BioStat Solutions, Inc’s team includes highly experienced statistical geneticists who are familiar with the complexities of pharmacogenomic and pharmacogenetic analyses. Our goal is to assist our clients to identify genetic effects in candidate gene and genome wide association studies for either efficacy or toxicity of drugs. Our team members also includes statisticians, molecular biologists, epidemiologists, ensuring a combination of experts when interpreting results. Our expertise in single marker as well as genome wide studies allows us to incorporate statistical methodologies that best fit the design of your study. When you have need in designing a study or combining studies with different designs our experts can help. Proper design prior to study initiation can reduce time and downstream effort. In addition to our statistical, genetic and clinical experience, we recognize the role of epidemiological factors in the analysis and interpretation of pharmacogenomic data. Therefore we consider ascertainment bias, ethnicity, gender, age and other factors that could influence the analyses of pharmacogenomics and pharmacogenetics data. Use of statistical genetic tools for pharmacogenomic and pharmacogenetic studies include:      • Candidate Gene Associations Studies      • Genome Wide Association Studies      • Linkage Analyses      • Population Stratification      • Admixture • Multiple Comparison Adjustment • Imputation • Haplotype Analyses • Validation of Significant Findings • SNP Selection Strategies Due to the complexity of pharmacogenomic analyses we strive to integrate statistical findings with clinical knowledge and gene functions when interpreting the potential effect of a gene or region on either the efficacy or toxicity of a given drug.