bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detecting coordinated regulation of multi-protein complexes using logic analysis of gene expression
Authors: Sprinzak Einat , Cokus Shawn J , Yeates Todd O , Eisenberg David , Pellegrini Matteo 
Published: December 2009

Abstract

Background
Many of the functional units in cells are multi-protein complexes such as RNA polymerase, the ribosome, and the proteasome. For such units to work together, one might expect a high level of regulation to enable co-appearance or repression of sets of complexes at the required time. However, this type of coordinated regulation between whole complexes is difficult to detect by existing methods for analyzing mRNA co-expression. We propose a new methodology that is able to detect such higher order relationships.

Results
We detect coordinated regulation of multiple protein complexes using logic analysis of gene expression data. Specifically, we identify gene triplets composed of genes whose expression profiles are found to be related by various types of logic functions. In order to focus on complexes, we associate the members of a gene triplet with the distinct protein complexes to which they belong. In this way, we identify complexes related by specific kinds of regulatory relationships. For example, we may find that the transcription of complex C is increased only if the transcription of both complex A AND complex B is repressed. We identify hundreds of examples of coordinated regulation among complexes under various stress conditions. Many of these examples involve the ribosome. Some of our examples have been previously identified in the literature, while others are novel. One notable example is the relationship between the transcription of the ribosome, RNA polymerase and mannosyltransferase II, which is involved in N-linked glycan processing in the Golgi.

Conclusions
The analysis proposed here focuses on relationships among triplets of genes that are not evident when genes are examined in a pairwise fashion as in typical clustering methods. By grouping gene triplets, we are able to decipher coordinated regulation among sets of three complexes. Moreover, using all triplets that involve coordinated regulation with the ribosome, we derive a large network involving this essential cellular complex. In this network we find that all multi-protein complexes that belong to the same functional class are regulated in the same direction as a group (either induced or repressed).
Contrasting identity-by-descent estimators, association studies, and linkage analyses using the Framingham Heart Study data
Authors: Marchani Elizabeth E , Di Yanming , Choi Yoonha , Cheung Charles , Su Ming , Boehm Frederick , Thompson Elizabeth A , Wijsman Ellen M 
Published: December 2009

Abstract
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 aims were: 1) to compare identity-by-descent sharing estimates from variable amounts of data; 2) to apply each of these estimates to a case-control association study designed to control for relatedness among samples; and 3) to contrast these results to those obtained using model-based and model-free linkage analysis methods.
Assessing the impact of global versus local ancestry in association studies
Authors: Kang Sun Jung , Larkin Emma K , Song Yeunjoo , Barnholtz-Sloan Jill , Baechle Dan , Feng Tao , Zhu Xiaofeng 
Published: December 2009

Abstract

Background
To account for population stratification in association studies, principal-components analysis is often performed on single-nucleotide polymorphisms (SNPs) across the genome. Here, we use Framingham Heart Study (FHS) Genetic Analysis Workshop 16 data to compare the performance of local ancestry adjustment for population stratification based on principal components (PCs) estimated from SNPs in a local chromosomal region with global ancestry adjustment based on PCs estimated from genome-wide SNPs.

Methods
Standardized height residuals from unrelated adults from the FHS Offspring Cohort were averaged from longitudinal data. PCs of SNP genotype data were calculated to represent individual's ancestry either 1) globally using all SNPs across the genome or 2) locally using SNPs in adjacent 20-Mbp regions within each chromosome. We assessed the extent to which there were differences in association studies of height depending on whether PCs for global, local, or both global and local ancestry were included as covariates.

Results
The correlations between local and global PCs were low (r < 0.12), suggesting variability between local and global ancestry estimates. Genome-wide association tests without any ancestry adjustment demonstrated an inflated type I error rate that decreased with adjustment for local ancestry, global ancestry, or both. A known spurious association was replicated for SNPs within the lactase gene, and this false-positive association was abolished by adjustment with local or global ancestry PCs.

Conclusion
Population stratification is a potential source of bias in this seemingly homogenous FHS population. However, local and global PCs derived from SNPs appear to provide adequate information about ancestry.
Principal-component-based population structure adjustment in the North American Rheumatoid Arthritis Consortium data: impact of single-nucleotide polymorphism set and analysis method
Authors: Peloso Gina M , Timofeev Nadia , Lunetta Kathryn L 
Published: December 2009

Abstract
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 (PCA) has become a popular method for identifying and adjusting for subtle population structure in association studies. Using the Genetic Analysis Workshop 16 (GAW16) NARAC data, we explore two unresolved issues concerning the use of genome-wide PCA to account for population structure in genetic associations studies: the choice of single-nucleotide polymorphism (SNP) subset and the choice of adjustment model. We computed PCs for subsets of genome-wide SNPs with varying levels of LD. The first two PCs were similar for all subsets and the first three PCs were associated with case status for all subsets. When the PCs associated with case status were included as covariates in an association model, the reduction in genomic inflation factor was similar for all SNP sets. Several models have been proposed to account for structure using PCs, but it is not yet clear whether the different methods will result in substantively different results for association studies with individuals of European descent. We compared genome-wide association p-values and results for two positive-control SNPs previously associated with rheumatoid arthritis using four PC adjustment methods as well as no adjustment and genomic control. We found that in this sample, adjusting for the continuous PCs or adjusting for discrete clusters identified using the PCs adequately accounts for the case-control population structure, but that a recently proposed randomization test performs poorly.
Accommodating population stratification in case-control association analysis: a new test and its application to genome-wide study on rheumatoid arthritis
Authors: Zhang Yufang , Xiao Xiangjun , Wang Kai 
Published: December 2009

Abstract
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 study design. Unlike some other methods for handling population stratification, we treat the cases as a population and the controls as another one even though each of them may be a mixture of several sub-populations. A likelihood-ratio test is used to test whether the allele frequency of a testing single-nucleotide polymorphism in the case population is the same as that in the control population. This new test is applied to the Genetic Analysis Workshop 16 Problem 1 data on rheumatoid arthritis. Compared with the Pearson chi-square genotype test, the association strength of many single-nucleotide polymorphisms is decreased while the signal at the HLA region on 6p21 is maintained.
Detecting susceptibility genes for rheumatoid arthritis based on a novel sliding-window approach
Authors: Sha Qiuying , Tang Rui , Zhang Shuanglin 
Published: December 2009

Abstract
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, without an optimal solution. In this study, we propose a new approach for genetic association analysis based on a variable-sized sliding-window framework. This approach employs principal component analysis to find the optimal window size. Using the bisection algorithm in window size searching, the proposed method tackles the exhaustive computation problem. It is more efficient and effective than currently available approaches. We conduct the genome-wide association study in Genetic Analysis Workshop 16 (GAW16) Problem 1 data using the proposed method. Our method successfully identified several susceptibility genes that have been reported by other researchers and additional candidate genes for follow-up studies.
Elastic-net regularization approaches for genome-wide association studies of rheumatoid arthritis
Authors: Cho Seoae , Kim Haseong , Oh Sohee , Kim Kyunga , Park Taesung 
Published: December 2009

AbstractThe 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 genome. However, many challenges exist in detecting disease-causing genes among the thousands of SNPs. Examples include multicollinearity and multiple testing issues, especially when a large number of correlated SNPs are simultaneously tested. Multicollinearity can often occur when predictor variables in a multiple regression model are highly correlated, and can cause imprecise estimation of association. In this study, we propose a simple stepwise procedure that identifies disease-causing SNPs simultaneously by employing elastic-net regularization, a variable selection method that allows one to address multicollinearity. At Step 1, the single-marker association analysis was conducted to screen SNPs. At Step 2, the multiple-marker association was scanned based on the elastic-net regularization. The proposed approach was applied to the rheumatoid arthritis (RA) case-control data set of Genetic Analysis Workshop 16. While the selected SNPs at the screening step are located mostly on chromosome 6, the elastic-net approach identified putative RA-related SNPs on other chromosomes in an increased proportion. For some of those putative RA-related SNPs, we identified the interactions with sex, a well known factor affecting RA susceptibility.
Incorporating multiple-marker information to detect risk loci for rheumatoid arthritis
Authors: Wang Xuexia , Qin Huaizhen , Sha Qiuying 
Published: December 2009

AbstractIn 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 and multiple genetic polymorphisms. In the proposed approach, we measured the genetic association between a disease and a single-nucleotide polymorphism window by the newly developed likelihood ratio test-principal components statistic, and performed a sliding-window technique to detect disease susceptibility windows. We split the whole sample into two sub-samples, each of which contained a portion of cases and controls. In the first stage, we selected the topRwindows by the statistics based on the first sub-sample, and in the second stage, we claimed significant windows by false-discovery rate correction on thep-values of the statistics based on the second sub-sample. By applying the new approach to the Genetic Analysis Workshop 16 Problem 1 data set, we detected 212 out of 531,601 windows to be responsible for rheumatoid arthritis. Except for chromosomes 4 and 18, each of the other 20 autosomes was found to harbor risk windows. Our results supported the findings of some rheumatoid arthritis susceptibility genes identified in the literature. In addition, we identified several new single-nucleotide polymorphism windows for follow-up studies.
Two-stage joint selection method to identify candidate markers from genome-wide association studies
Authors: Wu Zheyang , Aporntewan Chatchawit , Ballard David H , Lee Ji Young , Lee Joon Sang , Zhao Hongyu 
Published: December 2009

AbstractThe 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 interactions with other genes. As a result, these genes may not be identified by single-marker methods that are widely used in genome-wide association studies. To explore this possibility in real data, we carried out a two-stage model selection procedure of joint single-nucleotide polymorphism (SNP) analysis to detect genes associated with rheumatoid arthritis (RA) using Genetic Analysis Workshop 16 genome-wide association study data. In the first stage, the genetic markers were screened through an exhaustive two-dimensional search, through which promising SNP and SNP pairs were identified. Then, LASSO was used to choose putative SNPs from the candidates identified in the first stage. We then use the RA data collected by the Wellcome Trust Case Control Consortium to validate the putative genetic factors. Balancing computational load and statistical power, this method detects joint effects that may fail to emerge from single-marker analysis. Based on our proposed approach, we not only replicated the identification of important RA risk genes, but also found novel genes and their epistatic effects on RA. To our knowledge, this is the first two-dimensional scan based analysis for a real genome-wide association study.
Replication of recently identified associated single-nucleotide polymorphisms from six autoimmune diseases in Genetic Analysis Workshop 16 rheumatoid arthritis data
Authors: Deshmukh Harshal , Kim-Howard Xana , Nath Swapan K 
Published: December 2009

Abstract
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 Workshop 16 rheumatoid arthritis (RA) data we sought to replicate the genetic association between single-nucleotide polymorphisms (SNPs) identified in recent genome-wide association studies (GWAS) on RA and five other autoimmune diseases. We identified 164 significantly associated non-HLA SNPs (p < 10-5) from 16 GWAS and 13 candidate gene studies on six different autoimmune diseases, including RA, systemic lupus erythematosus, type 1 diabetes, Crohn disease, multiple sclerosis, and celiac disease. Using both direct and imputation-based association test, we replicated 16 shared susceptibility regions involving RA and at least one of the other autoimmune diseases. We also identified hidden population structure within cases and controls in Genetic Analysis Workshop 16 RA data and assessed the effect of population structure on the shared autoimmunity regions. Because multiple autoimmune diseases share common genetic origin, these could be areas of immense interest for further genetic and clinical association studies.
Genome-wide association studies using single-nucleotide polymorphisms versus haplotypes: an empirical comparison with data from the North American Rheumatoid Arthritis Consortium
Authors: Shim Heejung , Chun Hyonho , Engelman Corinne D , Payseur Bret A 
Published: December 2009

AbstractThe 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 the choice of haplotype-based vs. individual-SNP approaches is expected to affect the results of association studies, few empirical comparisons of method performance have been reported on the genome-wide scale in the same set of individuals. To measure the relative ability of the two strategies to detect associations, we used a large dataset from the North American Rheumatoid Arthritis Consortium to: 1) partition the genome into haplotype blocks, 2) associate haplotypes with disease, and 3) compare the results with individual-SNP association mapping. Although some associations were shared across methods, each approach uniquely identified several strong candidate regions. Our results suggest that the application of both haplotype-based and individual-SNP testing to GWAS should be adopted as a routine procedure.
Using a latent growth curve model for an integrative assessment of the effects of genetic and environmental factors on multiple phenotypes
Authors: Hamid Jemila S , Roslin Nicole M , Paterson Andrew D , Beyene Joseph 
Published: December 2009

Abstract
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 quantitative traits (systolic blood pressure, high-density lipoprotein, low-density lipoprotein, and triglycerides) simultaneously in a multivariate framework that allows us to study their change over time, assess individual variation, and investigate cross-phenotype relationships. Environmental, demographic, and lifestyle covariates are included at different levels of the model as time-varying or time-invariant, as appropriate. To investigate the change over time attributed to genetic factors, we use candidate markers that have previously been shown to be associated with the quantitative traits. We illustrate our approach using independent observations from the offspring cohort of the Framingham Heart Study data.
Application of sex-specific single-nucleotide polymorphism filters in genome-wide association data
Authors: Ling Hua , Hetrick Kurt , Bailey-Wilson Joan E , Pugh Elizabeth W 
Published: December 2009

Abstract
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 by sex quality control filters were missed by filters of call rate at 95% and Hardy-Weinberg equilibrium at 10-6. We applied a subset of these sex-specific quality control filters to eight chromosomes in the Framingham Heart Study samples genotyped by Affymetrix 500 k SNP arrays, and identified another two single-nucleotide polymorphisms that failed to be picked up by the above global filters.
A genome-wide association scan for rheumatoid arthritis data by Hotelling's T2tests
Authors: Chen Lianfu , Zhong Ming , Chen Wei Vivien , Amos Christopher I , Fan Ruzong 
Published: December 2009

Abstract
We performed a genome-wide association scan on the North American Rheumatoid Arthritis Consortium (NARAC) data using Hotelling's T
2 tests, i.e., T

H
based on allele coding and T

G
based on genotype coding. The objective was to identify associations between single-nucleotide polymorphisms (SNPs) or markers and rheumatoid arthritis. In specific candidate gene regions, we evaluated the performance of Hotelling's T
2 tests. Then Hotelling's T
2 tests were used as a tool to identify new regions that contain SNPs showing strong associations with disease. As expected, the strongest association evidence was found in the region of the HLA-DRB1 locus on chromosome 6. In the region of the TRAF1-C5 genes, we identified two SNPs, rs2900180 and rs3761847, with the largest and the second largest T

H
and T

G
scores among all SNPs on chromosome 9. We also identified one SNP, rs2476601, in the region of the PTPN22 gene that had the largest T

H
score and the second largest T

G
score among all SNPs on chromosome 1. In addition, SNPs with the largest T

H
score on each chromosome were identified. These SNPs may be located in the regions of genes that have modest effects on rheumatoid arthritis. These regions deserve further investigation.
Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects
Authors: Kim Yoonhee , Wojciechowski Robert , Sung Heejong , Mathias Rasika A , Wang Li , Klein Alison P , Lenroot Rhoshel K , Malley James , Bailey-Wilson Joan E 
Published: December 2009

Abstract
Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets.
Identification of gene-gene interaction using principal components
Authors: Li Jia , Tang Rui , Biernacka Joanna M , de Andrade Mariza 
Published: December 2009

Abstract
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 effects is of interest. Using the Genetic Analysis Workshop 16 data from the North American Rheumatoid Arthritis Consortium, we propose an approach to screen for SNP-SNP interaction using a two-stage method and an approach for detecting gene-gene interactions using principal components. We selected a set of 17 rheumatoid arthritis candidate genes to assess both approaches. Our approach using principal components holds promise in detecting gene-gene interactions. However, further study is needed to evaluate the power and the feasibility for a whole genome-wide association analysis using the principal components approach.
Application of Bayesian classification with singular value decomposition method in genome-wide association studies
Authors: Kwon Soonil , Cui Jinrui , Rhodes Shannon L , Tsiang Donald , Rotter Jerome I , Guo Xiuqing 
Published: December 2009

AbstractTo 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 studies (GWAS), we developed the iterative Bayesian variable selection method and successfully applied it to the simulated rheumatoid arthritis data provided by the Genetic Analysis Workshop 15 (GAW15). One drawback for applying our iterative Bayesian variable selection method is the relatively long running time required for evaluation of GWAS data. To improve computing speed, we recently developed a Bayesian classification with singular value decomposition (BCSVD) method. We have applied the BCSVD method here to the rheumatoid arthritis data distributed by GAW16 Problem 1 and demonstrated that the BCSVD method works well for analyzing GWAS data.