Statistical and Bioinformatics Consulting

Genome wide association studies allow us to evaluate the role of common variants across the human genome. Using raw genotypic data generated by our Genotyping Core or provided by you, we offer the following services:

• QC processing and report generation
• Case-control and family-based GWAS analysis
• Imputation
• Meta-analysis
• Population stratification analysis
• Assistance with interpretation and production of publication-ready graphs

Genotyping may be run on specific, targeted regions. Such regions are often candidates for disease gene locations. We provide analysis for externally or internally typed candidate gene studies and offer:

• Tag SNP selection
• QC of genotype and sample parameters (e.g., Hardy Weinberg Equilibrium)
• Analysis of case-control and family-based data
• Interaction analysis
• Generation of analysis and custom reports
• Graphical interpretation

Results across several different experiments may be combined to provide a larger sample and analysis set. We are experienced in all aspects of data management and analysis of large-scaled meta-analysis projects. Our group is heavily involved in several large Consortiums conducting meta-analysis including the Alzheimer’s Disease Genetics Consortium (ADGC).

We can help with meta-analysis including:

• Data retrieval and organization
  
• QC of final data set
  
• Analysis of common and rare variants
  
• Interpretation and generation of reports

Both gene-by-gene (GxG) and gene-by-environment (GxE) analyses seek to look beyond the single-gene disease hypothesis. By investigating the modifying effects of genes in relation to other genes, we can explore genomic interaction. Environmental factors can be considered as they relate to specific genetic architecture. In the analysis of GxG and GxE we provide:

• QC of genotypic and environmental data
• Statistical analysis of environmental factors
• Statistical analysis of interactions using software such as PLINK, SAS and R
• Customized reports

Linkage analysis uses familial genetic data to look for disease transmission in families. Such analysis can help to pinpoint areas of the genome to be followed up with more detailed mapping or sequencing. We are able to combine the information from families with genetic data along with results from other types of studies, including case-control, as well. Our analyses include:

• Analysis of familial data including large, multi-generational extended families and smaller families
• QC of genetic data including verifying relationship status within the pedigree
• Calculation of parametric and non-parametric LOD scores
• Interpretation of linkage intervals found in analysis
• Family plots including haplotype results
• Customized reports

RNASeq provides a comprehensive and highly sensitive method for evaluating gene expression at the level of the transcriptome. With RNAseq analysis, novel transcripts may be identified. Additionally, this approach utilizes next-generation sequence data to enable identification of alternative gene spicing, gene fusions, SNPs, miRNA and long noncoding RNAs. This area of research is rapidly evolving with the development of new methods and analytic pipelines. Our internal bioinformatics and statistical scripts and pipelines are based both on the latest scientific expert guidelines as well as our in-house experience.

We provide the following services:

• Assistance with study design (in coordination with the HIHG’s Sequencing Core)
• QC checking and report
• Temporary storage and management of sequence data
• Alignment and generation of FPKM or counts file
• Differential gene expression
• Pathway and Gene Set Enrichment analysis
• De novo RNAseq for non-model organisms
• Assistance with interpretation and manuscript generation

Gene expression analysis remains a cost-effective and informative approach for transcriptome analysis. Available platforms (e.g. Affymetrix GeneChip Exon 1.0 ST Array) provide both gene-based and exon-level coverage of known genes. This allows for targeted gene expression and alternative splice discovery. For standard projects, see our example analysis workflow INSERT WEBLINK.

We provide the following services:

• Assistance with study design
• Sample size calculations
• Data processing and QC (e.g., data normalization)
• Differential gene expression analysis
• Unsupervised cluster analysis
• Pathway and Gene Ontology (GO) analysis
• Gene Set Enrichment Analysis (GSEA)
• Interpretation and assistance with publications

DNA Methylation array is a cost-effective and genome-wide approach for methylation analysis. Available platforms (e.g. Illumina 450K) provide examination of ~480,000 CpG sites throughout the genome, including gene regions and CpG islands. The CpG sites located across the gene include the promoter region, 5’UTR, 1st exon, gene region, and 3’UTR. CpG island coverage includes >90% of known islands in addition to predicted CpG islands. This approach allows for a sensitive assessment of targeted CpG site methylation.

We provide the following services:

• Assistance with study design
• Sample size calculations
• Data processing and QC (e.g., data normalization)
• Differential DNA methylation analysis
• Unsupervised cluster analysis
• Pathway and Gene Ontology (GO) analysis
• Gene Set Enrichment Analysis (GSEA)
• Interpretation and assistance with publications

We are experienced working with multi-ethnic and admixed samples. We apply a range of statistical analyses including principal component analysis to examine population stratification and hidden Markov models to examine local genomic ancestry as well as categorization of ancestry proportions. This information can be useful to classify the ethnicity of individuals, provide covariates for genetic disease studies, or to examine population genetics questions.