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1.  Identifying regions of disease-related variants in admixed populations with the summation partition approach 
BMC Proceedings  2016;10(Suppl 7):131-134.
We propose a new method for identifying disease-related regions of single nucleotide variants in recently admixed populations. We use principal component analysis to derive both global and local ancestry information. We then use the summation partition approach to search for disease-related regions based on both rare variants and the local ancestral information of each region. We demonstrate this method using individuals with high systolic blood pressure from a sample of unrelated Mexican American subjects provided in the 19th Genetic Analysis Workshop.
doi:10.1186/s12919-016-0018-9
PMCID: PMC5133488  PMID: 27980624
2.  Network-guided interaction mining for the blood pressure phenotype of unrelated individuals in genetic analysis workshop 19 
BMC Proceedings  2016;10(Suppl 7):333-336.
Interactions between genes are an important part of the genetic architecture of complex diseases. In this paper, we use literature-guided individual genes known to be associated with type 2 diabetes (referred to as “seed genes”) to create a larger list of genes that share implied or direct networks with these seed genes. This larger list of genes are known to interact with each other, but whether they interact in ways to influence hypertension in individuals presents an interesting question. Using Genetic Analysis Workshop data on individuals with diabetes, for which only case-control labels of hypertension are known, we offer a foray into identification of diabetes-related gene interactions that are associated with hypertension. We use the approach of Lo et al. (Proc Natl Acad Sci U S A 105: 12387-12392, 2008), which creates a score to identify pairwise significant gene associations. We find that the genes GCK and PAX4, formerly known to be found within similar coexpression and pathway networks but without specific direct interactions, do, in fact, show significant joint interaction effects for hypertension.
doi:10.1186/s12919-016-0052-7
PMCID: PMC5133535  PMID: 27980658
3.  SNP set analysis for detecting disease association using exon sequence data 
BMC Proceedings  2011;5(Suppl 9):S91.
Rare variants are believed to play an important role in disease etiology. Recent advances in high-throughput sequencing technology enable investigators to systematically characterize the genetic effects of both common and rare variants. We introduce several approaches that simultaneously test the effects of common and rare variants within a single-nucleotide polymorphism (SNP) set based on logistic regression models and logistic kernel machine models. Gene-environment interactions and SNP-SNP interactions are also considered in some of these models. We illustrate the performance of these methods using the unrelated individuals data from Genetic Analysis Workshop 17. Three true disease genes (FLT1, PIK3C3, and KDR) were consistently selected using the proposed methods. In addition, compared to logistic regression models, the logistic kernel machine models were more powerful, presumably because they reduced the effective number of parameters through regularization. Our results also suggest that a screening step is effective in decreasing the number of false-positive findings, which is often a big concern for association studies.
doi:10.1186/1753-6561-5-S9-S91
PMCID: PMC3287933  PMID: 22373133
5.  Combining multiple family-based association studies 
BMC Proceedings  2007;1(Suppl 1):S162.
While high-throughput genotyping technologies are becoming readily available, the merit of using these technologies to perform genome-wide association studies has not been established. One major concern is that for studies of complex diseases and traits, the whole-genome approach requires such large sample sizes that both recruitment and genotyping pose considerable challenge. Here we propose a novel statistical method that boosts the effective sample size by combining data obtained from several studies. Specifically, we consider a situation in which various studies have genotyped non-overlapping subjects at largely non-overlapping sets of markers. Our approach, which exploits the local linkage disequilibrium structure without assuming an explicit population model, opens up the possibility of improving statistical power by incorporating existing data into future association studies.
PMCID: PMC2367479  PMID: 18466508
6.  Controlling for false positive findings of trans-hubs in expression quantitative trait loci mapping 
BMC Proceedings  2007;1(Suppl 1):S157.
In the fast-developing field of expression quantitative traits loci (eQTL) studies, much interest has been concentrated on detecting genomic regions containing transcriptional regulators that influence multiple expression phenotypes (trans-hubs). In this paper, we develop statistical methods for eQTL mapping and propose a new procedure for investigating candidate trans-hubs. We use data from the Genetic Analysis Workshop 15 to illustrate our methods. After correlations among expressions were accounted for, the previously detected trans-hubs are no longer significant. Our results suggest that conclusions regarding regulation hot spots should be treated with great caution.
PMCID: PMC2367467  PMID: 18466502

Results 1-6 (6)