| Title: | Sibling validation of polygenic risk scores and complex trait prediction |
| Journal: | Scientific Reports |
| Published: | 6 Aug 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32764582/ |
| DOI: | https://doi.org/10.1038/s41598-020-69927-7 |
| URL: | https://www.nature.com/articles/s41598-020-69927-7.pdf |
Publication 3462
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Abstract
We test 26 polygenic predictors using tens of thousands of genetic siblings from the UK Biobank (UKB), for whom we have SNP genotypes, health status, and phenotype information in late adulthood. Siblings have typically experienced similar environments during childhood, and exhibit negligible population stratification relative to each other. Therefore, the ability to predict differences in disease risk or complex trait values between siblings is a strong test of genomic prediction in humans. We compare validation results obtained using non-sibling subjects to those obtained among siblings and find that typically most of the predictive power persists in between-sibling designs. In the case of disease risk we test the extent to which higher polygenic risk score (PRS) identifies the affected sibling, and also compute Relative Risk Reduction as a function of risk score threshold. For quantitative traits we examine between-sibling differences in trait values as a function of predicted differences, and compare to performance in non-sibling pairs. Example results: Given 1 sibling with normal-range PRS score (< 84 percentile, < + 1 SD) and 1 sibling with high PRS score (top few percentiles, i.e. > + 2 SD), the predictors identify the affected sibling about 70-90% of the time across a variety of disease conditions, including Breast Cancer, Heart Attack, Diabetes, etc. 55-65% of the time the higher PRS sibling is the case. For quantitative traits such as height, the predictor correctly identifies the taller sibling roughly 80 percent of the time when the (male) height difference is 2 inches or more.</p>
3 Authors
- Louis Lello
- Timothy G. Raben
- Stephen D. H. Hsu
1 Application
| Application ID | Title |
|---|---|
| 15326 | Compressed Sensing and high-dimensional statistical methods in complex trait genomics |
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