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Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.
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Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Análise da Randomização Mendeliana , Locos de Características Quantitativas , Humanos , Análise da Randomização Mendeliana/métodos , Estudo de Associação Genômica Ampla/métodos , Especificidade de Órgãos/genética , Modelos Genéticos , Polimorfismo de Nucleotídeo ÚnicoRESUMO
Cigarette smoking adversely affects many aspects of human health, and epigenetic responses to smoking may reflect mechanisms that mediate or defend against these effects. Prior studies of smoking and DNA methylation (DNAm), typically measured in leukocytes, have identified numerous smoking-associated regions (e.g., AHRR). To identify smoking-associated DNAm features in typically inaccessible tissues, we generated array-based DNAm data for 916 tissue samples from the GTEx (Genotype-Tissue Expression) project representing 9 tissue types (lung, colon, ovary, prostate, blood, breast, testis, kidney, and muscle). We identified 6,350 smoking-associated CpGs in lung tissue (n = 212) and 2,735 in colon tissue (n = 210), most not reported previously. For all 7 other tissue types (sample sizes 38-153), no clear associations were observed (false discovery rate 0.05), but some tissues showed enrichment for smoking-associated CpGs reported previously. For 1,646 loci (in lung) and 22 (in colon), smoking was associated with both DNAm and local gene expression. For loci detected in both lung and colon (e.g., AHRR, CYP1B1, CYP1A1), top CpGs often differed between tissues, but similar clusters of hyper- or hypomethylated CpGs were observed, with hypomethylation at regulatory elements corresponding to increased expression. For lung tissue, 17 hallmark gene sets were enriched for smoking-associated CpGs, including xenobiotic- and cancer-related gene sets. At least four smoking-associated regions in lung were impacted by lung methylation quantitative trait loci (QTLs) that co-localize with genome-wide association study (GWAS) signals for lung function (FEV1/FVC), suggesting epigenetic alterations can mediate the effects of smoking on lung health. Our multi-tissue approach has identified smoking-associated regions in disease-relevant tissues, including effects that are shared across tissue types.
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Fumar Cigarros , Metilação de DNA , Masculino , Feminino , Humanos , Metilação de DNA/genética , Epigênese Genética , Estudo de Associação Genômica Ampla , Fumar/efeitos adversos , Fumar/genética , Expressão GênicaRESUMO
Inorganic arsenic is highly toxic and carcinogenic to humans. Exposed individuals vary in their ability to metabolize arsenic, and variability in arsenic metabolism efficiency (AME) is associated with risks of arsenic-related toxicities. Inherited genetic variation in the 10q24.32 region, near the arsenic methyltransferase (AS3MT) gene, is associated with urine-based measures of AME in multiple arsenic-exposed populations. To identify potential causal variants in this region, we applied fine mapping approaches to targeted sequencing data generated for exposed individuals from Bangladeshi, American Indian, and European American populations (n = 2,357, 557, and 648 respectively). We identified three independent association signals for Bangladeshis, two for American Indians, and one for European Americans. The size of the confidence sets for each signal varied from 4 to 85 variants. There was one signal shared across all three populations, represented by the same SNP in American Indians and European Americans (rs191177668) and in strong linkage disequilibrium (LD) with a lead SNP in Bangladesh (rs145537350). Beyond this shared signal, differences in LD patterns, minor allele frequency (MAF) (e.g., rs12573221 ~13% in Bangladesh ~0.2% among American Indians), and/or heterogeneity in effect sizes across populations likely contributed to the apparent population specificity of the additional identified signals. One of our potential causal variants influences AS3MT expression and nearby DNA methylation in numerous GTEx tissue types (with rs4919690 as a likely causal variant). Several SNPs in our confidence sets overlap transcription factor binding sites and cis-regulatory elements (from ENCODE). Taken together, our analyses reveal multiple potential causal variants in the 10q24.32 region influencing AME, including a variant shared across populations, and elucidate potential biological mechanisms underlying the impact of genetic variation on AME.
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Intoxicação por Arsênico , Arsênio , Arsenicais , Humanos , Arsênio/toxicidade , Arsênio/metabolismo , Intoxicação por Arsênico/genética , Arsenicais/metabolismo , Metilação de DNA , Metiltransferases/genética , Metiltransferases/metabolismo , Polimorfismo de Nucleotídeo Único/genética , Cromossomos Humanos Par 10RESUMO
BACKGROUND: We used a polygenic score for externalizing behavior (extPGS) and structural MRI to examine potential pathways from genetic liability to conduct problems via the brain across the adolescent transition. METHODS: Three annual assessments of child conduct problems, attention-deficit/hyperactivity problems, and internalizing problems were conducted across across 9-13 years of age among 4,475 children of European ancestry in the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®). RESULTS: The extPGS predicted conduct problems in each wave (R2 = 2.0%-2.9%). Bifactor models revealed that the extPRS predicted variance specific to conduct problems (R2 = 1.7%-2.1%), but also variance that conduct problems shared with other measured problems (R2 = .8%-1.4%). Longitudinally, extPGS predicted levels of specific conduct problems (R2 = 2.0%), but not their slope of change across age. The extPGS was associated with total gray matter volume (TGMV; R2 = .4%) and lower TGMV predicted both specific conduct problems (R2 = 1.7%-2.1%) and the variance common to all problems in each wave (R2 = 1.6%-3.1%). A modest proportion of the polygenic liability specific to conduct problems in each wave was statistically mediated by TGMV. CONCLUSIONS: Across the adolescent transition, the extPGS predicted both variance specific to conduct problems and variance shared by all measured problems. The extPGS also was associated with TGMV, which robustly predicted conduct problems. Statistical mediation analyses suggested the hypothesis that polygenic variation influences individual differences in brain development that are related to the likelihood of conduct problems during the adolescent transition, justifying new research to test this causal hypothesis.
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Transtorno da Conduta , Imageamento por Ressonância Magnética , Herança Multifatorial , Humanos , Adolescente , Criança , Masculino , Feminino , Transtorno da Conduta/genética , Transtorno da Conduta/diagnóstico por imagem , Transtorno da Conduta/fisiopatologia , Estudos Longitudinais , Desenvolvimento do Adolescente/fisiologia , Encéfalo/diagnóstico por imagem , Transtorno do Deficit de Atenção com Hiperatividade/genética , Comportamento do Adolescente/fisiologia , Substância Cinzenta/diagnóstico por imagem , Substância Cinzenta/patologiaRESUMO
BACKGROUND: There is growing consensus that researchers should offer to return genetic results to participants, but returning results in lower-resource countries has received little attention. In this study, we return results on genetic susceptibility to arsenic toxicity to participants in a Bangladeshi cohort exposed to arsenic through naturally-contaminated drinking water. We examine the impact on behavioral changes related to exposure reduction. METHODS: We enrolled participants from the Health Effects of Arsenic Longitudinal Study who had (1) high arsenic (≥150 µg/g creatinine) in a recent urine sample and (2) existing data on genetic variants impacting arsenic metabolism efficiency (AS3MT and FTCD). We used genetic data to recruit three study groups, each with n = 103: (1) efficient metabolizers (low-risk), (2) inefficient metabolizers (high-risk), and (3) a randomly-selected control group (NCT05072132). At baseline, all participants received information on the effects of arsenic and how to reduce exposure by switching to a low arsenic well. The two intervention groups also received their arsenic metabolism efficiency status (based on their genetic results). Changes in behavior and arsenic exposure were assessed using questionnaires and urine arsenic measures after six months. RESULTS: Clear decreases in urine arsenic after six months were observed for all three groups. The inefficient group self-reported higher levels of attempted switching to lower arsenic wells than the other groups; however, there was no detectable difference in urine arsenic reduction among the three groups. Participants showed strong interest in receiving genetic results and found them useful. The inefficient group experienced higher levels of anxiety than the other groups. Among the efficient group, that receiving genetic results did not appear to hinder behavioral change. CONCLUSION: Returning genetic results increased self-reported exposure-reducing behaviors but did not have a detectable impact on reducing urine arsenic over and above a one-on-one educational intervention.
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Intoxicação por Arsênico , Arsênio , Humanos , Arsênio/toxicidade , Bangladesh/epidemiologia , Privacidade Genética , Estudos Longitudinais , Intoxicação por Arsênico/epidemiologia , Intoxicação por Arsênico/genética , MetiltransferasesRESUMO
MOTIVATION: Trans-acting expression quantitative trait loci (eQTLs) collectively explain a substantial proportion of expression variation, yet are challenging to detect and replicate since their effects are often individually weak. A large proportion of genetic effects on distal genes are mediated through cis-gene expression. Cis-association (between SNP and cis-gene) and gene-gene correlation conditional on SNP genotype could establish trans-association (between SNP and trans-gene). Both cis-association and gene-gene conditional correlation have effects shared across relevant tissues and conditions, and trans-associations mediated by cis-gene expression also have effects shared across relevant conditions. RESULTS: We proposed a Cross-Condition Mediation analysis method (CCmed) for detecting cis-mediated trans-associations with replicable effects in relevant conditions/studies. CCmed integrates cis-association and gene-gene conditional correlation statistics from multiple tissues/studies. Motivated by the bimodal effect-sharing patterns of eQTLs, we proposed two variations of CCmed, CCmedmost and CCmedspec for detecting cross-tissue and tissue-specific trans-associations, respectively. We analyzed data of 13 brain tissues from the Genotype-Tissue Expression (GTEx) project, and identified trios with cis-mediated trans-associations across brain tissues, many of which showed evidence of trans-association in two replication studies. We also identified trans-genes associated with schizophrenia loci in at least two brain tissues. AVAILABILITY AND IMPLEMENTATION: CCmed software is available at http://github.com/kjgleason/CCmed. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Inorganic arsenic (iAs) is a carcinogen, and exposure to iAs via food and water is a global public health problem. iAs-contaminated drinking water alone affects >100 million people worldwide, including ~50 million in Bangladesh. Once absorbed into the blood stream, most iAs is converted to mono-methylated (MMA) and then di-methylated (DMA) forms, facilitating excretion in urine. Arsenic metabolism efficiency varies among individuals, in part due to genetic variation near AS3MT (arsenite methyltransferase; 10q24.32). To identify additional arsenic metabolism loci, we measured protein-coding variants across the human exome for 1,660 Bangladeshi individuals participating in the Health Effects of Arsenic Longitudinal Study (HEALS). Among the 19,992 coding variants analyzed exome-wide, the minor allele (A) of rs61735836 (p.Val101Met) in exon 3 of FTCD (formiminotransferase cyclodeaminase) was associated with increased urinary iAs% (P = 8x10-13), increased MMA% (P = 2x10-16) and decreased DMA% (P = 6x10-23). Among 2,401 individuals with arsenic-induced skin lesions (an indicator of arsenic toxicity and cancer risk) and 2,472 controls, carrying the low-efficiency A allele (frequency = 7%) was associated with increased skin lesion risk (odds ratio = 1.35; P = 1x10-5). rs61735836 is in weak linkage disequilibrium with all nearby variants. The high-efficiency/major allele (G/Valine) is human-specific and eliminates a start codon at the first 5´-proximal Kozak sequence in FTCD, suggesting selection against an alternative translation start site. FTCD is critical for catabolism of histidine, a process that generates one-carbon units that can enter the one-carbon/folate cycle, which provides methyl groups for arsenic metabolism. In our study population, FTCD and AS3MT SNPs together explain ~10% of the variation in DMA% and support a causal effect of arsenic metabolism efficiency on arsenic toxicity (i.e., skin lesions). In summary, this work identifies a coding variant in FTCD associated with arsenic metabolism efficiency, providing new evidence supporting the established link between one-carbon/folate metabolism and arsenic toxicity.
Assuntos
Amônia-Liases/genética , Arsênio/toxicidade , Glutamato Formimidoiltransferase/genética , Metiltransferases/genética , Adulto , Alelos , Amônia-Liases/fisiologia , Arsênio/metabolismo , Intoxicação por Arsênico , Bangladesh , Exposição Ambiental , Feminino , Ácido Fólico/metabolismo , Frequência do Gene/genética , Glutamato Formimidoiltransferase/fisiologia , Humanos , Masculino , Metilação , Metiltransferases/metabolismo , Enzimas Multifuncionais , Mutação de Sentido Incorreto , Razão de Chances , Fenótipo , Polimorfismo de Nucleotídeo Único/genética , Fatores de Risco , Dermatopatias/induzido quimicamente , Dermatopatias/genética , Poluentes Químicos da ÁguaRESUMO
[This corrects the article DOI: 10.1371/journal.pgen.1007984.].
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The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is "mediation" by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "cis-mediators" of trans-eQTLs, including those "cis-hubs" involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.
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Perfilação da Expressão Gênica/métodos , Genômica/métodos , Locos de Características Quantitativas , Bases de Dados Genéticas , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Polimorfismo de Nucleotídeo Único , Seleção Genética , Distribuição TecidualRESUMO
Genome-wide association studies (GWAS) have identified more than 90 susceptibility loci for breast cancer, but the underlying biology of those associations needs to be further elucidated. More genetic factors for breast cancer are yet to be identified but sample size constraints preclude the identification of individual genetic variants with weak effects using traditional GWAS methods. To address this challenge, we utilized a gene-level expression-based method, implemented in the MetaXcan software, to predict gene expression levels for 11,536 genes using expression quantitative trait loci and examine the genetically-predicted expression of specific genes for association with overall breast cancer risk and estrogen receptor (ER)-negative breast cancer risk. Using GWAS datasets from a Challenge launched by National Cancer Institute, we identified TP53INP2 (tumor protein p53-inducible nuclear protein 2) at 20q11.22 to be significantly associated with ER-negative breast cancer (Z = -5.013, p = 5.35×10-7, Bonferroni threshold = 4.33×10-6). The association was consistent across four GWAS datasets, representing European, African and Asian ancestry populations. There are 6 single nucleotide polymorphisms (SNPs) included in the prediction of TP53INP2 expression and five of them were associated with estrogen-receptor negative breast cancer, although none of the SNP-level associations reached genome-wide significance. We conducted a replication study using a dataset outside of the Challenge, and found the association between TP53INP2 and ER-negative breast cancer was significant (p = 5.07x10-3). Expression of HP (16q22.2) showed a suggestive association with ER-negative breast cancer in the discovery phase (Z = 4.30, p = 1.70x10-5) although the association was not significant after Bonferroni adjustment. Of the 249 genes that are 250 kb within known breast cancer susceptibility loci identified from previous GWAS, 20 genes (8.0%) were statistically significant associated with ER-negative breast cancer (p<0.05), compared to 582 (5.2%) of 11,287 genes that are not close to previous GWAS loci. This study demonstrated that expression-based gene mapping is a promising approach for identifying cancer susceptibility genes.
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Neoplasias da Mama/genética , Receptor alfa de Estrogênio/genética , Haptoglobinas/genética , Proteínas Nucleares/genética , Neoplasias da Mama/patologia , Feminino , Regulação Neoplásica da Expressão Gênica , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Polimorfismo de Nucleotídeo ÚnicoRESUMO
There is a growing recognition that gene-environment interaction (G × E) plays a pivotal role in the development and progression of complex diseases. Despite a wealth of genetic data on various complex diseases/traits generated from association and sequencing studies, detecting G × E via genome-wide analysis remains challenging due to power issues. In genome-wide G × E studies, a common strategy to improve power is to first conduct a filtering test and retain only the genetic variants that pass the filtering step for subsequent G × E analyses. Two-stage, multistage, and unified tests have been proposed to jointly consider the filtering statistics in G × E tests. However, such G × E tests based on data from a single study may still be underpowered. Meanwhile, large-scale consortia have been formed to borrow strength across studies and populations. In this work, motivated by existing single-study G × E tests with filtering and the needs for meta-analysis G × E approaches based on consortia data, we propose a meta-analysis framework for detecting gene-based G × E effects, and introduce meta-analysis-based filtering statistics in the gene-level G × E tests. Simulations demonstrate the advantages of the proposed method-the ofGEM test. We apply the proposed tests to existing data from two breast cancer consortia to identify the genes harboring genetic variants with age-dependent penetrance (i.e., gene-age interaction effects). We develop an R software package ofGEM for the proposed meta-analysis tests.
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Interação Gene-Ambiente , Fatores Etários , Idade de Início , Neoplasias da Mama/genética , Simulação por Computador , Feminino , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Modelos Genéticos , Penetrância , Fatores de RiscoRESUMO
BACKGROUND: It is well-known that methylation changes occur as humans age, however, understanding how age-related changes in DNA methylation vary by sex is lacking. In this study, we characterize the effect of age on DNA methylation in a sex-specific manner and determine if these effects vary by genomic context. We used the Illumina HumanMethylation 450 K array and DNA derived from whole blood for 400 adult participants (189 males and 211 females) from Bangladesh to identify age-associated CpG sites and regions and characterize the location of these age-associated sites with respect to CpG islands (vs. shore, shelf, or open sea) and gene regions (vs. intergenic). We conducted a genome-wide search for age-associated CpG sites (among 423,604 sites) using a reference-free approach to adjust for cell type composition (the R package RefFreeEWAS) and performed an independent replication analysis of age-associated CpGs. RESULTS: The number of age-associated CpGs (p < 5 x 10- 8) were 986 among men and 3479 among women of which 2027(63.8%) and 572 (64.1%) replicated (using Bonferroni adjusted p < 1.2 × 10- 5). For both sexes, age-associated CpG sites were more likely to be hyper-methylated with increasing age (compared to hypo-methylated) and were enriched in CpG islands and promoter regions compared with other locations and all CpGs on the array. Although we observed strong correlation between chronological age and previously-developed epigenetic age models (r ≈ 0.8), among our top (based on lowest p-value) age-associated CpG sites only 12 for males and 44 for females are included in these prediction models, and the median chronological age compared to predicted age was 44 vs. 51.7 in males and 45 vs. 52.1 in females. CONCLUSIONS: Our results describe genome-wide features of age-related changes in DNA methylation. The observed associations between age and methylation were generally consistent for both sexes, although the associations tended to be stronger among women. Our population may have unique age-related methylation changes that are not captured in the established methylation-based age prediction model we used, which was developed to be non-tissue-specific.
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Envelhecimento/genética , Sangue/metabolismo , Metilação de DNA , Adulto , Idoso , Bangladesh , Ilhas de CpG/genética , Epigênese Genética , Feminino , Predisposição Genética para Doença/genética , Genoma Humano/genética , Humanos , Masculino , Pessoa de Meia-Idade , Caracteres SexuaisRESUMO
Leukocyte telomere length (LTL) is a heritable trait with two potential sources of heritability (h2): inherited variation in non-telomeric regions (e.g., SNPs that influence telomere maintenance) and variability in the lengths of telomeres in gametes that produce offspring zygotes (i.e., "direct" inheritance). Prior studies of LTL h2 have not attempted to disentangle these two sources. Here, we use a novel approach for detecting the direct inheritance of telomeres by studying the association between identity-by-descent (IBD) sharing at chromosome ends and phenotypic similarity in LTL. We measured genome-wide SNPs and LTL for a sample of 5069 Bangladeshi adults with substantial relatedness. For each of the 6318 relative pairs identified, we used SNPs near the telomeres to estimate the number of chromosome ends shared IBD, a proxy for the number of telomeres shared IBD (Tshared). We then estimated the association between Tshared and the squared pairwise difference in LTL ((ΔLTL)2) within various classes of relatives (siblings, avuncular, cousins, and distant), adjusting for overall genetic relatedness (Ï). The association between Tshared and (ΔLTL)2 was inverse among all relative pair types. In a meta-analysis including all relative pairs (Ï > 0.05), the association between Tshared and (ΔLTL)2 (P = 0.01) was stronger than the association between Ï and (ΔLTL)2 (P = 0.43). Our results provide strong evidence that telomere length (TL) in parental germ cells impacts TL in offspring cells and contributes to LTL h2 despite telomere "reprogramming" during embryonic development. Applying our method to larger studies will enable robust estimation of LTL h2 attributable to direct transmission of telomeres.
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Leucócitos/metabolismo , Leucócitos/patologia , Pais , Polimorfismo de Nucleotídeo Único , Homeostase do Telômero , Telômero/genética , Adolescente , Adulto , Idoso , Feminino , Seguimentos , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Adulto JovemRESUMO
Gene expression and its regulation can vary substantially across tissue types. In order to generate knowledge about gene expression in human tissues, the Genotype-Tissue Expression (GTEx) program has collected transcriptome data in a wide variety of tissue types from post-mortem donors. However, many tissue types are difficult to access and are not collected in every GTEx individual. Furthermore, in non-GTEx studies, the accessibility of certain tissue types greatly limits the feasibility and scale of studies of multi-tissue expression. In this work, we developed multi-tissue imputation methods to impute gene expression in uncollected or inaccessible tissues. Via simulation studies, we showed that the proposed methods outperform existing imputation methods in multi-tissue expression imputation and that incorporating imputed expression data can improve power to detect phenotype-expression correlations. By analyzing data from nine selected tissue types in the GTEx pilot project, we demonstrated that harnessing expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx tissues. More importantly, we showed that by using GTEx data as a reference, one can impute expression levels in inaccessible tissues in non-GTEx expression studies.
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Regulação da Expressão Gênica , Genótipo , Locos de Características Quantitativas , Transcriptoma , Humanos , Fenótipo , Projetos Piloto , Reprodutibilidade dos TestesRESUMO
BACKGROUND: Leucocyte telomere length (TL) is a potential biomarker of ageing and risk for age-related disease. Leucocyte TL is heritable and shows substantial differences by race/ethnicity. Recent genome-wide association studies (GWAS) report ~10 loci harbouring SNPs associated with leucocyte TL, but these studies focus primarily on populations of European ancestry. OBJECTIVE: This study aims to enhance our understanding of genetic determinants of TL across populations. METHODS: We performed a GWAS of TL using data on 5075 Bangladeshi adults. We measured TL using one of two technologies (qPCR or a Luminex-based method) and used standardised variables as TL phenotypes. RESULTS: Our results replicate previously reported associations in the TERC and TERT regions (P=2.2×10-8 and P=6.4×10-6, respectively). We observed a novel association signal in the RTEL1 gene (intronic SNP rs2297439; P=2.82×10-7) that is independent of previously reported TL-associated SNPs in this region. The minor allele for rs2297439 is common in South Asian populations (≥0.25) but at lower frequencies in other populations (eg, 0.07 in Northern Europeans). Among the eight other previously reported association signals, all were directionally consistent with our study, but only rs8105767 (ZNF208) was nominally significant (P=0.003). SNP-based heritability estimates were as high as 44% when analysing close relatives but much lower when analysing distant relatives only. CONCLUSIONS: In this first GWAS of TL in a South Asian population, we replicate some, but not all, of the loci reported in prior GWAS of individuals of European ancestry, and we identify a novel second association signal at the RTEL1 locus.
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Povo Asiático/genética , DNA Helicases/genética , Estudo de Associação Genômica Ampla , Telômero/genética , Predisposição Genética para Doença , Humanos , Polimorfismo de Nucleotídeo Único/genéticaRESUMO
Identifying gene-environment interactions is a central challenge in the quest to understand susceptibility to complex, multi-factorial diseases. Developing an understanding of how inter-individual variability in inherited genetic variation alters the effects of environmental exposures will enhance our knowledge of disease mechanisms and improve our ability to predict disease and target interventions to high-risk sub-populations. Limited progress has been made identifying gene-environment interactions in the epidemiological setting using existing statistical approaches for genome-wide searches for interaction. In this paper, we describe a novel two-step approach using omics data to conduct genome-wide searches for gene-environment interactions. Using existing genome-wide SNP data from a large Bangladeshi cohort study specifically designed to assess the effect of arsenic exposure on health, we evaluated gene-arsenic interactions by first conducting genome-wide searches for SNPs that modify the effect of arsenic on molecular phenotypes (gene expression and DNA methylation features). Using this set of SNPs showing evidence of interaction with arsenic in relation to molecular phenotypes, we then tested SNP-arsenic interactions in relation to skin lesions, a hallmark characteristic of arsenic toxicity. With the emergence of additional omics data in the epidemiologic setting, our approach may have the potential to boost power for genome-wide interaction research, enabling the identification of interactions that will enhance our understanding of disease etiology and our ability to develop interventions targeted at susceptible sub-populations.
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Intoxicação por Arsênico/genética , Arsênio/toxicidade , Interação Gene-Ambiente , Predisposição Genética para Doença , Animais , Metilação de DNA/genética , Epistasia Genética , Regulação da Expressão Gênica/efeitos dos fármacos , Humanos , Fenótipo , Polimorfismo de Nucleotídeo ÚnicoRESUMO
BACKGROUND: Chronic arsenic exposure is associated with increased risk for arsenical skin lesions, cancer, and other adverse health outcomes. One potential mechanism of arsenic toxicity is telomere dysfunction. However, prior epidemiological studies of arsenic exposure, telomere length (TL), and skin lesion are small and cross-sectional. We investigated the associations between arsenic exposure and TL and between baseline TL and incident skin lesion risk among individuals participating in the Health Effects of Arsenic Longitudinal Study in Bangladesh (2000-2009). METHODS: Quantitative PCR was used to measure the average TL of peripheral blood DNA collected at baseline. The association between baseline arsenic exposure (well water and urine) and TL was estimated in a randomly-selected subcohort (nâ¯=â¯1469). A nested case-control study (466 cases and 464 age- and sex-matched controls) was used to estimate the association between baseline TL and incident skin lesion risk (diagnosed <â¯8 years after baseline). RESULTS: No association was observed between arsenic exposure (water or urine) and TL. Among incident skin lesion cases and matched controls, we observed higher skin lesion risk among individuals with shorter TL (Ptrend =â¯1.5 × 10-5) with odds ratios of 2.60, 1.59, and 1.10 for the first (shortest), second, and third TL quartiles compared to the fourth (longest). CONCLUSIONS: Arsenic exposure was not associated with TL among Bangladeshi adults, suggesting that leukocyte TL may not reflect a primary mode of action for arsenic's toxicity. However, short TL was associated with increased skin lesion risk, and may be a biomarker of arsenic susceptibility modifying arsenic's effect on skin lesion risk.
Assuntos
Arsênio , Exposição Ambiental , Telômero , Adulto , Arsênio/toxicidade , Bangladesh , Estudos de Casos e Controles , Estudos Transversais , Feminino , Humanos , Estudos Longitudinais , Masculino , Telômero/efeitos dos fármacosRESUMO
OBJECTIVES: Relative telomere length (RTL) is a potential biomarker of aging and risk for chronic disease. Previously, we developed a probe-based RTL assay on Luminex platform, where probes for Telomere (T) and reference gene (R) for a given DNA sample were tested in a single well. Here, we describe a method of pooling multiple samples in one well to increase the throughput and cost-effectiveness. METHODS: We used four different microbeads for the same T-probe and four different microbeads for the same R-probe. Each pair of probe sets were hybridized to DNA in separate plates and then pooled in a single plate for all the subsequent steps. We used DNA samples from 60 independent individuals and repeated in multiple batches to test the precision. RESULTS: The precision was good to excellent with Intraclass correlation coefficient (ICC) of 0.908 (95% CI 0.856-0.942). More than 67% of the variation in the RTL could be explained by sample-to-sample variation; less than 0.1% variation was due to batch-to-batch variation and 0.3% variation was explained by bead-to-bead variation. We increased the throughput of RTL Luminex assay from 60 to 240 samples per run. The new assay was validated against the original Luminex assay without pooling (r = 0.79, P = 1.44 × 10-15 ). In an independent set of samples (n = 550), the new assay showed a negative correlation of RTL with age (r = -0.41), a result providing external validation for the method. CONCLUSION: We describe a novel high throughput pooled-sample multiplex Luminex assay for RTL with good to excellent precision suitable for large-scale studies.
Assuntos
Ensaios de Triagem em Larga Escala/métodos , Encurtamento do Telômero/fisiologia , Telômero/química , Adulto , Idoso , Ensaios de Triagem em Larga Escala/economia , Humanos , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Adulto JovemRESUMO
Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has been a focus of research for several years. In this article, select examples of G×E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G×E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G×Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G×E in several examples. Future studies may benefit from these lessons learned.
Assuntos
Doença/etiologia , Interação Gene-Ambiente , Pesquisa Biomédica , Doença/genética , Exposição Ambiental , Predisposição Genética para Doença , Variação Genética , Estudo de Associação Genômica Ampla/métodos , Humanos , Modelos BiológicosRESUMO
A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.