Xihong Lin

Jun 13, 2024 | nature.com | Zachary R McCaw |Xihong Lin

AbstractWithin population biobanks, incomplete measurement of certain traits limits the power for genetic discovery. Machine learning is increasingly used to impute the missing values from the available data. However, performing genome-wide association studies (GWAS) on imputed traits can introduce spurious associations, identifying genetic variants that are not associated with the original trait.

Dec 1, 2023 | nature.com | Xihong Lin

AbstractExisting SNP-heritability estimators that leverage summary statistics from genome-wide association studies (GWAS) are much less efficient (i.e., have larger standard errors) than the restricted maximum likelihood (REML) estimators which require access to individual-level data.

Sep 25, 2023 | nature.com | Haoyu Zhang |Jingning Zhang |Thomas Ahearn |Zhi Yu |TONY CHEN |Montserrat Garcia-Closas | +1 more

AbstractPolygenic risk scores (PRSs) increasingly predict complex traits; however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning.