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1.
Bioinformatics ; 2019 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-31697315

RESUMO

MOTIVATION: The human microbiota is the collection of microorganisms colonizing the human body, and plays an integral part in human health. A growing trend in microbiome analysis is to construct a network to estimate the co-occurrence patterns among taxa through precision matrices. Existing methods do not facilitate investigation into how these networks change with respect to covariates. RESULTS: We propose a new model called Microbiome Differential Network Estimation (MDiNE) to estimate network changes with respect to a binary covariate. The counts of individual taxa in the samples are modelled through a multinomial distribution whose probabilities depend on a latent Gaussian random variable. A sparse precision matrix over all the latent terms determines the co-occurrence network among taxa. The model fit is obtained and evaluated using Hamiltonian Monte Carlo methods. The performance of our model is evaluated through an extensive simulation study, and is shown to outperform existing methods in terms of estimation of network parameters. We also demonstrate an application of the model to estimate changes in the intestinal microbial network topology with respect to Crohn's disease. AVAILABILITY: MDiNE is implemented in a freely available R package: https://github.com/kevinmcgregor/mdine. SUPPLEMENTARY INFORMATION: A file containing supplemental material has been submitted with this manuscript.

2.
BMC Med Genomics ; 12(1): 144, 2019 10 24.
Artigo em Inglês | MEDLINE | ID: mdl-31651337

RESUMO

BACKGROUND: Systemic sclerosis (SSc) is a rare autoimmune connective tissue disease whose pathogenesis remains incompletely understood. Increasing evidence suggests that both genetic susceptibilities and changes in DNA methylation influence pivotal biological pathways and thereby contribute to the disease. The role of DNA methylation in SSc has not been fully elucidated, because existing investigations of DNA methylation predominantly focused on nucleotide CpGs within restricted genic regions, and were performed on samples containing mixed cell types. METHODS: We performed whole-genome bisulfite sequencing on purified CD4+ T lymphocytes from nine SSc patients and nine controls in a pilot study, and then profiled genome-wide cytosine methylation as well as genetic variations. We adopted robust statistical methods to identify differentially methylated genomic regions (DMRs). We then examined pathway enrichment associated with genes located in these DMRs. We also tested whether changes in CpG methylation were associated with adjacent genetic variation. RESULTS: We profiled DNA methylation at more than three million CpG dinucleotides genome-wide. We identified 599 DMRs associated with 340 genes, among which 54 genes exhibited further associations with adjacent genetic variation. We also found these genes were associated with pathways and functions that are known to be abnormal in SSc, including Wnt/ß-catenin signaling pathway, skin lesion formation and progression, and angiogenesis. CONCLUSION: The CD4+ T cell DNA cytosine methylation landscape in SSc involves crucial genes in disease pathogenesis. Some of the methylation patterns are also associated with genetic variation. These findings provide essential foundations for future studies of epigenetic regulation and genome-epigenome interaction in SSc.

3.
Artigo em Inglês | MEDLINE | ID: mdl-31642105

RESUMO

OBJECTIVES: To study the frequency of suicidal ideation and its association with clinical and neurobiological correlates among cognitively intact autosomal dominant Alzheimer's disease (ADAD) at-risk individuals. METHODS/DESIGN: In a cross-sectional study of 183 ADAD at-risk individuals (91 mutation carriers and 92 noncarriers), we compared the frequency of suicidal ideation among carriers and noncarriers. Linear mixed-effects models with family-level random effects evaluated the relationships between geriatric depression scale (GDS), neuropsychiatric inventory-questionnaire (NPI-Q), and suicidal ideation scores among all ADAD at-risk individuals. An interaction term was added to the regression models to evaluate the interactions of suicidal ideation and mutation status on neuropsychiatric symptoms. RESULTS: Twenty-six (14.20%) ADAD at-risk individuals (13 [14.28%] carriers and 13 [14.13%] noncarriers) had suicidal ideation. The frequency of suicidal ideation did not differ between carriers and noncarriers. Suicidal ideation was associated with higher GDS among all ADAD at-risk individuals. When stratified into mutation carrier status, noncarriers with suicidal ideation had higher GDS than carriers. There was no statistically significant association between suicidal ideation and NPI-Q among ADAD at-risk individuals. Awareness of mutation status, neuropsychological performances, and cerebrospinal fluid AD biomarkers were not associated with suicidal ideation among carriers and noncarriers. CONCLUSIONS: Suicidal ideation is common among cognitively intact ADAD at-risk individuals. While ADAD at-risk individuals with suicidal ideation have greater depressive symptoms, noncarriers with suicidal ideation have higher GDS scores than carriers. Interestingly, awareness of the mutation status was not associated with suicidal ideation in our study. Early identification of suicidal thoughts can facilitate timely interventions to prevent suicidal behaviours. Keywords autosomal dominant Alzheimer's diseasedominantly inherited Alzheimer's networkneuropsychiatric symptomssuicidal ideation.

4.
Mol Cancer Res ; 2019 Sep 17.
Artigo em Inglês | MEDLINE | ID: mdl-31530633

RESUMO

Large-scale genomic studies have detailed the molecular landscape of tumors from patients with high-grade serous ovarian cancers (HGSC) who underwent primary debulking surgery and correlated the identified subgroups to survival. In recent years, there is increased use of neoadjuvant chemotherapy (NACT) for patients with HGSC and while abundant data exist for patients who underwent primary debulking, little data are available on the cancer cells remaining after NACT that could lead to recurrences. We aimed to analyze gene expression profiles of NACT-treated HGSC tumor samples, and correlate them to treatment response and outcome. Tumor samples were collected from patients with stage III or IV HGSC (NACT cohort, N = 57) at the time of surgery and diagnosis (biopsy samples N = 8). Tumor content was validated by histologic examination and bioinformatics. Gene expression analysis was performed using a tailored NanoString-based assay, while sequencing was performed using MiSeq. A cross-validated survival classifier revealed patient clusters with either a "Better" or "Worse" prognostic outcome. The association with overall survival remained significant after controlling for clinical variables, and differential gene expression, gene set enrichment analyses, and the appropriate survival models were used to assess the associations between alterations in gene expression in cancer cells remaining after NACT and outcome. Pathway-based analysis of the differentially expressed genes revealed comparatively high levels of cell cycle and DNA repair gene expression in the poor outcome group. IMPLICATIONS: Our work suggests mRNA expression patterns in key genes following NACT may reflect response to treatment and outcome in patient with HGSC.

5.
PLoS Genet ; 15(8): e1008344, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31469826

RESUMO

Pancreatic adenocarcinoma (PC) is a lethal malignancy that is familial or associated with genetic syndromes in 10% of cases. Gene-based surveillance strategies for at-risk individuals may improve clinical outcomes. However, familial PC (FPC) is plagued by genetic heterogeneity and the genetic basis for the majority of FPC remains elusive, hampering the development of gene-based surveillance programs. The study was powered to identify genes with a cumulative pathogenic variant prevalence of at least 3%, which includes the most prevalent PC susceptibility gene, BRCA2. Since the majority of known PC susceptibility genes are involved in DNA repair, we focused on genes implicated in these pathways. We performed a region-based association study using the Mixed-Effects Score Test, followed by leave-one-out characterization of PC-associated gene regions and variants to identify the genes and variants driving risk associations. We evaluated 398 cases from two case series and 987 controls without a personal history of cancer. The first case series consisted of 109 patients with either FPC (n = 101) or PC at ≤50 years of age (n = 8). The second case series was composed of 289 unselected PC cases. We validated this discovery strategy by identifying known pathogenic BRCA2 variants, and also identified SMG1, encoding a serine/threonine protein kinase, to be significantly associated with PC following correction for multiple testing (p = 3.22x10-7). The SMG1 association was validated in a second independent series of 532 FPC cases and 753 controls (p<0.0062, OR = 1.88, 95%CI 1.17-3.03). We showed segregation of the c.4249A>G SMG1 variant in 3 affected relatives in a FPC kindred, and we found c.103G>A to be a recurrent SMG1 variant associating with PC in both the discovery and validation series. These results suggest that SMG1 is a novel PC susceptibility gene, and we identified specific SMG1 gene variants associated with PC risk.

6.
Clin Epigenetics ; 11(1): 110, 2019 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-31366403

RESUMO

OBJECTIVE: To compare DNA methylation in subjects positive vs negative for anti-citrullinated protein antibodies (ACPA), a key serological marker of rheumatoid arthritis (RA) risk. METHODS: With banked serum from a random subset (N = 3600) of a large general population cohort, we identified ACPA-positive samples and compared them to age- and sex-matched ACPA-negative controls. We used a custom-designed methylome panel to conduct targeted bisulfite sequencing of 5 million CpGs located in regulatory or hypomethylated regions of DNA from whole blood (red blood cell lysed). Using binomial regression models, we investigated the differentially methylated regions (DMRs) between ACPA-positive vs ACPA-negative subjects. An independent set of T cells from RA patients was used to "validate" the differentially methylated sites. RESULTS: We measured DNA methylation in 137 subjects, of whom 63 were ACPA-positive, 66 were ACPA-negative, and 8 had self-reported RA. We identified 1303 DMRs of relevance, of which one third (402) had underlying genetic effects. These DMRs were enriched in intergenic CpG islands (CGI) and CGI shore regions. Furthermore, the genes associated with these DMRs were enriched in pathways related to Epstein-Barr virus infection and immune response. In addition, 80 (38%) of 208 RA-specific DMRs were replicated in T cells from RA samples. CONCLUSIONS: Sequencing-based high-resolution methylome mapping revealed biologically relevant DNA methylation changes in asymptomatic individuals positive for ACPA that overlap with those seen in RA. Pathway analyses suggested roles for viral infections, which may represent the effect of environmental triggers upstream of disease onset.

7.
Sci Rep ; 9(1): 8838, 2019 Jun 20.
Artigo em Inglês | MEDLINE | ID: mdl-31221986

RESUMO

Prostate cancer (PCa) is the most common cancer amongst men. A novel androgen receptor (AR) antagonist, enzalutamide (ENZA) has recently been demonstrated to enhance the effect of radiation (XRT) by impairing the DNA damage repair process. This study aimed to identify a radiosensitive gene signature induced by ENZA in the PCa cells and to elucidate the biological pathways which influence this radiosensitivity. We treated LNCaP (AR-positive, hormone-sensitive PCa cells) and C4-2 (AR-positive, hormone-resistant PCa cells) cells with ENZA alone and in combination with androgen deprivation therapy (ADT) and XRT. Using one-way ANOVA on the gene expression profiling, we observed significantly differentially expressed (DE) genes in inflammation-and metabolism-related genes in hormone-sensitive and hormone-resistant PCa cell lines respectively. Survival analysis in both the TCGA PRAD and GSE25136 datasets suggested an association between the expression of these genes and time to recurrence. These results indicated that ENZA alone or in combination with ADT enhanced the effect of XRT through immune and inflammation-related pathways in LNCaP cells and metabolic-related pathways in C4-2 cells. Kaplan-Meier analysis and Cox proportional hazard models showed that low expression of all the candidate genes except for PTPRN2 were associated with tumor progression and recurrence in a PCa cohort.

8.
J Child Psychol Psychiatry ; 60(11): 1183-1190, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31049953

RESUMO

BACKGROUND: Internalising and externalising problems commonly co-occur in childhood. Yet, few developmental models describing the structure of child psychopathology appropriately account for this comorbidity. We evaluate a model of childhood psychopathology that separates the unique and shared contribution of individual psychological symptoms into specific internalising, externalising and general psychopathology factors and assess how these general and specific factors predict long-term outcomes concerning criminal behaviour, academic achievement and affective symptoms in three independent cohorts. METHODS: Data were drawn from independent birth cohorts (Avon Longitudinal Study of Parents and Children (ALSPAC), N = 11,612; Generation R, N = 7,946; Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN), N = 408). Child psychopathology was assessed between 4 and 8 years using a range of diagnostic and questionnaire-based measures, and multiple informants. First, structural equation models were used to assess the fit of hypothesised models of shared and unique components of psychopathology in all cohorts. Once the model was chosen, linear/logistic regressions were used to investigate whether these factors were associated with important outcomes such as criminal behaviour, academic achievement and well-being from late adolescence/early adulthood. RESULTS: The model that included specific factors for internalising/externalising and a general psychopathology factor capturing variance shared between symptoms regardless of their classification fits well for all of the cohorts. As hypothesised, general psychopathology factor scores were predictive of all outcomes of later functioning, while specific internalising factor scores predicted later internalising outcomes. Specific externalising factor scores, capturing variance not shared by any other psychological symptoms, were not predictive of later outcomes. CONCLUSIONS: Early symptoms of psychopathology carry information that is syndrome-specific as well as indicative of general vulnerability and the informant reporting on the child. The 'general psychopathology factor' might be more relevant for long-term outcomes than specific symptoms. These findings emphasise the importance of considering the co-occurrence of common internalising and externalising problems in childhood when considering long-term impact.

9.
Genet Epidemiol ; 43(4): 373-401, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-30635941

RESUMO

In Mendelian randomization (MR), inference about causal relationship between a phenotype of interest and a response or disease outcome can be obtained by constructing instrumental variables from genetic variants. However, MR inference requires three assumptions, one of which is that the genetic variants only influence the outcome through phenotype of interest. Pleiotropy, that is, the situation in which some genetic variants affect more than one phenotype, can invalidate these genetic variants for use as instrumental variables; thus a naive analysis will give biased estimates of the causal relation. Here, we present new methods (constrained instrumental variable [CIV] methods) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists and when the pleiotropic phenotypes are available. We demonstrate that a smoothed version of CIV performs approximate selection of genetic variants that are valid instruments, and provides unbiased estimates of the causal effects. We provide details on a number of existing methods, together with a comparison of their performance in a large series of simulations. CIV performs robustly across different pleiotropic violations of the MR assumptions. We also analyzed the data from the Alzheimer's disease (AD) neuroimaging initiative (ADNI; Mueller et al., 2005. Alzheimer's Dementia, 11(1), 55-66) to disentangle causal relationships of several biomarkers with AD progression.


Assuntos
Pleiotropia Genética/fisiologia , Análise da Randomização Mendeliana/métodos , Algoritmos , Fatores de Confusão (Epidemiologia) , Estudos de Associação Genética , Variação Genética , Humanos , Modelos Genéticos , Fenótipo
11.
BMC Proc ; 12(Suppl 9): 20, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30275877

RESUMO

Using data on 680 patients from the GAW20 real data set, we conducted Mendelian randomization (MR) studies to explore the causal relationships between methylation levels at selected probes (cytosine-phosphate-guanine sites [CpGs]) and high-density lipoprotein (HDL) changes (ΔHDL) using single-nucleotide polymorphisms (SNPs) as instrumental variables. Several methods were used to estimate the causal effects at CpGs of interest on ΔHDL, including a newly developed method that we call constrained instrumental variables (CIV). CIV performs automatic SNP selection while providing estimates of causal effects adjusted for possible pleiotropy, when the potentially-pleiotropic phenotypes are measured. For CpGs in or near the 10 genes identified as associated with ΔHDL using a family-based VC-score test, we compared CIV to Egger regression and the two-stage least squares (TSLS) method. All 3 approaches selected at least 1CpG in 2 genes-RNMT;C18orf19 and C6orf141-as showing a causal relationship with ΔHDL.

12.
BMC Proc ; 12(Suppl 9): 30, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30263044

RESUMO

Epigenome association studies that test a large number of methylation sites suffer from stringent multiple-testing corrections. This study's goals were to investigate region-based associations between DNA methylation sites and lipid-level changes in response to the treatment with fenofibrate in the GAW20 data and to investigate whether improvements in power could be obtained by taking into account correlations between DNA methylation at neighboring cytosine-phosphate-guanine (CpG) sites. To this end, we applied both a recently developed block-based data-dimension-reduction approach and a region-based variance-component (VC) linear mixed model to GAW20 data. We compared analyses of unrelated individuals with familial data. The region-based VC approach using unrelated (independent) individuals identified the gene LGALS9C as significantly associated with changes in triglycerides. However, univariate tests of individual CpG sites yielded no valid statistically significant results.

13.
Biometrics ; 2018 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-30168593

RESUMO

DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called "DMCHMM" which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. Our proposed method is different from other HMM methods since it profiles methylation of each sample separately, hence exploiting inter-CpG autocorrelation within samples, and it is more flexible than previous approaches by allowing multiple hidden states. Using simulations, we show that DMCHMM has the best performance among several competing methods. An analysis of cell-separated blood methylation profiles is also provided.

14.
BMC Bioinformatics ; 19(1): 295, 2018 Aug 08.
Artigo em Inglês | MEDLINE | ID: mdl-30089455

RESUMO

BACKGROUND: Polygenic risk scores (PRS) describe the genomic contribution to complex phenotypes and consistently account for a larger proportion of variance in outcome than single nucleotide polymorphisms (SNPs) alone. However, there is little consensus on the optimal data input for generating PRS, and existing approaches largely preclude the use of imputed posterior probabilities and strand-ambiguous SNPs i.e., A/T or C/G polymorphisms. Our ability to predict complex traits that arise from the additive effects of a large number of SNPs would likely benefit from a more inclusive approach. RESULTS: We developed PRS-on-Spark (PRSoS), a software implemented in Apache Spark and Python that accommodates different data inputs and strand-ambiguous SNPs to calculate PRS. We compared performance between PRSoS and an existing software (PRSice v1.25) for generating PRS for major depressive disorder using a community cohort (N = 264). We found PRSoS to perform faster than PRSice v1.25 when PRS were generated for a large number of SNPs (~ 17 million SNPs; t = 42.865, p = 5.43E-04). We also show that the use of imputed posterior probabilities and the inclusion of strand-ambiguous SNPs increase the proportion of variance explained by a PRS for major depressive disorder (from 4.3% to 4.8%). CONCLUSIONS: PRSoS provides the user with the ability to generate PRS using an inclusive and efficient approach that considers a larger number of SNPs than conventional approaches. We show that a PRS for major depressive disorder that includes strand-ambiguous SNPs, calculated using PRSoS, accounts for the largest proportion of variance in symptoms of depression in a community cohort, demonstrating the utility of this approach. The availability of this software will help users develop more informative PRS for a variety of complex phenotypes.

15.
Psychol Methods ; 2018 Aug 13.
Artigo em Inglês | MEDLINE | ID: mdl-30102054

RESUMO

Motivated by the goal of expanding currently existing Genotype × Environment interaction (G × E) models to simultaneously include multiple genetic variants and environmental exposures in a parsimonious way, we developed a novel method to estimate the parameters in a G × E model, where G is a weighted sum of genetic variants (genetic score) and E is a weighted sum of environments (environmental score). The approach uses alternating optimization, an iterative process where the genetic score weights, the environmental score weights, and the main model parameters are estimated in turn, assuming the other parameters are constant. This technique can be used to construct relatively complex interaction models that are constrained to a particular structure, and hence contain fewer parameters. We present the model as a 2-way interaction longitudinal mixed model, for which ordinary linear regression is a special case, but it can easily be extended to be compatible with k-way interaction models and generalized linear mixed models. The model is implemented in R (LEGIT package) and using SAS macros (LEGIT_SAS). Through simulations, we demonstrate the power and validity of this approach even with small sample sizes. Furthermore, we present examples from the Maternal Adversity, Vulnerability, and Neurodevelopment (MAVAN) study where we improve significantly upon already existing models using alternating optimization. (PsycINFO Database Record

16.
Gynecol Oncol ; 148(3): 553-558, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-29395310

RESUMO

OBJECTIVE: The expression of homologous recombination (HR) genes in high grade ovarian cancer (HGOC) samples from debulking surgeries were correlated to outcomes in patients selected for chemotherapy treatment regimens. STUDY DESIGN: RNA was extracted from 96 fresh frozen tumor samples from debulking surgeries from chemotherapy naïve patients with HGOC (primary derived surgeries (PDS), n = 55) or following neoadjuvant chemotherapy treatment (NACT), n = 41). The samples were selected for high tumor content by a gynecological pathologist, and cancer cell content was further confirmed using a percent tumor content covariate, and mutation score covariate analysis. Gene expression analysis was performed using a tailored NanoString-based Pancancer Pathway Panel. Cox proportional hazard regression models were used to assess the associations between the expression of 19 HR genes and survival. RESULTS: In the PDS group, over-expression of six HR genes (C11orf30, NBN, FANCF, FANCC, FANCB, RAD50) was associated with improved outcome, in contrast to the NACT group where four HR genes (BRCA2, TP53, FANCB, RAD51) were associated with worse outcome. With the adding extent of debulking as a covariate, three HR genes (NBN, FANCF, RAD50), and only one HR gene (RAD51) remained significantly associated with survival in PDS and NACT groups, respectively. CONCLUSION: Distinct HR expression profiles define subgroups associated with overall outcome in patients that are exposed to neoadjuvant chemotherapy and not only chemotherapy-naïve patients.


Assuntos
Antineoplásicos/uso terapêutico , Carcinoma Endometrioide/genética , Procedimentos Cirúrgicos de Citorredução , Terapia Neoadjuvante , Neoplasias Císticas, Mucinosas e Serosas/genética , Neoplasias Ovarianas/genética , Reparo de DNA por Recombinação/genética , Idoso , Proteína BRCA1/genética , Proteína BRCA2/genética , Antígeno Ca-125/sangue , Carcinoma Endometrioide/sangue , Carcinoma Endometrioide/tratamento farmacológico , Carcinoma Endometrioide/patologia , Proteínas de Ciclo Celular/genética , Enzimas Reparadoras do DNA/genética , Proteínas de Ligação a DNA/genética , Proteína do Grupo de Complementação C da Anemia de Fanconi/genética , Proteína do Grupo de Complementação F da Anemia de Fanconi/genética , Proteínas de Grupos de Complementação da Anemia de Fanconi/genética , Feminino , Perfilação da Expressão Gênica , Humanos , Proteínas de Membrana/sangue , Pessoa de Meia-Idade , Gradação de Tumores , Proteínas de Neoplasias/genética , Neoplasias Císticas, Mucinosas e Serosas/sangue , Neoplasias Císticas, Mucinosas e Serosas/tratamento farmacológico , Neoplasias Císticas, Mucinosas e Serosas/patologia , Proteínas Nucleares/genética , Neoplasias Ovarianas/sangue , Neoplasias Ovarianas/tratamento farmacológico , Neoplasias Ovarianas/patologia , Ovariectomia , PTEN Fosfo-Hidrolase/genética , Prognóstico , Modelos de Riscos Proporcionais , Rad51 Recombinase/genética , Proteínas Repressoras/genética , Taxa de Sobrevida , Transcriptoma , Proteína Supressora de Tumor p53/genética
17.
Biol Sex Differ ; 9(1): 10, 2018 02 20.
Artigo em Inglês | MEDLINE | ID: mdl-29463315

RESUMO

BACKGROUND: Sexual dimorphism in DNA methylation levels is a recurrent epigenetic feature in different human cell types and has been implicated in predisposition to disease, such as psychiatric and autoimmune disorders. To elucidate the genetic origins of sex-specific DNA methylation, we examined DNA methylation levels in fibroblast cell lines and blood cells from individuals with different combinations of sex chromosome complements and sex phenotypes focusing on a single autosomal region--the differentially methylated region (DMR) in the promoter of the zona pellucida binding protein 2 (ZPBP2) as a reporter. RESULTS: Our data show that the presence of the sex determining region Y (SRY) was associated with lower methylation levels, whereas higher X chromosome dosage in the absence of SRY led to an increase in DNA methylation levels at the ZPBP2 DMR. We mapped the X-linked modifier of DNA methylation to the long arm of chromosome X (Xq13-q21) and tested the impact of mutations in the ATRX and RLIM genes, located in this region, on methylation levels. Neither ATRX nor RLIM mutations influenced ZPBP2 methylation in female carriers. CONCLUSIONS: We conclude that sex-specific methylation differences at the autosomal locus result from interaction between a Y-linked factor SRY and at least one X-linked factor that acts in a dose-dependent manner.

19.
Genet Epidemiol ; 42(3): 233-249, 2018 04.
Artigo em Inglês | MEDLINE | ID: mdl-29423954

RESUMO

Predicting a phenotype and understanding which variables improve that prediction are two very challenging and overlapping problems in the analysis of high-dimensional (HD) data such as those arising from genomic and brain imaging studies. It is often believed that the number of truly important predictors is small relative to the total number of variables, making computational approaches to variable selection and dimension reduction extremely important. To reduce dimensionality, commonly used two-step methods first cluster the data in some way, and build models using cluster summaries to predict the phenotype. It is known that important exposure variables can alter correlation patterns between clusters of HD variables, that is, alter network properties of the variables. However, it is not well understood whether such altered clustering is informative in prediction. Here, assuming there is a binary exposure with such network-altering effects, we explore whether the use of exposure-dependent clustering relationships in dimension reduction can improve predictive modeling in a two-step framework. Hence, we propose a modeling framework called ECLUST to test this hypothesis, and evaluate its performance through extensive simulations. With ECLUST, we found improved prediction and variable selection performance compared to methods that do not consider the environment in the clustering step, or to methods that use the original data as features. We further illustrate this modeling framework through the analysis of three data sets from very different fields, each with HD data, a binary exposure, and a phenotype of interest. Our method is available in the eclust CRAN package.


Assuntos
Doença/genética , Modelos Genéticos , Adolescente , Algoritmos , Criança , Pré-Escolar , Análise por Conglomerados , Simulação por Computador , Bases de Dados como Assunto , Epigênese Genética , Regulação da Expressão Gênica , Humanos , Imagem por Ressonância Magnética
20.
Sci Rep ; 8(1): 220, 2018 01 09.
Artigo em Inglês | MEDLINE | ID: mdl-29317680

RESUMO

Performance of a recently developed test for association between multivariate phenotypes and sets of genetic variants (MURAT) is demonstrated using measures of bone mineral density (BMD). By combining individual-level whole genome sequenced data from the UK10K study, and imputed genome-wide genetic data on individuals from the Study of Osteoporotic Fractures (SOF) and the Osteoporotic Fractures in Men Study (MrOS), a data set of 8810 individuals was assembled; tests of association were performed between autosomal gene-sets of genetic variants and BMD measured at lumbar spine and femoral neck. Distributions of p-values obtained from analyses of a single BMD phenotype are compared to those from the multivariate tests, across several region definitions and variant weightings. There is evidence of increased power with the multivariate test, although no new loci for BMD were identified. Among 17 genes highlighted either because there were significant p-values in region-based association tests or because they were in well-known BMD genes, 4 windows in 2 genes as well as 6 single SNPs in one of these genes showed association at genome-wide significant thresholds with the multivariate phenotype test but not with the single-phenotype test, Sequence Kernel Association Test (SKAT).


Assuntos
Densidade Óssea/genética , Estudo de Associação Genômica Ampla/normas , Fraturas por Osteoporose/genética , Polimorfismo de Nucleotídeo Único , Idoso , Exoma , Feminino , Colo do Fêmur/patologia , Estudo de Associação Genômica Ampla/métodos , Humanos , Vértebras Lombares/patologia , Masculino , Fraturas por Osteoporose/patologia , Fenótipo
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