medRxivGenetic & Genomic MedicinePreprint
Deleterious Variants Contribute Minimal Excess Risk in Large-Scale Testing
Yen‐Tsung HuangiD, En‐Yu LaiiD, Jia-Ying SuiD, Hsueh-Ju Lu, Yen‐Lin CheniD, Jer-Yuarn WuiD, Chunyu WeiiD, Ling‐Hui LiiD, Cathy S.J. FanniD, Hsin‐Chou YangiD, Chien-Hsiun CheniD, Hung‐Hsin CheniD, Yi-Min LiuiD, Ming-Fang TsaiiD, Erh‐Chan YehiD, Chih-Kuang ChengiD, Yen‐Po WangiD, Nai‐Fang ChiiD, I‐Cheng LeeiD, Harn-Shen CheniD, Yun‐Cheng Hsieh, Yi‐Chu LiaoiD, Shao‐Jung HsuiD, Shuo‐Ming OuiD, Kuan‐Lin LaiiD, , Chang‐Hsun HsiehiD
Abstract
DNA sequencing of patients with rare disorders has been highly successful in identifying "causal variants" for numerous conditions. However, there are many reports of healthy individuals who harbor these deleterious variants, leading to the concept of incomplete penetrance and doubt about the utility of genetic testing in clinical practice and population screening. As the deleterious variants are rare, the penetrance of these variants in the population is largely unknown. We analyzed the genetic and clinical data from 486,956 participants of the Taiwan Precision Medicine Initiative (TPMI) to determine the risk difference between those with and without deleterious variants. In all, we analyzed 292 disease-relevant variants and their clinical outcomes to assess their association. We found that only 15 variants show a risk difference exceeding 5% between those with or without the variants. In essence, 87.3% of deleterious variants exhibit minimal risk differences, suggesting a limited impact on the individual and population levels. Our analysis revealed increasing trends with age in six cardiovascular and degenerative diseases and bell-shaped trends in two cancers. Additionally, we identified three clinical outcomes exhibiting a dose-response relationship with the number of deleterious variants. Our findings show that large-scale testing of deleterious variants found in the literature is not warranted, except for those exhibiting large disease risk differences.
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 1
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