The Biobank Japan Project

The paper develops a framework to quantify predictive uncertainty in biological age estimates, revealing hidden dimensions of aging and improving risk interpretation.

The paper establishes a global consortium to discover rare genetic associations by integrating data from multiple biobanks, identifying 514 gene-trait associations, many detectable only through cross-ancestry meta-analysis.

The paper introduces scLinaX, a method to quantify X chromosome inactivation escape using scRNA-seq, revealing heterogeneity across cell types and tissues.

This study shows that polygenic risk scores from multi-ancestry GWAS perform better in understudied populations than single-ancestry scores, except when the understudied group is small.

The paper uses deep learning and genome-wide association studies to identify genetic loci associated with right heart structure and function in 40,000 individuals.

This paper introduces PolyPred and PolyPred+ to improve cross-population polygenic risk score accuracy by addressing LD, MAF, and causal effect size differences using fine-mapping and non-European training data.

This study conducts a trans-ancestry GWAS of rheumatoid arthritis, identifying 124 loci, 34 novel, and shows improved polygenic risk score performance across populations.

Fine-mapping 148 complex traits in three biobanks with combined n = 811,261 yields 4,518 causal variant-trait pairs, 285 of them repeating in more than one population.

The paper uses supervised learning to fine-map cis-eQTLs, identifying 20,913 additional putative causal eQTLs and 310 candidate genes.

This study combines multiple GWAS of ageing traits to identify genetic components associated with human ageing and finds LPA and VCAM1 as potential drug targets.

The paper investigates the association between age-related mosaic chromosomal alterations and infection risk in 767,891 individuals without blood cancer, finding increased infection susceptibility linked to mCAs.