Dynamic mapping of genes controlling cancer stem cell proliferation

The growing evidence that cancer originates from stem cells holds a great promise to eliminate this disease by designing specific drug therapies for removing cancer stem cells. Translation of this knowledge into predictive tests for the clinic is hampered due to the lack of methods to discriminate cancer stem cells from non-cancer stem cells. Here, we address this issue by describing a conceptual strategy for identifying the genetic origins of cancer stem cells. The strategy incorporates a high-dimensional group of differential equations that characterizes the proliferation, differentiation, and reprogramming of cancer stem cells in a dynamic cellular and molecular system. The deployment of robust mathematical models will help uncover and explain many still unknown aspects of cell behavior, tissue function, and network organization related to the formation and division of cancer stem cells. The statistical method developed allows biologically meaningful hypotheses about the genetic control mechanisms of carcinogenesis and metastasis to be tested in a quantitative manner

Imputation and quality control steps for combining multiple genome-wide datasets

The electronic MEdical Records and GEnomics (eMERGE) network brings together DNA biobanks linked to electronic health records (EHRs) from multiple institutions. Approximately 52,000 DNA samples from distinct individuals have been genotyped using genome-wide SNP arrays across the nine sites of the network. The eMERGE Coordinating Center and the Genomics Workgroup developed a pipeline to impute and merge genomic data across the different SNP arrays to maximize sample size and power to detect associations with a variety of clinical endpoints. The 1000 Genomes cosmopolitan reference panel was used for imputation. Imputation results were evaluated using the following metrics: accuracy of imputation, allelic R2 (estimated correlation between the imputed and true genotypes), and the relationship between allelic R2 and minor allele frequency. Computation time and memory resources required by two different software packages (BEAGLE and IMPUTE2) were also evaluated. A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms. We present lessons learned and describe the pipeline implemented here to impute and merge genomic data sets. The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR

EMR-linked GWAS study: Investigation of variation landscape of loci for Body Mass Index in children

Common variation at the loci harboring the fat mass and obesity gene (FTO), MC4R and TMEM18 are consistently reported as being associated with obesity and body mass index especially in adult population. In order to confirm this effect in pediatric population five European ancestry cohorts from pediatric eMERGE-II network (CCHMC-BCH) were evaluated.Method:Data on 5049 samples of European ancestry were obtained from the Electronic Medical Records (EMRs) of two large academic centers in five different genotyped cohorts. For all available samples, gender, age, height and weight were collected and Body Mass Index (BMI) was calculated. To account for age and sex differences in BMI, BMI z-scores were generated using 2000 Centers of Disease Control and Prevention (CDC) growth charts. A Genome-wide association study (GWAS) was performed with BMI z-score. After removing missing data and outliers based on principal components (PC) analyses, 2860 samples were used for the GWAS study. The association between each SNP and BMI was tested using linear regression adjusting for age, gender, and PC by cohort. The effects of SNPs were modeled assuming additive, recessive and dominant effects of the minor allele. Meta-analysis was conducted using a weighted z-score approach. Results:The mean age of subjects was 9.8 years (range 2-19). The proportion of male subjects was 56%. In these cohorts, 14% of samples had a BMI≥95% and 28%≥85%. Meta analyses produced a signal at 16q12 genomic region with the best result of p=1.43x10E-07 (p (rec)=7.34E-08) for the single nucleotide polymorphism (SNP) rs8050136 at the first intron of FTO gene (z=5.26) and with no heterogeneity between cohorts (p=0.77). Imputation in this region using dense 1000-Genome and Hapmap CEU samples revealed 71 SNPs with