Prediction methods have become an integral part of biomedical and biotechnological research. However, their clinical interpretations are largely based on biochemical or molecular data, but not clinical data. Here, we focus on improving the reliability and clinical applicability of prediction algorithms. We assembled and curated two large non-overlapping large databases of clinical phenotypes. These phenotypes were caused by missense variations in 44 and 63 genes associated with Mendelian diseases. We used these databases to establish and validate the model, allowing us to improve the predictions obtained from EVmutation, SNAP2 and PoPMuSiC 2.1. The predictions of clinical effects suffered from a lack of specificity, which appears to be the common constraint of all recently used prediction methods, although predictions mediated by these methods are associated with nearly absolute sensitivity. We introduced evidence-based tailoring of the default settings of the prediction methods; this tailoring substantially improved the prediction outcomes. Additionally, the comparisons of the clinically observed and theoretical variations led to the identification of large previously unreported pools of variations that were under negative selection during molecular evolution. The evolutionary variation analysis approach described here is the first to enable the highly specific identification of likely disease-causing missense variations that have not yet been associated with any clinical phenotype.

• MeSH
• Algorithms MeSH
• Ectodysplasins genetics   MeSH
• Phenotype MeSH
• Genetic Variation MeSH
• Genetic Diseases, Inborn genetics   MeSH
• Genomics MeSH
• Glucosephosphate Dehydrogenase genetics   MeSH
• Hemoglobins genetics   MeSH
• Hepatocyte Nuclear Factor 4 genetics   MeSH
• Humans MeSH
• Mutation, Missense MeSH
• Models, Genetic * MeSH
• Evolution, Molecular MeSH
• Mutation * MeSH
• Likelihood Functions MeSH
• Proteomics MeSH
• Protein Tyrosine Phosphatase, Non-Receptor Type 11 genetics   MeSH
• Computational Biology methods   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• EDA protein, human MeSH Browser
• Ectodysplasins MeSH
• G6PD protein, human MeSH Browser
• Glucosephosphate Dehydrogenase MeSH
• hemoglobin B MeSH Browser
• Hemoglobins MeSH
• Hepatocyte Nuclear Factor 4 MeSH
• HNF4A protein, human MeSH Browser
• PTPN11 protein, human MeSH Browser
• Protein Tyrosine Phosphatase, Non-Receptor Type 11 MeSH

Maturity onset diabetes of the young (MODY) represents a diabetes type which has an enormous clinical impact. It significantly alters treatment, refines a patient's prognosis and enables early detection of diabetes in relatives. Nevertheless, when diabetes is manifested the vast majority of MODY patients are not correctly diagnosed, but mostly falsely included among patients with type 1 or type 2 diabetes, in many cases permanently. The aim of this article is to offer a simple and comprehensible guide for recognizing individuals with MODY hidden among adult patients with another type of long-term diabetes and in women with gestational diabetes.

• Keywords
• Differential diagnosis, Gestational diabetes, MODY, Type 1 diabetes mellitus, Type 2 diabetes mellitus,
• MeSH
• Diabetes Mellitus, Type 2 * classification   diagnosis   MeSH
• Child MeSH
• Humans MeSH
• Adolescent MeSH
• Child, Preschool MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Adolescent MeSH
• Male MeSH
• Child, Preschool MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

• Keywords
• GCK-MODY, HNF1A-MODY, HNF4A-MODY, MODY, diagnostics, differential diagnosis,
• Publication type
• Journal Article MeSH

Computational methods that allow predicting the effects of nonsynonymous substitutions are an integral part of exome studies. Here, we validated and improved their specificity by performing a comprehensive bioinformatics analysis combined with experimental and clinical data on a model of glucokinase (GCK): 8835 putative variations, including 515 disease-associated variations from 1596 families with diagnoses of monogenic diabetes (GCK-MODY) or persistent hyperinsulinemic hypoglycemia of infancy (PHHI), and 126 variations with available or newly reported (19 variations) data on enzyme kinetics. We also proved that high frequency of disease-associated variations found in patients is closely related to their evolutionary conservation. The default set prediction methods predicted correctly the effects of only a part of the GCK-MODY-associated variations and completely failed to predict the normoglycemic or PHHI-associated variations. Therefore, we calculated evidence-based thresholds that improved significantly the specificity of predictions (≤75%). The combined prediction analysis even allowed to distinguish activating from inactivating variations and identified a group of putatively highly pathogenic variations (EVmutation score <-7.5 and SNAP2 score >70), which were surprisingly underrepresented among MODY patients and thus under negative selection during molecular evolution. We suggested and validated the first robust evidence-based thresholds, which allow improved, highly specific predictions of disease-associated GCK variations.

• MeSH
• Enzyme Activation MeSH
• Diabetes Mellitus, Type 2 genetics   metabolism   MeSH
• Glucokinase chemistry   genetics   MeSH
• Kinetics MeSH
• Humans MeSH
• Evolution, Molecular MeSH
• Disease Susceptibility MeSH
• Amino Acid Substitution * MeSH
• Computational Biology * methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Glucokinase MeSH