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Volume 3 Supplement 7
Genetic Analysis Workshop 16. Go to conference site.
St Louis, MO, USA17-20 September 2008
Page 1 of 3
Genome-wide association studies (GWAS) have quickly become the norm in dissecting the genetic basis of complex diseases. Family-based association approaches have the advantages of being robust to possible hidd...
Rheumatoid arthritis (RA) is a complex, chronic inflammatory disease implicated to have several plausible candidate loci; however, these may not account for all the genetic variations underlying RA. Common dis...
For Genetic Analysis Workshop 16 Problem 1, we provided data for genome-wide association analysis of rheumatoid arthritis. Single-nucleotide polymorphism (SNP) genotype data were provided for 868 cases and 119...
Genetic Analysis Workshop 16 (GAW16) Problem 2 presented data from the Framingham Heart Study (FHS), an observational, prospective study of risk factors for cardiovascular disease begun in 1948. Data have been...
The Genetic Analysis Workshop (GAW) 16 Problem 3 comprises simulated phenotypes emulating the lipid domain and its contribution to cardiovascular disease risk. For each replication there were 6,476 subjects in...
Several methods have been proposed to impute genotypes at untyped markers using observed genotypes and genetic data from a reference panel. We used the Genetic Analysis Workshop 16 rheumatoid arthritis case-co...
We performed a genome-wide association scan on the North American Rheumatoid Arthritis Consortium (NARAC) data using Hotelling's T2 tests, i.e., T H based on allele coding and T ...
Due to the growing need to combine data across multiple studies and to impute untyped markers based on a reference sample, several analytical tools for imputation and analysis of missing genotypes have been de...
We have conducted a genome-wide association study on the Genetic Analysis Workshop (GAW) 16 rheumatoid arthritis data using a multilocus score test based on wavelet transform proposed recently by the authors. ...
To analyze multiple single-nucleotide polymorphisms simultaneously when the number of markers is much larger than the number of studied individuals, as is the situation we have in genome-wide association studi...
The results from association studies are usually summarized by a measure of evidence of association (frequentist or Bayesian probability values) that does not directly reflect the impact of the detected signal...
The availability of very large number of markers by modern technology makes genome-wide association studies very popular. The usual approach is to test single-nucleotide polymorphisms (SNPs) one at a time for ...
There is a long-established association between rheumatoid arthritis and HLA-DRĪ²1. The shared epitope (SE) allele is an indicator of the presence of any of the HLA-DRĪ²1 alleles associated with RA. Other autoan...
The North American Rheumatoid Arthritis Consortium case-control study collected case participants across the United States and control participants from New York. More than 500,000 single-nucleotide polymorphi...
With the recent rapid improvements in high-throughout genotyping techniques, researchers are facing a very challenging task of large-scale genetic association analysis, especially at the whole-genome level, wi...
We conducted a search for non-chromosome 6 genes that may increase risk for rheumatoid arthritis (RA). Our approach was to retrospectively ascertain three "extreme" subsamples from the North American Rheumatoi...
Single-locus analysis is often used to analyze genome-wide association (GWA) data, but such analysis is subject to severe multiple comparisons adjustment. Multivariate logistic regression is proposed to fit a ...
Currently, genome-wide association studies (GWAS) are conducted by collecting a massive number of SNPs (i.e., large p) for a relatively small number of individuals (i.e., small n) and associations are made betwee...
Genetic association of population-based quantitative trait data has traditionally been analyzed using analysis of variance (ANOVA). However, violations of certain statistical assumptions may lead to false-posi...
Many phenotypes may be influenced by the prenatal environment of the mother and/or maternal care, and these maternal effects may have a heritable component. We have implemented in the computer program SOLAR a ...
Genome-wide associations between single-nucleotide polymorphisms and clinical traits were simultaneously conducted using penalized orthogonal-components regression. This method was developed to identify the ge...
Because multiple loci control complex diseases, there is great interest in testing markers simultaneously instead of one by one. In this paper, we applied two model selection algorithms: the stochastic search ...
Population stratification is one of the major causes of spurious associations in association studies. A unified association approach based on principal-component analysis can overcome the effect of population ...
The selective genotyping approach in quantitative genetics means genotyping only individuals with extreme phenotypes. This approach is considered an efficient way to perform gene mapping, and can be applied in...
Most genetic association studies only genotype a small proportion of cataloged single-nucleotide polymorphisms (SNPs) in regions of interest. With the catalogs of high-density SNP data available (e.g., HapMap)...
The current trend in genome-wide association studies is to identify regions where the true disease-causing genes may lie by evaluating thousands of single-nucleotide polymorphisms (SNPs) across the whole genom...
The goal of this paper is to search for two-locus combinations that are jointly associated with rheumatoid arthritis using the data set of Genetic Analysis Workshop 16 Problem 1. We use a two-stage strategy to...
We compared family-based single-marker association analysis using Merlin and multi-marker analysis using LASSO (least absolute shrinkage and selection operator) for the low-density lipoprotein phenotype at the...
In genome-wide association studies, new schemes are needed to incorporate multiple-locus information. In this article, we proposed a two-stage sliding-window approach to detect associations between a disease a...
The interaction among multiple genes and environmental factors can affect an individual's susceptibility to disease. Some genes may not show strong marginal associations when they affect disease risk through i...
We present computationally simple association tests based on haplotype sharing that can be easily applied to genome-wide association studies, while allowing use of fast (but not likelihood-based) haplotyping a...
Many autoimmune diseases share similar underlying pathology and have a tendency to cluster within families, giving rise to the concept of shared susceptibility genes among them. In the Genetic Analysis Worksho...
The Genetic Analysis Workshop 16 rheumatoid arthritis data include a set of 868 cases and 1194 controls genotyped at 545,080 single-nucleotide polymorphisms (SNPs) from the Illumina 550 k chip. We focus on inv...
Using single-nucleotide polymorphisms (SNPs), we sought to predict classical class I and class II human leukocyte antigen (HLA) alleles, and test for their associations with rheumatoid arthritis (RA) in the No...
Recent genome-wide association studies on several complex diseases have focused on individual single-nucleotide polymorphism (SNP) analysis; however, not many studies have reported interactions among genes per...
The high genomic density of the single-nucleotide polymorphism (SNP) sets that are typically surveyed in genome-wide association studies (GWAS) now allows the application of haplotype-based methods. Although t...
We performed a whole-genome association study of rheumatoid arthritis susceptibility using Illumina 550k single-nucleotide polymorphism (SNP) genotypes of 868 cases and 1194 controls from the North American Rh...
The importance of considering confounding due to population stratification in genome-wide association analysis using case-control designs has been a source of debate. Armitage's trend test, together with some ...
Genome-wide association studies often involve testing hundreds of thousands of single-nucleotide polymorphisms (SNPs). These tests may be highly correlated because of linkage disequilibrium among SNPs. Multipl...
After performing a genome-wide association study, it is often difficult to know which regions to follow up, especially when no one marker reaches genome-wide significance. Researchers frequently focus on their...
In high-dimensional studies such as genome-wide association studies, the correction for multiple testing in order to control total type I error results in decreased power to detect modest effects. We present a...
Determining the most promising single-nucleotide polymorphisms (SNPs) presents a challenge in genome-wide association studies, when hundreds of thousands of association tests are conducted. The power to detect...
Metabolic syndrome, by definition, is the manifestation of multiple, correlated metabolic impairments. It is known to have both strong environmental and genetic contributions. However, isolating genetic varian...
Rheumatoid arthritis is inherited in a complex manner. So far several single susceptibility genes, such as PTPN22, STAT4, and TRAF1-C5, have been identified. However, it is presumed that some genes may interact t...
We propose the use of latent growth curve model to assess the influence of genetic, environmental, demographic, and lifestyle factors on multiple phenotypes related to coronary heart disease. We model four qua...
The phrase "mendelian randomization" has become associated with the use of genetic polymorphisms to uncover causal relationships between phenotypic variables. The statistical methods useful in mendelian random...
Multiple single-nucleotide polymorphisms have been associated with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels. In this paper, we eva...
Cardiovascular diseases are associated with combinations of phenotypic traits, which are in turn caused by a combination of environmental and genetic factors. Because of the diversity of pathways that may lead...
Genetic epidemiology studies often adjust for numerous potential confounders, yet the influences of confounder misclassification and selection bias are rarely considered. We used simulated data to evaluate the...
Citation Impact 2023
Source Normalized Impact per Paper (SNIP): 0.893
SCImago Journal Rank (SJR): 0.475
Usage 2023
Downloads: 640,615
Altmetric mentions: 196