Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and utilize it to estimate the genetic correlation due to predicted expression between pairs of traits. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases.

The lack of minority data, despite a collaboration of eight universities and 13 individual laboratories, highlights the urgent need for a dedicated national effort to prioritize diversity in research. Our study expands the understanding of pharmacogenetic analyses in racially/ethnically diverse populations and advances the foundation for precision medicine in at-risk and understudied minority populations.

The key factors underlying the development of allergic diseases—the propensity for a minority of individuals to develop dysfunctional responses to harmless environmental molecules—remain undefined. We report a pathway of immune counter-regulation that suppresses the development of aeroallergy and shrimp-induced anaphylaxis. In mice, signaling through epithelially expressed dectin-1 suppresses the development of type 2 immune responses through inhibition of interleukin-33 (IL-33) secretion and the subsequent recruitment of IL-13–producing innate lymphoid cells. Although this homeostatic pathway is functional in respiratory epithelial cells from healthy humans, it is dramatically impaired in epithelial cells from asthmatic and chronic rhinosinusitis patients, resulting in elevated IL-33 production. Moreover, we identify an association between a single-nucleotide polymorphism (SNP) in the dectin-1 gene loci and reduced pulmonary function in two cohorts of asthmatics. This intronic SNP is a predicted eQTL (expression quantitative trait locus) that is associated with reduced dectin-1 expression in human tissue. We identify invertebrate tropomyosin, a ubiquitous arthropod-derived molecule, as an immunobiologically relevant dectin-1 ligand that normally serves to restrain IL-33 release and dampen type 2 immunity in healthy individuals. However, invertebrate tropomyosin presented in the context of impaired dectin-1 function, as observed in allergic individuals, leads to unrestrained IL-33 secretion and skewing of immune responses toward type 2 immunity. Collectively, we uncover a previously unrecognized mechanism of protection against allergy to a conserved recognition element omnipresent in our environment.

Background-Asthma, an inflammatory disorder of the airways, is the most common chronic disease of children worldwide. There are significant racial/ethnic disparities in asthma prevalence, morbidity and mortality among U.S. children. This trend is mirrored in obesity, which may share genetic and environmental risk factors with asthma. The majority of asthma biomedical research has been performed in populations of European decent.

Single allele study designs, commonly used in genome-wide association studies (GWAS) as well as the more recently developed whole genome sequencing (WGS) studies, are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p-values are declared statistically significant due to issues arising from multiple testing and type I errors. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, making it difficult to separate true associations from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at the overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios.