Hidetoshi Kitajima

Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution.

Abstract Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals.

DNA methylation of the Klf14 gene region in whole blood cells provides prediction for the chronic inflammation in the adipose tissue

Krüppel-Like Factor 14 (KLF14) gene, which appears to be a master regulator of gene expression in the adipose tissue and have previously been associated with BMI and Type 2 diabetes (T2D) by large genome-wide association studies. In order to find predictive biomarkers for the development of T2D, it is necessary to take epigenomic changes affected by environmental factors into account. This study focuses on ageing and obesity, which are T2D risk factors, and examines epigenetic changes and inflammatory changes. We investigated DNA methylation changes in the Klf14 promoter region in different organs of mice for comparing aging and weight. We found that methylation levels of these sites were increased with aging and weight in the spleen, the adipose tissue, the kidney, the lung, the colon and the whole blood cells. In addition, in the spleen, the adipose tissue and the whole blood, these epigenetic changes were also significantly associated with inflammatory levels. Moreover, not only Klf14, but also expression levels of some downstream genes were decreased with methylation in the spleen, the adipose tissue and the whole blood cells. Taken together, our results suggest that methylation changes of Klf14 in those tissues may be associated with changes in gene expression and inflammation on the adipose tissue of obesity and T2D. In addition, the methylation changes in the whole blood cells may serve as a predictive epigenetic biomarker for the development of T2D.