A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> The Human Gene Mutation Database (HGMD) represents a comprehensive core collection of data on published germline mutations in nuclear genes underlying human inherited disease. By September 1997, the database contained nearly 12 000 different lesions in a total of 636 different genes, with new entries currently accumulating at a rate of over 2000 per annum. Although originally established for the scientific study of mutational mechanisms in human genes, HGMD has acquired a much broader utility to researchers, physicians and genetic counsellors so that it was made publicly available at http://uwcm.ac.uk/uwcm/mg/hgmd0.html in April 1996. Mutation data in HGMD are accessible on the basis of every gene being allocated one web page per mutation type, if data of that type are present. Meaningful integration with phenotypic, structural and mapping information has been accomplished through bi-directional links between HGMD and both the Genome Database (GDB) and Online Mendelian Inheritance in Man (OMIM), Baltimore, USA. Hypertext links have also been established to Medline abstracts through Entrez , and to a collection of 458 reference cDNA sequences also used for data checking. Being both comprehensive and fully integrated into the existing bioinformatics structures relevant to human genetics, HGMD has established itself as the central core database of inherited human gene mutations. <s> BIB001 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> The biomedical world relies heavily on the definition of pharmaceutical targets as anessential step in the drug design process. It is therefore tempting to apply thismodel to genetic diseases as well. However, whereas the model applies well togain-of-function genetic diseases, it is less suited to most loss-of-function geneticdiseases. Most common diseases, as well as gain-of-function genetic diseases, arecharacterized by the activation of specific pathways or the ectopic activity ofproteins, which make well identified targets. By contrast, loss-of-function geneticdiseases are caused by the impairment of one protein, with potentially distributedconsequences. For such diseases, the definition of a pharmaceutical target is lessprecise, and the identification of pharmaceutically-relevant targets may bedifficult. This critical but largely ignored aspect of loss-of-function geneticdiseases should be taken into consideration to avoid the commitment of resources toinappropriate strategies in the search for treatments. <s> BIB002 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> McKusick's Online Mendelian Inheritance in Man (OMIM®; http://www.ncbi.nlm.nih.gov/omim), a knowledgebase of human genes and phenotypes, was originally published as a book, Mendelian Inheritance in Man, in 1966. The content of OMIM is derived exclusively from the published biomedical literature and is updated daily. It currently contains 18 961 full-text entries describing phenotypes and genes. To date, 2239 genes have mutations causing disease, and 3770 diseases have a molecular basis. Approximately 70 new entries are added and 700 entries are updated per month. OMIM® is expanding content and organization in response to shifting biological paradigms and advancing biotechnology. <s> BIB003 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> Many patients suffering from developmental disorders harbor submicroscopic deletions or duplications that, by affecting the copy number of dosage-sensitive genes or disrupting normal gene expression, lead to disease. However, many aberrations are novel or extremely rare, making clinical interpretation problematic and genotype-phenotype correlations uncertain. Identification of patients sharing a genomic rearrangement and having phenotypic features in common leads to greater certainty in the pathogenic nature of the rearrangement and enables new syndromes to be defined. To facilitate the analysis of these rare events, we have developed an interactive web-based database called DECIPHER (Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources) which incorporates a suite of tools designed to aid the interpretation of submicroscopic chromosomal imbalance, inversions, and translocations. DECIPHER catalogs common copy-number changes in normal populations and thus, by exclusion, enables changes that are novel and potentially pathogenic to be identified. DECIPHER enhances genetic counseling by retrieving relevant information from a variety of bioinformatics resources. Known and predicted genes within an aberration are listed in the DECIPHER patient report, and genes of recognized clinical importance are highlighted and prioritized. DECIPHER enables clinical scientists worldwide to maintain records of phenotype and chromosome rearrangement for their patients and, with informed consent, share this information with the wider clinical research community through display in the genome browser Ensembl. By sharing cases worldwide, clusters of rare cases having phenotype and structural rearrangement in common can be identified, leading to the delineation of new syndromes and furthering understanding of gene function. <s> BIB004 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> We have developed an online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs). Reported TASs were common [median risk allele frequency 36%, interquartile range (IQR) 21%−53%] and were associated with modest effect sizes [median odds ratio (OR) 1.33, IQR 1.20–1.61]. Among 20 genomic annotation sets, reported TASs were significantly overrepresented only in nonsynonymous sites [OR = 3.9 (2.2−7.0), p = 3.5 × 10−7] and 5kb-promoter regions [OR = 2.3 (1.5−3.6), p = 3 × 10−4] compared to SNPs randomly selected from genotyping arrays. Although 88% of TASs were intronic (45%) or intergenic (43%), TASs were not overrepresented in introns and were significantly depleted in intergenic regions [OR = 0.44 (0.34−0.58), p = 2.0 × 10−9]. Only slightly more TASs than expected by chance were predicted to be in regions under positive selection [OR = 1.3 (0.8−2.1), p = 0.2]. This new online resource, together with bioinformatic predictions of the underlying functionality at trait/disease-associated loci, is well-suited to guide future investigations of the role of common variants in complex disease etiology. <s> BIB005 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> Recent genome-wide studies have reported novel associations between common polymorphisms and susceptibility to many major infectious diseases in humans. In parallel, an increasing number of rare mutations underlying susceptibility to specific phenotypes of infectious disease have been described. Together, these developments have highlighted a key role for host genetic variation in determining the susceptibility to infectious disease. They have also provided insights into the genetic architecture of infectious disease susceptibility and identified immune molecules and pathways that are directly relevant to the human host defence. <s> BIB006 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> Genome-wide association studies (GWASs) have enabled the discovery of common genetic variation contributing to normal and pathological traits and clinical drug responses, but recognizing the precise targets of these associations is now the major challenge. Here, we review recent approaches to the functional follow-up of GWAS loci, including fine mapping of GWAS signal(s), prioritization of putative functional SNPs by the integration of genetic epidemiological and bioinformatic methods, and in vitro and in vivo experimental verification of predicted molecular mechanisms for identifying the targeted genes. The majority of GWAS-identified variants fall in noncoding regions of the genome. Therefore, this review focuses on strategies for assessing likely mechanisms affected by noncoding variants; such mechanisms include transcriptional regulation, noncoding RNA function, and epigenetic regulation. These approaches have already accelerated progress from genetic studies to biological knowledge and might ultimately guide the development of prognostic, preventive, and therapeutic measures. <s> BIB007 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> Cardiovascular disease is the most common cause of death worldwide, and hypertension is the major risk factor. Mendelian hypertension elucidates mechanisms of blood pressure regulation. Here we report six missense mutations in PDE3A (encoding phosphodiesterase 3A) in six unrelated families with mendelian hypertension and brachydactyly type E (HTNB). The syndrome features brachydactyly type E (BDE), severe salt-independent but age-dependent hypertension, an increased fibroblast growth rate, neurovascular contact at the rostral-ventrolateral medulla, altered baroreflex blood pressure regulation and death from stroke before age 50 years when untreated. In vitro analyses of mesenchymal stem cell-derived vascular smooth muscle cells (VSMCs) and chondrocytes provided insights into molecular pathogenesis. The mutations increased protein kinase A-mediated PDE3A phosphorylation and resulted in gain of function, with increased cAMP-hydrolytic activity and enhanced cell proliferation. Levels of phosphorylated VASP were diminished, and PTHrP levels were dysregulated. We suggest that the identified PDE3A mutations cause the syndrome. VSMC-expressed PDE3A deserves scrutiny as a therapeutic target for the treatment of hypertension. <s> BIB008 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) at the National Center for Biotechnology Information (NCBI) is a freely available archive for interpretations of clinical significance of variants for reported conditions. The database includes germline and somatic variants of any size, type or genomic location. Interpretations are submitted by clinical testing laboratories, research laboratories, locus-specific databases, OMIM®, GeneReviews™, UniProt, expert panels and practice guidelines. In NCBI's Variation submission portal, submitters upload batch submissions or use the Submission Wizard for single submissions. Each submitted interpretation is assigned an accession number prefixed with SCV. ClinVar staff review validation reports with data types such as HGVS (Human Genome Variation Society) expressions; however, clinical significance is reported directly from submitters. Interpretations are aggregated by variant-condition combination and assigned an accession number prefixed with RCV. Clinical significance is calculated for the aggregate record, indicating consensus or conflict in the submitted interpretations. ClinVar uses data standards, such as HGVS nomenclature for variants and MedGen identifiers for conditions. The data are available on the web as variant-specific views; the entire data set can be downloaded via ftp. Programmatic access for ClinVar records is available through NCBI's E-utilities. Future development includes providing a variant-centric XML archive and a web page for details of SCV submissions. <s> BIB009 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> The NHGRI-EBI GWAS Catalog has provided data from published genome-wide association studies since 2008. In 2015, the database was redesigned and relocated to EMBL-EBI. The new infrastructure includes a new graphical user interface (www.ebi.ac.uk/gwas/), ontology supported search functionality and an improved curation interface. These developments have improved the data release frequency by increasing automation of curation and providing scaling improvements. The range of available Catalog data has also been extended with structured ancestry and recruitment information added for all studies. The infrastructure improvements also support scaling for larger arrays, exome and sequencing studies, allowing the Catalog to adapt to the needs of evolving study design, genotyping technologies and user needs in the future. <s> BIB010 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Genetic Associations <s> Background ::: Identification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 5257 individuals, exceeding the single cohort size of previous studies by more than a factor of 2. <s> BIB011
Since many of human diseases are proven to have a genetic component, the discovery of genetic variants underlying diseases is one of the major challenges of human genetics. Impact of the individual genetic constitution on the development of the disease may be very different: from highly heritable Mendelian disorders, e.g. sicklecell anemia (OMIM:#603903) or Duchenne muscular dystrophy (OMIM:#310200) caused by mutations in certain genes, to infectious diseases BIB006 caused by an external pathogenic agent. Several thousands of the human diseases are caused by the dysregulation of a single gene: loss BIB002 or modification BIB008 of its function. These diseases are usually rare and aggregate in families which carry a specific mutation. In addition to medical significance, such monogenic diseases allow connecting phenotypes and genes and thus providing us with clues about gene functions. Therefore, one of the main and widely used sources of knowledge for gene prioritization is OMIM BIB003 . OMIM provides a constantly updating catalog of hereditary disorders and associated genes. Although OMIM initiated as a database of Mendelian diseases, now in addition to more than 5000 single-gene traits, it coves about 700 complex and 150 non-disease phenotypes. Besides OMIM, several other databases collect information about rare monogenic diseases and disease-causing variants: OrphaNet  , DECIPHER BIB004 , ClinVar BIB009 , HGMD BIB001 . In addition to comprehensive catalogs of human monogenic diseases, one can obtain genotype and phenotype information on animal models of human diseases from species-specific databases e.g. Mouse Genome Database  and Rat Genome Database  . Unlike Mendelian diseases, complex diseases cannot be explained by a single mutation with high effect but thought to be the result of interactions between multiple genetic and environmental factors. Genome-wide association studies (GWAS) discover common single nucleotide polymorphisms (SNPs), which allelic states significantly correlate with disease status. NHGRI-EBI GWAS Catalog BIB010 provides a curated and regularly updated lists of published GWAS, and contains over 60,000 of SNP-trait associations from 3411 publications. However, effects of such risk variants identified in GWAS are much weaker than effects of Mendelian variants. Moreover, linking a variant with its effect on a certain gene may be a challenging task because, in contrast with Mendelian disease variants, only a small fraction of GWAS hits has an obvious effect on the protein, e.g. missense substitution or frameshift. Most of significant GWAS variants locate in intronic or intergenic regions BIB005 . Mapping them to the closest gene may not always be correct, and considering regulatory annotations for mapping of silent GWAS variants to genes appears to be a better strategy BIB007 . Joehanes et al. BIB011 have shown, that about half of GWAS variants lay in expression quantitative trait loci (eQTL) -genome regions with markers correlated with expression levels of one or several genes.