Comparative genomics approaches accurately predict deleterious variants in plants

Recent advances in genome resequencing have led to increased interest in prediction of the functional consequences of genetic variants. Variants at phylogenetically conserved sites are of particular interest, because they are more likely than variants at phylogenetically variable sites to have deleterious effects on fitness and contribute to phenotypic variation. Numerous comparative genomic approaches have been developed to predict deleterious variants, but the approaches are nearly always assessed based on their ability to identify known disease-causing mutations in humans. Determining the accuracy of deleterious variant predictions in nonhuman species is important to understanding evolution, domestication, and potentially to improving crop quality and yield. To examine our ability to predict deleterious variants in plants we generated a curated database of 2,910 Arabidopsis thaliana mutants with known phenotypes. We evaluated seven approaches and found that while all performed well, their relative ranking differed from prior benchmarks in humans. We conclude that deleterious mutations can be reliably predicted in A. thaliana and likely other plant species, but that the relative performance of various approaches does not necessarily translate from one species to another

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in the Human Interleukin 12B Gene: Identification and Structural Characterization

Background: Interleukin -12B (IL12B) polymorphism has been identified as a factor in the development of various Immunological disorders and cancer. The objective of this study was to identify the non-synonymous SNPs (nsSNPs) with the strongest predicted negative impact on the function of the IL12B protein.Methods: We employed a variety of computational methods, including SIFT, PolyPhen2, PROVEAN, SNAP2 to determine the functional impact of nsSNPs. Also, In order to investigate the potential association of nsSNPs in the IL12B gene with disease, a computational analysis was conducted using PhD-SNP, SNP&GO, and Pmut. Additionally, I-mutant and MuPro were employed to predict protein stability, while ConSurf was used to identify functional domains and conserved amino acid residues within the protein. Furthermore, SOPMA was used in combination with Project Hope and MutPred2 to predict the impact of mutations on both the structure and function of proteins. Finally, we used GeneMania to analyze the gene-gene interactions of the IL12B gene with other genes.Results: Our results indicate that nine nsSNPs (G72C, G86C, C90R, C131S, Y136D, P235L, V254G, Y258H and P259S) were found to be potentially deleterious in the IL12B gene.Conclusion: Our study emphasizes the significance of identifying functional and structural polymorphisms in the IL12B gene, as they may reveal potential therapeutic targets and provide insight into the underlying mechanisms of related diseases. Further experimental investigation is necessary to fully explore the role of these nsSNPs in disease pathogenesis.Keywords: Interleukin 12B; deleterious nsSNPs; Polymorphisms.; Computational analysis

Genome-wide association study for calving performance using high-density genotypes in dairy and beef cattle

peer-reviewedBackground Calving difficulty and perinatal mortality are prevalent in modern-day cattle production systems. It is well-established that there is a genetic component to both traits, yet little is known about their underlying genomic architecture, particularly in beef breeds. Therefore, we performed a genome-wide association study using high-density genotypes to elucidate the genomic architecture of these traits and to identify regions of the bovine genome associated with them. Results Genomic regions associated with calving difficulty (direct and maternal) and perinatal mortality were detected using two statistical approaches: (1) single-SNP (single nucleotide polymorphism) regression and (2) a Bayesian approach. Data included high-density genotypes on 770 Holstein-Friesian, 927 Charolais and 963 Limousin bulls. Several novel or previously identified genomic regions were detected but associations differed by breed. For example, two genomic associations, one each on chromosomes 18 and 2 explained 2.49 % and 3.13 % of the genetic variance in direct calving difficulty in the Holstein-Friesian and Charolais populations, respectively. Imputed Holstein-Friesian sequence data was used to refine the genomic regions responsible for significant associations. Several candidate genes on chromosome 18 were identified and four highly significant missense variants were detected within three of these genes (SIGLEC12, CTU1, and ZNF615). Nevertheless, only CTU1 contained a missense variant with a putative impact on direct calving difficulty based on SIFT (0.06) and Polyphen (0.95) scores. Using imputed sequence data, we refined a genomic region on chromosome 4 associated with maternal calving difficulty in the Holstein-Friesian population and found the strongest association with an intronic variant in the PCLO gene. A meta-analysis was performed across the three breeds for each calving performance trait to identify common variants associated with these traits in the three breeds. Our results suggest that a portion of the genetic variation in calving performance is common to all three breeds. Conclusion The genomic architecture of calving performance is complex and mainly influenced by many polymorphisms of small effect. We identified several associations of moderate effect size but the majority were breed-specific, indicating that breed-specific alleles exist for calving performance or that the linkage phase between genotyped allele and causal mutation varies between breeds

Performance of in silico tools for the evaluation of UGT1A1 missense variants

Variations in the gene encoding uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) are particularly important because they have been associated with hyperbilirubinemia in Gilbert’s and Crigler–Najjar syndromes as well as with changes in drug metabolism. Several variants associated with these phenotypes are nonsynonymous single-nucleotide polymorphisms (nsSNPs). Bioinformatics approaches have gained increasing importance in predicting the functional significance of these variants. This study was focused on the predictive ability of bioinformatics approaches to determine the pathogenicity of human UGT1A1 nsSNPs, which were previously characterized at the protein level by in vivo and in vitro studies. Using 16 Web algorithms, we evaluated 48 nsSNPs described in the literature and databases. Eight of these algorithms reached or exceeded 90% sensitivity and six presented a Matthews correlation coefficient above 0.46. The best-performing method was MutPred, followed by Sorting Intolerant from Tolerant (SIFT). The prediction measures varied significantly when predictors such us SIFT, polyphen-2, and Prediction of Pathological Mutations on Proteins were run with their native alignment generated by the tool, or with an input alignment that was strictly built with UGT1A1 orthologs and manually curated. Our results showed that the prediction performance of some methods based on sequence conservation analysis can be negatively affected when nsSNPs are positioned at the hypervariable or constant regions of UGT1A1 ortholog sequences

Computational Methods to Work as First-Pass Filter in Deleterious SNP Analysis of Alkaptonuria

A major challenge in the analysis of human genetic variation is to distinguish functional from nonfunctional SNPs. Discovering these functional SNPs is one of the main goals of modern genetics and genomics studies. There is a need to effectively and efficiently identify functionally important nsSNPs which may be deleterious or disease causing and to identify their molecular effects. The prediction of phenotype of nsSNPs by computational analysis may provide a good way to explore the function of nsSNPs and its relationship with susceptibility to disease. In this context, we surveyed and compared variation databases along with in silico prediction programs to assess the effects of deleterious functional variants on protein functions. In other respects, we attempted these methods to work as first-pass filter to identify the deleterious substitutions worth pursuing for further experimental research. In this analysis, we used the existing computational methods to explore the mutation-structure-function relationship in HGD gene causing alkaptonuria

Genetic Epidemiology of Glucose-6-Dehydrogenase Deficiency in the Arab World

A systematic search was implemented using four literature databases (PubMed, Embase, Science Direct and Web of Science) to capture all the causative mutations of Glucose-6-phosphate dehydrogenase (G6PD) deficiency (G6PDD) in the 22 Arab countries. Our search yielded 43 studies that captured 33 mutations (23 missense, one silent, two deletions, and seven intronic mutations), in 3,430 Arab patients with G6PDD. The 23 missense mutations were then subjected to phenotypic classification using in silico prediction tools, which were compared to the WHO pathogenicity scale as a reference. These in silico tools were tested for their predicting efficiency using rigorous statistical analyses. Of the 23 missense mutations, p.S188F, p.I48T, p.N126D, and p.V68M, were identified as the most common mutations among Arab populations, but were not unique to the Arab world, interestingly, our search strategy found four other mutations (p.N135T, p.S179N, p.R246L, and p.Q307P) that are unique to Arabs. These mutations were exposed to structural analysis and molecular dynamics simulation analysis (MDSA), which predicting these mutant forms as potentially affect the enzyme function. The combination of the MDSA, structural analysis, and in silico predictions and statistical tools we used will provide a platform for future prediction accuracy for the pathogenicity of genetic mutations.QUST-CAS-FALL-15/16-24 gran

Cataloguing functionally relevant polymorphisms in gene DNA ligase I: a computational approach

A computational approach for identifying functionally relevant SNPs in gene LIG1 has been proposed. LIG1 is a crucial gene which is involved in excision repair pathways and mutations in this gene may lead to increase sensitivity towards DNA damaging agents. A total of 792 SNPs were reported to be associated with gene LIG1 in dbSNP. Different web server namely SIFT, PolyPhen, CUPSAT, FASTSNP, MAPPER and dbSMR were used to identify potentially functional SNPs in gene LIG1. SIFT, PolyPhen and CUPSAT servers predicted eleven nsSNPs to be intolerant, thirteen nsSNP to be damaging and two nsSNPs have the potential to destabilize protein structure. The nsSNP rs11666150 was predicted to be damaging by all three servers and its mutant structure showed significant increase in overall energy. FASTSNP predicted twenty SNPs to be present in splicing modifier binding sites while rSNP module from MAPPER server predicted nine SNPs to influence the binding of transcription factors. The results from the study may provide vital clues in establishing affect of polymorphism on phenotype and in elucidating drug response

In Silico profiling of deleterious amino acid substitutions of potential pathological importance in haemophlia A and haemophlia B

<p>Abstract</p> <p>Background</p> <p>In this study, instead of current biochemical methods, the effects of deleterious amino acid substitutions in <it>F8 and F9 </it>gene upon protein structure and function were assayed by means of computational methods and information from the databases. Deleterious substitutions of <it>F8 and F9 </it>are responsible for Haemophilia A and Haemophilia B which is the most common genetic disease of coagulation disorders in blood. Yet, distinguishing deleterious variants of <it>F8 and F9 </it>from the massive amount of nonfunctional variants that occur within a single genome is a significant challenge.</p> <p>Methods</p> <p>We performed an <it>in silico </it>analysis of deleterious mutations and their protein structure changes in order to analyze the correlation between mutation and disease. Deleterious nsSNPs were categorized based on empirical based and support vector machine based methods to predict the impact on protein functions. Furthermore, we modeled mutant proteins and compared them with the native protein for analysis of protein structure stability.</p> <p>Results</p> <p>Out of 510 nsSNPs in <it>F8</it>, 378 nsSNPs (74%) were predicted to be 'intolerant' by SIFT, 371 nsSNPs (73%) were predicted to be 'damaging' by PolyPhen and 445 nsSNPs (87%) as 'less stable' by I-Mutant2.0. In <it>F9</it>, 129 nsSNPs (78%) were predicted to be intolerant by SIFT, 131 nsSNPs (79%) were predicted to be damaging by PolyPhen and 150 nsSNPs (90%) as less stable by I-Mutant2.0. Overall, we found that I-Mutant which emphasizes support vector machine based method outperformed SIFT and PolyPhen in prediction of deleterious nsSNPs in both <it>F8 </it>and <it>F9</it>.</p> <p>Conclusions</p> <p>The models built in this work would be appropriate for predicting the deleterious amino acid substitutions and their functions in gene regulation which would be useful for further genotype-phenotype researches as well as the pharmacogenetics studies. These <it>in silico </it>tools, despite being helpful in providing information about the nature of mutations, may also function as a first-pass filter to determine the substitutions worth pursuing for further experimental research in other coagulation disorder causing genes.</p