13 research outputs found
Steve Hamilton Collection
No full textTo listen to the audio recordings from this collection please go to the following link.
Steve Hamilton Oral History Collectio
1972-1973 Alpha Tau Omega 1
No full textAlpha Tau Omega was founded at Jacksonville State University in March 1969. Shown 1972-1973 members and little sisters of the greek organization gather outside near the First National Bank in Jacksonville, AL. Members were Jerry Starnes, Bob Green, Bill Lynch, Mike Whisonant, Tom Roberson, Lee Thompson, Steve Harrison, David Kendrick, Doug Holmes, Larry McDow, Mike Canada, Bill Linscott, Lester Willson, David Bibb, Charlie Mangieri, Tom Eames, Robert Snead, Keith Absher, Tim Conrad, Bruce Henderson, Wyatt Jones, Mac Payne, Charles Kicker, Stanley Traylor, Craig Glasgow, Len Black, Rick Totten, Rick Foster, Clarence Crump, Joe Clifton, Joe Bailey, Bill Adams, Joe Caiola, Anthony Romano, Eddie Copeland, Steven Taylor, Randy Mosley, Gary Giedinghagen, John Wilks, David Buttram, Barry Starr, Chris Hooten, Tyger Shields, Ben Jones, Randy Dobsen, Steve Bridges, Pete Yates, David Miles, Jack Nunnaly, Don Ivey, Richard Mastraioni, Gene Pigg, Randy Harris, Steve Vincent, Marshall Clay, Pete Holley, Harold Calloway, Teri Stowe, William Stanely. Little Sisters were Ann Scalici, Patti Jameison, Remona Sharp, Debbie Maynard, Mary Jane Snider, Carol Livingston, Betty Cornelius, Trisha Hallmark, Debra Hannah, Gwen Adair, Lisa Hubbard, Sherry Blackerby, Bethann Sadler, Karen Collingsworth, Penny Hill, Terry Locke, Angie Troncale, Debi Smith, Denise Hubbard, Carmen James, Jackie Atchinson, Sharon Musick, Belinda Moree, Kathy Hamilton, Amy Lewis, Alicia Benefield, Kathy Camp, Nedra Hunt, Becky Luker.https://digitalcommons.jsu.edu/lib-ac-histimg/43494/thumbnail.jp
Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders
No full textElevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate-increasing and heart rate-decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets
Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution
No full textWaist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻¹³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index
No full textObesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and similar to 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 x 10(-8)), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation
Metabolic and inflammatory biomarkers are associated with epigenetic aging acceleration estimates in the GOLDN study
No full textAbstract Background Recently, epigenetic age acceleration—or older epigenetic age in comparison to chronological age—has been robustly associated with mortality and various morbidities. However, accelerated epigenetic aging has not been widely investigated in relation to inflammatory or metabolic markers, including postprandial lipids. Methods We estimated measures of epigenetic age acceleration in 830 Caucasian participants from the Genetics Of Lipid Lowering Drugs and diet Network (GOLDN) considering two epigenetic age calculations based on differing sets of 5′-Cytosine-phosphate-guanine-3′ genomic site, derived from the Horvath and Hannum DNA methylation age calculators, respectively. GOLDN participants underwent a standardized high-fat meal challenge after fasting for at least 8 h followed by timed blood draws, the last being 6 h postmeal. We used adjusted linear mixed models to examine the association of the epigenetic age acceleration estimate with fasting and postprandial (0- and 6-h time points) low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglyceride (TG) levels as well as five fasting inflammatory markers plus adiponectin. Results Both DNA methylation age estimates were highly correlated with chronological age (r > 0.90). We found that the Horvath and Hannum measures of epigenetic age acceleration were moderately correlated (r = 0.50). The regression models revealed that the Horvath age acceleration measure exhibited marginal associations with increased postprandial HDL (p = 0.05), increased postprandial total cholesterol (p = 0.06), and decreased soluble interleukin 2 receptor subunit alpha (IL2sRα, p = 0.02). The Hannum measure of epigenetic age acceleration was inversely associated with fasting HDL (p = 0.02) and positively associated with postprandial TG (p = 0.02), interleukin-6 (IL6, p = 0.007), C-reactive protein (C-reactive protein, p = 0.0001), and tumor necrosis factor alpha (TNFα, p = 0.0001). Overall, the observed effect sizes were small and the association of the Hannum residual with inflammatory markers was attenuated by adjustment for estimated T cell type percentages. Conclusions Our study demonstrates that epigenetic age acceleration in blood relates to inflammatory biomarkers and certain lipid classes in Caucasian individuals of the GOLDN study. Future studies should consider epigenetic age acceleration in other tissues and extend the analysis to other ethnic groups
Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways.
No full textGenome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders
Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder
No full textGenetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.Peer reviewe
Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs
No full textMost psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders
Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs
No full textMost psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.c.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders
