Genetic association studies have rapidly become a major tool for identifying the genetic basis of common human diseases. The advent of cost-effective genotyping coupled with large collections of samples linked to clinical outcomes and quantitative traits now make it possible to systematically characterize genotype-phenotype relationships in diverse populations and extensive datasets. To capitalize on these […]
Using genetic data from an obesity candidate gene study of self-reported African Americans and European Americans, we investigated the number of Ancestry Informative Markers (AIMs) and candidate gene SNPs necessary to infer continental ancestry. Proportions of African and European ancestry were assessed with STRUCTURE (K 2), using 276 AIMs. These reference values were compared to […]
Since publication of the human genome in 2003, geneticists have been interested in risk variant associations to resolve the etiology of traits and complex diseases. The International HapMap Consortium undertook an effort to catalog all common variation across the genome (variants with a minor allele frequency (MAF) of at least 5% in one or more […]
Multiple sclerosis (MS) is a neurodegenerative, autoimmune disease of the central nervous system, and numerous studies have shown that MS has a strong genetic component. Independent studies to identify MS-associated genes have often indicated multiple signals in physically close genomic regions, although by their proximity it is not always clear if these data indicate redundant […]
Gene-gene interactions are proposed as an important component of the genetic architecture of complex diseases, and are just beginning to be evaluated in the context of genome-wide association studies (GWAS). In addition to detecting epistasis, a benefit to interaction analysis is that it also increases power to detect weak main effects. We conducted a knowledge-driven […]
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 […]