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Gan, S.

Publications and source records attributed to Gan, S..

2 recordsLinked to original sources

The Quantification of Antibody Elements and Receptors subunit expression using qPCR: The Design of VH, VL, CH, CL, FcR subunits primers for a more holistic view of the immune system.

The expression levels of Immunoglobulin elements and their receptors are important markers for health and disease. Within the immunoglobulin locus, the constant regions and the variable region families are associated with certain pathologies, yet a holistic view of the interaction between the expression of the multiple genes remain to be fully characterized. There is thus an important need to quantify antibody elements, their receptors and the receptor subunits in blood (PBMC cDNA) for both screening and detailed studies of such associations. Leveraging on qPCR, we designed primers for all V{kappa} 1-6, VH1-7, V{lambda}1-11, nine CH isotypes, C{kappa}, C{kappa}, C{lambda}1 &3, Fc{varepsilon}RI ,{beta}, and {gamma} subunits, all three Fc{gamma}R and their subunits, and FcR. Validating this on a volunteer PBMC cDNA, we show a qPCR primer set repertoire that can quantify the relative expression of all the above genes to GAPDH housekeeping gene, with implications and uses in both clinical monitoring and research.

immunology

PheGWAS: A new dimension to visualize GWAS across multiple phenotypes

MotivationPheGWAS was developed to enhance exploration of phenome-wide pleiotropy at the genome-wide level through the efficient generation of a dynamic visualization combining Manhattan plots from GWAS with PheWAS to create a three-dimensional "landscape". Pleiotropy in sub-surface GWAS significance strata can be explored in a sectional view plotted within user defined levels. Further complexity reduction is achieved by confining to a single chromosomal section. Comprehensive genomic and phenomic coordinates can be displayed. ResultsPheGWAS is demonstrated using summary data from Global Lipids Genetics Consortium (GLGC) GWAS across multiple lipid traits. For single and multiple traits PheGWAS highlighted all eight-eight and sixty-nine loci respectively. Further, the genes and SNPs reported in GLGC were identified using additional functions implemented within PheGWAS. Not only is PheGWAS capable of identifying independent signals but also provide insights to local genetic correlation (verified using HESS) and in identifying the potential regions that share causal variants across phenotypes (verified using colocalization tests). Availability and ImplementationThe PheGWAS software and code are freely available at (https://github.com/georgeg0/PheGWAS). Contacta.doney@dundee.ac.uk, g.z.george@dundee.ac.uk

bioinformatics