Using Ontology Fingerprints to evaluate genome-wide association study results

We describe an approach to characterize genes or phenotypes via ontology fingerprints which are composed of Gene Ontology (GO) terms overrepresented among those PubMed abstracts linked to the genes or phenotypes. We then quantify the biological relevance between genes and phenotypes by comparing their ontology fingerprints to calculate a similarity score. We validated this approach by correctly identifying genes belong to their biological pathways with high accuracy, and applied this approach to evaluate GWA study by ranking genes associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, LPL, and CETP for HDL; LDLR, APOE and APOB for LDL; and LPL, APOA1 and APOB for triglyceride. In addition, we identified some top ranked genes linking to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation

Recent advances in understanding the genetic architecture of type 2 diabetes

Genome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c). At the end of 2011, established loci (P 0.05] variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations. Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci. Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered. Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes

Toxic effects of indocyanine green, infracyanine green, and trypan blue on the human retinal pigmented epithelium

Background: Indocyanine green, infracyanine green, and trypan blue are frequently used as aids to visualize structures removed during vitreoretinal surgery. But they may have toxic effects on the retina. We therefore compared the acute and chronic toxicities of these stains on cultured human retinal pigmented epithelial (RPE) cells using clinically relevant concentrations and an identical experimental setup for each agent. Methods: Monolayers of RPE cells were incubated with various concentrations of indocyanine green, infracyanine green (each at 0.005%, 0.05%, and 0.5%) or trypan blue (0.05%, 0.06%, 0.1%, 0.15%, and 0.5%) for 5min (acute exposure) or 6 days (chronic exposure). Using the propidium iodide assay, acute cytotoxicity was monitored at 15-min intervals for up to 3h. Chronic cytotoxicity was assessed by monitoring cell calcein esterase activity, cell proliferation, and cell morphology (viability) after 6 days of exposure. Results: Indocyanine and infracyanine green induced acute and chronic toxicities at a concentration above 0.05%. Trypan blue evoked no acute toxicity, but it was chronically cytotoxic at all tested concentrations. Conclusions: Despite thorough rinsing after application, significant amounts of the not sufficiently water soluble indocyanine and infracyanine green are retained after surgery by the eye. Trypan blue, being more water-soluble than ICG, is probably retained to the least degree. This circumstance is fortunate given that trypan blue exhibits a chronic cytotoxicity comparable to ICG at all clinically relevant concentrations. During vitrectomy, surgeons should aim to expose retinal tissue to only low concentrations of these stains and for as short a period as possibl

Metabolic and cardiovascular traits: an abundance of recently identified common genetic variants

Genome-wide association studies are providing new insights into the genetic basis of metabolic and cardiovascular traits. In the past 3 years, common variants in ∼50 loci have been strongly associated with metabolic and cardiovascular traits. Several of these loci have implicated genes without a previously known connection with metabolism. Further studies will be required to characterize the full impact of these loci on metabolism. Many of the identified loci include multiple independent variants that influence the same metabolic or cardiovascular trait and a few loci harbor independent variants that each influence distinct traits. The total proportion of trait heritability explained by variants identified so far is still modest (typically <10%). Future studies will build on these successes by identifying additional common and rare variants and by determining the functional impact of the underlying alleles and genes

A radiation hybrid map of the proximal short arm of the human X chromosome spanning incontinentia pigmenti 1 (IP1) translocation breakpoints

Radiation hybrid mapping was used in combination with physical mapping techniques to order and estimate distances between 14 loci in the proximal region of the short arm of the human X chromosome. A panel of radiation hybrids containing human X-chromosomal fragments was generated from a Chinese hamster-human cell hybrid containing an X chromosome as its only human DNA. Sixty-seven radiation hybrids were screened by Southern hybridization with sets of probes that mapped to the region Xp11.4-Xcen to generate a radiation hybrid map of the area. A physical map of 14 loci was constructed based on the segregation of the loci in the hybrid clones. Using pulsed-field gel electrophoresis (PFGE) analyses and a somatic cell hybrid mapping panel containing naturally occurring X; autosome translocations, the order of the 14 loci was verified and the loci nearest to the X-chromosomal translocation breakpoints associated with the disease incontinentia pigmenti 1 (IP1) were identified. The radiation hybrid panel will be useful as a mapping resource for determining the location, order, and distances between other genes and polymorphic loci in this region as well as for generating additional region-specific DNA markers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29766/1/0000104.pd

The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases

The Metabolic Syndrome in Men (METSIM) study is a population-based study including 10,197 Finnish men examined in 2005–2010. The aim of the study is to investigate nongenetic and genetic factors associated with the risk of T2D and CVD, and with cardiovascular risk factors. The protocol includes a detailed phenotyping of the participants, an oral glucose tolerance test, fasting laboratory measurements including proton NMR measurements, mass spectometry metabolomics, adipose tissue biopsies from 1,400 participants, and a stool sample. In our ongoing follow-up study, we have, to date, reexamined 6,496 participants. Extensive genotyping and exome sequencing have been performed for essentially all METSIM participants, and >2,000 METSIM participants have been whole-genome sequenced. We have identified several nongenetic markers associated with the development of diabetes and cardiovascular events, and participated in several genetic association studies to identify gene variants associated with diabetes, hyperglycemia, and cardiovascular risk factors. The generation of a phenotype and genotype resource in the METSIM study allows us to proceed toward a “systems genetics” approach, which includes statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein, or metabolite levels, to provide a global view of the molecular architecture of complex traits

A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for nextâ generation sequencing

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate Fâ distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of highâ dimensional genotype data. It is shown that approximate Fâ distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135654/1/gepi22014_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135654/2/gepi22014-sup0001-Suppmat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135654/3/gepi22014.pd