小柯机器人

近日，美国加州大学洛杉矶分校Bogdan Pasaniuc等研究人员合作发现，个人多基因风险分数估计的巨大不确定性会影响基于贝叶斯多基因风险评分（PRS）的风险分层。该项研究成果于2021年12月20日在线发表在《自然—遗传学》杂志上。

研究人员表明，PRS方法可以估计个体PRS的方差，并可以通过后验抽样产生良好校准的可信区间。对于英国生物库中的13个真实性状（n=291,273个无血缘关系的"英国白人"），研究人员观察到个体PRS估计值的巨大变异，这影响了对基于PRS的分层解释；对不同性状进行平均，只有0.8%（s.d.=1.6%）PRS点估计值在前十位的个体具有相应95%可信区间是完全包含在前十位的。研究人员为个人PRS方差的期望值提供了一个分析性估计，并作为SNP遗传率、因果SNP数量和样本量的函数。这些结果显示了将个体PRS估计的不确定性纳入后续分析的重要性。

据介绍，虽然PRS的队列级准确性已被广泛评估，但PRS的不确定性仍未被充分探索。

附：英文原文

Title: Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification

Author: Ding, Yi, Hou, Kangcheng, Burch, Kathryn S., Lapinska, Sandra, Priv, Florian, Vilhjlmsson, Bjarni, Sankararaman, Sriram, Pasaniuc, Bogdan

Issue&Volume: 2021-12-20

Abstract: Although the cohort-level accuracy of polygenic risk scores (PRSs)—estimates of genetic value at the individual level—has been widely assessed, uncertainty in PRSs remains underexplored. In the present study, we show that Bayesian PRS methods can estimate the variance of an individual’s PRS and can yield well-calibrated credible intervals via posterior sampling. For 13 real traits in the UK Biobank (n=291,273 unrelated ‘white British’), we observe large variances in individual PRS estimates which impact interpretation of PRS-based stratification; averaging across traits, only 0.8% (s.d.=1.6%) of individuals with PRS point estimates in the top decile have corresponding 95% credible intervals fully contained in the top decile. We provide an analytical estimator for the expectation of individual PRS variance as a function of SNP heritability, number of causal SNPs and sample size. Our results showcase the importance of incorporating uncertainty in individual PRS estimates into subsequent analyses.

DOI: 10.1038/s41588-021-00961-5

Source: https://www.nature.com/articles/s41588-021-00961-5

Nature Genetics：《自然—遗传学》，创刊于1992年。隶属于施普林格·自然出版集团，最新IF：41.307
官方网址：https://www.nature.com/ng/
投稿链接：https://mts-ng.nature.com/cgi-bin/main.plex