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Asthma is a complex heterogeneous disease caused by gene-environment interactions. Although numerous genome-wide association studies have been conducted, these interactions have not been systemically investigated. We sought to identify genetic factors associated with the asthma phenotype in 66,857 subjects from the Health Examination Study, Cardiovascular Disease Association Study, and Korea Association Resource Study cohorts. We investigated asthma-associated gene-environment (smoking status) interactions at the level of single nucleotide polymorphisms, genes, and gene sets. We identified two potentially novel ( SETDB1 and ZNF8 ) and five previously reported ( DM4C , DOCK8 , MMP20 , MYL7 , and ADCY9 ) genes associated with increased asthma risk. Numerous gene ontology processes, including regulation of T cell differentiation in the thymus (GO:0033081), were significantly enriched for asthma risk. Functional annotation analysis confirmed the causal relationship between five genes (two potentially novel and three previously reported genes) and asthma through genome-wide functional prediction scores (combined annotation-dependent depletion, deleterious annotation of genetic variants using neural networks, and RegulomeDB). Our findings elucidate the genetic architecture of asthma and improve the understanding of its biological mechanisms. However, further studies are necessary for developing preventive treatments based on environmental factors and understanding the immune system mechanisms that contribute to the etiology of asthma.

We investigated ethnicity-specific exonic variants of type 2 diabetes (T2D) and its related clinical phenotypes in an East Asian population. We performed whole-exome sequencing in 917 T2D case and control subjects, and the findings were validated by exome array genotyping in 3,026 participants. In silico replication was conducted for seven nonsynonymous variants in an additional 13,122 participants. Single-variant and gene-based association tests for T2D were analyzed. A total of 728,838 variants were identified by whole-exome sequencing. Among nonsynonymous variants, PAX4 Arg192His increased risk of T2D and GLP1R Arg131Gln decreased risk of T2D in genome-wide significance (odds ratio [OR] 1.48, P = 4.47 × 10 -16 and OR 0.84, P = 3.55 × 10 -8 , respectively). Another variant at PAX4 192 codon Arg192Ser was nominally associated with T2D (OR 1.62, P = 5.18 × 10 -4 ). In T2D patients, PAX4 Arg192His was associated with earlier age at diagnosis, and GLP1R Arg131Gln was associated with decreased risk of cardiovascular disease. In control subjects without diabetes, the PAX4 Arg192His was associated with higher fasting glucose and GLP1R Arg131Gln was associated with lower fasting glucose and HbA 1c level. Gene-based analysis revealed that SLC30A8 was most significantly associated with decreased risk of T2D ( P = 1.0 × 10 -4 ). In summary, we have identified nonsynonymous variants associated with risk of T2D and related phenotypes in Koreans., (© 2018 by the American Diabetes Association.)

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis -expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants., Competing Interests: Conflict of interest statement: S.E.L., J. Laramie, and R.G.T. were employees of Complete Genomics during this study. T.M.T. is an employee of Regeneron Pharmaceuticals. D.A. is an employee of Vertex Pharmaceuticals.

To gain insight into potential regulatory mechanisms through which the effects of variants at four established type 2 diabetes (T2D) susceptibility loci (CDKAL1, CDKN2A-B, IGF2BP2 and KCNQ1) are mediated, we undertook transancestral fine-mapping in 22 086 cases and 42 539 controls of East Asian, European, South Asian, African American and Mexican American descent. Through high-density imputation and conditional analyses, we identified seven distinct association signals at these four loci, each with allelic effects on T2D susceptibility that were homogenous across ancestry groups. By leveraging differences in the structure of linkage disequilibrium between diverse populations, and increased sample size, we localised the variants most likely to drive each distinct association signal. We demonstrated that integration of these genetic fine-mapping data with genomic annotation can highlight potential causal regulatory elements in T2D-relevant tissues. These analyses provide insight into the mechanisms through which T2D association signals are mediated, and suggest future routes to understanding the biology of specific disease susceptibility loci., (© The Author 2016. Published by Oxford University Press.)