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Biomedical subjects

Yingzhe Zhang

Publications and source records attributed to Yingzhe Zhang.

2 recordsLinked to original sources

Rare Coding Variants Reveal Distinct Genetic Architectures Across Multidimensional Sleep Phenotypes.

Sleep and circadian traits have been widely studied using common variants, but the contribution of rare coding variation remains unclear. We analyzed rare coding variants in 397,065 whole-exome sequenced UK Biobank participants across 36 sleep phenotypes from self-report, diagnoses, sleep medication use and accelerometry, and meta-analyzed results with 171,536 whole-genome sequenced All of Us participants of diverse ancestries, with replication in the Mass General Brigham Biobank (N = 31,275). We identified 260 genes associated with sleep phenotypes, including novel associations with sleep medication use in 29 genes and 24 out of 29 have not previously been reported with any sleep phenotypes. We observed modest but significant rare variant heritability and strong genetic correlations between sleep medication use, insomnia and fatigue. Temporal gene expression trajectory analyses indicate that genes associated with self-reported sleep traits show constant high prenatal expression, whereas genes linked to sleep medication phenotypes exhibit peak expression in the late prenatal period. These findings highlight distinct biological mechanisms captured by different measurement sources of sleep phenotypes and reveal rare-variant-informed targets for therapeutic discovery.

exome sequencing

Characterizing the Uncertainty, Misclassification and Inconsistency of Polygenic Prediction.

Polygenic risk scores (PRSs) hold promise for precision medicine, yet their clinical translation is hindered by substantial uncertainty in individual risk estimates and often limited agreement in risk stratification across multiple PRSs for the same disease. We develop a unified inferential framework to calibrate PRS point estimates and uncertainties for both quantitative traits and binary phenotypes, and to characterize how PRS accuracy, uncertainty, pairwise correlation jointly determine misclassification and classification inconsistency. We show, both theoretically and empirically, that individual- and population-level misclassification and inconsistency rates are highly predictable in independent datasets. We further evaluate PRS integration and uncertainty-aware probabilistic thresholding strategies that reduce misclassification and improve concordance in risk stratification. Together, these results demonstrate that instability in PRS-based classification is a predictable statistical consequence of uncertainty and establish a principled foundation for incorporating uncertainty into PRS-based risk interpretation, communication, and clinical decision-making.

Journal Article