regSNPs: a strategy for prioritizing regulatory single nucleotide substitutions

Pharmacogenomics: A Genetic Approach to Drug Development and Therapy

The majority of the well-known pharmacogenomics research used in the medical sciences contributes to our understanding of medication interactions. It has a significant impact on treatment and drug development. The broad use of pharmacogenomics is required for the progress of therapy. The main focus is on how genes and an intricate gene system affect the body's reaction to medications. Novel biomarkers that help identify a patient group that is more or less likely to respond to a certain medication have been discovered as a result of recent developments in the field of clinical therapeutics. It aims to improve customized therapy by giving the appropriate drug at the right dose at the right time and making sure that the right prescriptions are issued. A combination of genetic, environmental, and patient variables that impact the pharmacokinetics and/or pharmacodynamics of medications results in interindividual variance in drug response. Drug development, illness susceptibility, and treatment efficacy are all impacted by pharmacogenomics. The purpose of this work is to give a review that might serve as a foundation for the creation of new pharmacogenomics applications, techniques, or strategies.

The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies

The Human Gene Mutation Database (HGMD^®) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD ( http://www.hgmd.org ) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc.

The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies

The Human Gene Mutation Database (HGMD^®) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc.

GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding

MOTIVATION

The majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies.

RESULTS

We present GERV (generative evaluation of regulatory variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor's canonical motif and associated co-factor motifs. We show that GERV outperforms existing methods in predicting single-nucleotide polymorphisms associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked single-nucleotide polymorphisms and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis.

AVAILABILITY AND IMPLEMENTATION

The implementation of GERV and related data are available at http://gerv.csail.mit.edu/.

Predicting functional regulatory SNPs in the human antimicrobial peptide genes DEFB1 and CAMP in tuberculosis and HIV/AIDS

Single nucleotide polymorphisms (SNPs) in transcription factor binding sites (TFBSs) within gene promoter region or enhancers can modify the transcription rate of genes related to complex diseases. These SNPs can be called regulatory SNPs (rSNPs). Data compiled from recent projects, such as the 1000 Genomes Project and ENCODE, has revealed essential information used to perform in silico prediction of the molecular and biological repercussions of SNPs within TFBS. However, most of these studies are very limited, as they only analyze SNPs in coding regions or when applied to promoters, and do not integrate essential biological data like TFBSs, expression profiles, pathway analysis, homotypic redundancy (number of TFBSs for the same TF in a region), chromatin accessibility and others, which could lead to a more accurate prediction. Our aim was to integrate different data in a biologically coherent method to analyze the proximal promoter regions of two antimicrobial peptide genes, DEFB1 and CAMP, that are associated with tuberculosis (TB) and HIV/AIDS. We predicted SNPs within the promoter regions that are more likely to interact with transcription factors (TFs). We also assessed the impact of homotypic redundancy using a novel approach called the homotypic redundancy weight factor (HWF). Our results identified 10 SNPs, which putatively modify the binding affinity of 24 TFs previously identified as related to TB and HIV/AIDS expression profiles (e.g. KLF5, CEBPA and NFKB1 for TB; FOXP2, BRCA1, CEBPB, CREB1, EBF1 and ZNF354C for HIV/AIDS; and RUNX2, HIF1A, JUN/AP-1, NR4A2, EGR1 for both diseases). Validating with the OregAnno database and cell-specific functional/non functional SNPs from additional 13 genes, our algorithm performed 53% sensitivity and 84.6% specificity to detect functional rSNPs using the DNAseI-HUP database. We are proposing our algorithm as a novel in silico method to detect true functional rSNPs in antimicrobial peptide genes. With further improvement, this novel method could be applied to other promoters in order to design probes and to discover new drug targets for complex diseases.

ReMo-SNPs: a new software tool for identification of polymorphisms in regions and motifs genome-wide

Studies of complex genetic diseases have revealed many risk factors of small effect, but the combined amount of heritability explained is still low. Genome-wide association studies are often underpowered to identify true effects because of the very large number of parallel tests. There is, therefore, a great need to generate data sets that are enriched for those markers that have an increased a priori chance of being functional, such as markers in genomic regions involved in gene regulation. ReMo-SNPs is a computational program developed to aid researchers in the process of selecting functional SNPs for association analyses in user-specified regions and/or motifs genome-wide. The useful feature of automatic selection of genotyped markers in the user-provided material makes the output data ready to be used in a following association study. In this article we describe the program and its functions. We also validate the program by including an example study on three different transcription factors and results from an association study on two psychiatric phenotypes. The flexibility of the ReMo-SNPs program enables the user to study any region or sequence of interest, without limitation to transcription factor binding regions and motifs. The program is freely available at: http://www.neuro.ki.se/ReMo-SNPs/.

Impact of human pathogenic micro-insertions and micro-deletions on post-transcriptional regulation

Small insertions/deletions (INDELs) of ≤21 bp comprise 18% of all recorded mutations causing human inherited disease and are evident in 24% of documented Mendelian diseases. INDELs affect gene function in multiple ways: for example, by introducing premature stop codons that either lead to the production of truncated proteins or affect transcriptional efficiency. However, the means by which they impact post-transcriptional regulation, including alternative splicing, have not been fully evaluated. In this study, we collate disease-causing INDELs from the Human Gene Mutation Database (HGMD) and neutral INDELs from the 1000 Genomes Project. The potential of these two types of INDELs to affect binding-site affinity of RNA-binding proteins (RBPs) was then evaluated. We identified several sequence features that can distinguish disease-causing INDELs from neutral INDELs. Moreover, we built a machine-learning predictor called PinPor (predicting pathogenic small insertions and deletions affecting post-transcriptional regulation, http://watson.compbio.iupui.edu/pinpor/) to ascertain which newly observed INDELs are likely to be pathogenic. Our results show that disease-causing INDELs are more likely to ablate RBP-binding sites and tend to affect more RBP-binding sites than neutral INDELs. Additionally, disease-causing INDELs give rise to greater deviations in binding affinity than neutral INDELs. We also demonstrated that disease-causing INDELs may be distinguished from neutral INDELs by several sequence features, such as their proximity to splice sites and their potential effects on RNA secondary structure. This predictor showed satisfactory performance in identifying numerous pathogenic INDELs, with a Matthews correlation coefficient (MCC) value of 0.51 and an accuracy of 0.75.

GWAS3D: Detecting human regulatory variants by integrative analysis of genome-wide associations, chromosome interactions and histone modifications

Interpreting the genetic variants located in the regulatory regions, such as enhancers and promoters, is an indispensable step to understand molecular mechanism of complex traits. Recent studies show that genetic variants detected by genome-wide association study (GWAS) are significantly enriched in the regulatory regions. Therefore, detecting, annotating and prioritizing of genetic variants affecting gene regulation are critical to our understanding of genotype-phenotype relationships. Here, we developed a web server GWAS3D to systematically analyze the genetic variants that could affect regulatory elements, by integrating annotations from cell type-specific chromatin states, epigenetic modifications, sequence motifs and cross-species conservation. The regulatory elements are inferred from the genome-wide chromosome interaction data, chromatin marks in 16 different cell types and 73 regulatory factors motifs from the Encyclopedia of DNA Element project. Furthermore, we used these function elements, as well as risk haplotype, binding affinity, conservation and P-values reported from the original GWAS to reprioritize the genetic variants. Using studies from low-density lipoprotein cholesterol, we demonstrated that our reprioritizing approach was effective and cell type specific. In conclusion, GWAS3D provides a comprehensive annotation and visualization tool to help users interpreting their results. The web server is freely available at http://jjwanglab.org/gwas3d.