Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders. - Ligthart, Symen; Vaez, Ahmad; Võsa, Urmo; Stathopoulou, Maria G; de Vries, Paul S; Prins, Bram P; van der Most, Peter J; Tanaka, Toshiko; Naderi, Elnaz; Rose, Lynda M; Wu, Ying; Karlsson, Robert; et al. (Am J Hum Genet, 2018)
C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits. - Tachmazidou, Ioanna; Süveges, Dániel; Min, Josine L; Ritchie, Graham R S; Steinberg, Julia; Walter, Klaudia; Iotchkova, Valentina; Schwartzentruber, Jeremy; Huang, Jie; Memari, Yasin; McCarthy, Shane; Crawford, Andrew A; et al. (Am J Hum Genet, 2017)
Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.
Keywords: Anthropometry; Body Height; Cohort Studies; Databases, Genetic; DNA Methylation; Female; Genetic Variation; Genome, Human; Genome-Wide Association Study; Humans; Lipodystrophy; Male; Meta-Analysis as Topic; Obesity; Physical Chromosome Mapping; Quantitative Trait Loci; Sequence Analysis, DNA; Sex Characteristics; Syndrome; United Kingdom

Modulation of genetic associations with serum urate levels by body-mass-index in humans. - Huffman, Jennifer E; Albrecht, Eva; Teumer, Alexander; Mangino, Massimo; Kapur, Karen; Johnson, Toby; Kutalik, Zoltán; Pirastu, Nicola; Pistis, Giorgio; Lopez, Lorna M; Haller, Toomas; Salo, Perttu; et al. (PLoS One, 2015)
We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 x 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDARADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.