Browsing by Subject "Genetic mapping"

Now showing 1 - 2 of 2
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    Assessing the Genetic Risk for Alcohol Use Disorders
    (National Institute on Alcohol Abuse and Alcoholism, 2012) Foroud, Tatiana; Phillips, Tamara J.; Medical and Molecular Genetics, School of Medicine
    The past two decades have witnessed a revolution in the field of genetics which has led to a rapid evolution in the tools and techniques available for mapping genes that contribute to genetically complex disorders such as alcohol dependence. Research in humans and in animal models of human disease has provided important new information. Among the most commonly applied approaches used in human studies are family studies, case-control studies, and genome-wide association studies. Animal models have been aimed at identifying genetic regions or individual genes involved in different aspects of alcoholism, using such approaches as quantitative trait locus analysis, genome sequencing, knockout animals, and other sophisticated molecular genetic techniques. All of these approaches have led to the identification of several genes that seem to influence the risk for alcohol dependence, which are being further analyzed. Newer studies, however, also are attempting to look at the genetic basis of alcoholism at the level of the entire genome, moving beyond the study of individual genes toward analyses of gene interactions and gene networks in the development of this devastating disease.
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    Hippocampal Surface Mapping of Genetic Risk Factors in AD via Sparse Learning Models
    (Office of the Vice Chancellor for Research, 2012-04-13) Wan, Jing; Kim, Sungeun; Inlow, Mark; Nho, Kwangsik; Swaminathan, Shanker; Risacher, Shannon L.; Fang, Shiaofen; Weiner, Michael W.; Beg, M. Faisal; Wang, Lei; Saykin, Andrew J.; Shen, Li; ADNI
    Genetic mapping of hippocampal shape, an under-explored area, has strong potential as a neurodegeneration biomarker for AD and MCI. This study investigates the genetic effects of top candidate single nucleotide polymorphisms (SNPs) on hippocampal shape features as quantitative traits (QTs) in a large cohort. FS+LDDMM was used to segment hippocampal surfaces from MRI scans and shape features were extracted after surface registration. Elastic net (EN) and sparse canonical correlation analysis (SCCA) were proposed to examine SNP-QT associations, and compared with multiple regression (MR). Although similar in power, EN yielded substantially fewer predictors than MR. Detailed surface mapping of global and localized genetic effects were identified by MR and EN to reveal multi-SNP-single-QT relationships, and by SCCA to discover multi-SNP-multi-QT associations. Shape analysis identified stronger SNP-QT correlations than volume analysis. Sparse multivariate models have greater power to reveal complex SNP-QT relationships. Genetic analysis of quantitative shape features has considerable potential for enhancing mechanistic understanding of complex disorders like AD.