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PLoS Genet ; 15(7): e1008203, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31269027


Polymorphic inversions contribute to adaptation and phenotypic variation. However, large multi-centric association studies of inversions remain challenging. We present scoreInvHap, a method to genotype inversions from SNP data for genome-wide association studies (GWASs), overcoming important limitations of current methods and outperforming them in accuracy and applicability. scoreInvHap calls individual inversion-genotypes from a similarity score to the SNPs of experimentally validated references. It can be used on different sources of SNP data, including those with low SNP coverage such as exome sequencing, and is easily adaptable to genotype new inversions, either in humans or in other species. We present 20 human inversions that can be reliably and easily genotyped with scoreInvHap to discover their role in complex human traits, and illustrate a first genome-wide association study of experimentally-validated human inversions. scoreInvHap is implemented in R and it is freely available from Bioconductor.

Eur Respir J ; 53(4)2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-30765504


RATIONALE: We aimed to identify differentially methylated regions (DMRs) in cord blood DNA associated with childhood lung function, asthma and chronic obstructive pulmonary disease (COPD) across the life course. METHODS: We meta-analysed epigenome-wide data of 1688 children from five cohorts to identify cord blood DMRs and their annotated genes, in relation to forced expiratory volume in 1 s (FEV1), FEV1/forced vital capacity (FVC) ratio and forced expiratory flow at 75% of FVC at ages 7-13 years. Identified DMRs were explored for associations with childhood asthma, adult lung function and COPD, gene expression and involvement in biological processes. RESULTS: We identified 59 DMRs associated with childhood lung function, of which 18 were associated with childhood asthma and nine with COPD in adulthood. Genes annotated to the top 10 identified DMRs were HOXA5, PAOX, LINC00602, ABCA7, PER3, CLCA1, VENTX, NUDT12, PTPRN2 and TCL1A. Differential gene expression in blood was observed for 32 DMRs in childhood and 18 in adulthood. Genes related with 16 identified DMRs were associated with respiratory developmental or pathogenic pathways. INTERPRETATION: Our findings suggest that the epigenetic status of the newborn affects respiratory health and disease across the life course.

J Allergy Clin Immunol ; 143(6): 2062-2074, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30579849


BACKGROUND: Epigenetic mechanisms, including methylation, can contribute to childhood asthma. Identifying DNA methylation profiles in asthmatic patients can inform disease pathogenesis. OBJECTIVE: We sought to identify differential DNA methylation in newborns and children related to childhood asthma. METHODS: Within the Pregnancy And Childhood Epigenetics consortium, we performed epigenome-wide meta-analyses of school-age asthma in relation to CpG methylation (Illumina450K) in blood measured either in newborns, in prospective analyses, or cross-sectionally in school-aged children. We also identified differentially methylated regions. RESULTS: In newborns (8 cohorts, 668 cases), 9 CpGs (and 35 regions) were differentially methylated (epigenome-wide significance, false discovery rate < 0.05) in relation to asthma development. In a cross-sectional meta-analysis of asthma and methylation in children (9 cohorts, 631 cases), we identified 179 CpGs (false discovery rate < 0.05) and 36 differentially methylated regions. In replication studies of methylation in other tissues, most of the 179 CpGs discovered in blood replicated, despite smaller sample sizes, in studies of nasal respiratory epithelium or eosinophils. Pathway analyses highlighted enrichment for asthma-relevant immune processes and overlap in pathways enriched both in newborns and children. Gene expression correlated with methylation at most loci. Functional annotation supports a regulatory effect on gene expression at many asthma-associated CpGs. Several implicated genes are targets for approved or experimental drugs, including IL5RA and KCNH2. CONCLUSION: Novel loci differentially methylated in newborns represent potential biomarkers of risk of asthma by school age. Cross-sectional associations in children can reflect both risk for and effects of disease. Asthma-related differential methylation in blood in children was substantially replicated in eosinophils and respiratory epithelium.

BMC Bioinformatics ; 18(1): 553, 2017 Dec 14.
Artigo em Inglês | MEDLINE | ID: mdl-29237399


BACKGROUND: DNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usually occur at the regional level: for example, genomic structural variants can affect methylation patterns in regions up to several megabases in length. Existing DMR methods provide lists of Differentially Methylated Regions (DMRs) of up to only few kilobases in length, and cannot check if a target region is differentially methylated. Therefore, these methods are not suitable to evaluate methylation changes in large regions. To address these limitations, we developed a new DMR approach based on redundancy analysis (RDA) that assesses whether a target region is differentially methylated. RESULTS: Using simulated and real datasets, we compared our approach to three common DMR detection methods (Bumphunter, blockFinder, and DMRcate). We found that Bumphunter underestimated methylation changes and blockFinder showed poor performance. DMRcate showed poor power in the simulated datasets and low specificity in the real data analysis. Our method showed very high performance in all simulation settings, even with small sample sizes and subtle methylation changes, while controlling type I error. Other advantages of our method are: 1) it estimates the degree of association between the DMR and the outcome; 2) it can analyze a targeted or region of interest; and 3) it can evaluate the simultaneous effects of different variables. The proposed methodology is implemented in MEAL, a Bioconductor package designed to facilitate the analysis of methylation data. CONCLUSIONS: We propose a multivariate approach to decipher whether an outcome of interest alters the methylation pattern of a region of interest. The method is designed to analyze large target genomic regions and outperforms the three most popular methods for detecting DMRs. Our method can evaluate factors with more than two levels or the simultaneous effect of more than one continuous variable, which is not possible with the state-of-the-art methods.

Metilação de DNA/genética , Genoma/genética , Genômica/métodos , Neoplasias da Mama/genética , Bases de Dados Genéticas , Epigênese Genética , Feminino , Humanos
Hum Mol Genet ; 26(20): 4067-4085, 2017 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-29016858


Pre-pregnancy maternal obesity is associated with adverse offspring outcomes at birth and later in life. Individual studies have shown that epigenetic modifications such as DNA methylation could contribute. Within the Pregnancy and Childhood Epigenetics (PACE) Consortium, we meta-analysed the association between pre-pregnancy maternal BMI and methylation at over 450,000 sites in newborn blood DNA, across 19 cohorts (9,340 mother-newborn pairs). We attempted to infer causality by comparing the effects of maternal versus paternal BMI and incorporating genetic variation. In four additional cohorts (1,817 mother-child pairs), we meta-analysed the association between maternal BMI at the start of pregnancy and blood methylation in adolescents. In newborns, maternal BMI was associated with small (<0.2% per BMI unit (1 kg/m2), P < 1.06 × 10-7) methylation variation at 9,044 sites throughout the genome. Adjustment for estimated cell proportions greatly attenuated the number of significant CpGs to 104, including 86 sites common to the unadjusted model. At 72/86 sites, the direction of the association was the same in newborns and adolescents, suggesting persistence of signals. However, we found evidence for acausal intrauterine effect of maternal BMI on newborn methylation at just 8/86 sites. In conclusion, this well-powered analysis identified robust associations between maternal adiposity and variations in newborn blood DNA methylation, but these small effects may be better explained by genetic or lifestyle factors than a causal intrauterine mechanism. This highlights the need for large-scale collaborative approaches and the application of causal inference techniques in epigenetic epidemiology.

Herança Materna/genética , Obesidade/complicações , Resultado da Gravidez/genética , Adulto , Índice de Massa Corporal , Estudos de Coortes , Metilação de DNA/genética , Epigênese Genética/genética , Epigenômica/métodos , Feminino , Humanos , Recém-Nascido , Masculino , Herança Materna/fisiologia , Mães , Gravidez/fisiologia , Resultado da Gravidez/epidemiologia , Efeitos Tardios da Exposição Pré-Natal/genética , Efeitos Tardios da Exposição Pré-Natal/metabolismo
BMC Bioinformatics ; 18(1): 36, 2017 Jan 17.
Artigo em Inglês | MEDLINE | ID: mdl-28095799


BACKGROUND: Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor's methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples. RESULTS: To cover this need, we have developed MultiDataSet, a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment. CONCLUSIONS: MultiDataSet is a suitable class for data integration under R and Bioconductor framework.

Genômica/métodos , Software , Metilação de DNA , Expressão Gênica , Humanos , Análise Multivariada