Genome-Wide Fine-Mapping Of Diabetic Traits

Type 2 diabetes results from both genes and the environment. Mapping genetic loci in animal models can help identify genes that are involved in type 2 diabetes to better understand the disease. Heterogeneous stock (HS) rats are derived from eight inbred founder strains and maintained in a breeding strategy that minimizes inbreeding. HS rats have a highly recombinant genome, which allows for rapid fine-mapping of complex traits genome-wide. However, this results in a complicated set of relationships between animals that is non-existent in traditional genetic mapping methods. To fine-map traits involved in type 2 diabetes, multiple diabetic phenotypes were collected in 1,038 HS male rats and these animals were genotyped using the Affymetrix 10K SNP array. Following ancestral haplotype reconstruction, a mixed modeling approach was used to identify genetic loci involved in two phenotypes suggestive of diabetes: fasting glucose and glucose area under the curve after a glucose tolerance test. Sibship was used as a random effect in the model to account for the complex family relationships. A genome-wide significant marker interval was detected on chromosome 11 for fasting glucose with a 95% confidence interval of 5.75 Mb. Genome-wide significant marker intervals were also detected on chromosomes 1,3, 10, and 13 for glucose area under the curve, with the average 95% confidence interval for these loci being only 3.15 Mb. A multilocus modeling technique involving resample model averaging was applied to the fasting glucose phenotype. This technique determines how frequently each locus is detected when resampling a portion of the original data-set, thus reducing potential false positives. Multilocus modeling results for fasting glucose coincided with the significant marker interval demonstrated in the mixed modeling approach. Both approaches are effective at detecting significant marker intervals that are expected to be involved in the phenotype of interest with a greater resolution over traditional methods

A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes

ProcessDriver: A Computational Pipeline to Identify Copy Number Drivers and Associated Disrupted Biological Processes in Cancer

Copy number amplifications and deletions that are recurrent in cancer samples harbor genes that confer a fitness advantage to cancer tumor proliferation and survival. One important challenge in computational biology is to separate the causal (i.e., driver) genes from passenger genes in large, aberrated regions. Many previous studies focus on the genes within the aberration (i.e., cis genes), but do not utilize the genes that are outside of the aberrated region and dysregulated as a result of the aberration (i.e., trans genes). We propose a computational pipeline, called ProcessDriver, that prioritizes candidate drivers by relating cis genes to dysregulated trans genes and biological processes. ProcessDriver is based on the assumption that a driver cis gene should be closely associated with the dysregulated trans genes and biological processes, as opposed to previous studies that assume a driver cis gene should be the most correlated gene to the copy number of an aberrated region. We applied our method on breast, bladder and ovarian cancer data from the Cancer Genome Atlas database. Our results included previously known driver genes and cancer genes, as well as potentially novel driver genes. Additionally, many genes in the final set of drivers were linked to new tumor events after initial treatment using survival analysis. Our results highlight the importance of selecting driver genes based on their widespread downstream effects in trans

Identifying Regulators from Multiple Types of Biological Data in Cancer

Cancer genomes accumulate alterations that promote cancer cell proliferation and survival. Structural, genetic and epigenetic alterations that have a selective advantage for tumorigenesis affect key regulatory genes and microRNAs that in turn regulate the expression of many target genes. The goal of this dissertation is to leverage the alteration-rich landscape of cancer genomes to detect key regulatory genes and microRNAs. To this end, we designed a feature selection algorithm to identify DNA methylation signals around a gene that would highly predict its expression. We found that genes whose expression could be predicted by DNA methylation accurately were enriched in Gene Ontology terms related to the regulation of various biological processes. This suggests that genes controlled by DNA methylation are regulatory genes. We also developed two tools that infer relationships between regulatory genes and target genes leveraging structural and epigenetic data. The first tool, ProcessDriver integrates copy number alteration and gene expression datasets to identify copy number cancer driver genes, target genes of these drivers and the disrupted biological processes. Our results showed that driver genes selected by ProcessDriver are enriched in known cancer genes. Using survival analysis, we showed that drivers are linked to new tumor events after initial treatment. The second tool was developed to leverage structural and epigenetic data to infer interactions between regulatory genes and targets on a network-level. Our canonical correlation analysis-based approach utilized the DNA methylation or copy number states of potential regulators and the expression states of potential targets to score regulatory interactions. We then incorporated these regulatory interaction scores as prior knowledge in a dynamic Bayesian framework utilizing time series gene expression data. Our results indicated that the canonical correlation analysis-based scores reflect the true interactions between genes with high accuracy, and the accuracy can be further increased by using the scores as a prior in the dynamic Bayesian framework. Finally, we are developing an algorithm to detect cancer-related microRNAs, associated targets and disrupted biological processes. Our preliminary results suggest that the modules of miRNAs and target genes identified in this approach are enriched in known microRNA-gene interactions

Identifying factors which influence eating disorder risk during behavioral weight management: A consensus study

This study aimed to understand clinician, researcher and consumer views regarding factors which influence eating disorder (ED) risk during behavioral weight management, including individual risk factors, intervention strategies and delivery features. Eighty-seven participants were recruited internationally through professional and consumer organizations and social media and completed an online survey. Individual characteristics, intervention strategies (5-point scale) and delivery features (important/unimportant/unsure) were rated. Participants were mostly women (n = 81), aged 35-49 y, from Australia or United States, were clinicians and/or reported lived experience of overweight/obesity and/or ED. There was agreement (64% to 99%) that individual characteristics were relevant to ED risk, with history of ED, weight-based teasing/stigma and weight bias internalization having the highest agreement. Intervention strategies most frequently rated as likely to increase ED risk included those with a focus on weight, prescription (structured diets, exercise plans) and monitoring strategies, e.g., calorie counting. Strategies most frequently rated as likely to decrease ED risk included having a health focus, flexibility and inclusion of psychosocial support. Delivery features considered most important were who delivered the intervention (profession, qualifications) and support (frequency, duration). Findings will inform future research to quantitatively assess which of these factors predict eating disorder risk, to inform screening and monitoring protocols

Unpacking the Behavioural Components and Delivery Features of Early Childhood Obesity Prevention Interventions in the TOPCHILD Collaboration: A Systematic Review and Intervention Coding Protocol

INTRODUCTION: Little is known about how early (eg, commencing antenatally or in the first 12 months after birth) obesity prevention interventions seek to change behaviour and which components are or are not effective. This study aims to (1) characterise early obesity prevention interventions in terms of target behaviours, delivery features and behaviour change techniques (BCTs), (2) explore similarities and differences in BCTs used to target behaviours and (3) explore effectiveness of intervention components in preventing childhood obesity. METHODS AND ANALYSIS: Annual comprehensive systematic searches will be performed in Epub Ahead of Print/MEDLINE, Embase, Cochrane (CENTRAL), CINAHL, PsycINFO, as well as clinical trial registries. Eligible randomised controlled trials of behavioural interventions to prevent childhood obesity commencing antenatally or in the first year after birth will be invited to join the Transforming Obesity in CHILDren Collaboration. Standard ontologies will be used to code target behaviours, delivery features and BCTs in both published and unpublished intervention materials provided by trialists. Narrative syntheses will be performed to summarise intervention components and compare applied BCTs by types of target behaviours. Exploratory analyses will be undertaken to assess effectiveness of intervention components. ETHICS AND DISSEMINATION: The study has been approved by The University of Sydney Human Research Ethics Committee (project no. 2020/273) and Flinders University Social and Behavioural Research Ethics Committee (project no. HREC CIA2133-1). The study\u27s findings will be disseminated through peer-reviewed publications, conference presentations and targeted communication with key stakeholders. PROSPERO REGISTRATION NUMBER: CRD42020177408

Transforming Obesity Prevention for CHILDren (TOPCHILD) Collaboration: Protocol for a Systematic Review with Individual Participant Data Meta-Analysis of Behavioural Interventions for the Prevention of Early Childhood Obesity

INTRODUCTION: Behavioural interventions in early life appear to show some effect in reducing childhood overweight and obesity. However, uncertainty remains regarding their overall effectiveness, and whether effectiveness differs among key subgroups. These evidence gaps have prompted an increase in very early childhood obesity prevention trials worldwide. Combining the individual participant data (IPD) from these trials will enhance statistical power to determine overall effectiveness and enable examination of individual and trial-level subgroups. We present a protocol for a systematic review with IPD meta-analysis to evaluate the effectiveness of obesity prevention interventions commencing antenatally or in the first year after birth, and to explore whether there are differential effects among key subgroups. METHODS AND ANALYSIS: Systematic searches of Medline, Embase, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycInfo and trial registries for all ongoing and completed randomised controlled trials evaluating behavioural interventions for the prevention of early childhood obesity have been completed up to March 2021 and will be updated annually to include additional trials. Eligible trialists will be asked to share their IPD; if unavailable, aggregate data will be used where possible. An IPD meta-analysis and a nested prospective meta-analysis will be performed using methodologies recommended by the Cochrane Collaboration. The primary outcome will be body mass index z-score at age 24±6 months using WHO Growth Standards, and effect differences will be explored among prespecified individual and trial-level subgroups. Secondary outcomes include other child weight-related measures, infant feeding, dietary intake, physical activity, sedentary behaviours, sleep, parenting measures and adverse events. ETHICS AND DISSEMINATION: Approved by The University of Sydney Human Research Ethics Committee (2020/273) and Flinders University Social and Behavioural Research Ethics Committee (HREC CIA2133-1). Results will be relevant to clinicians, child health services, researchers, policy-makers and families, and will be disseminated via publications, presentations and media releases. PROSPERO REGISTRATION NUMBER: CRD42020177408

Unpacking the behavioural components and delivery features of early childhood obesity prevention interventions in the TOPCHILD Collaboration: a systematic review and intervention coding protocol.

INTRODUCTION: Little is known about how early (eg, commencing antenatally or in the first 12 months after birth) obesity prevention interventions seek to change behaviour and which components are or are not effective. This study aims to (1) characterise early obesity prevention interventions in terms of target behaviours, delivery features and behaviour change techniques (BCTs), (2) explore similarities and differences in BCTs used to target behaviours and (3) explore effectiveness of intervention components in preventing childhood obesity. METHODS AND ANALYSIS: Annual comprehensive systematic searches will be performed in Epub Ahead of Print/MEDLINE, Embase, Cochrane (CENTRAL), CINAHL, PsycINFO, as well as clinical trial registries. Eligible randomised controlled trials of behavioural interventions to prevent childhood obesity commencing antenatally or in the first year after birth will be invited to join the Transforming Obesity in CHILDren Collaboration. Standard ontologies will be used to code target behaviours, delivery features and BCTs in both published and unpublished intervention materials provided by trialists. Narrative syntheses will be performed to summarise intervention components and compare applied BCTs by types of target behaviours. Exploratory analyses will be undertaken to assess effectiveness of intervention components. ETHICS AND DISSEMINATION: The study has been approved by The University of Sydney Human Research Ethics Committee (project no. 2020/273) and Flinders University Social and Behavioural Research Ethics Committee (project no. HREC CIA2133-1). The study's findings will be disseminated through peer-reviewed publications, conference presentations and targeted communication with key stakeholders. PROSPERO REGISTRATION NUMBER: CRD42020177408

Transforming Obesity Prevention for CHILDren (TOPCHILD) Collaboration: protocol for a systematic review with individual participant data meta-analysis of behavioural interventions for the prevention of early childhood obesity.

INTRODUCTION: Behavioural interventions in early life appear to show some effect in reducing childhood overweight and obesity. However, uncertainty remains regarding their overall effectiveness, and whether effectiveness differs among key subgroups. These evidence gaps have prompted an increase in very early childhood obesity prevention trials worldwide. Combining the individual participant data (IPD) from these trials will enhance statistical power to determine overall effectiveness and enable examination of individual and trial-level subgroups. We present a protocol for a systematic review with IPD meta-analysis to evaluate the effectiveness of obesity prevention interventions commencing antenatally or in the first year after birth, and to explore whether there are differential effects among key subgroups. METHODS AND ANALYSIS: Systematic searches of Medline, Embase, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycInfo and trial registries for all ongoing and completed randomised controlled trials evaluating behavioural interventions for the prevention of early childhood obesity have been completed up to March 2021 and will be updated annually to include additional trials. Eligible trialists will be asked to share their IPD; if unavailable, aggregate data will be used where possible. An IPD meta-analysis and a nested prospective meta-analysis will be performed using methodologies recommended by the Cochrane Collaboration. The primary outcome will be body mass index z-score at age 24±6 months using WHO Growth Standards, and effect differences will be explored among prespecified individual and trial-level subgroups. Secondary outcomes include other child weight-related measures, infant feeding, dietary intake, physical activity, sedentary behaviours, sleep, parenting measures and adverse events. ETHICS AND DISSEMINATION: Approved by The University of Sydney Human Research Ethics Committee (2020/273) and Flinders University Social and Behavioural Research Ethics Committee (HREC CIA2133-1). Results will be relevant to clinicians, child health services, researchers, policy-makers and families, and will be disseminated via publications, presentations and media releases. PROSPERO REGISTRATION NUMBER: CRD42020177408

Transforming Obesity Prevention for CHILDren (TOPCHILD) Collaboration: protocol for a systematic review with individual participant data meta-analysis of behavioural interventions for the prevention of early childhood obesity

Introduction Behavioural interventions in early life appear to show some effect in reducing childhood overweight and obesity. However, uncertainty remains regarding their overall effectiveness, and whether effectiveness differs among key subgroups. These evidence gaps have prompted an increase in very early childhood obesity prevention trials worldwide. Combining the individual participant data (IPD) from these trials will enhance statistical power to determine overall effectiveness and enable examination of intervention-covariate interactions. We present a protocol for a systematic review with IPD meta-analysis to evaluate the effectiveness of obesity prevention interventions commencing antenatally or in the first year after birth, and to explore whether there are differential effects among key subgroups