Fig. 4. Differential asthma associations with health-related phenotypes across subgroups.

A total of 10 different categories of health-related phenotypes (140 different measurements in total) were subjected to phenotype association analysis (see Methods for technical details and Supplementary Data 11 for the numbers of allocated cases and controls in each subgroup). We first computed phenotypes’ slope estimates of asthma associations within each subgroup and in the any-CDs group. The direction and strength of the association are characterized by the sign and absolute value of the slope, respectively. a Heterogeneous slope estimates related to blood count. We assessed the heterogeneity in these slope estimates across subgroups for each phenotype, and benchmarked against the slope value for that phenotype in the any-CDs group. Each phenotype is presented as a meta-plot, which shows the posterior means (as squares) and 95 percent confidence intervals (as error bars) of the slopes from subgroups 1 to 11 that were also discovered in UK Biobank (displayed from top to bottom). Slope estimates that are significantly less positive than the any-CDs group benchmark (marked by a vertical dashed line) are shown in blue, while those that are significantly more positive are shown in red; the respective subgroup numbers are also shown for significantly different subgroups. For example, subgroup 6 “Lung” exhibits many red-blood-cell-related phenotypes that are in significantly stronger associations with asthma likelihood than appear for the general population in the any-CDs group. b Heterogeneous slope estimates related to the local environment, diet, and physical activity. In the same fashion as shown in a, we display the meta-plots of the phenotypes in the categories of the local environment, diet, and physical activity. A distinct pattern of these phenotypes distinguishes subgroup 1 “Diabetes,” in which stronger associations of greenspace, air quality, salt intake, and exercise are evident.