collection-and-extraction-of-saliva-dna-for-next-generation-sequencing

Collection and Extraction of Saliva DNA for Next Generation Sequencing

The preferred source of DNA in human genetics research is blood, or cell lines derived from blood, as these sources yield large quantities of high quality ...

The Research Institute at Nationwide Children's Hospital

We propose a statistical model for linkage analysis of the longitudinal data. The proposed model is a mixed model based on the new Haseman and Elston model and allows several random effects. Specifically, the proposed model includes a random effect for correlation among sib pairs having one sibling in common, and one for the correlation among siblings from the same parents.

Linkage analysis of longitudinal data.

Most genotyping technologies for single nucleotide polymorphism (SNP) markers use standard clustering methods to 'call' the SNP genotypes. These methods are not always optimal in distinguishing the genotype clusters of a SNP because they do not take advantage of specific features of the genotype calling problem. In particular, when family data are available, pedigree information is ignored. Furthermore, prior information about the distribution of the measurements for each cluster can be used to choose an appropriate model-based clustering method and can significantly improve the genotype calls. One special genotyping problem that has never been discussed in the literature is that of genotyping of trisomic individuals, such as individuals with Down syndrome. Calling trisomic genotypes is a more complicated problem, and the addition of external information becomes very important.

Smarter clustering methods for SNP genotype calling.

Knowledge regarding genetic factors that influence pancreatic cancer risk is currently limited. To identify novel pancreatic cancer susceptibility loci, we conducted a two-stage genome-wide association study.

Pooling-based genome-wide association study implicates gamma-glutamyltransferase 1 (GGT1) gene in pancreatic carcinogenesis.

Cardiac abnormalities are one of the most common congenital defects observed in individuals with Down syndrome. Considerable research has implicated both folate deficiency and genetic variation in folate pathway genes with birth defects, including both congenital heart defects (CHD) and Down syndrome (DS). Here, we test variation in folate pathway genes for a role in the major DS-associated CHD atrioventricular septal defect (AVSD). In a group of 121 case families (mother, father, and proband with DS and AVSD) and 122 control families (mother, father, and proband with DS and no CHD), tag SNPs were genotyped in and around five folate pathway genes: 5,10-methylenetetrahyrdofolate reductase (MTHFR), methionine synthase (MTR), methionine synthase reductase (MTRR), cystathionine beta-synthase (CBS), and the reduced folate carrier (SLC19A1, RFC1). SLC19A1 was found to be associated with AVSD using a multilocus allele-sharing test. Individual SNP tests also showed nominally significant associations with odds ratios of between 1.34 and 3.78, depending on the SNP and genetic model. Interestingly, all marginally significant SNPs in SLC19A1 are in strong linkage disequilibrium (r(2)> or = 0.8) with the nonsynonymous coding SNP rs1051266 (c.80A>G), which has previously been associated with nonsyndromic cases of CHD. In addition to SLC19A1, the known functional polymorphism MTHFR c.1298A was over-transmitted to cases with AVSD (P=0.05) and under-transmitted to controls (P=0.02). We conclude, therefore, that disruption of the folate pathway contributes to the incidence of AVSD among individuals with DS.

Variation in folate pathway genes contributes to risk of congenital heart defects among individuals with Down syndrome.

In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains.

An eQTL biological data visualization challenge and approaches from the visualization community.

The authors conducted a genetic linkage study of families that have both autism spectrum disorder (ASD) and language-impaired probands to find common communication impairment loci. The hypothesis was that these families have a high genetic loading for impairments in language ability, thus influencing the language and communication deficits of the family members with ASD. Comprehensive behavioral phenotyping of the families also enabled linkage analysis of quantitative measures, including normal, subclinical, and disordered variation in all family members for the three general autism symptom domains: social, communication, and compulsive behaviors.

Article	Year
Linkage analysis of longitudinal data. BMC genetics\| PubMed ID: 14975095	2003
Smarter clustering methods for SNP genotype calling. Bioinformatics (Oxford, England)\| PubMed ID: 18826959	2008
Pooling-based genome-wide association study implicates gamma-glutamyltransferase 1 (GGT1) gene in pancreatic carcinogenesis. Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]\| PubMed ID: 20484958	2010
Variation in folate pathway genes contributes to risk of congenital heart defects among individuals with Down syndrome. Genetic epidemiology\| PubMed ID: 20718043	2010
An eQTL biological data visualization challenge and approaches from the visualization community. BMC bioinformatics\| PubMed ID: 22607587	2012
A genome scan for loci shared by autism spectrum disorder and language impairment. The American journal of psychiatry\| PubMed ID: 24170272	2014

Soo Yeon Cheong

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