孟德尔随机化
生命银行
生物
全基因组关联研究
疾病
痛风
生物信息学
生物标志物
内科学
医学
基因型
遗传学
单核苷酸多态性
遗传变异
基因
作者
Nasa Sinnott-Armstrong,Yosuke Tanigawa,David Amar,Nina Mars,Christian Benner,Matthew Aguirre,Guhan Ram Venkataraman,Michael Wainberg,Hanna M. Ollila,Tuomo Kiiskinen,Aki S. Havulinna,James P. Pirruccello,Junyang Qian,Anna Shcherbina,Fátima Rodríguez,Themistocles L. Assimes,Vineeta Agarwala,Robert Tibshirani,Trevor Hastie,Samuli Ripatti,Jonathan K. Pritchard,Mark J. Daly,Manuel A. Rivas
出处
期刊:Nature Genetics
[Nature Portfolio]
日期:2021-01-18
卷期号:53 (2): 185-194
被引量:536
标识
DOI:10.1038/s41588-020-00757-z
摘要
Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.
科研通智能强力驱动
Strongly Powered by AbleSci AI