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Polygenic Traits

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Relative Risk

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...

Review and Preview

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

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Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

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Metrics for Evaluating Polygenic Risk Scores.

Stuart G Baker¹

¹Division of Cancer Prevention, Biometry Research Group, National Cancer Institute, Bethesda, MD, USA.

JNCI Cancer Spectrum

|December 27, 2021

Summary

This summary is machine-generated.

Evaluating polygenic risk scores for cancer prediction requires appropriate metrics. The minimum test tradeoff metric is recommended for its direct relation to clinical utility, unlike AUC or subset relative risk.

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Published on: August 15, 2019

Area of Science:

Genetics and Genomics
Cancer Epidemiology
Biostatistics

Background:

Polygenic risk scores (PRS) are increasingly used to predict cancer incidence based on genetic variants.
The choice of metric significantly impacts the interpretation of PRS predictive performance.

Purpose of the Study:

To compare three metrics for evaluating PRS predictive performance: Area Under the Curve (AUC), Subset Relative Risk (SRR), and Minimum Test Tradeoff (MTT).
To determine which metric best reflects the clinical utility of PRS in cancer risk prediction.

Main Methods:

Comparative analysis of three distinct metrics for evaluating PRS.
Mathematical demonstration that SRR is a direct re-labeling of AUC.
Evaluation of MTT in relation to expected clinical utility.

Main Results:

Subset Relative Risk (SRR) was shown to be equivalent to the Area Under the Curve (AUC).
The Minimum Test Tradeoff (MTT) metric was found to be directly associated with expected clinical utility.

Conclusions:

AUC and SRR provide limited insight into the practical clinical utility of PRS.
The Minimum Test Tradeoff (MTT) is the recommended metric for evaluating PRS due to its direct correlation with clinical utility in cancer risk prediction.