AI Article Synopsis

  • * Individuals with extreme traits or high risk for serious diseases are more effectively identified through a small number of rare variants rather than through numerous common variants that have minimal effects.
  • * By integrating rare variants from related genes into a single genetic risk model, we created a more effective tool for predicting disease risk across different populations, outperforming traditional methods based on common variants.

Article Abstract

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ~10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared with common-variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.

Download full-text PDF

Source
http://dx.doi.org/10.1126/science.abo1131DOI Listing

Publication Analysis

Top Keywords

rare penetrant
12
penetrant mutations
8
common diseases
8
common variants
8
risk
5
rare
4
mutations confer
4
confer severe
4
severe risk
4
common
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!