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1.  Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies 
PLoS Medicine  2014;11(10):e1001748.
Using data from two population-based birth cohorts, Danielle Belgrave and colleagues examine the evidence for atopic march in developmental profiles for allergic disorders.
Please see later in the article for the Editors' Summary
Background
The term “atopic march” has been used to imply a natural progression of a cascade of symptoms from eczema to asthma and rhinitis through childhood. We hypothesize that this expression does not adequately describe the natural history of eczema, wheeze, and rhinitis during childhood. We propose that this paradigm arose from cross-sectional analyses of longitudinal studies, and may reflect a population pattern that may not predominate at the individual level.
Methods and Findings
Data from 9,801 children in two population-based birth cohorts were used to determine individual profiles of eczema, wheeze, and rhinitis and whether the manifestations of these symptoms followed an atopic march pattern. Children were assessed at ages 1, 3, 5, 8, and 11 y. We used Bayesian machine learning methods to identify distinct latent classes based on individual profiles of eczema, wheeze, and rhinitis. This approach allowed us to identify groups of children with similar patterns of eczema, wheeze, and rhinitis over time.
Using a latent disease profile model, the data were best described by eight latent classes: no disease (51.3%), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%). When latent variable modelling was carried out separately for the two cohorts, similar results were obtained. Highly concordant patterns of sensitisation were associated with different profiles of eczema, rhinitis, and wheeze. The main limitation of this study was the difference in wording of the questions used to ascertain the presence of eczema, wheeze, and rhinitis in the two cohorts.
Conclusions
The developmental profiles of eczema, wheeze, and rhinitis are heterogeneous; only a small proportion of children (∼7% of those with symptoms) follow trajectory profiles resembling the atopic march.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Our immune system protects us from viruses, bacteria, and other pathogens by recognizing specific molecules on the invader's surface and initiating a sequence of events that culminates in the death of the pathogen. Sometimes, however, our immune system responds to harmless materials (allergens such as pollen) and triggers allergic, or atopic, symptoms. Common atopic symptoms include eczema (transient dry itchy patches on the skin), wheeze (high pitched whistling in the chest, a symptom of asthma), and rhinitis (sneezing or a runny nose in the absence of a cold or influenza). All these symptoms are very common during childhood, but recent epidemiological studies (examinations of the patterns and causes of diseases in a population) have revealed age-related changes in the proportions of children affected by each symptom. So, for example, eczema is more common in infants than in school-age children. These findings have led to the idea of “atopic march,” a natural progression of symptoms within individual children that starts with eczema, then progresses to wheeze and finally rhinitis.
Why Was This Study Done?
The concept of atopic march has led to the initiation of studies that aim to prevent the development of asthma in children who are thought to be at risk of asthma because they have eczema. Moreover, some guidelines recommend that clinicians tell parents that children with eczema may later develop asthma or rhinitis. However, because of the design of the epidemiological studies that support the concept of atopic march, children with eczema who later develop wheeze and rhinitis may actually belong to a distinct subgroup of children, rather than representing the typical progression of atopic diseases. It is important to know whether atopic march adequately describes the natural history of atopic diseases during childhood to avoid the imposition of unnecessary strategies on children with eczema to prevent asthma. Here, the researchers use machine learning techniques to model the developmental profiles of eczema, wheeze, and rhinitis during childhood in two large population-based birth cohorts by taking into account time-related (longitudinal) changes in symptoms within individuals. Machine learning is a data-driven approach that identifies structure within the data (for example, a typical progression of symptoms) using unsupervised learning of latent variables (variables that are not directly measured but are inferred from other observable characteristics).
What Did the Researchers Do and Find?
The researchers used data from two UK birth cohorts—the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Manchester Asthma and Allergy Study (MAAS)—for their study (9,801 children in total). Both studies enrolled children at birth and monitored their subsequent health at regular review clinics. At each review clinic, information about eczema, wheeze, and rhinitis was collected from the parents using validated questionnaires. The researchers then used these data and machine learning methods to identify groups of children with similar patterns of onset of eczema, wheeze, and rhinitis over the first 11 years of life. Using a type of statistical model called a latent disease profile model, the researchers found that the data were best described by eight latent classes—no disease (51.3% of the children), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%).
What Do These Findings Mean?
These findings show that, in two large UK birth cohorts, the developmental profiles of eczema, wheeze, and rhinitis were heterogeneous. Most notably, the progression of symptoms fitted the profile of atopic march in fewer than 7% of children with symptoms. The researchers acknowledge that their study has some limitations. For example, small differences in the wording of the questions used to gather information from parents about their children's symptoms in the two cohorts may have slightly affected the findings. However, based on their findings, the researchers propose that, because eczema, wheeze, and rhinitis are common, these symptoms often coexist in individuals, but as independent entities rather than as a linked progression of symptoms. Thus, using eczema as an indicator of subsequent asthma risk and assigning “preventative” measures to children with eczema is flawed. Importantly, clinicians need to understand the heterogeneity of patterns of atopic diseases in children and to communicate this variability to parents when advising them about the development and resolution of atopic symptoms in their children.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001748.
The UK National Health Service Choices website provides information about eczema (including personal stories), asthma (including personal stories), and rhinitis
The US National Institute of Allergy and Infectious Diseases provides information about atopic diseases
The UK not-for-profit organization Allergy UK provides information about atopic diseases and a description of the atopic march
MedlinePlus encyclopedia has pages on eczema, wheezing, and rhinitis (in English and Spanish)
MedlinePlus provides links to further resources about allergies, eczema, and asthma (in English and Spanish)
Information about ALSPAC and MAAS is available
Wikipedia has pages on machine learning and latent disease profile models (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001748
PMCID: PMC4204810  PMID: 25335105
2.  Effects of BMI, Fat Mass, and Lean Mass on Asthma in Childhood: A Mendelian Randomization Study 
PLoS Medicine  2014;11(7):e1001669.
In this study, Granell and colleagues used Mendelian randomization to investigate causal effects of BMI, fat mass, and lean mass on current asthma at age 7½ years in the Avon Longitudinal Study of Parents and Children (ALSPAC) and found that higher BMI increases the risk of asthma in mid-childhood.
Please see later in the article for the Editors' Summary
Background
Observational studies have reported associations between body mass index (BMI) and asthma, but confounding and reverse causality remain plausible explanations. We aim to investigate evidence for a causal effect of BMI on asthma using a Mendelian randomization approach.
Methods and Findings
We used Mendelian randomization to investigate causal effects of BMI, fat mass, and lean mass on current asthma at age 7½ y in the Avon Longitudinal Study of Parents and Children (ALSPAC). A weighted allele score based on 32 independent BMI-related single nucleotide polymorphisms (SNPs) was derived from external data, and associations with BMI, fat mass, lean mass, and asthma were estimated. We derived instrumental variable (IV) estimates of causal risk ratios (RRs). 4,835 children had available data on BMI-associated SNPs, asthma, and BMI. The weighted allele score was strongly associated with BMI, fat mass, and lean mass (all p-values<0.001) and with childhood asthma (RR 2.56, 95% CI 1.38–4.76 per unit score, p = 0.003). The estimated causal RR for the effect of BMI on asthma was 1.55 (95% CI 1.16–2.07) per kg/m2, p = 0.003. This effect appeared stronger for non-atopic (1.90, 95% CI 1.19–3.03) than for atopic asthma (1.37, 95% CI 0.89–2.11) though there was little evidence of heterogeneity (p = 0.31). The estimated causal RRs for the effects of fat mass and lean mass on asthma were 1.41 (95% CI 1.11–1.79) per 0.5 kg and 2.25 (95% CI 1.23–4.11) per kg, respectively. The possibility of genetic pleiotropy could not be discounted completely; however, additional IV analyses using FTO variant rs1558902 and the other BMI-related SNPs separately provided similar causal effects with wider confidence intervals. Loss of follow-up was unlikely to bias the estimated effects.
Conclusions
Higher BMI increases the risk of asthma in mid-childhood. Higher BMI may have contributed to the increase in asthma risk toward the end of the 20th century.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The global burden of asthma, a chronic (long-term) condition caused by inflammation of the airways (the tubes that carry air in and out of the lungs), has been rising steadily over the past few decades. It is estimated that, nowadays, 200–300 million adults and children worldwide are affected by asthma. Although asthma can develop at any age, it is often diagnosed in childhood—asthma is the most common chronic disease in children. In people with asthma, the airways can react very strongly to allergens such as animal fur or to irritants such as cigarette smoke, becoming narrower so that less air can enter the lungs. Exercise, cold air, and infections can also trigger asthma attacks, which can be fatal. The symptoms of asthma include wheezing, coughing, chest tightness, and shortness of breath. Asthma cannot be cured, but drugs can relieve its symptoms and prevent acute asthma attacks.
Why Was This Study Done?
We cannot halt the ongoing rise in global asthma rates without understanding the causes of asthma. Some experts think obesity may be one cause of asthma. Obesity, like asthma, is increasingly common, and observational studies (investigations that ask whether individuals exposed to a suspected risk factor for a condition develop that condition more often than unexposed individuals) in children have reported that body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) is positively associated with asthma. Observational studies cannot prove that obesity causes asthma because of “confounding.” Overweight children with asthma may share another unknown characteristic (confounder) that actually causes both obesity and asthma. Moreover, children with asthma may be less active than unaffected children, so they become overweight (reverse causality). Here, the researchers use “Mendelian randomization” to assess whether BMI has a causal effect on asthma. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the effect of a modifiable risk factor and the outcome of interest. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. So, if a higher BMI leads to asthma, genetic variants associated with increased BMI should be associated with an increased risk of asthma.
What Did the Researchers Do and Find?
The researchers investigated causal effects of BMI, fat mass, and lean mass on current asthma at age 7½ years in 4,835 children enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC, a long-term health project that started in 1991). They calculated an allele score for each child based on 32 BMI-related genetic variants, and estimated associations between this score and BMI, fat mass and lean mass (both measured using a special type of X-ray scanner; in children BMI is not a good indicator of “fatness”), and asthma. They report that the allele score was strongly associated with BMI, fat mass, and lean mass, and with childhood asthma. The estimated causal relative risk (risk ratio) for the effect of BMI on asthma was 1.55 per kg/m2. That is, the relative risk of asthma increased by 55% for every extra unit of BMI. The estimated causal relative risks for the effects of fat mass and lean mass on asthma were 1.41 per 0.5 kg and 2.25 per kg, respectively.
What Do These Findings Mean?
These findings suggest that a higher BMI increases the risk of asthma in mid-childhood and that global increases in BMI toward the end of the 20th century may have contributed to the global increase in asthma that occurred at the same time. It is possible that the observed association between BMI and asthma reported in this study is underpinned by “genetic pleiotropy” (a potential limitation of all Mendelian randomization analyses). That is, some of the genetic variants included in the BMI allele score could conceivably also increase the risk of asthma. Nevertheless, these findings suggest that public health interventions designed to reduce obesity may also help to limit the global rise in asthma.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001669.
The US Centers for Disease Control and Prevention provides information on asthma and on all aspects of overweight and obesity (in English and Spanish)
The World Health Organization provides information on asthma and on obesity (in several languages)
The UK National Health Service Choices website provides information about asthma, about asthma in children, and about obesity (including real stories)
The Global Asthma Report 2011 is available
The Global Initiative for Asthma released its updated Global Strategy for Asthma Management and Prevention on World Asthma Day 2014
Information about the Avon Longitudinal Study of Parents and Children is available
MedlinePlus provides links to further information on obesity in children, on asthma, and on asthma in children (in English and Spanish
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001669
PMCID: PMC4077660  PMID: 24983943
3.  Lack of Identification in Semiparametric Instrumental Variable Models With Binary Outcomes 
American Journal of Epidemiology  2014;180(1):111-119.
A parameter in a statistical model is identified if its value can be uniquely determined from the distribution of the observable data. We consider the context of an instrumental variable analysis with a binary outcome for estimating a causal risk ratio. The semiparametric generalized method of moments and structural mean model frameworks use estimating equations for parameter estimation. In this paper, we demonstrate that lack of identification can occur in either of these frameworks, especially if the instrument is weak. In particular, the estimating equations may have no solution or multiple solutions. We investigate the relationship between the strength of the instrument and the proportion of simulated data sets for which there is a unique solution of the estimating equations. We see that this proportion does not appear to depend greatly on the sample size, particularly for weak instruments (ρ2 ≤ 0.01). Poor identification was observed in a considerable proportion of simulated data sets for instruments explaining up to 10% of the variance in the exposure with sample sizes up to 1 million. In an applied example considering the causal effect of body mass index (weight (kg)/height (m)2) on the probability of early menarche, estimates and standard errors from an automated optimization routine were misleading.
doi:10.1093/aje/kwu107
PMCID: PMC4070936  PMID: 24859275
Avon Longitudinal Study of Parents and Children; generalized method of moments; identifiability; identification; instrumental variables; semiparametric methods; structural mean model; weak instruments
4.  Validating childhood asthma in an epidemiological study using linked electronic patient records 
BMJ Open  2014;4(4):e005345.
Objective
To investigate the performance of parent-reported data in identifying physician-confirmed asthma.
Design and setting
Validation study using linkage between the Avon Longitudinal Study of Parents and Children (ALSPAC) and electronic patient records held within the General Practice Research Database (GPRD).
Participants
Participants were those eligible to participate in ALSPAC who also had a record in the GPRD; this included 765 individuals, just under 4% of ALSPAC-eligible participants. The analysis was based on 141 participants with complete parent-reported asthma data.
Primary and secondary outcome measures
The main GPRD outcome measure was whether a child had a diagnosis of asthma before they were nine. Parent-reported measures were doctor diagnosis of asthma (before mean age 7.5 years), various outcomes based on wheezing and breathlessness recorded longitudinally between 6 months and 8.5 years. Secondary outcomes were bronchial hyper-responsiveness (BHR), forced expiratory volume in 1 s/forced vital capacity ratio and skin prick test responses.
Results
Among the 141 participants with complete parent-reported data, 26 (18%) had an asthma diagnosis before age nine. Using general practitioner (GP)-recorded asthma as the gold standard, the question ‘Has a doctor ever diagnosed your child with asthma?’ was both sensitive (88.5%) and specific (95.7%). ‘Ever wheezed’ had the highest sensitivity (100%) but low specificity (60%). More specific definitions were obtained by restricting to those who had wheezed on more than one occasion, experienced frequent wheeze and/or wheezed after the age of 3, but these measures had low sensitivities. BHR only identified 50% of those with a GP-recorded diagnosis.
Conclusions
Parental reports of a doctor's diagnosis agree well with a GP-recorded diagnosis. High specificity for asthma can be achieved by using detailed wheezing questions, although these definitions are likely to exclude mild cases of asthma. Our study shows that linkage between observational studies and electronic patient records has the potential to enhance epidemiological research.
doi:10.1136/bmjopen-2014-005345
PMCID: PMC4010849  PMID: 24760357
Epidemiology; Primary Care
5.  Genome-wide association study of body mass index in 23,000 individuals with and without asthma 
Background
Both asthma and obesity are complex disorders that are influenced by environmental and genetic factors. Shared genetic factors between asthma and obesity have been proposed to partly explain epidemiological findings of co-morbidity between these conditions.
Objective
To identify genetic variants that are associated with body mass index (BMI) in asthmatic children and adults, and to evaluate if there are differences between the genetics of BMI in asthmatics and healthy individuals.
Methods
In total, 19 studies contributed with genome-wide analysis study (GWAS) data from more than 23,000 individuals with predominantly European descent, of whom 8,165 are asthmatics.
Results
We report associations between several DENND1B variants (p=2.2×10−7 for rs4915551) on chromosome 1q31 and BMI from a meta-analysis of GWAS data using 2,691 asthmatic children (screening data). The top DENND1B SNPs were next evaluated in seven independent replication data sets comprising 2,014 asthmatics, and rs4915551 was nominally replicated (p<0.05) in two of the seven studies and of borderline significance in one (p=0.059). However, strong evidence of effect heterogeneity was observed and overall, the association between rs4915551 and BMI was not significant in the total replication data set, p=0.71. Using a random effects model, BMI was overall estimated to increase by 0.30 kg/m2 (p=0.01 for combined screening and replication data sets, N=4,705) per additional G allele of this DENND1B SNP. FTO was confirmed as an important gene for adult and childhood BMI regardless of asthma status.
Conclusions and Clinical Relevance
DENND1B was recently identified as an asthma susceptibility gene in a GWAS on children, and here we find evidence that DENND1B variants may also be associated with BMI in asthmatic children. However, the association was overall not replicated in the independent data sets and the heterogeneous effect of DENND1B points to complex associations with the studied diseases that deserve further study.
doi:10.1111/cea.12054
PMCID: PMC3608930  PMID: 23517042
Association; Asthma; BMI; Genetics; Genome-wide; Obesity
7.  Associations of Different Phenotypes of Wheezing Illness in Early Childhood with Environmental Variables Implicated in the Aetiology of Asthma 
PLoS ONE  2012;7(10):e48359.
Rationale
Asthma is a complex heterogeneous disease that has increased in prevalence in many industrialised countries. However, the causes of asthma inception remain elusive. Consideration of sub-phenotypes of wheezing may reveal important clues to aetiological risk factors.
Methods
Longitudinal phenotypes capturing population heterogeneity in wheezing reports from birth to 7 years were derived using latent class analysis in the Avon Longitudinal Study of Parents and Children (ALSPAC). Probability of class membership was used to examine the association between five wheezing phenotypes (transient early, prolonged early, intermediate-onset, late-onset, persistent) and early life risk factors for asthma.
Results
Phenotypes had similar patterns and strengths of associations with early environmental factors. Comparing transient early with prolonged early wheezing showed a similar pattern of association with most exposure variables considered in terms of the direction of the effect estimates but with prolonged early wheezing tending to have stronger associations than transient early wheezing except for parity and day care attendance.
Conclusions
Associations with early life risk factors suggested that prolonged early wheeze might be a severe form of transient early wheezing. Although differences were found in the associations of early life risk factors with individual phenotypes, these did not point to novel aetiological pathways. Persistent wheezing phenotype has features suggesting overlap of early and late-onset phenotypes.
doi:10.1371/journal.pone.0048359
PMCID: PMC3485223  PMID: 23118993
8.  Genome-wide association and large scale follow-up identifies 16 new loci influencing lung function 
Artigas, María Soler | Loth, Daan W | Wain, Louise V | Gharib, Sina A | Obeidat, Ma’en | Tang, Wenbo | Zhai, Guangju | Zhao, Jing Hua | Smith, Albert Vernon | Huffman, Jennifer E | Albrecht, Eva | Jackson, Catherine M | Evans, David M | Cadby, Gemma | Fornage, Myriam | Manichaikul, Ani | Lopez, Lorna M | Johnson, Toby | Aldrich, Melinda C | Aspelund, Thor | Barroso, Inês | Campbell, Harry | Cassano, Patricia A | Couper, David J | Eiriksdottir, Gudny | Franceschini, Nora | Garcia, Melissa | Gieger, Christian | Gislason, Gauti Kjartan | Grkovic, Ivica | Hammond, Christopher J | Hancock, Dana B | Harris, Tamara B | Ramasamy, Adaikalavan | Heckbert, Susan R | Heliövaara, Markku | Homuth, Georg | Hysi, Pirro G | James, Alan L | Jankovic, Stipan | Joubert, Bonnie R | Karrasch, Stefan | Klopp, Norman | Koch, Beate | Kritchevsky, Stephen B | Launer, Lenore J | Liu, Yongmei | Loehr, Laura R | Lohman, Kurt | Loos, Ruth JF | Lumley, Thomas | Al Balushi, Khalid A | Ang, Wei Q | Barr, R Graham | Beilby, John | Blakey, John D | Boban, Mladen | Boraska, Vesna | Brisman, Jonas | Britton, John R | Brusselle, Guy G | Cooper, Cyrus | Curjuric, Ivan | Dahgam, Santosh | Deary, Ian J | Ebrahim, Shah | Eijgelsheim, Mark | Francks, Clyde | Gaysina, Darya | Granell, Raquel | Gu, Xiangjun | Hankinson, John L | Hardy, Rebecca | Harris, Sarah E | Henderson, John | Henry, Amanda | Hingorani, Aroon D | Hofman, Albert | Holt, Patrick G | Hui, Jennie | Hunter, Michael L | Imboden, Medea | Jameson, Karen A | Kerr, Shona M | Kolcic, Ivana | Kronenberg, Florian | Liu, Jason Z | Marchini, Jonathan | McKeever, Tricia | Morris, Andrew D | Olin, Anna-Carin | Porteous, David J | Postma, Dirkje S | Rich, Stephen S | Ring, Susan M | Rivadeneira, Fernando | Rochat, Thierry | Sayer, Avan Aihie | Sayers, Ian | Sly, Peter D | Smith, George Davey | Sood, Akshay | Starr, John M | Uitterlinden, André G | Vonk, Judith M | Wannamethee, S Goya | Whincup, Peter H | Wijmenga, Cisca | Williams, O Dale | Wong, Andrew | Mangino, Massimo | Marciante, Kristin D | McArdle, Wendy L | Meibohm, Bernd | Morrison, Alanna C | North, Kari E | Omenaas, Ernst | Palmer, Lyle J | Pietiläinen, Kirsi H | Pin, Isabelle | Polašek, Ozren | Pouta, Anneli | Psaty, Bruce M | Hartikainen, Anna-Liisa | Rantanen, Taina | Ripatti, Samuli | Rotter, Jerome I | Rudan, Igor | Rudnicka, Alicja R | Schulz, Holger | Shin, So-Youn | Spector, Tim D | Surakka, Ida | Vitart, Veronique | Völzke, Henry | Wareham, Nicholas J | Warrington, Nicole M | Wichmann, H-Erich | Wild, Sarah H | Wilk, Jemma B | Wjst, Matthias | Wright, Alan F | Zgaga, Lina | Zemunik, Tatijana | Pennell, Craig E | Nyberg, Fredrik | Kuh, Diana | Holloway, John W | Boezen, H Marike | Lawlor, Debbie A | Morris, Richard W | Probst-Hensch, Nicole | Kaprio, Jaakko | Wilson, James F | Hayward, Caroline | Kähönen, Mika | Heinrich, Joachim | Musk, Arthur W | Jarvis, Deborah L | Gläser, Sven | Järvelin, Marjo-Riitta | Stricker, Bruno H Ch | Elliott, Paul | O’Connor, George T | Strachan, David P | London, Stephanie J | Hall, Ian P | Gudnason, Vilmundur | Tobin, Martin D
Nature Genetics  2011;43(11):1082-1090.
Pulmonary function measures reflect respiratory health and predict mortality, and are used in the diagnosis of chronic obstructive pulmonary disease (COPD). We tested genome-wide association with the forced expiratory volume in 1 second (FEV1) and the ratio of FEV1 to forced vital capacity (FVC) in 48,201 individuals of European ancestry, with follow-up of top associations in up to an additional 46,411 individuals. We identified new regions showing association (combined P<5×10−8) with pulmonary function, in or near MFAP2, TGFB2, HDAC4, RARB, MECOM (EVI1), SPATA9, ARMC2, NCR3, ZKSCAN3, CDC123, C10orf11, LRP1, CCDC38, MMP15, CFDP1, and KCNE2. Identification of these 16 new loci may provide insight into the molecular mechanisms regulating pulmonary function and into molecular targets for future therapy to alleviate reduced lung function.
doi:10.1038/ng.941
PMCID: PMC3267376  PMID: 21946350
9.  A Comprehensive Evaluation of Potential Lung Function Associated Genes in the SpiroMeta General Population Sample 
PLoS ONE  2011;6(5):e19382.
Rationale
Lung function measures are heritable traits that predict population morbidity and mortality and are essential for the diagnosis of chronic obstructive pulmonary disease (COPD). Variations in many genes have been reported to affect these traits, but attempts at replication have provided conflicting results. Recently, we undertook a meta-analysis of Genome Wide Association Study (GWAS) results for lung function measures in 20,288 individuals from the general population (the SpiroMeta consortium).
Objectives
To comprehensively analyse previously reported genetic associations with lung function measures, and to investigate whether single nucleotide polymorphisms (SNPs) in these genomic regions are associated with lung function in a large population sample.
Methods
We analysed association for SNPs tagging 130 genes and 48 intergenic regions (+/−10 kb), after conducting a systematic review of the literature in the PubMed database for genetic association studies reporting lung function associations.
Results
The analysis included 16,936 genotyped and imputed SNPs. No loci showed overall significant association for FEV1 or FEV1/FVC traits using a carefully defined significance threshold of 1.3×10−5. The most significant loci associated with FEV1 include SNPs tagging MACROD2 (P = 6.81×10−5), CNTN5 (P = 4.37×10−4), and TRPV4 (P = 1.58×10−3). Among ever-smokers, SERPINA1 showed the most significant association with FEV1 (P = 8.41×10−5), followed by PDE4D (P = 1.22×10−4). The strongest association with FEV1/FVC ratio was observed with ABCC1 (P = 4.38×10−4), and ESR1 (P = 5.42×10−4) among ever-smokers.
Conclusions
Polymorphisms spanning previously associated lung function genes did not show strong evidence for association with lung function measures in the SpiroMeta consortium population. Common SERPINA1 polymorphisms may affect FEV1 among smokers in the general population.
doi:10.1371/journal.pone.0019382
PMCID: PMC3098839  PMID: 21625484
10.  Genome-wide association study identifies five loci associated with lung function 
Repapi, Emmanouela | Sayers, Ian | Wain, Louise V | Burton, Paul R | Johnson, Toby | Obeidat, Ma’en | Zhao, Jing Hua | Ramasamy, Adaikalavan | Zhai, Guangju | Vitart, Veronique | Huffman, Jennifer E | Igl, Wilmar | Albrecht, Eva | Deloukas, Panos | Henderson, John | Granell, Raquel | McArdle, Wendy L | Rudnicka, Alicja R | Barroso, Inês | Loos, Ruth J F | Wareham, Nicholas J | Mustelin, Linda | Rantanen, Taina | Surakka, Ida | Imboden, Medea | Wichmann, H Erich | Grkovic, Ivica | Jankovic, Stipan | Zgaga, Lina | Hartikainen, Anna-Liisa | Peltonen, Leena | Gyllensten, Ulf | Johansson, Åsa | Zaboli, Ghazal | Campbell, Harry | Wild, Sarah H | Wilson, James F | Gläser, Sven | Homuth, Georg | Völzke, Henry | Mangino, Massimo | Soranzo, Nicole | Spector, Tim D | Polašek, Ozren | Rudan, Igor | Wright, Alan F | Heliövaara, Markku | Ripatti, Samuli | Pouta, Anneli | Naluai, Åsa Torinsson | Olin, Anna-Carin | Torén, Kjell | Cooper, Matthew N | James, Alan L | Palmer, Lyle J | Hingorani, Aroon D | Wannamethee, S Goya | Whincup, Peter H | Smith, George Davey | Ebrahim, Shah | McKeever, Tricia M | Pavord, Ian D | MacLeod, Andrew K | Morris, Andrew D | Porteous, David J | Cooper, Cyrus | Dennison, Elaine | Shaheen, Seif | Karrasch, Stefan | Schnabel, Eva | Schulz, Holger | Grallert, Harald | Bouatia-Naji, Nabila | Delplanque, Jérôme | Froguel, Philippe | Blakey, John D | Britton, John R | Morris, Richard W | Holloway, John W | Lawlor, Debbie A | Hui, Jennie | Nyberg, Fredrik | Jarvelin, Marjo-Riitta | Jackson, Cathy | Kähönen, Mika | Kaprio, Jaakko | Probst-Hensch, Nicole M | Koch, Beate | Hayward, Caroline | Evans, David M | Elliott, Paul | Strachan, David P | Hall, Ian P | Tobin, Martin D
Nature genetics  2009;42(1):36-44.
Pulmonary function measures are heritable traits that predict morbidity and mortality and define chronic obstructive pulmonary disease (COPD). We tested genome-wide association with forced expiratory volume in 1 s (FEV1) and the ratio of FEV1 to forced vital capacity (FVC) in the SpiroMeta consortium (n = 20,288 individuals of European ancestry). We conducted a meta-analysis of top signals with data from direct genotyping (n ≤ 32,184 additional individuals) and in silico summary association data from the CHARGE Consortium (n = 21,209) and the Health 2000 survey (n ≤ 883). We confirmed the reported locus at 4q31 and identified associations with FEV1 or FEV1/FVC and common variants at five additional loci: 2q35 in TNS1 (P = 1.11 × 10−12), 4q24 in GSTCD (2.18 × 10−23), 5q33 in HTR4 (P = 4.29 × 10−9), 6p21 in AGER (P = 3.07 × 10−15) and 15q23 in THSD4 (P = 7.24 × 10−15). mRNA analyses showed expression of TNS1, GSTCD, AGER, HTR4 and THSD4 in human lung tissue. These associations offer mechanistic insight into pulmonary function regulation and indicate potential targets for interventions to alleviate respiratory disease.
doi:10.1038/ng.501
PMCID: PMC2862965  PMID: 20010834
11.  Glutathione-S-transferase genes and asthma phenotypes: a Human Genome Epidemiology (HuGE) systematic review and meta-analysis including unpublished data 
Background Oxidative stress is thought to be involved in the pathogenesis of asthma. Glutathione-S-transferase (GST) enzymes, which play an important role in antioxidant defences, may therefore influence asthma risk. Two common deletion polymorphisms of GSTM1 and GSTT1 genes and the GSTP1 Ile105Val polymorphism have been associated with asthma in children and adults, but results are inconsistent across studies.
Methods Systematic review and meta-analysis of the effects of GST genes on asthma, wheezing and bronchial hyper-responsiveness (BHR), with inclusion of unpublished data from three studies, including the large Avon Longitudinal Study of Parents and Children (ALSPAC). Random effect or fixed effect models were used as appropriate, and sensitivity analyses were performed to assess the impact of study characteristics and quality on pooled results.
Results The meta-analyses of GSTM1 (n = 22 studies) and GSTT1 (n = 19) showed increased asthma risk associated with the null genotype, but there was extreme between-study heterogeneity and publication bias and the association disappeared when meta-analysis was restricted to the largest studies. Meta-analysis of GSTP1 Ile105Val (n = 17) and asthma suggested a possible protective effect of the Val allele, but heterogeneity was extreme. Few studies evaluated wheezing and BHR and most reported no associations, although weak evidence was found for positive associations of GSTM1 null and GSTP1 Val allele with wheezing and a negative association of GSTP1 Val allele with BHR.
Conclusions Our findings do not support a substantial role of GST genes alone in the development of asthma. Future studies of large size should focus on interactions of GST genes with environmental oxidative exposures and with other genes involved in antioxidant pathways. Quality of study conduct and reporting needs to be improved to increase credibility of the evidence accumulating over time.
doi:10.1093/ije/dyp337
PMCID: PMC2846443  PMID: 20032267
Meta-analysis; systematic review; glutathione-S-transferase genes; GSTM1 gene; GSTT1 gene; GSTP1 gene; asthma; wheezing; bronchial responsiveness; The Avon Longitudinal Study of Parents and Children (ALSPAC)
12.  Carrier Status for the Common R501X and 2282del4 Filaggrin Mutations Is Not Associated with Hearing Phenotypes in 5377 Children from the ALSPAC Cohort 
PLoS ONE  2009;4(6):e5784.
Background
Filaggrin is a major protein in the epidermis. Several mutations in the filaggrin gene (FLG) have been associated with a number of conditions. Filaggrin is expressed in the tympanic membrane and could alter its mechanical properties, but the relationship between genetic variation in FLG and hearing has not yet been tested.
Methodology/Principal Findings
We examined whether loss-of function mutations R501X and 2282del4 in the FLG gene affected hearing in children. Twenty eight hearing variables representing five different aspects of hearing at age nine years in 5,377 children from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort were tested for association with these mutations. No evidence of association was found between R501X or 2282del4 (or overall FLG mutation carrier status) and any of the hearing phenotypes analysed.
Conclusions/Significance
In conclusion, carrier status for common filaggrin mutations does not affect hearing in children.
doi:10.1371/journal.pone.0005784
PMCID: PMC2685991  PMID: 19492053
13.  Mothers' anxiety during pregnancy is associated with asthma in their children 
Background
Maternal stress in early life has been associated with the development of asthma in children, although it is unclear whether there are any critical periods of exposure. The association of asthma with prenatal exposure to maternal stress has not been reported.
Objective
We tested whether prenatal and postnatal anxiety and/or depression in pregnant women predicted the risk of their offspring developing asthma in childhood.
Methods
The Avon Longitudinal Study of Parents and Children is a population-based birth cohort recruited during pregnancy. Data were available on maternal anxiety scores and asthma at age 7½ years in 5810 children. Anxiety was assessed at 18 and 32 weeks of gestation by using the validated Crown-Crisp Experiential Index. Asthma was defined at age 7½ years as doctor-diagnosed asthma with current symptoms or treatment in the previous 12 months. Multivariable logistic regression was used to determine the association of prenatal anxiety with asthma (odds ratio; 95% CI).
Results
Independent of postnatal anxiety and adjusted for a number of likely confounders, there was a higher likelihood of asthma at age 7½ years (odds ratio, 1.64; 95% CI, 1.25-2.17) in children of mothers in the highest compared with lowest quartile of anxiety scores at 32 weeks of gestation, with evidence for a dose-response (P value for trend <0.001).
Conclusions
Maternal anxiety symptoms as an indicator of stress during fetal life may program the development of asthma during childhood.
doi:10.1016/j.jaci.2009.01.042
PMCID: PMC2726292  PMID: 19348924
Anxiety; pregnancy; prenatal programming; asthma; child; ALSPAC, Avon Longitudinal Study of Parents and Children; HPA, Hypothalamo-pituitary-adrenal; OR, Odds ratio

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