Irina Culminskaya, Alexander M Kulminski, Anatoli I Yashin
{"title":"Coordinated Action of Biological Processes during Embryogenesis Can Cause Genome-Wide Linkage Disequilibrium in the Human Genome and Influence Age-Related Phenotypes.","authors":"Irina Culminskaya, Alexander M Kulminski, Anatoli I Yashin","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>A role of non-Mendelian inheritance in genetics of complex, age-related traits is becoming increasingly recognized. Recently, we reported on two inheritable clusters of SNPs in extensive genome-wide linkage disequilibrium (LD) in the Framingham Heart Study (FHS), which were associated with the phenotype of premature death. Here we address biologically-related properties of these two clusters. These clusters have been unlikely selected randomly because they are functionally and structurally different from matched sets of randomly selected SNPs. For example, SNPs in LD from each cluster are highly significantly enriched in genes (p=7.1×10<sup>-22</sup> and p=5.8×10<sup>-18</sup>), in general, and in short genes (p=1.4×10<sup>-47</sup> and p=4.6×10<sup>-7</sup>), in particular. Mapping of SNPs in LD to genes resulted in two, partly overlapping, networks of 1764 and 4806 genes. Both these networks were gene enriched in developmental processes and in biological processes tightly linked with development including biological adhesion, cellular component organization<i>,</i> locomotion, localization, signaling, (p<10<sup>-4</sup>, q<10<sup>-4</sup> for each category). Thorough analysis suggests connections of these genetic networks with different stages of embryogenesis and highlights biological interlink of <i>specific</i> processes enriched for genes from these networks. The results suggest that coordinated action of biological processes during embryogenesis may generate genome-wide networks of genetic variants, which may influence complex age-related phenotypes characterizing health span and lifespan.</p>","PeriodicalId":72218,"journal":{"name":"Annals of gerontology and geriatric research","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5367637/pdf/nihms850301.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34867931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anil N Makam, Oanh K Nguyen, Jie Zhou, Kenneth J Ottenbacher, Ethan A Halm
{"title":"Trends in Long-Term Acute Care Hospital Use in Texas from 2002-2011.","authors":"Anil N Makam, Oanh K Nguyen, Jie Zhou, Kenneth J Ottenbacher, Ethan A Halm","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objective: </strong>To assess regional trends in long-term acute care hospital (LTAC) use over time.</p><p><strong>Design setting participants: </strong>Retrospective study using 100% Texas Medicare data. Separate cohorts were created for each year from 2002-2011, which included all beneficiaries residing in 23 hospital referral regions (HRRs) with continuous enrollment in Parts A and B in the previous and current year, or until death.</p><p><strong>Measurements: </strong>LTAC utilization rate was defined as the number of individuals with a LTAC stay per 100,000 Medicare beneficiaries residing in the HRR. Baseline LTAC use at the HRR-level was categorized by tertiles of use in 2002.</p><p><strong>Results: </strong>Overall, LTAC use increased 35% from 2002-2011 and coincided with major Medicare policy changes. However, there were marked regional differences in LTAC utilization trends. From 2002-2011, HRRs in the lowest tertile of baseline LTAC use, which included regions with 0 to 1 LTAC facilities in 2002, had an increase in utilization by 211%, from 190 to 591 individuals per 100,000 persons. In contrast, HRRs in the highest tertile of baseline LTAC use, which included some of the most densely LTAC-bedded regions in the country, experienced a 21% decline (915 to 719 individuals per 100,000 persons; p<0.001 for interaction of LTAC utilization and tertile of baseline use).</p><p><strong>Conclusion: </strong>These findings suggest substantial regional variation in the trends in LTAC use over time. Further research is needed to estimate how much of this variation is due to differences in clinical need due to increasing number of severely ill older adults versus regional market supply.</p>","PeriodicalId":72218,"journal":{"name":"Annals of gerontology and geriatric research","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4686275/pdf/nihms733409.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10242580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anatoliy I Yashin, Deqing Wu, Konstantin G Arbeev, Liubov S Arbeeva, Igor Akushevich, Alexander Kulminski, Irina Culminskaya, Eric Stallard, Svetlana V Ukraintseva
{"title":"Genetic Structures of Population Cohorts Change with Increasing Age: Implications for Genetic Analyses of Human aging and Life Span.","authors":"Anatoliy I Yashin, Deqing Wu, Konstantin G Arbeev, Liubov S Arbeeva, Igor Akushevich, Alexander Kulminski, Irina Culminskaya, Eric Stallard, Svetlana V Ukraintseva","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Correcting for the potential effects of population stratification is an important issue in genome wide association studies (GWAS) of complex traits. Principal component analysis (PCA) of the genetic structure of the population under study with subsequent incorporation of the first several principal components (PCs) in the GWAS regression model is often used for this purpose.</p><p><strong>Problem: </strong>For longevity related traits such a correction may negatively affect the accuracy of genetic analyses. This is because PCs may capture genetic structure induced by mortality selection processes in genetically heterogeneous populations.</p><p><strong>Data and methods: </strong>We used the Framingham Heart Study data on life span and on individual genetic background to construct two sets of PCs. One was constructed to separate population stratification due to differences in ancestry from that induced by mortality selection. The other was constructed using genetic data on individuals of different ages without attempting to separate the ancestry effects from the mortality selection effects. The GWASs of human life span were performed using the first 20 PCs from each of the selected sets to control for possible population stratification.</p><p><strong>Results: </strong>The results indicated that the GWAS that used the PC set separating population stratification induced by mortality selection from differences in ancestry produced stronger genetic signals than the GWAS that used PCs without such separation.</p><p><strong>Conclusion: </strong>The quality of genetic estimates in GWAS can be improved when changes in genetic structure caused by mortality selection are taken into account in controlling for possible effects of population stratification.</p>","PeriodicalId":72218,"journal":{"name":"Annals of gerontology and geriatric research","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4398390/pdf/nihms665969.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33233544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Plasminogen Activator Inhibitor-1, Body Fat and Insulin Action in Aging Women.","authors":"Shawna McMillin, A S Ryan","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Plasminogen activator inhibitor-1 (PAI-1) over-expression is linked to obesity, insulin resistance, and age. We hypothesized that aerobically trained women athletes would have reduced PAI-1 regardless of age compared to sedentary controls and levels would be associated with hyperinsulinemia. Plasma PAI-1 was measured in women athletes who were young (YA, n=19, VO<sub>2max</sub>=53.7±1.1ml/kg/min) and older (OA, n=18, VO<sub>2max</sub>=46.6±1.5ml/kg/min) and compared to 19 sedentary controls (YC, n=6, VO<sub>2max</sub>=35.9±1.2ml/kg/min; OC, n=13, VO<sub>2max</sub>=22.1±1.7ml/kg/min). PAI-1 levels did not differ between YA and OA but was 23% higher in OC compared to OA (P<0.05). PAI-1 was inversely associated with VO<sub>2max</sub>, directly to %body fat, and subcutaneous abdominal fat, fasting leptin, insulin, and first-phase and second-phase insulin response during a hyperglycemic clamp. The current results suggest that older athletes have low PAI-1 levels possibly due to high levels of physical fitness, reduced body fat, and increased insulin action and may contribute to low atherothrombosis and improved cardiovascular health.</p>","PeriodicalId":72218,"journal":{"name":"Annals of gerontology and geriatric research","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539000/pdf/nihms714656.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34007604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}