Age at puberty and risk of asthma: A Mendelian randomisation study

Minelli, Cosetta; Plaat, Diana A van der; Leynaert, Bénédicte; Granell, Raquel; Amaral, André F.S.; Pereira, Miguel; Mahmoud, Osama; Potts, James; Sheehan, Nuala A.; Bowden, Jack; Thompson, John R.; Jarvis, Déborah; Smith, George Davey; Henderson, John

doi:10.1371/journal.pmed.1002634

Cited by 56 publications

(53 citation statements)

References 59 publications

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“…children 32. However, Minelli et al6 reported a 7% increased risk of adult asthma in male children, which is similar to our findings. Several mechanisms may be proposed to explain our findings.…”

supporting

confidence: 92%

Early pubertal maturation and risk of childhood asthma: A Mendelian randomization and longitudinal study

Chen

Fan

Yang

et al. 2019

Allergy

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Background: Studies on early puberty and incident asthma have reported inconsistent results and are mainly performed in females. In this longitudinal study, we investigated the causal relationship between pubertal maturation and asthma through Mendelian randomization (MR) and explored the joint effect of overweightness and early pubertal maturation on asthma. Methods:We used data from the Taiwan Children Health Study with longitudinal follow-ups of 2991 children aged 11-17 years. Six puberty-related single-nucleotide polymorphisms (combined into a weighted allelic score) were used to yield genetic instrumental variables for early puberty. Early pubertal maturation was defined as reaching a certain pubertal stage earlier than the median age for that stage. Incident asthma cases were calculated by excluding children with a history of asthma prior to that age. Results:The results of MR analysis revealed that early pubertal maturation was associated with active asthma (OR = 1.18; 95% CI: 1.08-1.28); this effect was significant in male children. Early pubertal maturation significantly increased the risk of incident asthma outcomes at 12 and 17 years of age in both sexes (hazard ratio = 2.15; 95% CI: 1.21-3.84). Taking non-overweight and non-early puberty children as the reference group, we observed a synergistic effect of overweightness and early pubertal maturation on asthma risk (OR = 1.08; 95% CI: 1.04-1.11) in children of both sexes. Conclusions:Early screening and intervention for obesity are recommended to prevent future early pubertal onset and asthma occurrence. K E Y W O R D S asthma, early puberty, longitudinal study, Mendelian randomization analysis, overweightness | 893 CHEN Et al.

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supporting

confidence: 92%

Early pubertal maturation and risk of childhood asthma: A Mendelian randomization and longitudinal study

Chen

Fan

Yang

et al. 2019

Allergy

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“…As highlighted above, a significant genetic correlation might arise because of a causal effect of these risk factors on asthma risk. [83][84][85][86] At least for obesity, such a causal effect does not appear to extend to other allergies, 87 which could potentially explain the difference in genetic correlation between AOA and COA.…”

Section: Discussionmentioning

confidence: 99%

Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct

Ferreira

Mathur

Vonk

et al. 2019

The American Journal of Human Genetics

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The extent to which genetic risk factors are shared between childhood-onset (COA) and adult-onset (AOA) asthma has not been estimated. On the basis of data from the UK Biobank study (n ¼ 447,628), we found that the variance in disease liability explained by common variants is higher for COA (onset at ages between 0 and 19 years; h 2 g ¼ 25.6%) than for AOA (onset at ages between 20 and 60 years; h 2 g ¼ 10.6%). The genetic correlation (r g ) between COA and AOA was 0.67. Variation in age of onset among COA-affected individuals had a low heritability (h 2 g ¼ 5%), which we confirmed in independent studies and also among AOA-affected individuals. To identify subtype-specific genetic associations, we performed a genome-wide association study (GWAS) in the UK Biobank for COA (13,962 affected individuals) and a separate GWAS for AOA (26,582 affected individuals) by using a common set of 300,671 controls for both studies. We identified 123 independent associations for COA and 56 for AOA (37 overlapped); of these, 98 and 34, respectively, were reproducible in an independent study (n ¼ 262,767). Collectively, 28 associations were not previously reported. For 96 COA-associated variants, including five variants that represent COA-specific risk factors, the risk allele was more common in COA-than in AOA-affected individuals. Conversely, we identified three variants that are stronger risk factors for AOA. Variants associated with obesity and smoking had a stronger contribution to the risk of AOA than to the risk of COA. Lastly, we identified 109 likely target genes of the associated variants, primarily on the basis of correlated expression quantitative trait loci (up to n ¼ 31,684). GWAS informed by age of onset can identify subtype-specific risk variants, which can help us understand differences in pathophysiology between COA and AOA and so can be informative for drug development.

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“…The most commonly used two-sample methods were: MR-Egger regression (N=10 studies); weighted median estimator (N=9); IVW (N=8), mostly as the main analysis, with only 3 studies specifying whether a fixed-effect or a random-effects model was used; weighted mode estimator (N=3). All studies used MR-Egger but none reported an I 2 GX value, although one mentioned the limited variability in instrument strengths as an explanation for the limited power of the MR-Egger analysis 13 . A one-sample MR method was also used in 3 studies: 2SLS (N=2) and a maximum likelihood method 3 (N=1).…”

Section: Examples Of Published Ukb Studiesmentioning

confidence: 99%

The use of two-sample methods for Mendelian randomization analyses on single large datasets

Minelli

Greco

Plaat

et al. 2020

Preprint

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Tel: +44 (0) 207 5947758 2 Key Messages • Current availability of phenotypic and genetic data from large biobanks, such as UK Biobank, has led to increasing use of one-sample Mendelian randomization (MR) to investigate causal relationships in epidemiological research • Robust MR methods have been developed to address pleiotropy, but they assume independence between the gene-exposure and gene-outcome association estimates; this holds in two-sample MR but not in one-sample MR • We illustrate the practical implications, in terms of bias and precision of the MR causal effect estimate, of using robust two-sample methods in one-sample MR studies performed within large biobanks • Two-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger regression • MR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrumental strength is very high Abstract Background: With genome-wide association data for many exposures and outcomes now available from large biobanks, one-sample Mendelian randomization (MR) is increasingly used to investigate causal relationships. Many robust MR methods are available to address pleiotropy, but these assume independence between the gene-exposure and geneoutcome association estimates. Unlike in two-sample MR, in one-sample MR the two estimates are obtained from the same individuals, and the assumption of independence does not hold in the presence of confounding. Methods: With simulations mimicking a typical study in UK Biobank we assessed the performance, in terms of bias and precision of the MR estimate, of the fixed-effect and (multiplicative) random-effects meta-analysis method, weighted median estimator, weighted mode estimator and MR-Egger regression, used in both one-sample and two-sample data.We considered scenarios differing for: presence/absence of a true causal effect; amount of confounding; presence and type of pleiotropy (none, balanced or directional). Results:Even in the presence of substantial correlation due to confounding, all methods performed well when used in one-sample MR except for MR-Egger, which resulted in bias reflecting direction and magnitude of the confounding. Such bias was much reduced in the presence of very high variability in instrumental strength across variants (I 2 GX of 97%). Conclusions:Two-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger. MR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrumental strength is very high.

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Age at puberty and risk of asthma: A Mendelian randomisation study

Cited by 56 publications

References 59 publications

Early pubertal maturation and risk of childhood asthma: A Mendelian randomization and longitudinal study

Early pubertal maturation and risk of childhood asthma: A Mendelian randomization and longitudinal study

Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct

The use of two-sample methods for Mendelian randomization analyses on single large datasets

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