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1.  Testing optimally weighted combination of variants for hypertension 
BMC Proceedings  2014;8(Suppl 1):S59.
Testing rare variants directly is possible with next-generation sequencing technology. In this article, we propose a sliding-window-based optimal-weighted approach to test for the effects of both rare and common variants across the whole genome. We measured the genetic association between a disease and a combination of variants of a single-nucleotide polymorphism window using the newly developed tests TOW and VW-TOW and performed a sliding-window technique to detect disease-susceptible windows. By applying the new approach to unrelated individuals of Genetic Analysis Workshop 18 on replicate 1 chromosome 3, we detected 3 highly susceptible windows across chromosome 3 for diastolic blood pressure and identified 10 of 48,176 windows as the most promising for both diastolic and systolic blood pressure. Seven of 9 top variants influencing diastolic blood pressure and 8 of 9 top variants influencing systolic blood pressure were found in or close to our top 10 windows.
PMCID: PMC4143713  PMID: 25519394
2.  Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data 
BMC Proceedings  2014;8(Suppl 1):S91.
Increasing evidence shows that complex diseases are caused by both common and rare variants. Recently, several statistical methods for detecting associations of rare variants have been developed, including the test for testing the effect of an optimally weighted combination of variants (TOW) developed by our group in 2012. These methodologies consider phenotype measurement at only one time point. Because many sequence data have been developed on population cohorts that contain phenotype measurements at multiple time points, such as the data set provided in the Genetic Analysis Workshop 18 (GAW18), we extend TOW from phenotype measurement at one time point to phenotype measurements at multiple time points. We then apply the newly proposed method to the GAW18 data set and compare the power of the new method with TOW using only one phenotype measurement. The application results show that the newly proposed method jointly modeling phenotype measurements at all time points has increased power over TOW.
PMCID: PMC4143720  PMID: 25519418
3.  Detection of rare variant effects in association studies: extreme values, iterative regression, and a hybrid approach 
BMC Proceedings  2011;5(Suppl 9):S112.
We develop statistical methods for detecting rare variants that are associated with quantitative traits. We propose two strategies and their combination for this purpose: the iterative regression strategy and the extreme values strategy. In the iterative regression strategy, we use iterative regression on residuals and a multimarker association test to identify a group of significant variants. In the extreme values strategy, we use individuals with extreme trait values to select candidate genes and then test only these candidate genes. These two strategies are integrated into a hybrid approach through a weighting technology. We apply the proposed methods to analyze the Genetic Analysis Workshop 17 data set. The results show that the hybrid approach is the most powerful approach. Using the hybrid approach, the average power to detect causal genes for Q1 is about 40% and the powers to detect FLT1 and KDR are 100% and 68% for Q1, respectively. The powers to detect VNN3 and BCHE are 34% and 30% for Q2, respectively.
PMCID: PMC3287836  PMID: 22373188
4.  A combinatorial approach for detecting gene-gene interaction using multiple traits of Genetic Analysis Workshop 16 rheumatoid arthritis data 
BMC Proceedings  2009;3(Suppl 7):S43.
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 to have a significant effect on the disease, while each of them only plays a modest role. We propose a new combinatorial association test to detect the gene-gene interaction in the rheumatoid arthritis data using multiple traits: disease status, anti-cyclic citrullinated peptide, and immunoglobulin M. Existing gene-gene interaction tests only use the information on a single trait at a time. In this article, we propose a new multivariate combinatorial searching method that utilizes multiple traits at the same time. Multivariate combinatorial searching method is conducted by incorporating the multiple traits with various techniques of feature selection to search for a set of disease-susceptibility genes that may interact. By analyzing three panels of markers, we have identified a significant gene-gene interaction between PTPN22 and TRAF1-C5.
PMCID: PMC2795942  PMID: 20018035
5.  Incorporating multiple-marker information to detect risk loci for rheumatoid arthritis 
BMC Proceedings  2009;3(Suppl 7):S28.
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 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 top R windows by the statistics based on the first sub-sample, and in the second stage, we claimed significant windows by false-discovery rate correction on the p-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.
PMCID: PMC2795925  PMID: 20018018
6.  Application of seventeen two-locus models in genome-wide association studies by two-stage strategy 
BMC Proceedings  2009;3(Suppl 7):S26.
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 reduce the computational burden associated with performing an exhaustive two-locus search across the genome. In the first stage, the full set of 531,689 single-nucleotide polymorphisms was screened using univariate testing. In the second stage, all pairs made from the 500 single-nucleotide polymorphisms with the lowest p-values from the first stage were evaluated under each of 17 two-locus models. Our analyses identified a two-locus combination - rs6939589 and rs11634386 - that proved to be significantly associated with rheumatoid arthritis under a Rec × Rec model (p-value = 0.045 after adjusting for multiple tests and multiple models).
PMCID: PMC2795923  PMID: 20018016
7.  Detecting susceptibility genes for rheumatoid arthritis based on a novel sliding-window approach 
BMC Proceedings  2009;3(Suppl 7):S14.
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.
PMCID: PMC2795910  PMID: 20018003
8.  Genome-wide association tests by two-stage approaches with unified analysis of families and unrelated individuals 
BMC Proceedings  2007;1(Suppl 1):S140.
Multiple testing is a problem in genome-wide or region-wide association studies. In this report, we consider a study design given by the Genetic Analysis Workshop 15 (GAW15) Problem 3 – nuclear families (parents with their affected children) and unrelated controls. Based on this design, we propose three two-stage approaches to deal with the problem of multiple testing. The tests in the first stage, statistically independent of the association test used in the second stage, are used to screen or select single-nucleotide polymorphisms (SNPs). Then, in the second stage, a family-based association test is performed on a much smaller set of selected SNPs. Thus, the problem of multiple testing is much less severe. Our simulation studies and application to the dense SNP data of chromosome 6 in the GAW15 Problem 3 show that the two-stage methods are more powerful than the one-stage method (using the family-based association test only).
PMCID: PMC2367544  PMID: 18466484
9.  A method dealing with a large number of correlated traits in a linkage genome scan 
BMC Proceedings  2007;1(Suppl 1):S84.
We propose a method to perform linkage genome scans for many correlated traits in the Genetic Analysis Workshop 15 (GAW15) data. The proposed method has two steps: first, we use a clustering method to find the tight clusters of the traits and use the first principal component (PC) of the traits in each cluster to represent the cluster; second, we perform a linkage scan for each cluster by using the representative trait of the cluster. The results of applying the method to the GAW15 Problem 1 data indicate that most of the traits in the same cluster have the same regulators, and the representative trait measure, the first PC, can explain a large part of the total variation of all the traits in each cluster. Furthermore, considering one cluster of traits at a time may yield more linkage signals than considering traits individually.
PMCID: PMC2367490  PMID: 18466587
10.  Genome-wide association tests by using block information in family data 
BMC Proceedings  2007;1(Suppl 1):S149.
By applying an association test to analyze the data sets from Genetic Analysis Workshop 15 Problem 3, we compare power using different haplotype-block information. The results from using both of the two different coding schemes show that the test using tight blocks with limited haplotype diversity within each block is more powerful than that using evenly spaced blocks, and the latter is more powerful than that using single-marker blocks. By using carefully chosen haplotype blocks, the power of association tests may be enhanced.
PMCID: PMC2359871  PMID: 18466493

Results 1-10 (10)