Genome Wide Association Studies (GWAS) are a standard approach for large-scale common variation characterization and for identification of single loci predisposing to disease. However, due to issues of moderate sample sizes and particularly multiple testing correction, many variants of smaller effect size are not detected within a single allele analysis framework. Thus, small main effects […]
Understanding how genetic variants impact the regulation and expression of genes is important for forging mechanistic links between variants and phenotypes in personal genomics studies. In this work, we investigate statistical interactions among variants that alter gene expression and identify 79 genes showing highly significant interaction effects consistent with genetic heterogeneity. Of the 79 genes, […]
Simulated data is a necessary first step in the evaluation of new analytic methods because in simulated data the true effects are known. To successfully develop novel statistical and computational methods for genetic analysis, it is vital to simulate datasets consisting of single nucleotide polymorphisms (SNPs) spread throughout the genome at a density similar to […]
It is well established that the risk of developing multiple sclerosis is substantially increased in the relatives of affected individuals and that most of this increase is genetically determined. The observed pattern of familial recurrence risk has long suggested that multiple variants are involved, but it has proven difficult to identify individual risk variants and […]
Often in human genetic analysis, multiple tables of single nucleotide polymorphism (SNP) statistics are shown alongside a Haploview style correlation plot. Readers are then asked to make inferences that incorporate knowledge across these multiple sets of results. To better facilitate a collective understanding of all available data, we developed a Ruby-based web application, LD-Plus, to […]
Gene-centric analysis tools for genome-wide association study data are being developed both to annotate single locus statistics and to prioritize or group single nucleotide polymorphisms (SNPs) prior to analysis. These approaches require knowledge about the relationships between SNPs on a genotyping platform and genes in the human genome. SNPs in the genome can represent broader […]
Multifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification – the classification error. The combination of variables that produces […]
Parallel multifactor dimensionality reduction is a tool for large-scale analysis of gene-gene and gene-environment interactions. The MDR algorithm was redesigned to allow an unlimited number of study subjects, total variables and variable states, and to remove restrictions on the order of interactions being analyzed. In addition, the algorithm is markedly more efficient, with approximately 150-fold […]
Most genotype-phenotype studies have historically lacked population diversity, impacting the generalizability of findings and thereby limiting the ability to equitably implement precision medicine. This well-documented problem has generated much interest in the ascertainment of new cohorts with an emphasis on multiple dimensions of diversity, including race/ethnicity, gender, age, socioeconomic status, disability, and geography. The most […]
Most genotype-phenotype studies have historically lacked population diversity, impacting the generalizability of findings and thereby limiting the ability to equitably implement precision medicine. This well-documented problem has generated much interest in the ascertainment of new cohorts with an emphasis on multiple dimensions of diversity, including race/ethnicity, gender, age, socioeconomic status, disability, and geography. The most […]
Most genotype-phenotype studies have historically lacked population diversity, impacting the generalizability of findings and thereby limiting the ability to equitably implement precision medicine. This well-documented problem has generated much interest in the ascertainment of new cohorts with an emphasis on multiple dimensions of diversity, including race/ethnicity, gender, age, socioeconomic status, disability, and geography. The most […]