Polymarker and HLA DQA1 Genetic Markers in Forensic Casework

1995 ◽  
pp. 37-38 ◽  
Author(s):  
R. Espinheira ◽  
T. Ribeiro ◽  
H. Geada ◽  
L. Reys
Keyword(s):  
Genetic Markers ◽  
Forensic Casework ◽  
Hla Dqa1

scholarly journals Type 1 diabetes susceptibility determined by HLA alleles and CTLA-4 and insulin genes polymorphisms in Brazilians

2009 ◽  
Vol 53 (3) ◽  
pp. 368-373 ◽  
Author(s):  
Alessandro Clayton Souza Ferreira ◽  
Karina Braga Gomes ◽  
Ivan Barbosa Machado Sampaio ◽  
Vanessa Cristina de Oliveira ◽  
Victor Cavalcanti Pardini ◽  
...  
Keyword(s):  
Genetic Markers ◽  
Multifactorial Disease ◽  
Environmental Aspects ◽  
Diabetes Susceptibility ◽  
Time Resolved ◽  
Hla Alleles ◽  
Significant Difference ◽  
Hla Dqa1

INTRODUCTION:Type 1A diabetes mellitus (T1ADM) is a multifactorial disease in which genetic and environmental aspects are important to its development. The association of genetic variations with disease has been demonstrated in several studies; however, the role of some gene loci has not yet been fully elucidated. OBJECTIVE:To compare the frequency of HLA alleles and polymorphism in CTLA-4 and insulin genes in Brazilians with T1ADM and individuals without the disease, as well as to identify genetic markers that are able to discriminate between diabetic and non-diabetic individuals. METHODS: The presence of HLA DQB1, DQA1 and DRB1 alleles, as well as the -2221 MspI polymorphism in the insulin gene and 49 A/G in the CTLA-4 gene were identified by the "Time-resolved fluorometer" technique after hybridization with probes labeled with Eu (III) / Sm (III) and Tb (III). RESULTS: The DQB1 *0302 and DQA1 *03 alleles were identified as predisposed to T1ADM, and the DQB1 *0301 allele presented a protective effect against the disease.The DQA1 label proved to be able to differentiate between 71.13% of the diabetic and non-diabetic individuals.This value increased to 82.47% when the DQB1 label was added. No significant difference in the frequency of polymorphisms in the insulin and CTLA-4 genes was observed between the two groups. CONCLUSIONS: The genetic markers that best characterized and discriminated diabetic and non-diabetic individuals were the HLA DQA1 and DQB1.alleles.

The Development and Evaluation of New Genetic Markers for the Application of PCR to Forensic Casework

1992 ◽  
pp. 29-31 ◽  
Author(s):  
R. Reynolds ◽  
R. Saiki ◽  
M. Grow ◽  
N. Fildes ◽  
G. McClure ◽  
...  
Keyword(s):  
Genetic Markers ◽  
Forensic Casework

HLA-DQA1 and HLA-DQB1 Genetic Markers and Clinical Presentation in Celiac Disease

2002 ◽  
Vol 34 (5) ◽  
pp. 548-554 ◽  
Author(s):  
Paul Zubillaga ◽  
Maria Concepcion Vidales ◽  
Itziar Zubillaga ◽  
Victor Ormaechea ◽  
Nerea García-Urkía ◽  
...  
Keyword(s):  
Celiac Disease ◽  
Genetic Markers ◽  
Clinical Presentation ◽  
Hla Dqa1

Retrofitting massively parallel sequencing (MPS) for HLA-DQA1 and polymarker (PM) in forensic casework

2021 ◽  
Author(s):  
Elaine J. Lewis ◽  
Erin Weaver ◽  
Audrey Hoyle ◽  
Robert Lagacé ◽  
Fabio Oldoni ◽  
...  
Keyword(s):  
Massively Parallel Sequencing ◽  
Massively Parallel ◽  
Parallel Sequencing ◽  
Forensic Casework ◽  
Hla Dqa1

530: Termining Risk for Prostate Cancer at Prostate Biopsy using Genetic Markers

2005 ◽  
Vol 173 (4S) ◽  
pp. 144-145
Author(s):  
Robert K. Nam ◽  
William Zhang ◽  
John Trachtenberg ◽  
Michael A.S. Jewett ◽  
Steven Narod
Keyword(s):  
Prostate Cancer ◽  
Genetic Markers ◽  
Prostate Biopsy

New Genetic Markers May Help Predict Melanoma Outcome

2008 ◽  
Vol 39 (4) ◽  
pp. 9
Author(s):  
BRUCE JANCIN
Keyword(s):  
Genetic Markers

Memory Processes, Aging, Cognitive Decline, and Neurodegenerative Diseases

2006 ◽  
Vol 11 (4) ◽  
pp. 304-311 ◽  
Author(s):  
Lars-Göran Nilsson
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Decline ◽  
Neurodegenerative Disease ◽  
Genetic Markers ◽  
Brain Activity ◽  
Memory Performance ◽  
Modeling And Simulations ◽  
Aging And Health ◽  
Explained Variance ◽  
Different Levels

This paper presents four domains of markers that have been found to predict later cognitive impairment and neurodegenerative disease. These four domains are (1) data patterns of memory performance, (2) cardiovascular factors, (3) genetic markers, and (4) brain activity. The critical features of each domain are illustrated with data from the longitudinal Betula Study on memory, aging, and health ( Nilsson et al., 1997 ; Nilsson et al., 2004 ). Up to now, early signs regarding these domains have been examined one by one and it has been found that they are associated with later cognitive impairment and neurodegenerative disease. However, it was also found that each marker accounts for only a very small part of the total variance, implying that single markers should not be used as predictors for cognitive decline or neurodegenerative disease. It is discussed whether modeling and simulations should be used as tools to combine markers at different levels to increase the amount of explained variance.

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