A Bayesian method for finding interactions in genomic studies

An important step in building a multiple regression model is the selection of predictors. In genomic and epidemiologic studies, datasets with a small sample size and a large number of predictors are common. In such settings, most standard methods for identifying a good subset of predictors are unstable. Furthermore, there is an increasing emphasis towards identification of interactions, which has not been studied much in the statistical literature. We propose a method, called BSI (Bayesian Selection of Interactions), for selecting predictors in a regression setting when the number of predictors is considerably larger than the sample size with a focus towards selecting interactions. Latent variables are used to infer subset choices based on the posterior distribution. Inference about interactions is implemented by a constraint on the latent variables. The posterior distribution is computed using the Gibbs Sampling methods. The finite-sample properties of the proposed method are assessed by simulation studies. We illustrate the BSI method by analyzing data from a hypertension study involving Single Nucleotide Polymorphisms (SNPs)

Public Trust in Health Information Sharing: A Measure of System Trust

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142931/1/hesr12654.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142931/2/hesr12654_am.pd

Identification of genes associated with complex traits by testing the genetic dissimilarity between individuals

Abstract Using the exome sequencing data from 697 unrelated individuals and their simulated disease phenotypes from Genetic Analysis Workshop 17, we develop and apply a gene-based method to identify the relationship between a gene with multiple rare genetic variants and a phenotype. The method is based on the Mantel test, which assesses the correlation between two distance matrices using a permutation procedure. Using up to 100,000 permutations to estimate the statistical significance in 200 replicate data sets, we found that the method had 5.1% type I error at an α level of 0.05 and had various power to detect genes with simulated genetic associations. FLT1 and KDR had the most significant correlations with Q1 and were replicated 170 and 24 times, respectively, in 200 simulated data sets using a Bonferroni corrected p-value of 0.05 as a threshold. These results suggest that the distance correlation method can be used to identify genotype-phenotype association when multiple rare genetic variants in a gene are involved.http://deepblue.lib.umich.edu/bitstream/2027.42/112957/1/12919_2011_Article_1171.pd

Influence of apolipoprotein E genotype variation on the means, variances, and correlations of plasma lipids and apolipoproteins in children

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65894/1/j.1469-1809.1999.6340311.x.pd

Identification of correlated genetic variants jointly associated with rheumatoid arthritis using ridge regression

Abstract 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 variation of anti-cyclic citrullinated peptide (anti-CCP), a newly identified clinical risk factor for development of rheumatoid arthritis. Within a 2.7-Mbp region on chromosome 6 around the well studied HLA-DRB1 locus, ridge regression identified a single-nucleotide polymorphism that was associated with anti-CCP variation when including the additive effects of other single-nucleotide polymorphisms in a multivariable analysis, but that showed only a weak direct association with anti-CCP. This suggests that multivariable methods can be used to identify potentially relevant genetic variants in regions of interest that would be difficult to detect based on direct associations.http://deepblue.lib.umich.edu/bitstream/2027.42/117369/1/12919_2009_Article_2814.pd

Negotiating Deliberative Ideals in Theory and Practice: A Case Study in “Hybrid Design”

Much literature on deliberation is derived from ideal theory. However, deliberations are inevitably non-ideal in two ways: (1) many deliberative ideals are in tension with each other; and 2) intended balancing of ideals cannot be attained perfectly amidst the messiness of real-world recruitment and conversation. This essay explores both kinds of non-ideality in respect to a case study: the 2011 community deliberative processes on a state public health “biobank,” the Michigan BioTrust for Health. We follow two recommendations from major contemporary theorists of deliberation: to be transparent about how competing deliberative goals are negotiated in deliberative design; and to publicize case studies that report associated struggles and results. We present our “hybrid design” that sought to negotiate tensions within three families of deliberative goals: goals of representation and inclusion; goals of discourse-framing; and goals of political impact. We offer deliberative facilitators tentative suggestions based on this case study, concluding deliberations need not be “ideal” to be transformative

A scan statistic for identifying chromosomal patterns of SNP association

We have developed a single nucleotide polymorphism (SNP) association scan statistic that takes into account the complex distribution of the human genome variation in the identification of chromosomal regions with significant SNP associations. This scan statistic has wide applicability for genetic analysis, whether to identify important chromosomal regions associated with common diseases based on whole-genome SNP association studies or to identify disease susceptibility genes based on dense SNP positional candidate studies. To illustrate this method, we analyzed patterns of SNP associations on chromosome 19 in a large cohort study. Among 2,944 SNPs, we found seven regions that contained clusters of significantly associated SNPs. The average width of these regions was 35 kb with a range of 10–72 kb. We compared the scan statistic results to Fisher's product method using a sliding window approach, and detected 22 regions with significant clusters of SNP associations. The average width of these regions was 131 kb with a range of 10.1–615 kb. Given that the distances between SNPs are not taken into consideration in the sliding window approach, it is likely that a large fraction of these regions represents false positives. However, all seven regions detected by the scan statistic were also detected by the sliding window approach. The linkage disequilibrium (LD) patterns within the seven regions were highly variable indicating that the clusters of SNP associations were not due to LD alone. The scan statistic developed here can be used to make gene-based or region-based SNP inferences about disease association. Genet. Epidemiol . 2006. © 2006 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55838/1/20173_ftp.pd