Statistical models for genetics data are often surprisingly challenging, and often require advanced and new statistical methods. This project continues to investigate a number of such areas, including, for example, a global analysis of X-chromosome dosage compensation. We begin by noting that Drosophila has a special dosage compensation complex, which upregulates the male X chromosome about two-fold relative to the autosomes, thus maintaining X-versus-autosomal genic balance. However, this complex is only present in the soma, not in the germline. Nevertheless, germline tissues also display striking two-fold upregulation of genes on the male X-chromosome, as revealed by careful measurements of gene expression using microarrays (Gupta, Malley, Oliver, et al., 2006). Analysis of published data from mouse and worm expression arrays reveals a similar balance between X and autosomal genes. Taken together, these results (with indicate that multiple means have evolved to achieve the same end) emphasize our fundamental ignorance of the underlying transcription-linked process that is being regulated. We note that this paper by Gupta, Malley, Oliver, et al. (J. Biology, Feb. 2006) was accessed more than 8,500 times in the year following its appearance in Feburary 2006, and was the third most accessed paper in this journal over that time period. More recently we have undertaken the study of genome wide associations and how statistical learning machines can be applied to such ultra large data (500K or 1,000K snps), with the aim of locating the most predictive genes or snps among the available features and understanding how linkage disequilibrium compromises or assists these detection methods.

Agency
National Institute of Health (NIH)
Institute
Center for Information Technology (CIT)
Type
Intramural Research (Z01)
Project #
1Z01CT000268-10
Application #
7733764
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
10
Fiscal Year
2008
Total Cost
$280,192
Indirect Cost
Name
Center for Information Technology
Department
Type
DUNS #
City
State
Country
United States
Zip Code
Konig, I R; Malley, J D; Weimar, C et al. (2007) Practical experiences on the necessity of external validation. Stat Med 26:5499-511
O'Hanlon, Terrance P; Carrick, Danielle Mercatante; Targoff, Ira N et al. (2006) Immunogenetic risk and protective factors for the idiopathic inflammatory myopathies: distinct HLA-A, -B, -Cw, -DRB1, and -DQA1 allelic profiles distinguish European American patients with different myositis autoantibodies. Medicine (Baltimore) 85:111-27
Gupta, Vaijayanti; Parisi, Michael; Sturgill, David et al. (2006) Global analysis of X-chromosome dosage compensation. J Biol 5:3
Stojanov, Silvia; Hoffmann, Florian; Kery, Anja et al. (2006) Cytokine profile in PFAPA syndrome suggests continuous inflammation and reduced anti-inflammatory response. Eur Cytokine Netw 17:90-7
O'Hanlon, Terrance P; Rider, Lisa G; Mamyrova, Gulnara et al. (2006) HLA polymorphisms in African Americans with idiopathic inflammatory myopathy: allelic profiles distinguish patients with different clinical phenotypes and myositis autoantibodies. Arthritis Rheum 54:3670-81
Mamyrova, Gulnara; O'Hanlon, Terrance P; Monroe, Jason B et al. (2006) Immunogenetic risk and protective factors for juvenile dermatomyositis in Caucasians. Arthritis Rheum 54:3979-87
O'Hanlon, Terrance P; Carrick, Danielle Mercatante; Arnett, Frank C et al. (2005) Immunogenetic risk and protective factors for the idiopathic inflammatory myopathies: distinct HLA-A, -B, -Cw, -DRB1 and -DQA1 allelic profiles and motifs define clinicopathologic groups in caucasians. Medicine (Baltimore) 84:338-49
Parisi, Michael; Nuttall, Rachel; Edwards, Pamela et al. (2004) A survey of ovary-, testis-, and soma-biased gene expression in Drosophila melanogaster adults. Genome Biol 5:R40
O'Hanlon, Terrance; Koneru, Bhanu; Bayat, Elham et al. (2004) Immunogenetic differences between Caucasian women with and those without silicone implants in whom myositis develops. Arthritis Rheum 50:3646-50
Jerebko, Anna K; Summers, Ronald M; Malley, James D et al. (2003) Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees. Med Phys 30:52-60

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