Correcting for Genomic Inflation Leads to Loss of Power in Large-Scale Genome-Wide Association Study Meta-Analysis.

Inflation in genome-wide association studies (GWAS) summary statistics represents a major challenge, for which correction methods have been developed. These include the genomic control (GC) method, which uses the λ-value to correct summary statistics, and the linkage disequilibrium score regression (LDSR) method, which uses the LDSR intercept. By using type 2 diabetes (T2D) as an exemplar, we explore factors influencing λ-values and the impact of these corrections on association signals.

Insights into the molecular underpinning of type 2 diabetes complications.

Type 2 diabetes (T2D) complications pose a significant global health challenge. Omics technologies have been employed to investigate these complications and identify the biological pathways involved. In this review, we focus on four major T2D complications: diabetic kidney disease, diabetic retinopathy, diabetic neuropathy, and cardiovascular complications.

Disentangling the consequences of type 2 diabetes on targeted metabolite profiles using causal inference and interaction QTL analyses.

Circulating metabolite levels have been associated with type 2 diabetes (T2D), but the extent to which T2D affects metabolite levels and their genetic regulation remains to be elucidated. In this study, we investigate the interplay between genetics, metabolomics, and T2D risk in the UK Biobank dataset using the Nightingale panel composed of 249 metabolites, 92% of which correspond to lipids (HDL, IDL, LDL, VLDL) and lipoproteins. By integrating these data with large-scale T2D GWAS from the DIAMANTE meta-analysis through Mendelian randomization analyses, we find 79 metabolites with a causal association to T2D, all spanning lipid-related classes except for Glucose and Tyrosine.

Diffusion controls local versus dispersed inheritance of histones during replication and shapes epigenomic architecture.

The dynamics of inheritance of histones and their associated modifications across cell divisions can have major consequences on maintenance of the cellular epigenomic state. Recent experiments contradict the long-held notion that histone inheritance during replication is always local, suggesting that active and repressed regions of the genome exhibit fundamentally different histone dynamics independent of transcription-coupled turnover. Here we develop a stochastic model of histone dynamics at the replication fork and demonstrate that differential diffusivity of histones in active versus repressed chromatin is sufficient to quantitatively explain these recent experiments.