2022 Citation Impact
0.914 - SNIP (Source Normalized Impact per Paper)
0.506 - SJR (SCImago Journal Rank)
2023 Usage
640,615 downloads
196 Altmetric mentions
Page 62 of 71
Genome-wide association studies, which analyzes hundreds of thousands of single-nucleotide polymorphisms to identify disease susceptibility genes, are challenging because the work involves intensive computatio...
In this paper we test for association between copy number variation and diabetes in a subset of individuals from the Framingham Heart Study. We used the 500 k SNP data and called copy number variation using tw...
Established loci for rheumatoid arthritis (RA), including HLA-DRB1 and PTPN22, do not fully account for the genetic component of susceptibility to the disease. One possible source of as yet undiscovered susceptib...
Multivariate techniques are an important area of investigation for studying contributions of multiple genetic variants to disease onset and pathology. We analyzed the Genetic Analysis Workshop 16 North America...
Due to the high-dimensionality of single-nucleotide polymorphism (SNP) data, region-based methods are an attractive approach to the identification of genetic variation associated with a certain phenotype. A co...
Both imprinting and maternal effects could lead to parent-of-origin patterns in complex traits of human disorders. Statistical methods that differentiate these two effects and identify them simultaneously by u...
We investigated efficient case-control association analysis using family data. The outcome of interest was coronary heart disease. We employed existing and new methods that take into account the correlations a...
The Framingham Heart Study is a well known longitudinal cohort study. In recent years, the community-based Framingham Heart Study has embarked on genome-wide association studies. In this paper, we present a Fr...
We examined the properties of growth mixture modeling in finding longitudinal quantitative trait loci in a genome-wide association study. Two software packages are commonly used in these analyses: Mplus and th...
It is well known that conventional association tests can lead to excessive false positives when there is population stratification. We propose a new test for detecting genetic association with a case-control s...
Population structure occurs when a sample is composed of individuals with different ancestries and can result in excess type I error in genome-wide association studies. Genome-wide principal-component analysis...
To overcome the "spurious" association caused by population stratification in population-based association studies, we propose a principal-component based method that can use both family and unrelated samples ...
We explored the utility of population- and pedigree-based analyses using the Framingham Heart Study genome-wide 50 k single-nucleotide polymorphism marker data provided for Genetic Analysis Workshop 16. Our ai...
Over the past decade, genetic analysis has shifted from linkage studies, which identify broad regions containing putative trait loci, to genome-wide association studies, which detect the association of a marke...
Recently, gene set analysis (GSA) has been extended from use on gene expression data to use on single-nucleotide polymorphism (SNP) data in genome-wide association studies. When GSA has been demonstrated on SN...
We describe an empirical Bayesian linear model for integration of functional gene annotation data with genome-wide association data. Using case-control study data from the North American Rheumatoid Arthritis C...
Gene identification using linkage, association, or genome-wide expression is often underpowered. We propose that formal combination of information from multiple gene-identification approaches may lead to the i...
Rheumatoid arthritis (RA) is three times more common in females than in males, suggesting that sex may play a role in modifying genetic associations with disease. We have addressed this hypothesis by performin...
Genome-wide association studies are often limited in their ability to attain their full potential due to the sheer volume of information created. We sought to use the random forest algorithm to identify single...
After more than 200 genome-wide association studies, there have been some successful identifications of a single novel locus. Thus, the identification of single-nucleotide polymorphisms (SNP) with interaction ...
Knowledge of simulated genetic effects facilitates interpretation of methodological studies. Genetic interactions for common disorders are likely numerous and weak. Using the 200 replicates of the Genetic Anal...
Rheumatoid arthritis (RA, MIM 180300) is a chronic and complex autoimmune disease. Using the North American Rheumatoid Arthritis Consortium (NARAC) data set provided in Genetic Analysis Workshop 16 (GAW16), we...
Fifteen known type 2 diabetes (T2D) gene variants were assessed for their associations with T2D status in 228 T2D families from the Framingham Heart Study (FHS) Original, Offspring, and Children Cohorts. Bayes...
Random forest is an efficient approach for investigating not only the effects of individual markers on a trait but also the effect of the interactions among the markers in genetic association studies. This app...
Using the North American Rheumatoid Arthritis Consortium genome-wide association dataset, we applied ridged, multiple least-squares regression to identify genetic variants with apparent unique contributions to...
Genome-wide association studies (GWAS) have helped to reveal genetic mechanisms of complex diseases. Although commonly used genotyping technology enables us to determine up to a million single-nucleotide polym...
The objective of this study was to detect interactions between relevant single-nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA). Data from Problem 1 of the Genetic Analysis Workshop 16...
We applied a penalized regression approach to single-nucleotide polymorphisms in regions on chromosomes 1, 6, and 9 of the North American Rheumatoid Arthritis Consortium data. Results were compared with a stan...
In genome-wide association studies, high-level statistical analyses rely on the validity of the called genotypes, and different genotype calling algorithms (GCAs) have been proposed. We compared the GCAs Bayes...
We explored five sex-specific quality control filters in North American Rheumatoid Arthritis Consortium's Illumina 550 k datasets. Three X chromosome and three autosomal single-nucleotide polymorphisms flagged...
Results from whole-genome association studies of many common diseases are now available. Increasingly, these are being incorporated into meta-analyses to increase the power to detect weak associations with mea...
We used data reduction and clustering methods to identify five phenotypically homogeneous groups of study participants with similar profiles for cardiovascular disease risk factors. We constructed both qualita...
While recently performed genome-wide association studies have advanced the identification of genetic variants predisposing to type 2 diabetes (T2D), the potential application of these novel findings for diseas...
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...
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...
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...
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...
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 ...
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...
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...
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...
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)...
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 ...
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...
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 ...
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 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. ...
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 ...
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...
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...
2022 Citation Impact
0.914 - SNIP (Source Normalized Impact per Paper)
0.506 - SJR (SCImago Journal Rank)
2023 Usage
640,615 downloads
196 Altmetric mentions