Utility Of Polygenic Scores Research Articles

Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling

Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores’ ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10−19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.

Polygenic scores for social science: Clarification, consensus, and controversy.

In this response, I focus on clarifying my arguments, highlighting consensus, and addressing competing views about the utility of polygenic scores (PGSs) for social science. I also discuss an assortment of expansions to my arguments and suggest alternative approaches. I conclude by reiterating the need for caution and appropriate scientific skepticism.

Polygenic scores in biomedical research.

Public health strategies aimed at disease prevention or early detection and intervention have the potential to advance human health worldwide. However, their success depends on the identification of risk factors that underlie disease burden in the general population. Genome-wide association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in common complex diseases or traits. By calculating a weighted sum of the number of trait-associated alleles harboured by an individual, a polygenic score (PGS), also called a polygenic risk score (PRS), can be constructed that reflects an individual’s estimated genetic predisposition for a given phenotype. Here, we ask six experts to give their opinions on the utility of these probabilistic tools, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use. In this Viewpoint, we asked six experts to give their opinions on the utility of polygenic scores, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use.

Polygenic association with severity and long-term outcome in eating disorder cases

About 20% of individuals with anorexia nervosa (AN) remain chronically ill. Therefore, early identification of poor outcome could improve care. Genetic research has identified regions of the genome associated with AN. Patients with anorexia nervosa were identified via the Swedish eating disorder quality registers Stepwise and Riksät and invited to participate in the Anorexia Nervosa Genetics Initiative. First, we associated genetic information longitudinally with eating disorder severity indexed by scores on the Clinical Impairment Assessment (CIA) in 2843 patients with lifetime AN with or without diagnostic migration to other forms of eating disorders followed for up to 16 years (mean = 5.3 years). Second, we indexed the development of a severe and enduring eating disorder (SEED) by a high CIA score plus a follow-up time ≥5 years. We associated individual polygenic scores (PGSs) indexing polygenic liability for AN, schizophrenia, and body mass index (BMI) with severity and SEED. After multiple testing correction, only the BMI PGS when calculated with traditional clumping and p value thresholding was robustly associated with disorder severity (βPGS = 1.30; 95% CI: 0.72, 1.88; p = 1.2 × 10–5) across all p value thresholds at which we generated the PGS. However, using the alternative PGS calculation method PRS-CS yielded inconsistent results for all PGS. The positive association stands in contrast to the negative genetic correlation between BMI and AN. Larger discovery GWASs to calculate PGS will increase power, and it is essential to increase sample sizes of the AN GWASs to generate clinically meaningful PGS as adjunct risk prediction variables. Nevertheless, this study provides the first evidence of potential clinical utility of PGSs for eating disorders.