RATIONALE
Current guidelines do not sufficiently capture the heterogenous nature of asthma leading to suboptimal management and treatment strategies. A more comprehensive classification of asthma into biologically meaningful subgroups is needed. Given its position on the central biological dogma, as the ‘ome closest to phenotype, reflecting genetics, environment and their interactions, metabolomics represents a novel and compelling approach to accurately identifying asthma endotypes; i.e. subtypes defined by their functional or pathobiological mechanisms, with the potential for clinical translation.
METHODS
We performed plasma metabolomic profiling of 1155 asthmatic children from the Genetics of Asthma in Costa Rica Study (GACRS) across four profiling platforms covering a broad range of the metabolome. We generated patient similarity networks for each platform that connected asthmatics via edges representing patient-to-patient similarity in their metabolomic profiles (controlling for age, sex and body mass index). We then fused the four platform-specific networks using Similarity Network Fusion and performed spectral clustering on the resulting fused similarity network to identify metabo-endotypes. We explored phenotypic and clinical differences across the metabo-endotypes using ANOVA and chi-square tests. For validation we recapitulated the endotypes in an independent population of asthmatic children (CAMP, n=911) to determine whether the same between-endotype differences were observed. Finally, we identified metabolomic and genomic drivers of validated metabo-endotype membership, meta-analyzing findings from GACRS and CAMP.
RESULTS
We identified five metabo-endotypes in GACRS and observed significant differences across metabo-endotypes in asthma-relevant phenotypes pre-bronchodilator (p-ANOVA=8.3x10-5) and post-bronchodilator (p-ANOVA=1.8x10-5) FEV1/FVC ratio; use of inhaled (p-ANOVA=5.0x10-13) and oral (p-ANOVA=0.007) steroids, and airway hyperresponsiveness to methacholine (AHR PC20<16.8, p-ANOVA=8.4x10-7)p-ANOVA=8.4x10-7). Furthermore, there was a significant difference in eosinophil count (p-ANOVA=0.009). The recapitulated metabo-endotypes in CAMP displayed significant differences (p<0.05) in many of the same phenotypes. (Table 1). The “most-severe” asthma endotype was defined by the lowest FEV1/FVC ratio, and the highest prevalence of AHR and oral steroid usage. It was characterized by higher levels of glycerolipids and lysophospholipids, as well as lower levels of nucleotides relative to the other endotypes. These findings suggest dysregulation of pulmonary surfactant homeostasis in the “most-severe” endotype and were supported by the genetic analysis, which found that members of this endotype were more likely to carry variants in key pulmonary surfactant regulation genes such as BACH3 (meta-analyzed p=5.2x10-4) and BMP3 (meta-analyzed p=8.5x10-4).
CONCLUSIONS
These findings demonstrate that clinically meaningful endotypes can be derived and validated using metabolomic data, and that interrogating the drivers of these metabo-endotypes can help understand their pathophysiology and identify therapeutic targets.

BACKGROUND: Runs of homozygosity (ROH) are long stretches of homozygous genotypes in a person’s genome that result from the individual inheriting identical haplotypes from each of their two parents, often reflecting some degree of inbreeding. Long ROHs can be accurately inferred from genome-wide SNP data and can be used to indicate the level of inbreeding. We perform phenome-wide and comprehensive analysis of multi-omics data to identify the correlates of inbreeding.
METHODS: We used our tool KING to estimate inbreeding coefficients (F_ROH) in UK Biobank and MESA, using long ROHs (>3Mb) which may be indicative of identical by descent (IBD) rather than identical by state (IBS) sharing. We used data from the UK Biobank to perform association studies between the inbreeding coefficient and various cardiometabolic, pulmonary, body size, cognition, and socioeconomic traits using linear and logistic regression (1) across all participants (433,768) and (2) stratified by sex. Regression models were adjusted for sex, age, study site and principal components of ancestry. Data from the Multi-Ethnic Study of Atherosclerosis (MESA), which includes samples from people of diverse ancestries, was used to investigate the proteomic and transcriptomic consequences of inbreeding. Genes/proteins correlated with a higher inbreeding coefficient were found using a linear regression model and then they were used to identify significant pathways that may help to understand the effects of ROH and underlying mechanisms in the human genome.
RESULTS: In the UK Biobank, considering a Bonferroni P value cutoff 0.00025, inbreeding showed significant associations in 23 out of 200 traits (11.5%), including pulmonary traits (e.g., forced vital capacity FVC), body size traits (e.g., height), socioeconomic traits (e.g., Townsend deprivation index), and cognitive traits (e.g., fluid intelligence score). We identified sex differences of the inbreeding effect on the health outcomes, with generally larger effects on women than men. For example, the effect of inbreeding on the risk of diabetes was much stronger in women (OR1st-degree=6.0, P = 0.0002) than in men (P=0.54). In MESA, both the proteomics and transcriptomic analyses identified genes enriched for immune-related pathways, for example, stimulatory C-type lectin receptor signaling pathway (P < 0.05), which may suggest that inbreeding has an impact on human immunology.
CONCLUSION: Inbreeding affects significantly a large proportion of health outcome and molecular traits, and the effects may differ by sex. Future work could perform homozygosity mapping to detect genomic regions responsible for those outcomes in human phenotypes.