Denver Papillae Protocol for Objective Analysis of Fungiform Papillae

The goal of the Denver Papillae Protocol is to use a dichotomous key to define and prioritize the characteristics of fungiform papillae (FP) to ensure ...

University of Colorado Denver

Cardiovascular disease, metabolic syndrome, schizophrenia, diabetes, bipolar disorder, and autism are a few of the numerous complex diseases for which researchers are trying to decipher the genetic composition. One interest of geneticists is to determine the quantitative trait loci (QTLs) that underlie the genetic portion of these diseases and their risk factors. The difficulty for researchers is that the QTLs underlying these diseases are likely to have small to medium effects which will necessitate having large studies in order to have adequate power. Combining information across multiple studies provides a way for researchers to potentially increase power while making the most of existing studies.Here, we will explore some of the methods that are currently being used by geneticists to combine information across multiple genome-wide linkage studies. There are two main types of meta-analyses: (1) those that yield a measure of significance, such as Fisher's p-value method along with its extensions/modifications and the genome search meta-analysis (GSMA) method, and (2) those that yield a measure of a common effect size and the corresponding standard error, such as model-based methods and Bayesian methods. Some of these methods allow for the assessment of heterogeneity. This chapter will conclude with a recommendation for usage.

Methods for combining multiple genome-wide linkage studies.

The purpose of this study was to evaluate evidence for linkage to interrelated quantitative features of the metabolic syndrome (MetS). Data on eight quantitative MetS traits (body weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein (HDL) cholesterol, triglycerides (TGs), and fasting glucose and insulin measurements) and a 10 cM genome scan were available for 78 white families (n = 532 subjects). These data were used to conduct multipoint, multivariate linkage analyses, including tests for coincident linkage and complete pleiotropy. The strongest evidence for linkage from the bivariate analyses was observed on chromosome 1 (1p22.2) (HDL-TG; univariate lod score equivalent (lod(eq) = 3.99)) with stronger results from the trivariate analysis at the same location (HDL-TG-Insulin; lod(eq) = 4.32). Seven additional susceptibility regions (lod(eq) scores >1.9) were observed (1p36, 1q23, 2q21.2, 8q23.3, 14q23.2, 14q32.11, and 20p11.21). The results from this study indicate that several correlated traits of the MetS are influenced by the same gene(s) that account for some of the clustering of the MetS features.

Multivariate linkage scan for metabolic syndrome traits in families with type 2 diabetes.

Endothelial lipase (LIPG) and zinc finger protein 202 (ZNF202) are two pivotal genes in high density lipoprotein (HDL metabolism). We sought to determine their genetic contribution to variation in HDL-cholesterol levels by comprehensive resequencing of both genes in 235 individuals with high or low HDL-C levels. The selected subjects were 141 Whites (High HDL Group: n = 68, [Formula: see text] Low HDL Group: n = 73, [Formula: see text]) and 94 Hispanics (High HDL Group: n = 46, [Formula: see text] Low HDL Group: n = 48, [Formula: see text]). We identified a total of 185 and 122 sequence variants in LIPG and ZNF202, respectively. We found only two missense variants in LIPG (T111I and N396S) and two in ZNF202 (A154V and K259E). In both genes, there were several variants unique to either the low or high HDL group. For LIPG, the proportion of unique variants differed between the high and low HDL groups in both Whites (p = 0.022) and Hispanics (p = 0.017), but for ZNF202 this difference was observed only in Hispanics (p = 0.021). We also identified a common haplotype in ZNF202 among Whites that was significantly associated with the high HDL group (p = 0.013). These findings provide insights into the genetics of LIPG and ZNF202, and suggest that sequence variants occurring with high frequency in non-exonic regions may play a prominent role in modulating HDL-C levels in the general population.

Population-Based Resequencing of LIPG and ZNF202 Genes in Subjects with Extreme HDL Levels.

Statistics for next generation sequencing - meeting report.

Endothelial lipase (EL) plays a pivotal role in HDL metabolism. We sought to characterize EL and its interaction with HDL as well as its natural variants genetically, functionally and structurally. We screened our biethnic population sample (n = 802) for selected missense mutations (n = 5) and identified T111I as the only common variant. Multiple linear regression analyses in Hispanic subjects revealed an unexpected association between T111I and elevated LDL-C (p-value = 0.012) and total cholesterol (p-value = 0.004). We examined lipase activity of selected missense mutants (n = 10) and found different impacts on EL function, ranging from normal to complete loss of activity. EL-HDL lipidomic analyses indicated that EL has a defined remodeling of HDL without exhaustion of the substrate and a distinct and preference for several fatty acids that are lipid mediators and known for their potent pro- and anti-inflammatory properties. Structural studies using homology modeling revealed a novel α/β motif in the C-domain, unique to EL. The EL dimer was found to have the flexibility to expand and to bind various sizes of HDL particles. The likely impact of the all known missense mutations (n = 18) on the structure of EL was examined using molecular modeling and the impact they may have on EL lipase activity using a novel structure-function slope based on their structural free energy differences. The results of this multidisciplinary approach delineated the impact of EL and its variants on HDL. Moreover, the results suggested EL to have the capacity to modulate vascular health through its role in fatty acid-based signaling pathways.

Genetic and structure-function studies of missense mutations in human endothelial lipase.

Understanding the influence of taste perception on food choice has captured the interest of academics, industry, and the general public, the latter as evidenced by the extent of popular media coverage and use of the term supertaster. Supertasters are highly sensitive to the bitter tastant propylthiouracil (PROP) and its chemical relative phenylthiocarbamide. The well-researched differences in taste sensitivity to these bitter chemicals are partially controlled by variation in the TAS2R38 gene; however, this variation alone does not explain the supertaster phenomenon. It has been suggested that density of papillae, which house taste buds, may explain supertasting. To address the unresolved role of papillae, we used crowdsourcing in the museum-based Genetics of Taste Lab. This community lab is uniquely situated to attract both a large population of human subjects and host a team of citizen scientists to research population-based questions about human genetics, taste, and health. Using this model, we find that PROP bitterness is not in any way predicted by papillae density. This result holds within the whole sample, when divided into major diplotypes, and when correcting for age, sex, and genotype. Furthermore, it holds when dividing participants into oft-used taster status groups. These data argue against the use of papillae density in predicting taste sensitivity and caution against imprecise use of the term supertaster. Furthermore, it supports a growing volume of evidence that sets the stage for hypergeusia, a reconceptualization of heightened oral sensitivity that is not based solely on PROP or papillae density. Finally, our model demonstrates how community-based research can serve as a unique venue for both study participation and citizen science that makes scientific research accessible and relevant to people's everyday lives.

Crowdsourcing taste research: genetic and phenotypic predictors of bitter taste perception as a model.

The risk factors for acute episodes of respiratory disease in current and former smokers who do not have COPD are unknown.

Prediction of acute respiratory disease in current and former smokers with and without COPD.

Whole-genome sequencing (WGS) is becoming an affordable technology for the study of the genetics of complex traits. With any new technology, experimental designs and statistical methods, both old and new, must be evaluated. One design seeing a resurgence of interest is the use of families. Genetic Analysis Workshop 18 provided the opportunity to evaluate statistical methods applied to WGS data for family-based studies. We summarize the results of five contributions that used linkage in the context of WGS. The investigators took differing approaches, including assessment of false-positive rates in classic two-point linkage, the effects of heterogeneity on linkage and association tests, and the use of linkage to focus association tests. We describe the primary findings of each contribution and note challenges that are not new to those working in family designs or specific to WGS data; for example, choice of phenotype definition, covariate adjustment, and use of longitudinal data may produce different results, making comparisons challenging. We detail new issues brought about by WGS, such as the elevated genome-wide false-positive rate for classic two-point parametric linkage analysis, computational demands in multipoint calculations, and lack of clarity in how to best use linkage to focus association testing. Finally, we comment on when linkage may be helpful for WGS, highlighting where additional research is needed; for example, although linkage analysis has been successful in the study of rare variants of large effect, how to best use family information in the context of rare variants of moderate effect remains an open research question.

Challenges of linkage analysis in the era of whole-genome sequencing.

Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted for publication.

Genetic Analysis Workshop 18: Methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees.

Admixture mapping and association testing have been successfully applied to the detection of genes for complex diseases. Methods have also been developed to combine these approaches. As an initial step to determine the feasibility of combining admixture and association mapping in the context of whole genome sequencing, we have applied several methods to data from the Genetic Analysis Workshop 18. Here, we describe the steps necessary to carry out such a study from selection of reference populations and preprocessing of data through to the testing itself. We detected one significant result with a Bonferroni corrected p-value of 0.032 at single nucleotide polymorphism rs12639065. Computing local ancestry for Hispanic populations was challenging because there are relatively few methods by which to handle 3-way admixture, and publicly available Native American reference panels are scarce. However, combining admixture and association is a promising approach for detection of quantitative trait loci because it might be able to elevate the power of detection by combining 2 different sources of genetic signal.

Use of admixture and association for detection of quantitative trait loci in the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples (T2D-GENES) study.

Stephanie A. Santorico

Department of Mathematical and Statistical Sciences

University of Colorado Denver

Denver Papillae Protocol for Objective Analysis of Fungiform Papillae

Stephanie A. Santorico

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Mathematical and Statistical Sciences

University of Colorado Denver

Denver Papillae Protocol for Objective Analysis of Fungiform Papillae

Department of Mathematical and Statistical Sciences