Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability.

Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10-60). Applying an alternative multi-trait approach (Multi-Trait Analysis of GWAS), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outlines practical strategies for multi-trait testing.

TNS1 and NRXN1 Genes Interacting With Early-Life Smoking Exposure in Asthma-Plus-Eczema Susceptibility.

PURPOSE: Numerous genes have been associated with allergic diseases (asthma, allergic rhinitis, and eczema), but they explain only part of their heritability. This is partly because most previous studies ignored complex mechanisms such as gene-environment (G-E) interactions and complex phenotypes such as co-morbidity. However, it was recently evidenced that the co-morbidity of asthma-plus-eczema appears as a sub-entity depending on specific genetic factors. Besides, evidence also suggest that gene-by-early life environmental tobacco smoke (ETS) exposure interactions play a role in asthma, but were never investigated for asthma-plus-eczema. To identify genetic variants interacting with ETS exposure that influence asthma-plus-eczema susceptibility. METHODS: To conduct a genome-wide interaction study (GWIS) of asthma-plus-eczema according to ETS exposure, we applied a 2-stage strategy with a first selection of single nucleotide polymorphisms (SNPs) from genome-wide association meta-analysis to be tested at a second stage by interaction meta-analysis. All meta-analyses were conducted across 4 studies including a total of 5,516 European-ancestry individuals, of whom 1,164 had both asthma and eczema. RESULTS: Two SNPs showed significant interactions with ETS exposure. They were located in 2 genes, NRXN1 (2p16) and TNS1 (2q35), never reported associated and/or interacting with ETS exposure for asthma, eczema or more generally for allergic diseases. TNS1 is a promising candidate gene because of its link to lung and skin diseases with possible interactive effect with tobacco smoke exposure. CONCLUSIONS: This first GWIS of asthma-plus-eczema with ETS exposure underlines the importance of studying sub-phenotypes such as co-morbidities as well as G-E interactions to detect new susceptibility genes.

Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability.

Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.

Identification of OCA2 as a novel locus for the co-morbidity of asthma-plus-eczema.

BACKGROUND: Numerous genes have been associated with the three most common allergic diseases (asthma, allergic rhinitis or eczema) but these genes explain only a part of the heritability. In the vast majority of genetic studies, complex phenotypes such as co-morbidity of two of these diseases, have not been considered. This may partly explain missing heritability. OBJECTIVE: To identify genetic variants specifically associated with the co-morbidity of asthma-plus-eczema. METHODS: We first conducted a meta-analysis of four GWAS (Genome-Wide Association Study) of the combined asthma-plus-eczema phenotype (total of 8807 European-ancestry subjects of whom 1208 subjects had both asthma and eczema). To assess whether the association with SNP(s) was specific to the co-morbidity, we also conducted a meta-analysis of homogeneity test of association according to disease status ("asthma-plus-eczema" vs. the presence of only one disease "asthma only or eczema only"). We then used a joint test by combining the two test statistics from the co-morbidity-SNP association and the phenotypic heterogeneity of SNP effect meta-analyses. RESULTS: Seven SNPs were detected for specific association to the asthma-plus-eczema co-morbidity, two with significant and five with suggestive evidence using the joint test after correction for multiple testing. The two significant SNPs are located in the OCA2 gene (Oculocutaneous Albinism II), a new locus never detected for significant evidence of association with any allergic disease. This gene is a promising candidate gene, because of its link to skin and lung diseases, and to epithelial barrier and immune mechanisms. CONCLUSION: Our study underlines the importance of studying sub-phenotypes as co-morbidities to detect new susceptibility genes.

The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression.

Quantifying tissue-infiltrating immune and stromal cells provides clinically relevant information for various diseases. While numerous methods can quantify immune or stromal cells in human tissue samples from transcriptomic data, few are available for mouse studies. We introduce murine Microenvironment Cell Population counter (mMCP-counter), a method based on highly specific transcriptomic markers that accurately quantify 16 immune and stromal murine cell populations. We validated mMCP-counter with flow cytometry data and showed that mMCP-counter outperforms existing methods. We showed that mMCP-counter scores are predictive of response to immune checkpoint blockade in cancer mouse models and identify early immune impacts of Alzheimer's disease.