Browsing by Author "Arshad, Syed Hasan"

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    Changes in DNA methylation from pre- to post-adolescence are associated with pubertal exposures
    (BMC (part of Springer Nature), 2019-12-02) Han, Luhang; Zhang, Hongmei; Kaushal, Akhilesh; Rezwan, Faisal I.; Kadalayil, Latha; Karmaus, Wilfried; Henderson, A. John; Relton, Caroline L.; Ring, Susan; Arshad, Syed Hasan; Holloway, John W.
    Background Adolescence is a period characterized by major biological development, which may be associated with changes in DNA methylation (DNA-M). However, it is unknown to what extent DNA-M varies from pre- to post-adolescence, whether the pattern of changes is different between females and males, and how adolescence-related factors are associated with changes in DNA-M. Methods Genome-scale DNA-M at ages 10 and 18 years in whole blood of 325 subjects (n = 140 females) in the Isle of Wight (IOW) birth cohort was analyzed using Illumina Infinium arrays (450K and EPIC). Linear mixed models were used to examine DNA-M changes between pre- and post-adolescence and whether the changes were gender-specific. Adolescence-related factors and environmental exposure factors were assessed on their association with DNA-M changes. Replication of findings was attempted in the comparable Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Results In the IOW cohort, after controlling for technical variation and cell compositions at both pre- and post-adolescence, 15,532 cytosine–phosphate–guanine (CpG) sites (of 400,825 CpGs, 3.88%) showed statistically significant DNA-M changes from pre-adolescence to post-adolescence invariant to gender (false discovery rate (FDR) = 0.05). Of these 15,532 CpGs, 10,212 CpGs (66%) were replicated in the ALSPAC cohort. Pathway analysis using Ingenuity Pathway Analysis (IPA) identified significant biological pathways related to growth and development of the reproductive system, emphasizing the importance of this period of transition on epigenetic state of genes. In addition, in IOW, we identified 1179 CpGs with gender-specific DNA-M changes. In the IOW cohort, body mass index (BMI) at age 10 years, age of growth spurt, nonsteroidal drugs use, and current smoking status showed statistically significant associations with DNA-M changes at 15 CpGs on 14 genes such as the AHRR gene. For BMI at age 10 years, the association was gender-specific. Findings on current smoking status were replicated in the ALSPAC cohort. Conclusion Adolescent transition is associated with changes in DNA-M at more than 15K CpGs. Identified pathways emphasize the importance of this period of transition on epigenetic state of genes relevant to cell growth and immune system development.
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    Changes of DNA methylation are associated with changes in lung function during adolescence
    (BioMed Central, 2020-04-07) Sunny, Shadia Khan; Zhang, Hongmei; Rezwan, Faisal I.; Relton, Caroline L.; Henderson, A. John; Merid, Simon Kebede; Melén, Erik; Hallberg, Jenny; Arshad, Syed Hasan; Ewart, Susan; Holloway, John W.
    Background Adolescence is a significant period for the gender-dependent development of lung function. Prior studies have shown that DNA methylation (DNA-M) is associated with lung function and DNA-M at some cytosine-phosphate-guanine dinucleotide sites (CpGs) changes over time. This study examined whether changes of DNA-M at lung-function-related CpGs are associated with changes in lung function during adolescence for each gender, and if so, the biological significance of the detected CpGs. Methods Genome-scale DNA-M was measured in peripheral blood samples at ages 10 (n = 330) and 18 years (n = 476) from the Isle of Wight (IOW) birth cohort in United Kingdom, using Illumina Infinium arrays (450 K and EPIC). Spirometry was conducted at both ages. A training and testing method was used to screen 402,714 CpGs for their potential associations with lung function. Linear regressions were applied to assess the association of changes in lung function with changes of DNA-M at those CpGs potentially related to lung function. Adolescence-related and personal and family-related confounders were included in the model. The analyses were stratified by gender. Multiple testing was adjusted by controlling false discovery rate of 0.05. Findings were further examined in two independent birth cohorts, the Avon Longitudinal Study of Children and Parents (ALSPAC) and the Children, Allergy, Milieu, Stockholm, Epidemiology (BAMSE) cohort. Pathway analyses were performed on genes to which the identified CpGs were mapped. Results For females, 42 CpGs showed statistically significant associations with change in FEV1/FVC, but none for change in FEV1 or FVC. No CpGs were identified for males. In replication analyses, 16 and 21 of the 42 CpGs showed the same direction of associations among the females in the ALSPAC and BAMSE cohorts, respectively, with 11 CpGs overlapping across all the three cohorts. Through pathway analyses, significant biological processes were identified that have previously been related to lung function development. Conclusions The detected 11 CpGs in all three cohorts have the potential to serve as the candidate epigenetic markers for changes in lung function during adolescence in females
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    Epigenome-wide association study reveals duration of breastfeeding is associated with epigenetic differences in children
    (MDPI, 2020-05-20) Sherwood, William B.; Kothalawala, Dilini M.; Kadalayil, Latha; Ewart, Susan; Zhang, Hongmei; Karmaus, Wilfried; Arshad, Syed Hasan; Holloway, John W.; Rezwan, Faisal I.
    Several small studies have shown associations between breastfeeding and genome-wide DNA methylation (DNAm). We performed a comprehensive Epigenome-Wide Association Study (EWAS) to identify associations between breastfeeding and DNAm patterns in childhood. We analysed DNAm data from the Isle of Wight Birth Cohort at birth, 10, 18 and 26 years. The feeding method was categorized as breastfeeding duration >3 months and >6 months, and exclusive breastfeeding duration >3 months. EWASs using robust linear regression were performed to identify differentially methylated positions (DMPs) in breastfed and non-breastfed children at age 10 (false discovery rate of 5%). Differentially methylated regions (DMRs) were identified using comb-p. The persistence of significant associations was evaluated in neonates and individuals at 18 and 26 years. Two DMPs, in genes SNX25 and LINC00840, were significantly associated with breastfeeding duration >6 months at 10 years and was replicated for >3 months of exclusive breastfeeding. Additionally, a significant DMR spanning the gene FDFT1 was identified in 10-year-old children who were exposed to a breastfeeding duration >3 months. None of these signals persisted to 18 or 26 years. This study lends further support for a suggestive role of DNAm in the known benefits of breastfeeding on a child’s future health
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    Gaussian Bayesian network comparisons with graph ordering unknown
    (Elsevier, 2020-12-26) Zhang, Hongmei; Huang, Xianzheng; Han, Shengtong; Rezwan, Faisal I.; Karmaus, Wilfried; Arshad, Syed Hasan; Holloway, John W.
    A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons.
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    Prediction models for childhood asthma: a systematic review
    (Wiley, 2020-03-17) Kothalawala, Dilini M.; Kadalayil, Latha; Weiss, Veronique B. N.; Kyyaly, Mohammed Aref; Arshad, Syed Hasan; Holloway, John W.; Rezwan, Faisal I.
    Background The inability to objectively diagnose childhood asthma before age five often results in both under‐treatment and over‐treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school‐age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school‐age asthma. Methods Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school‐age children (6‐13 years). Validation studies were evaluated as a secondary objective. Results Twenty‐four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression‐based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression‐based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62‐0.83). Conclusion Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school‐age asthma prediction
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    Prediction of lung function in adolescence using epigenetic aging: a machine learning approach
    (MDPI, 2020-11-09) Md, Adnan Arefeen; Nimi, Sumaiya Tabassum; Rahman, M. Sohel; Arshad, Syed Hasan; Holloway, John W.; Rezwan, Faisal I.
    Epigenetic aging has been found to be associated with a number of phenotypes and diseases. A few studies have investigated its effect on lung function in relatively older people. However, this effect has not been explored in the younger population. This study examines whether lung function in adolescence can be predicted with epigenetic age accelerations (AAs) using machine learning techniques. DNA methylation based AAs were estimated in 326 matched samples at two time points (at 10 years and 18 years) from the Isle of Wight Birth Cohort. Five machine learning regression models (linear, lasso, ridge, elastic net, and Bayesian ridge) were used to predict FEV1 (forced expiratory volume in one second) and FVC (forced vital capacity) at 18 years from feature selected predictor variables (based on mutual information) and AA changes between the two time points. The best models were ridge regression (R2 = 75.21% ± 7.42%; RMSE = 0.3768 ± 0.0653) and elastic net regression (R2 = 75.38% ± 6.98%; RMSE = 0.445 ± 0.069) for FEV1 and FVC, respectively. This study suggests that the application of machine learning in conjunction with tracking changes in AA over the life span can be beneficial to assess the lung health in adolescence