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1.  A genome-wide association scan for rheumatoid arthritis data by Hotelling's T2 tests 
BMC Proceedings  2009;3(Suppl 7):S6.
We performed a genome-wide association scan on the North American Rheumatoid Arthritis Consortium (NARAC) data using Hotelling's T2 tests, i.e., TH based on allele coding and TG 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 T2 tests. Then Hotelling's T2 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 TH and TG scores among all SNPs on chromosome 9. We also identified one SNP, rs2476601, in the region of the PTPN22 gene that had the largest TH score and the second largest TG score among all SNPs on chromosome 1. In addition, SNPs with the largest TH 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.
PMCID: PMC2795960  PMID: 20018053
2.  Data for Genetic Analysis Workshop 16 Problem 1, association analysis of rheumatoid arthritis data 
BMC Proceedings  2009;3(Suppl 7):S2.
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 1194 controls that had been assayed using an Illumina 550 k platform. In addition, phenotypic data were provided from genotyping DRB1 alleles, which were classified according to the rheumatoid arthritis shared epitope, levels of anti-cyclic citrullinated peptide, and levels of rheumatoid factor IgM. Several questions could be addressed using the data, including analysis of genetic associations using single SNPs or haplotypes, as well as gene-gene and genetic analysis of SNPs for qualitative and quantitative factors.
PMCID: PMC2795916  PMID: 20018009
3.  Normalizing a large number of quantitative traits using empirical normal quantile transformation 
BMC Proceedings  2007;1(Suppl 1):S156.
Variance-components and regression-based methods are frequently used to map quantitative trait loci. The normality of the trait values is usually assumed and violation of this assumption can have a detrimental effect on the power and type I error of such analyses. Various transformations can be used, but appropriate transformations usually require careful analysis of individual traits, which is not feasible for data sets with a large number of traits like those in Problem 1 of Genetic Analysis Workshop 15 (GAW15). A semiparametric variance-components method can estimate the transformation along with the model parameters, but existing methods are computationally intensive. In this paper, we propose the use of empirical normal quantile transformation to normalize the scaled rank of trait values using an inverse normal transformation. Despite its simplicity and potential loss of information, this transformation is shown, by extensive simulations, to have good control of power and type I error, even when compared with the semiparametric method. To investigate the impact of such a transformation on real data sets, we apply variance-components and variance-regression methods to the expression data of GAW15 and compare the results before and after transformation.
PMCID: PMC2367615  PMID: 18466501
4.  Comparison of genome-wide single-nucleotide polymorphism linkage analyses in Caucasian and Hispanic NARAC families 
BMC Proceedings  2007;1(Suppl 1):S97.
We performed linkage analysis on families with rheumatoid arthritis, stratifying by ethnic origin. We compared results using either Kong and Cox nonparametric LOD scores or MOD score analysis using the software GeneHunter MODSCORE. We first applied SNPLINK to remove markers showing excess linkage disequilibrium from the SNPs in the Illumina IV SNP Linkage panel. In this analysis there were 659 self-reported Caucasian families and 29 self-reported Hispanic families in the NARAC collection. Chromosome 19 yielded MOD scores > 3.00 in the Hispanic group, while chromosomes 2, 6, 7, 11, and XY had MOD scores > 3.00 in the Caucasian group. We performed simulation studies to evaluate the empirical distribution of the MOD score for autosomal loci separately in Hispanics and Caucasians. Results showed genome-wide significant evidence for linkage in Caucasians for chromosomes 2q and 6p, but no significant evidence for any linkages in the Hispanics, including little evidence for linkage to chromosome 6p in this group. An examination of the difference of phenotypes in two ethnic groups suggested significantly earlier mean age of onset, higher percentage of anti-cyclic citrullinated peptide positive people, and lower percentage of affected people carrying shared epitopes in Hispanics than those in Caucasians. A larger sample size of the Hispanic group is needed to identify linkage regions.
PMCID: PMC2367594  PMID: 18466601
5.  Genome-wide single-nucleotide polymorphism linkage analyses of quantitative rheumatoid arthritis phenotypes in Caucasian NARAC families 
BMC Proceedings  2007;1(Suppl 1):S105.
We applied nonparametric quantitative trait linkage analysis to two rheumatoid arthritis quantitative phenotypes, IgM rheumatoid factor (RF) and anti-cyclic citrullinated peptide autoantibody titer measurements, using 5700 genome-wide Illumina single-nucleotide polymorphism genotypes on 658 Caucasian North American Rheumatoid Arthritis Consortium families. Peak LOD scores for both quantitative traits were located in the human leukocyte antigen region 6p21 (15.8 and 13.8 for RF and anti-cyclic citrullinated peptide, respectively) followed by 11p12 (3.2 and 3.6). In addition, there were LOD scores of 3.2 on 2q32 for RF and 3.6 on 4q24 for anti-cyclic citrullinated peptide. The resulting linkage signals for both phenotypes are very similar to previous results for rheumatoid arthritis as a qualitative variable, with rheumatoid factor measurements being most closely aligned. Interestingly, anti-cyclic citrullinated peptide exhibits a stronger linkage peak on 2p14 than rheumatoid factor and rheumatoid arthritis, and stronger linkage on 4q24. Finally, we used ordered subset analyses to determine if sub-ranges of these two traits increased rheumatoid arthritis linkage signals; however, our analyses did not reveal significant effects of the quantitative traits on rheumatoid arthritis linkage signals in this population.
PMCID: PMC2367581  PMID: 18466445
6.  Seeking gene relationships in gene expression data using support vector machine regression 
BMC Proceedings  2007;1(Suppl 1):S51.
Several genetic determinants responsible for individual variation in gene expression have been located using linkage and association analyses. These analyses have revealed regulatory relationships between genes. The heritability of expression variation as a quantitative phenotype reflects its underlying genetic architecture. Using support vector machine regression (SVMR) and gene ontological information, we proposed an approach to identify gene relationships in expression data provided by Genetic Analysis Workshop 15 that would facilitate subsequent genetic analyses. A group of related genes were selected for a shared biological theme, and SVMR was trained to form a regression model using the training gene expressions. The model was subsequently used to search for and capture similarly related genes. SVMR shows promising capability in modeling and seeking gene relationships through expression data.
PMCID: PMC2367560  PMID: 18466551
7.  Joint linkage and imprinting analyses of GAW15 rheumatoid arthritis and gene expression data 
BMC Proceedings  2007;1(Suppl 1):S53.
Genomic imprinting is a mechanism in which the expression of a gene copy depends upon the sex of the parent from which it was inherited. This mechanism is now well recognized in humans, and the deregulation of imprinted genes has been implicated in a number of diseases. In this study, we performed a genome-wide joint linkage and imprinting scan using two data sets provided by Genetic Analysis Workshop 15 (GAW15).
The first data set was high-risk rheumatoid arthritis families collected by the North American Rheumatoid Arthritis Consortium. We used both model-based and model-free methods of joint linkage and imprinting analyses. Although a genome scan of rheumatoid arthritis families using GENEHUNTER-MODSCORE suggested regions that might be imprinted, further analyses using variance-components method failed to obtain significant signals of imprinting. The second data set was Problem 1 of GAW15, which included single-nucleotide polymorphism genotypes and gene expression data for Centre d'Etude du Polymorphisme Humain pedigrees. A previous genome-wide linkage scan identified loci that may be regulators of gene expression: our genome-wide joint linkage and imprinting scan using a variance-components approach found significant signals for linkage.
Our linkage scan results suggest that imprinted genes are unlikely to be involved in susceptibility to rheumatoid arthritis. However, for expression level of TGFBR3 gene, we found a point-wise p-value of 0.03 for imprinting, but increase in the LOD score did not meet the required threshold to reliably identify imprinting as the correct mode of inheritance in genome-wide linkage scans.
PMCID: PMC2367552  PMID: 18466553
8.  Data for Genetic Analysis Workshop (GAW) 15 Problem 2, genetic causes of rheumatoid arthritis and associated traits 
BMC Proceedings  2007;1(Suppl 1):S3.
For Genetic Analysis Workshop 15 Problem 2, we organized data from several ongoing studies designed to identify genetic and environmental risk factors for rheumatoid arthritis. Data were derived from the North American Rheumatoid Arthritis Consortium (NARAC), collaboration among Canadian researchers, the European Consortium on Rheumatoid Arthritis Families (ECRAF), and investigators from Manchester, England. All groups used a common standard for defining rheumatoid arthritis, but NARAC also further selected for a more severe phenotype in the probands. Genotyping and family structures for microsatellite-based linkage analysis were provided from all centers. In addition, all centers but ECRAF have genotyped families for linkage analysis using SNPs and these data were additionally provided. NARAC also had additional data from a dense genotyping analysis of a region of chromosome 18 and results from candidate gene studies, which were provided. Finally, smoking influences risk for rheumatoid arthritis, and data were provided from the NARAC study on this behavior as well as some additional phenotypes measuring severity. Several questions could be evaluated using the data that were provided. These include comparing linkage analysis using single-nucleotide polymorphisms versus microsatellites and identifying credible regions of linkage outside the HLA region on chromosome 6p13, which has been extensively documented; evaluating the joint effects of smoking with genetic factors; and identifying more homogenous subsets of families for whom genetic susceptibility might be stronger, so that linkage and association studies may be more efficiently conducted.
PMCID: PMC2367518  PMID: 18466527

Results 1-8 (8)