Showing papers by "Nancy L. Saccone published in 2007"
TL;DR: Functional variants in both Tas2R16 and TAS2R38 correlate with alcohol consumption in African-American families, and it is found no evidence that TAS 2R38 haplotypes influence alcohol dependence in the COGA dataset.
Abstract: Background A novel family of G protein-coupled receptors, TAS2Rs, has recently been characterized and linked to sensitivity to bitter taste compounds. We have previously reported that a missense mutation in the TAS2R16 gene reduces the sensitivity of the receptor to bitter-taste stimuli and that it is associated with risk for alcohol dependence. Other family-based studies on the genetic transmittance of taste perception have previously demonstrated a correlation between genetic variation in TAS2R38 and sensitivity to bitter-taste compounds such as phenylthiocarbamide (PTC) and 6-n-propylthiouracil (PROP). Haplotypes resulting from 3 common nonsynonymous coding single-nucleotide polymorphisms in the TAS2R38 gene have been shown to alter receptor functions and taste sensitivity to PTC and PROP. The perceived bitterness of PROP has also been associated with oral sensation and drinking behaviors. Methods We used family-based association methods to test for association between TAS2R38 haplotypes and alcohol dependence as well as a measure of alcohol consumption (Maxdrinks) and age of onset of drinking behaviors in a sample of families densely affected with alcoholism. We have also extended our analysis of TAS2R16 to include the Maxdrinks phenotype. Results A positive correlation was observed between TAS2R38 haplotypes and Maxdrinks in Collaborative Study on the Genetics of Alcoholism (COGA) high-risk women of African-American origin. The common taster haplotype is significantly associated with a lower mean Maxdrinks compared with the other haplotypes. Similarly, the allele of TAS2R16 that is associated with a lower risk for alcohol dependence is also associated with lower mean Maxdrinks scores in African-American families. In contrast to the previously reported significant association between TAS2R16 and alcohol dependence, we found no evidence that TAS2R38 haplotypes influence alcohol dependence in the COGA dataset. Conclusion Functional variants in both TAS2R16 and TAS2R38 correlate with alcohol consumption in African-American families.
108 citations
Newcastle University1, Mayo Clinic2, French Institute of Health and Medical Research3, Hospital for Sick Children4, University of Göttingen5, Washington University in St. Louis6, Boston University7, Indian Statistical Institute8, Case Western Reserve University9, Duke University10, Cincinnati Children's Hospital Medical Center11, University of North Carolina at Chapel Hill12, University of Southampton13, University of Southern California14, Ontario Institute for Cancer Research15, University of Washington16, University of California, San Francisco17, Texas Biomedical Research Institute18
TL;DR: This data presents a novel probabilistic procedure called a “ ‘spatially checkpoints-based approach’” to characterize the immune system’s response to foreign invaders.
Abstract: National Institutes of Health (AR44422, N01-AR-7-2232, 5R01-HL049609-14, IR01-AG021917-01A1); Genome Canada and Associations AFP; Polyarctique-Groupe Taitbout; Rhumatisme et Travail; Arthritis Research Campaign; Unversity of Minnesota; Minnesota Supercomputing Institute; National Institute of General Medical Sciences (R01 GM31575)
9 citations
TL;DR: The results suggest that ANNs can serve as a useful method to analyze quantitative traits and are a potential tool for detecting gene × gene interactions, however, for the approach implemented here, optimizing the ANNs and obtaining stable results remains challenging.
Abstract: Using single-nucleotide polymorphism (SNP) genotypes and selected gene expression phenotypes from 14 CEPH (Centre d'Etude du Polymorphisme Humain) pedigrees provided for Genetic Analysis Workshop 15 (GAW15), we analyzed quantitative traits with artificial neural networks (ANNs) Our goals were to identify individual linkage signals and examine gene × gene interactions First, we used classical multipoint methods to identify phenotypes having nominal linkage evidence at two or more loci ANNs were then applied to sib-pair identity-by-descent (IBD) allele sharing across the genome as input variables and squared trait sums and differences for the sib pairs as output variables The weights of the trained networks were analyzed to assess the linkage evidence at each locus as well as potential interactions between them Loci identified by classical linkage analysis could also be identified by our ANN analysis However some ANN results were noisy, and our attempts to use cross-validated training to avoid overtraining and thereby improve results were only partially successful Potential interactions between loci with high-ranked weight measures were also evaluated, with the resulting patterns suggesting existence of both synergistic and antagonistic effects between loci Our results suggest that ANNs can serve as a useful method to analyze quantitative traits and are a potential tool for detecting gene × gene interactions However, for the approach implemented here, optimizing the ANNs and obtaining stable results remains challenging
4 citations
TL;DR: With the extensive microarray and SNP data provided for 14 CEPH families, groups explored multistage analyses, machine learning methods, network construction, and other techniques to try to answer questions about gene‐gene interaction, functional similarities, co‐regulated gene expression and the mapping of gene expression determinants.
Abstract: The complexity of data available in human genetics continues to grow at an explosive rate. With that growth, the challenges to understanding the meaning of the underlying information also grow. A currently popular approach to dissecting such information falls under the broad category of data mining. This can apply to any approach that tries to extract relevant information from large amounts of data, but often refers to methods that deal, in a non-linear fashion, with very large numbers of variables that cannot be simultaneously handled by more conventional statistical methods. To explore the usefulness of some of these approaches, 13 groups applied a variety of strategies to the first dataset provided to GAW 15 participants. With the extensive microarray and SNP data provided for 14 CEPH families, these groups explored multistage analyses, machine learning methods, network construction, and other techniques to try to answer questions about gene-gene interaction, functional similarities, co-regulated gene expression and the mapping of gene expression determinants, among others. In general, the methods offered strategies to provide a better understanding of the complex pathways involved in gene expression and function. These are still "works in progress," often exploratory in nature, but they provide insights into ways in which the data might be interpreted. Despite the still preliminary nature of some of these methods and the diversity of the approaches, some common themes emerged. The collection of papers and methods offer a starting point for further exploration of complex interactions in human genetic data now readily available.