Asthma is a heterogeneous syndrome with several causes.1
Research is beginning to uncover variations in the molecular roots and clinical manifestations of asthma.2
One research stream has discovered molecular genetic markers of asthma risk.3
Another research stream has characterised developmental and biological features of asthma that might represent so-called endotypes—ie, subtypes of disease with distinct pathobiology.5
Bringing these two streams of research together could uncover how recently discovered genetic risks relate to developmental and biological characteristics of asthma.
Our research seeks to address the question: when hypothesis-free methods such as genome-wide association studies (GWAS)7
make discoveries about heterogeneous syndromes such as asthma, what are these discoveries about? To achieve samples of tens of thousands, the largest asthma GWAS reduce this developmentally and biologically complex syndrome to a single outcome—for example, whether or not a physician has diagnosed asthma. Follow-up research is needed to fully understand discoveries from these studies. One approach is to work from the bottom up by tracing the path from variations in DNA sequences to differences in RNA transcription, subsequent protein production, and onwards up through disease pathogenesis to identify a process or molecule that can be targeted for intervention. A complementary approach is to work from the top down by characterising how genetic discoveries relate to individual differences in developmental and biological phenotypes, and then working backwards to devise interventions that can mitigate genetic risk.8
We aimed to take the top-down approach to identify developmental and biological signatures of asthma in individuals with high genetic risk. Once identified, these signatures can help to guide bottom-up molecular research. We aimed to construct a multilocus profile of genetic predisposition to asthma—a genetic risk score—from a comprehensive case-control GWAS of asthma9
and then to genotype this score in an unselected cohort followed up from birth to mid-life10
with extensive asthma phenotyping across nine assessments spanning the ages of 9—38 years. We then wanted to test how GWAS-discovered genetic risks were related to the development, persistence, and biological characteristics of asthma in this cohort.
Boys and girls in the Dunedin cohort who had higher genetic risk scores had a greater risk of developing asthma over the 38 years of follow-up than did those with lower genetic risk (HR 1·12, 95% CI 1·01—1·26), and developed asthma earlier in life (figure 1
). Genetic associations with asthma onset did not differ by sex (p=0·917).
Kaplan-Meier plots for development of asthma over time, by sex and genetic risk score
Kaplan-Meier plots show asthma onset in the whole Dunedin cohort according to sex (A) and high or low genetic risk score (B), and in girls only (C) and boys only (D) according to high or low genetic risk score. For illustrative purposes, high genetic risk was defined as a genetic risk score above the cohort median. Numbers at risk are reported at ages 0, 7, 15, 21, 26, 32, and 38 years.
Cohort members at higher genetic risk met criteria for current asthma at more assessments between the ages of 9 and 38 years than did those with lower genetic risk (IRR 1·16, 95% CI 1·03—1·30). Children at higher genetic risk were also more likely to develop life-course-persistent asthma (96 [11%] of 880 participants), defined by onset in childhood (before age 13 years) with recurrent symptoms up to age 38 years (RR 1·36, 95% CI 1·14—1·63; figure 2A
). The shift in the genetic risk distribution that underlies this effect is presented in the appendix
. Specifically, in the subset of cohort members with asthma onset in childhood (187 [21%] of 880 participants), those at higher genetic risk developed asthma earlier in life than did those with lower genetic risk (HR 1·17, 95% CI 1·01—1·34) and were more likely to develop life-course-persistent asthma (RR 1·20, 1·05—1·38; figure 2B
). Our receiver operating characteristic curve analysis showed that the genetic risk score slightly improves individual-level predictions of which participants with childhood-onset asthma developed life-course-persistent asthma (appendix
). We did sensitivity analyses that restricted the life-course-persistent subgroup to the 93 participants with asthma onset before the age of 9 years to test if results changed after exclusion of the three participants in the life-course-persistent group who developed asthma around the time of puberty. We noted that the results were unchanged (effect sizes for genetic associations with life-course-persistent asthma were RR 1·37 [95% CI 1·14—1·65] in the whole cohort and RR 1·20 [1·05—1·39] in participants who developed asthma in childhood).
Risk of developing life-course-persistent asthma according to genetic risk score
(A) Whole cohort (relative risk [RR] 1·36, 95% CI 1·14—1·63). (B) Subset of cohort members with asthma onset by age 13 years (RR 1·20, 1·05—1·38). Error bars show 95% CIs for point estimates. The genetic risk score is the count of risk alleles standardised to have a mean of 0 and an SD of 1.
The results of our analyses in the 305 (35%) participants who developed asthma during follow-up showed that those at higher genetic risk were at increased risk of atopy compared with those with asthma and a lower genetic risk (RR 1·07, 95% CI 1·01—1·14) across assessments at age 13, 21, and 32 years. Participants with asthma at higher genetic risk also had raised serum IgE concentrations (r=0·13; p=0·02) across assessments at age 11, 21, and 32 years.
Lung function analyses showed that participants with asthma at higher genetic risk were at increased risk of airway hyper-responsiveness to methacholine or albuterol relative to those with a lower genetic risk (RR 1·16, 95% CI 1·03—1·32) across nine assessments between the ages of 9 and 38 years. These analyses also showed that participants with asthma at higher genetic risk had poorer lung function, as measured by a post-albuterol ratio of forced expiratory volume in 1 s (FEV1
) to forced vital capacity (FVC), than did those at lower genetic risk (r
=0·13, p=0·03) and were more likely to develop incompletely reversible airflow obstruction, as defined by post-bronchodilator FEV1
-to-FVC ratios of more than 2 SD below the mean of participants who never developed asthma, than were those at lower genetic risk (RR 1·28, 95% CI 1·04—1·57) across assessments at ages 18, 26, 32, and 38 years. Genetic associations with lifetime risk for atopy, airway hyper-responsiveness, and incompletely reversible airflow obstruction in participants with asthma are shown in the appendix
Because smoking affects the lung function characteristics that we analysed,35
we did sensitivity analyses testing genetic associations with lung function phenotypes of asthma in non-smokers (figure 3
). By age 38 years, 214 (70%) of the 305 cohort members with asthma had smoked at some point during follow-up (in the full cohort, 627 [71%] of 880 had smoked). We previously constructed developmental phenotypes of smoking behaviour in the Dunedin cohort that identified when cohort members began smoking.36
We selectively excluded data from participants with asthma that were obtained after those individuals had begun smoking—eg, for a child who developed asthma at age 11 years and who took up smoking at age 18 years, observations at ages 11, 13, and 15 years were included in the analysis of airway hyper-responsiveness, whereas all observations from age 18 years onwards were excluded. Participants who developed asthma after starting smoking were excluded from the analysis entirely. Excluding smokers increased the magnitudes of associations between genetic risk and airway hyper-responsiveness (RR 1·38, 95% CI 1·16—1·64) and incompletely reversible airflow obstruction (RR 1·63, 1·10—2·41).
Lung function in non-smokers with asthma according to genetic risk score
(A) Predicted probability of airway hyper-responsiveness in 188 non-smokers with asthma and all 305 participants with asthma. (B) Predicted probability of incompletely reversible airflow obstruction in 105 non-smokers with asthma and all 305 participants with asthma. Probabilities were estimated from models including all participants with asthma and models including only non-smoking participants with asthma. The genetic risk score is the count of risk alleles standardised to have a mean of 0 and an SD of 1.
Our results showed substantial overlap between atopy, airway hyper-responsiveness, and incompletely reversible airflow obstruction in participants with asthma. For example, 55 (72%) of 76 cohort members with asthma and incompletely reversible airflow obstruction also had atopy and airway hyper-responsiveness during follow-up (figure 4A
). Individuals with asthma who did not develop atopy had genetic risk scores similar to those without asthma. Of participants with asthma who developed atopy, those who also developed airway hyper-responsiveness and incompletely reversible airflow obstruction had higher genetic risk scores than did those who had asthma and atopy only, and had the highest genetic risk scores of all participants (figure 4B
Atopy, airway hyper-responsiveness, and incompletely reversible airflow obstruction in participants with asthma
(A) Overlap between different biological characteristics of asthma. (B) Mean genetic risk within subgroups of participants with asthma. Participants with asthma who did not have atopy but who did have airway hyper-responsiveness or incompletely reversible airflow obstruction are grouped together because of the small number of cases in each group. Error bars show SE for subgroup means. The genetic risk score is the count of risk alleles standardised to have a mean of 0 and a SD of 1. AHR=airway hyper-responsiveness. IRAO=incompletely reversible airflow obstruction.
Over three decades of follow-up, cohort members at higher genetic risk missed more days of school or work because of asthma than did those at lower genetic risk (IRR 1·38, 95% CI 1·02—1·86) and were more likely to be admitted to hospital for breathing problems (HR 1·38, 1·07—1·79).
Genotype and family history provided similar amounts of information about asthma risk; effect sizes were similar for the genetic risk score and the family history score in models predicting developmental phenotypes of asthma, airway hyper-responsiveness, and asthma-related disruptions to daily life (table 2
). Family history was not associated with risks of participants with asthma manifesting atopy or incompletely reversible airflow obstruction.
Effect sizes for genetic risk score and family history score
Genotype-based and family-history-based measures provided different information about asthma risk. Cohort members' genetic risk scores were unrelated to their family history scores (r
). Adding statistical adjustments for family history to regression models testing genetic associations with asthma phenotypes and complications did not change the effect size or statistical significance estimated for the genetic risk score. The one exception was that after adjustment for family history, the genetic association with incidence of days of school or work missed because of asthma was no longer statistically significant (IRR 1·32, 95% CI 0·98—1·79; p=0·07).
Results from our population-level analyses advance asthma genetics beyond the original GWAS discoveries in three ways. First, we show that genetic risks predict which individuals with childhood-onset asthma remit and which develop life-course-persistent asthma, although these predictions are not sufficiently sensitive or specific to support immediate translation into clinical practice. Second, we elucidate a biological profile of asthma that arises from these genetic risks: atopic asthma characterised by airway hyper-responsiveness and leading to incompletely reversible airflow obstruction. Third, we describe the real-life effects of GWAS discoveries by quantifying genetic associations with missed school and work, and hospital admissions.
Cohort members at higher genetic risk developed asthma earlier in life, supporting GWAS reports of early-onset asthma as the developmental phenotype most strongly associated with discovered genetic risks (panel
We refined this developmental phenotype by showing that discovered genetic risks were specifically associated with a life-course-persistent developmental phenotype of asthma: of participants with childhood-onset asthma, those with a higher genetic risk were more likely to have persistent asthma up to age 38 years. Higher genetic risk asthma cases were also more likely to be atopic, supporting preliminary GWAS evidence that at least some asthma-associated loci are specific to atopic asthma.40
We also show that in participants with asthma who also developed atopy, higher genetic risk predisposes these individuals to airway hyper-responsiveness and the development of incompletely reversible airflow obstruction. Collectively, these findings describe a biological profile of asthma associated with GWAS discoveries. Research is now needed to trace molecular pathways from these variants to atopy, airway hyper-responsiveness, and incompletely reversible airflow obstruction in asthma.
We searched databases, including PubMed and the National Human Genome Research Institute GWAS Catalog, to identify the largest and most comprehensive genome-wide association studies (GWAS) of asthma using the search terms “asthma” and “genome-wide association study”. We did not apply any publication date restrictions and did the last search on Sept 1, 2012. We identified a GWAS done by the European GABRIEL Consortium,9
from which we selected single nucleotide polymorphisms (SNPs) that were genome-wide significant (p<5×10−8
) and that were at least partially independent of one another (R2
<0·6) to construct a genetic risk score.
Our results, when added to those of the original GWAS,9
show that genetic risks predict which childhood-onset asthma cases remit and which become life-course-persistent cases, describe a biological profile of the asthma that arises from these genetic risks, and show the real-life effects of GWAS discoveries by quantifying genetic associations with missed school and work, and hospital admissions. Because the effect sizes we noted were small, the predictions offered by genetic risk scores might be weak. More research is needed to clarify the clinical significance of the small increments in risk that can be predicted using SNPs.
Genetic associations with developmental and biological characteristics of asthma translated to an increased burden of asthma-related disruptions to daily life. Participants with asthma at higher genetic risk missed more days of school and work over three decades of follow-up and were more likely to be admitted to hospital for asthma than were those with a lower genetic risk. Magnitudes of genetic effects were small, with each SD increase in genetic risk score predicting an increase in risk for these disruptions to daily life by about a third. Nevertheless, results suggest that full mitigation of genetic risk could reduce the burden asthma imposes on patients, with potential benefits accruing to society through reductions in school and work absenteeism, and in health-care costs.
Finally, we tested whether recent GWAS discoveries captured information that might otherwise be obtained through a family history.41
GWAS discoveries for asthma provided novel information about inherited risk that was not captured by family history of asthma; the genetic risk score made independent, additive contributions to prediction models over and above family history. Both genotype-based and family-based risk measures predicted asthma onset and persistence, but genotype was a more consistent predictor of the biological characteristics of asthma. Surprisingly, genotypic risk was only weakly related to family history of asthma, with some cohort members with a negative family history carrying a high genetic risk for asthma. This lack of concordance between genotypic and family-based risk assessments is not unique to asthma. We previously noted this finding in patients with obesity and smoking problems,36
and others have reported similar results for some patients with cancers.41
In studies of diabetes and cancer, taking family history into account did not change effect sizes estimated for GWAS-discovered genetic risks,44
suggesting low correlation between genotypic and family-history risk measures. In turn, this lack of correlation suggests that family history captures more than the additive contributions of common variants. Replication is needed of the non-association between genetic risk and family history, using whole-genome scores.46
If replicated, future genetic discovery research in asthma could focus on genetic features of family history not detectable in GWAS, including rare variants, interactions among genes (epistasis) and, because family histories capture shared environments as well as genes, interactions between genes and environmental factors.8,47—49
Our findings also suggest that GWAS discoveries might help to identify which childhood-onset asthma cases are most likely to become life-course-persistent cases, even after accounting for differences in family histories of asthma. Because the effect sizes we recorded were small, the predictions offered by genetic risk scores might be weak. More work is needed to clarify the clinical significance of the small increments in risk that can be predicted using SNPs.50
We acknowledge several limitations. First, our analyses were based on cohort members of European descent and for whom follow-up has extended only to age 38 years, limiting the extent to which we can generalise the results. Replication of findings in non-white cohorts and extension of findings beyond mid-life are crucial. Nevertheless, this birth cohort is one of the longest running with detailed clinical and objective measurements pertinent to asthma, and the cohort is representative of the population studied; it originated as an unselected birth cohort and has been followed up with a 95% retention. Second, because our study did not include medical records, some cases of asthma and hospital admission might have been missed or misclassified, leading to underestimation of genetic effects. Third, our genetic risk score included only SNPs meeting genome-wide significance criteria in GWAS,9
and omitted two of these because of technical limitations of the genotyping array. The genotyped SNPs represent only a subset of asthma-causing variants, many of which remain undiscovered.51
Further, because the relative effect sizes of these SNPs on the developmental and biological characteristics of asthma that we analysed remain unknown, we did not weight the SNPs in our score. Whole-genome genetic risk score approaches that include hundreds or thousands of variants that meet more liberal significance criteria and that apply empirically defined weighting schemes might yield more comprehensive measures of genetic risk.52
Such whole-genome scores are a work in progress in the case of asthma; effect sizes reported in a recent whole-genome score study of asthma19
were similar to those in our study. Finally, because our genetic risk score approach studied the aggregate effect of several asthma-associated SNPs, we cannot address the question of molecular mechanism. Larger samples and meta-analyses are needed to assess the specific contributions of individual risk variants to developmental and biological characteristics of asthma. The contribution of our study is to guide such investigations by pointing to target phenotypes: early-onset, life-course-persistent asthma, and atopic asthma characterised by airway hyper-responsiveness and incompletely reversible airflow obstruction.
DWB, AC, MRS, and RJH did the scientific literature search; DWB, MRS, TEM, RP, and AC designed the study; TEM, MRS, RJH, RP, and AC obtained the data; DWB, HLH, RH, TEM, KS, BW, and AC analysed the data; DWB, TEM, MRS, RJH, and AC interpreted the data; DWB, TEM, and AC drafted the manuscript; and DWB, TEM, AC, MRS, RJH, RP, KS, BW, RH, and HLH provided critical revision.
We declare that we have no conflicts of interest.