Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.

14To understand the biological mechanisms underlying the thousands of genetic variants robustly associated with 15 complex traits, scalable methods that integrate GWAS and functional data generated by large-scale efforts are 16 needed. We derived a mathematical expression to compute PrediXcan results using summary data (S-17 PrediXcan) and showed its accuracy and robustness to misspecified reference populations. We compared S-18PrediXcan with existing methods and combined them into a best practice framework (MetaXcan) that 19integrates GWAS with QTL studies and reduces LD-confounded associations. We applied this framework to 44 20 GTEx tissues and 101 phenotypes from GWAS and meta-analysis studies, creating a growing catalog of 21 associations that captures the effects of gene expression variation on human phenotypes. Most of the 22 associations were tissue specific, indicating context specificity of the trait etiology. Colocalized significant 23 associations in unexpected tissues underscore the advantages of an agnostic scanning of multiple contexts to 24 increase the probability of detecting causal regulatory mechanisms. 25Prediction models, efficient software implementation, and association results are shared as a resource for 26 the research community.

Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restrict our ability to detect associations. Here we propose an efficient statistical method (MultiXcan) that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes. MultiXcan integrates evidence across multiple panels using multivariate regression, which naturally takes into account the correlation structure. We apply our method to simulated and real traits from the UK Biobank and show that, in realistic settings, we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed a summary result-based extension called S-MultiXcan, which we show yields highly concordant results with the individual level version when LD is well matched. Our multivariate model-based approach allowed us to use the individual level results as a gold standard to calibrate and develop a robust implementation of the summary-based extension. Results from our analysis as well as software and necessary resources to apply our method are publicly available.

13Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) 14 studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and 15 our ability to identify therapeutic targets. Gene-level association test methods such as PrediXcan can 16 prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental 17 and disease context restricts our ability to detect associations. Here we propose an efficient statistical 18 method that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability 19 to identify potential target genes: MulTiXcan. MulTiXcan integrates evidence across multiple panels 20 while taking into account their correlation. We apply our method to a broad set of complex traits available 21 from the UK Biobank and show that we can detect a larger set of significantly associated genes than 22 using each panel separately. To improve applicability, we developed an extension to work on summary 23 statistics: S-MulTiXcan, which we show yields highly concordant results with the individual level version. 24 Results from our analysis as well as software and necessary resources to apply our method are publicly 25 available. 26 Author summary 27 We develop a new method, MulTiXcan, to test the effect of gene expression regulation on complex traits, 28 integrating information available across multiple tissue studies. We show this approach has higher power 29 than traditional single-tissue methods. We extend this method to use only summary-statistics from public 30 GWAS. We apply these methods to over 200 complex traits available in the UK Biobank cohort, and 100 31 complex traits from public GWAS and discuss the findings. 32 Introduction 33Recent technological advances allow interrogation of the genome to a high level of coverage and precision, 34 enabling experimental studies that query the effect of genotype on both complex and molecular traits. 35Among these, GWAS have successfully associated genetic loci to human complex traits. GWAS meta-36 analyses with ever increasing sample sizes allow the detection of associated variants with smaller effect 37 sizes [1-3]. However, understanding the mechanism underlying these associations remains a challenging 38 problem, requiring follow-up studies and a wide array of techniques such as prioritization [4] and pathway 39 analysis [5]. 40Another approach is the study of quantitative trait loci (eQTLs), measuring association between 41 genotype and gene expression. These studies provide a wealth of biological information but tend to have 42 smaller sample sizes. A similar observation applies to QTL studies of other traits such methylation, 43 metabolites, or protein levels. 44The importance of gene expression regulation in complex traits [6][7][8][9] has motivated the integration 45 of eQTL studies and GWAS. To examine these mechanisms we developed PrediXcan [10], a method that 46 tests the mediating role of gene e...

Rationale: An increased cancer aggressiveness and mortality have been recently reported among patients with obstructive sleep apnea (OSA). Intermittent hypoxia (IH), a hallmark of OSA, enhances melanoma growth and metastasis in mice.Objectives: To assess whether OSA-related adverse cancer outcomes occur via IH-induced changes in host immune responses, namely tumor-associated macrophages (TAMs).Measurements and Main Results: Lung epithelial TC1 cell tumors were 84% greater in mice subjected to IH for 28 days compared with room air (RA). In addition, TAMs in IH-exposed tumors exhibited reductions in M1 polarity with a shift toward M2 protumoral phenotype. Although TAMs from tumors harvested from RA-exposed mice increased TC1 migration and extravasation, TAMs from IHexposed mice markedly enhanced such effects and also promoted proliferative rates and invasiveness of TC1 cells. Proliferative rates of melanoma (B16F10) and TC1 cells exposed to IH either in single culture or in coculture with macrophages (RAW 264.7) increased only when RAW 264.7 macrophages were concurrently present.Conclusions: Our findings support the notion that IH-induced alterations in TAMs participate in the adverse cancer outcomes reported in OSA.