scholarly journals Applicability of DNA pools on 500 K SNP microarrays for cost-effective initial screens in genomewide association studies

BMC Genomics ◽  
2007 ◽  
Vol 8 (1) ◽  
pp. 214 ◽  
Author(s):  
Sophia J Docherty ◽  
Lee M Butcher ◽  
Leonard C Schalkwyk ◽  
Robert Plomin
Keyword(s):  
Association Studies ◽  
Cost Effective ◽  
Snp Microarrays ◽  
Dna Pools ◽  
Genomewide Association

scholarly journals Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders

2021 ◽  
Author(s):  
Nevena V. Radonjić ◽  
Jonathan L. Hess ◽  
Paula Rovira ◽  
Ole Andreassen ◽  
Jan K. Buitelaar ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Correlations ◽  
Neuropsychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Genetic Etiology ◽  
Compulsive Disorder ◽  
Pairwise Correlations ◽  
Genomewide Association ◽  
Structural Brain

AbstractGenomewide association studies have found significant genetic correlations among many neuropsychiatric disorders. In contrast, we know much less about the degree to which structural brain alterations are similar among disorders and, if so, the degree to which such similarities have a genetic etiology. From the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium, we acquired standardized mean differences (SMDs) in regional brain volume and cortical thickness between cases and controls. We had data on 41 brain regions for: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), epilepsy, major depressive disorder (MDD), obsessive compulsive disorder (OCD), and schizophrenia (SCZ). These data had been derived from 24,360 patients and 37,425 controls. The SMDs were significantly correlated between SCZ and BD, OCD, MDD, and ASD. MDD was positively correlated with BD and OCD. BD was positively correlated with OCD and negatively correlated with ADHD. These pairwise correlations among disorders were correlated with the corresponding pairwise correlations among disorders derived from genomewide association studies (r = 0.494). Our results show substantial similarities in sMRI phenotypes among neuropsychiatric disorders and suggest that these similarities are accounted for, in part, by corresponding similarities in common genetic variant architectures.

scholarly journals A SNaPshot Assay for Determination of the Mannose-Binding Lectin Gene Variants and an Algorithm for Calculation of Haplogenotype Combinations

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 301
Author(s):  
Jana Mrazkova ◽  
Petr Sistek ◽  
Jan Lochman ◽  
Lydie Izakovicova Holla ◽  
Zdenek Danek ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Association Studies ◽  
Cost Effective ◽  
European Population ◽  
Mannose Binding Lectin ◽  
Nucleotide Polymorphisms ◽  
Online Application ◽  
Czech Population ◽  
Mannose Binding ◽  
Binding Lectin

Mannose-binding lectin (MBL) deficiency caused by the variability in the MBL2 gene is responsible for the susceptibility to and severity of various infectious and autoimmune diseases. A combination of six single nucleotide polymorphisms (SNPs) has a major impact on MBL levels in circulation. The aim of this study is to design and validate a sensitive and economical method for determining MBL2 haplogenotypes. The SNaPshot assay is designed and optimized to genotype six SNPs (rs1800451, rs1800450, rs5030737, rs7095891, rs7096206, rs11003125) and is validated by comparing results with Sanger sequencing. Additionally, an algorithm for online calculation of haplogenotype combinations from the determined genotypes is developed. Three hundred and twenty-eight DNA samples from healthy individuals from the Czech population are genotyped. Minor allele frequencies (MAFs) in the Czech population are in accordance with those present in the European population. The SNaPshot assay for MBL2 genotyping is a high-throughput, cost-effective technique that can be used in further genetic-association studies or in clinical practice. Moreover, a freely available online application for the calculation of haplogenotypes from SNPs is developed within the scope of this project.

scholarly journals Characterizing the mechanism behind the progression of NAFLD to hepatocellular carcinoma

2020 ◽  
pp. HEP36
Author(s):  
Pierre Nahon ◽  
Manon Allaire ◽  
Jean-Charles Nault ◽  
Valérie Paradis
Keyword(s):  
Hepatocellular Carcinoma ◽  
Fatty Liver Disease ◽  
Association Studies ◽  
Cost Effective ◽  
Genome Wide Association Studies ◽  
Alcoholic Fatty Liver ◽  
Genome Wide ◽  
High Prevalence ◽  
Prevalence Of Obesity ◽  
Alcoholic Fatty Liver Disease

Hepatocellular carcinoma (HCC) developed in non-alcoholic fatty liver disease (NAFLD) individuals presents substantial clinical and biological characteristics, which remain to be elucidated. Its occurrence in noncirrhotic patients raises issues regarding surveillance strategies, which cannot be considered as cost-effective given the high prevalence of obesity and metabolic syndrome, and furthermore delineates specific oncogenic process that could be targeted in the setting of primary or secondary prevention. In this context, the identification of a genetic heterogeneity modulating HCC risk as well as specific biological pathways have been made possible through genome-wide association studies, development of animal models and in-depth analyses of human samples at the pathological and genomic levels. These advances must be confirmed and pursued to pave the way for personalized management of NAFLD-related HCC.

scholarly journals Efficient phasing and imputation of low-coverage sequencing data using large reference panels

2020 ◽  
Author(s):  
S. Rubinacci ◽  
D.M. Ribeiro ◽  
R. Hofmeister ◽  
O. Delaneau
Keyword(s):  
Paradigm Shift ◽  
Rare Variants ◽  
Association Studies ◽  
Cost Effective ◽  
Human Populations ◽  
Sequencing Data ◽  
Snp Arrays ◽  
Genomic Studies ◽  
Low Coverage ◽  
The Impact

AbstractLow-coverage whole genome sequencing followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies. However, its competitiveness against SNP arrays is undermined as current imputation methods are computationally expensive and unable to leverage large reference panels.Here, we describe a method, GLIMPSE, for phasing and imputation of low-coverage sequencing datasets from modern reference panels. We demonstrate its remarkable performance across different coverages and human populations. It achieves imputation of a full genome for less than $1, outperforming existing methods by orders of magnitude, with an increased accuracy of more than 20% at rare variants. We also show that 1x coverage enables effective association studies and is better suited than dense SNP arrays to access the impact of rare variations. Overall, this study demonstrates the promising potential of low-coverage imputation and suggests a paradigm shift in the design of future genomic studies.

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