| Title: | Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes |
| Journal: | Journal of Personalized Medicine |
| Published: | 21 Jun 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34205563/ |
| DOI: | https://doi.org/10.3390/jpm11060582 |
| Citations: | 13 (2 in last 2 years) as of 8 Aug 2024 |
Publication 12697
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Abstract
One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight (n = 172,239) and comparative body size at age ten (n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.</p>
4 Authors
- Avigail Moldovan
- Yedael Y. Waldman
- Nadav Brandes
- Michal Linial
1 Application
| Application ID | Title |
|---|---|
| 26664 | Novel machine-learning framework for improved inference in GWAS |
Enabling scientific discoveries that improve human health