{"applicableToSamples":"NORMAL","active":true,"applicableBlocks":["SECTION","NON_GLIOMA_PATHOGENIC","SNV_PINNED","SNV_MANUAL","CNV_INTERPRETATION","SIGN_PLACE","CNV_QUERY","FORMATTED_TEXT","SNV_ACMG_SF","POLYGENIC_TRAITS","GENOTYPING_SELECTED_SNP","ANCESTRY","DISCLAIMER","SNV_QUERY","CLINICAL_TRIALS","ONCO_RECOMMENDATIONS","PATIENT_INFO","GLIOMA_PATHOGENIC","CNV_RESULT","PHARMACOGENETIC","OLIGOGENIC_TRAITS","GLIOMA_ASSOCIATED","ONCO_RELEVANT_SNV_MANUAL","SAMPLE_INFO","CLINVAR_PHENOTYPES"],"blocks":[{"id":"d79fefcf-e8bc-4d45-9e10-70bb79980612","reportTemplateId":"751fd39a-9226-465e-8b2a-439e27a20537","type":"POLYGENIC_TRAITS","ordering":0,"parameters":{"name":"Polygenic traits","description":"Polygenic traits are traits, such as height or weight, that are caused by the action of multiple genes. In addition to genetics, polygenic traits are influenced by many other factors, such as environment, socioeconomic factors, and lifestyle.<p><p>The genetic component of polygenic traits is assessed using polygenic risk score (PRS). PRS incorporates the effects of many genetic variants into one number that predicts the genetic predisposition for trait. <p><p>Each PRS value can be plotted on a PRS distribution frequency plot (the bell-shaped curve). For most people, their PRS values will be in the middle region, but for some, these values may deviate to the left or right of the average, which will indicate the presence of a lower or higher value of the polygenic trait.<p><p>To calculate polygenic traits, predictive models (logistic regression model and Cox regression model)  are used that take into account PRS values, as well as parameters such as sex and age (allowable range: from 18 to 80 years).<p><p>The logistic regression model allows you to determine the relationship between the presence or absence of a disease and predictor variables. The Cox regression model takes into account the age of onset of the disease and, accordingly, allows you to see the dynamics of the risk of developing the disease with age. More about comparing these models: [Ingram DD, Kleinman JC. Empirical comparisons of proportional hazards and logistic regression models. Stat Med. 1989 May;8(5):525-38](https://pubmed.ncbi.nlm.nih.gov/2727473/).<p><p>How well a statistical model predicts the presence or absence of a disease in a person is determined by the AUC (Area Under the Receiver Operating Characteristic Curve) index. The AUC value ranges from 50% to 100%, where higher values indicate that the model has more predictive power.<p><p>\nAs prediction results for polygenic traits, the following information is provided:\n* predicted trait value (using a logistic regression model);\n* the range of possible values (95% confidence interval), which includes the true value of the trait with a high degree of confidence (using a logistic regression model);\n* a comparison of the predicted value with the mean value of the trait;\n* the quality of model prediction observed using the AUC score (for calculation of genetic predisposition to a certain disease);\n* a display of a patient's PRS value on a PRS distribution graph;\n* a graph of disease risk versus age (for calculation of genetic predisposition to a certain disease).","selectedTraitTypes":["HEIGHT","WEIGHT","BMI","OBESITY","CAD","IBD","T2D","PROSTATE_CANCER","BREAST_CANCER","COLORECTAL_CANCER","TRUNK_FAT_MASS","TRUNK_FAT_FREE_MASS","HIP_CIRCUMFERENCE","IGH1","FASTING_GLUCOSE","HEMAGLOBIN","FASTING_INSULIN","HEMATOCRIT","BONE_MINERAL_DENSITY","CHRONIC_RENAL_FAILURE","ASTHMA","HYPERTHYRIODITIS","PULMONARY_FIBROSIS","ALZHEIMER","ALCO_CIRRHOSIS","PARKINSON","NEUROTICISM_SCORE","RISK_TAKING_BEHAVIOUR","HOSPITALIZED_COVID","VERY_SEVERE_COVID"],"addInterpretationBlock":true}}],"createdAt":"2026-02-27T11:34:55.974Z","updatedAt":"2026-03-03T15:15:14.655Z","id":"751fd39a-9226-465e-8b2a-439e27a20537","ownerId":"88edbf10-b671-4c15-b1df-4fe81158696c","shortName":"Polygenic Traits","fullName":null,"description":null}