEF2C as predicted by the original genome-wide association studies. Consistent with these results, miR-9 depletion affects retinal vasculature formation, demonstrating MIR-9-2 as a critical gene underpinning the associated trait. Importantly, we validated that other CNEs act as transcriptional enhancers that can be disrupted by conserved non-coding SNPs. This study uncovers disease-associated non-coding mutations that are deeply conserved, providing a path for in vivo testing to reveal their cis-regulated genes and biological roles.

PMut: a web-based tool for the annotation of pathological variants on proteins, 2017 update

We present here a full update of the PMut predictor, active since 2005 and with a large acceptance in the field of predicting Mendelian pathological mutations. PMut internal engine has been renewed, and converted into a fully featured standalone training and prediction engine that not only powers PMut web portal, but that can generate custom predictors with alternative training sets or validation schemas. PMut Web portal allows the user to perform pathology predictions, to access a complete repository of pre-calculated predictions, and to generate and validate new predictors. The default predictor performs with good quality scores (MCC values of 0.61 on 10-fold cross validation, and 0.42 on a blind test with SwissVar 2016 mutations). The PMut portal is freely accessible at http://mmb.irbbarcelona.org/PMut. A complete help and tutorial is available at http://mmb.irbbarcelona.org/PMut/help.

Nearly Neutral Evolution across the Drosophila melanogaster Genome

Under the nearly neutral theory of molecular evolution, the proportion of effectively neutral mutations is expected to depend upon the effective population size (Ne). Here, we investigate whether this is the case across the genome of Drosophila melanogaster using polymorphism data from North American and African lines. We show that the ratio of the number of nonsynonymous and synonymous polymorphisms is negatively correlated to the number of synonymous polymorphisms, even when the nonindependence is accounted for. The relationship is such that the proportion of effectively neutral nonsynonymous mutations increases by ∼45% as Ne is halved. However, we also show that this relationship is steeper than expected from an independent estimate of the distribution of fitness effects from the site frequency spectrum. We investigate a number of potential explanations for this and show, using simulation, that this is consistent with a model of genetic hitchhiking: Genetic hitchhiking depresses diversity at neutral and weakly selected sites, but has little effect on the diversity of strongly selected sites.

Esophageal cancer genetics in South Africa

Esophageal cancer (EC) is an extremely aggressive cancer with one of the highest mortality rates. The cancer is generally only diagnosed at the later stages and has a poor 5-year survival rate due to the limited treatment options. China and South Africa are two countries with a very high prevalence rate of EC. EC rates in South Africa have been on the increase, and esophageal squamous cell carcinoma is the predominant subtype and a primary cause of cancer-related deaths in the black and male mixed ancestry populations in South Africa. The incidence of EC is highest in the Eastern Cape Province, especially in the rural areas such as the Transkei, where the consumption of foods contaminated with Fusarium verticillioides is thought to play a major contributing role to the incidence of EC. China is responsible for almost half of all new cases of EC globally. In China, the prevalence of EC varies greatly. However, the two main areas of high prevalence are the southern Taihang Mountain area (Linxian, Henan Province) and the north Jiangsu area. In both countries, environmental toxins play a major role in increasing the chance that an individual will develop EC. These associative factors include tobacco use, alcohol consumption, nutritional deficiencies and exposure to environmental toxins. However, genetic polymorphisms also play a role in predisposing individuals to EC. These include single-nucleotide polymorphisms that can be found in both protein-coding genes and in non-coding sequences such as miRNAs. The aim of this review is to summarize the contribution of genetic polymorphisms to EC in South Africa and to compare and contrast this to the genetic polymorphisms observed in EC in the most comprehensively studied population group, the Chinese.

Acute exercise-induced enhancement of fear inhibition is moderated by BDNF Val66Met polymorphism

Rodent research indicates that acute physical exercise facilitates fear learning and inhibition. Expression of brain-derived neurotrophic factor (BDNF) may moderate the memory enhancing effects of acute exercise. We assessed the role of acute exercise in modulating extinction retention in humans, and investigated the extent to which the BDNF polymorphism influenced extinction retention. Seventy non-clinical participants engaged in a differential fear potentiated startle paradigm involving conditioning and extinction followed by random assignment to either intense exercise (n = 35) or no exercise (n = 35). Extinction retention was assessed 24 h later. Saliva samples were collected to index BDNF genotype. Exercised participants displayed significantly lower fear 24 h later relative to non-exercised participants. Moderation analyses indicated that after controlling for gender, the BDNF Val66Met polymorphism moderated the relationship between exercise and fear recovery 24 h later, such that exercise was associated with greater fear recovery in individuals with the Met allele. These findings provide initial evidence that acute exercise can impact fear extinction in humans and this effect is reduced in Met-allele carriers. This finding accords with the role of BDNF in extinction learning, and has implications for augmenting exposure-based therapies for anxiety disorders.