ORCID Profile
0000-0003-3278-1844
Current Organisation
Ambry Genetics (United States)
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Publisher: American Psychological Association (APA)
Date: 2012
DOI: 10.1037/A0028943
Abstract: This special section brings together behavioral, computational, mathematical, and neuroimaging approaches to understand the processes underlying category learning. Over the past decade, there has been growing convergence in research on categorization, with computational-mathematical models influencing the interpretation of brain imaging and neuropsychological data, and with cognitive neuroscience findings influencing the development and refinement of models. Classic debates between single-system and multiple-memory-system theories have become more nuanced and focused. Multiple brain areas and cognitive processes contribute to categorization, but theories differ markedly in whether and when those neurocognitive components are recruited for different aspects of categorization. The articles in this special section approach this issue from several erse angles.
Publisher: Public Library of Science (PLoS)
Date: 14-03-2012
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 05-2019
Publisher: Hindawi Limited
Date: 04-04-2019
DOI: 10.1155/2019/2310235
Abstract: Measurements of fasting glucose (FG) or glycated hemoglobin A1c (HbA1c) are two clinically approved approaches commonly used to determine glycemia, both of which are influenced by genetic factors. Obtaining accurate measurements of FG or HbA1c is not without its challenges, though. Measuring glycated serum protein (GSP) offers an alternative approach for assessing glycemia. The aim of this study was to estimate the heritability of GSP and GSP expressed as a percentage of total serum albumin (%GA) using a variance component approach and localize genomic regions (QTLs) that harbor genes likely to influence GSP and %GA trait variation in a large extended multigenerational pedigree from Jiri, Nepal ( n = 1,800 ). We also performed quantitative bivariate analyses to assess the relationship between GSP or %GA and several cardiometabolic traits. Additive genetic effects significantly influence variation in GSP and %GA levels ( p values: 1.15 × 10 − 5 and 3.39 × 10 − 5 , respectively). We localized a significant (LOD score = 3.18 ) and novel GSP QTL on chromosome 11q, which has been previously linked to type 2 diabetes. Two common ( MAF 0.4 ) SNPs within the chromosome 11 QTL were associated with GSP (adjusted p value 5.87 × 10 − 5 ): an intronic variant (rs10790184) in the DSCAML1 gene and a 3 ′ UTR variant (rs8258) in the CEP164 gene. Significant positive correlations were observed between GSP or %GA and blood pressure, and lipid traits ( p values: 0.0062 to 1.78 × 10 − 9 ). A significant negative correlation was observed between %GA and HDL cholesterol ( p = 1.12 × 10 − 5 ). GSP is influenced by genetic factors and can be used to assess glycemia and diabetes risk. Thus, GSP measurements can facilitate glycemic studies when accurate FG and/or HbA1c measurements are difficult to obtain. GSP can also be measured from frozen blood (serum) s les, which allows the prospect of retrospective glycemic studies using archived s les.
Publisher: Frontiers Media SA
Date: 2012
Location: United States of America
Location: United States of America
No related grants have been discovered for Matthew Johnson.