key: cord-349754-v6lll1xy authors: Zhu, Zhaozhong; Hasegawa, Kohei; Camargo, Carlos A.; Liang, Liming title: Investigating asthma heterogeneity through shared and distinct genetics: insights from genome-wide cross-trait analysis date: 2020-07-18 journal: J Allergy Clin Immunol DOI: 10.1016/j.jaci.2020.07.004 sha: doc_id: 349754 cord_uid: v6lll1xy Abstract: Asthma is a heterogeneous respiratory disease reflecting distinct pathobiological mechanisms. These mechanisms are based, at least partly, on different genetic factors shared by many other conditions, such as allergic diseases and obesity. Investigating the shared genetic effects enables better understanding the mechanisms of phenotypic correlations and is less subject to confounding by environmental factors. The increasing availability of large-scale genome-wide association study (GWAS) for asthma has enabled researchers to examine the genetic contributions to the epidemiological associations between asthma subtypes, and those between coexisting diseases/traits and asthma. Studies have found not only shared but also distinct genetic components between asthma subtypes, indicating that the heterogeneity is related to distinct genetics. This review summarizes a recently compiled analytical approach—genome-wide cross-trait analysis—to determine shared and distinct genetic architecture. The genome-wide cross-trait analysis features in several analytical aspects: genetic correlation, cross-trait meta-analysis, Mendelian randomization, polygenic risk score and functional analysis. In this article, we discuss in detail the scientific goals that can be achieved by these analyses, their advantages and limitations. We also make recommendations for future directions: 1) ethnicity-specific asthma GWASs, and 2) application of cross-trait methods to multi-omics data to dissect the heritability found in GWAS. Finally, these analytical approaches are also applicable to complex and heterogeneous traits beyond asthma. Traditionally, examining the phenotypic correlation or coexistence of other factors is a useful 135 way to investigate the heterogeneity of asthma. However, this approach may have residual 136 confounding and provide insufficient biological insight as to which underlying mechanism(s) 137 drive the association. A major advantage going from phenotypic correlations to genetic 138 correlations is to improve understanding of the mechanism(s)-shared genetic components can 139 be identified at different levels, from whole genome to individual variants, providing insights 140 into the reasons why asthma and coexistent diseases or traits are correlated. Furthermore, 141 genetic correlations are less subject to confounding by environmental factors for several reasons. 142 After adequately controlling for population ancestry, genetic correlation would occur only if the 143 germline genetic variant is causal or in linkage disequilibrium (LD) with the causal variant of 144 both traits. A purely environmental confounding factor (e.g., air pollution) would not lead to 145 genetic correlation because it is not associated with any genetic variant (Figure 2a and 2b) . In contrast, if an environmental factor is an intermediary step between the genetic variant and the 147 trait, then it is in the causal pathway, and it is not considered a confounder-i.e., it does not 148 create a false genetic correlation between the two traits (Figure 2c) . Population stratification is 149 arguably the only confounding factor in GWAS but it can be effectively controlled using 150 principal components from genome-wide genetic markers. 25 Once the genetic effect on diseases 151 and traits are robustly established, the genetic correlation between diseases and traits can be 152 reliably estimated and replicated. [26] [27] [28] [29] In the following sections, we will discuss a range of 153 detailed analyses that compile a comprehensive investigation between asthma and other 154 coexistent diseases or traits. applicable to asthma and many other diseases/traits. The design has been successfully applied to 170 the UK Biobank and GWAS consortia datasets, and determined the shared genetic architecture 171 between asthma and allergic diseases, 22 obesity, 12 and mental health disorders, 21 which were 172 reproducible in other studies. 26-29 A genome-wide cross-trait analysis features several analyses: 173 genetic correlation, cross-trait meta-analysis, Mendelian randomization, polygenic risk score, 174 and GWAS functional analysis. Each component is discussed in more detail in subsequent 175 sections and depicted in Figure 3 . A glossary of the cross-trait GWAS terminology may be 176 found in Table 1 . A summary of genome-wide cross-trait analysis methods may be found in 177 suggests obesity-to-asthma effects, reduction of body mass index (BMI) in patients with obesity 195 might counteract the genetic effect, thereby potentially preventing the development of asthma. Therefore, distinguishing horizontal pleiotropy from vertical pleiotropy where both contribute to 197 genetic correlations are important and can be challenging. We discuss methods for these analyses 198 in the sections below on cross-trait meta-analysis and Mendelian randomization. The sensitivity analyses showed that the Rg estimate from LDSC is unbiased to overlapping and asthma, 21 the use of ASSET identified 7 loci that are jointly associated with ADHD and 260 asthma, one locus with anxiety disorder and asthma, 10 loci with major depressive disorder and asthma. Of note, the human leukocyte antigen (HLA) region (chr6: 25-34Mb) was found to be 262 shared in the cross-trait meta-analysis of allergic disease and asthma as well as that between 263 major depressive disorder and asthma. The HLA region was commonly reported to have Definition Cross-trait meta-analysis A meta-analysis that tests the null hypothesis that none of the traits being examined is associated with the genetic variant. One genetic variant is tested at a time. Expression quantitative trait loci (eQTLs) Genetic variants that are associated with the gene expression levels. Assuming all genetic variants have some effect on a trait and their effect size follow a Gaussian distribution (called the infinitesimal model), the genetic correlation between two traits (A and B) measures the Pearson's correlation between the genetic variant effect on traits A and B. Genome-wide association study (GWAS) An analytical method that tests the association between each genetic variant and a specific phenotype (a disease status or a quantitative trait). One genetic variant is tested at a time. Human leukocyte antigen (HLA)/ major histocompatibility complex (MHC) region A genomic region of approximately 3.6 Mb genome sequence located on the chromosome 6p21, which is mainly known for its pervasive pleiotropic effect and immune-related function. The extended MHC region is at 25-34 Mb on chromosome 6. Horizontal pleiotropy A genetic variant or gene having independent effects on multiple traits, which do not have causal effect on each other. Variables that are associated with the modifiable exposure or risk factor of interest and affect the outcome only through the exposure or risk factor. Mendelian randomization An analytic approach that examines the causality of an observed association of a modifiable exposure or risk factor with an outcome of interest using one or more genetic instrumental variables. Polygenic risk score A score based on a set of disease/trait-associated genetic variants, commonly defined as weighted sum of their genotypes. Weights are chosen by their association effect on the disease/trait, directly from GWAS or further modified based on suitable statistical model incorporating all genetic variants on the genome. Vertical pleiotropy (genetic causality) A genetic variant or gene having an effect on a trait, which has causal effect on another trait. Advantages Disadvantages Examples of application in asthma or complex traits/PMID Gobal Initiative for Asthma. 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