Unlike monogenic diseases whose genetic determinants have been mapped? in extended human pedigrees using classical penetrance-based linkage? analysis methods, many common diseases involve multiple genetic and? environmental components and their interactions. The genetic analysis? of such complex phenotypes requires new statistical approaches for the? localization and evaluation of the relative importance of specific? quantitative trait loci (QTLs). In this application, the investigators? propose to develop/extend a number of new statistical genetic analytical? methods for the localization and evaluation of QTLs influencing common? human diseases. To address this critical area of genetic research, they? have formed a new collaborative scientific team combining two major? statistical genetic working groups based at the Southwest Foundation for? Medical Research (led by Dr. Blangero) and the University of California? at Los Angeles (led by Dr. Lange). Unlike most work in this nascent? field, they will concentrate their efforts on methods for the analysis? of common diseases and other complex phenotypes in extended human? pedigrees. In particular, they will focus largely on extensions of the? variance component method of linkage analysis. They will also further? develop and extend their linkage analysis software (SOLAR) so that it? becomes a comprehensive, yet easily used, package for the oligogenic? analysis of quantitative human variation.? ? The proposed research will address five specific aims: 1) The? investigators will develop and extend the variance component linkage? procedures to allow for general multivariate analysis, oligogenic? inheritance, epistasis, genotype x environment interaction, and? empirical genome-wide p-value evaluations; 2) They will develop more? efficient methods for the calculation of IBD probability matrices in? complex pedigrees; 3) A general variance component method for the? linkage analysis of discrete traits and the joint analysis of discrete? and quantitative traits will be formulated, tested, and implemented in? the SOLAR package; 4) Two new methods for fine mapping QTLs in extended? pedigrees will be developed and evaluated including joint? disequilibrium/linkage analysis using variance component model and a? novel gamete competition model; and 5) All of the above methods will be? incorporated into the software package (SOLAR) for linkage analysis of? complex traits in extended pedigrees.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Method to Extend Research in Time (MERIT) Award (R37)
Project #
5R37MH059490-10
Application #
7276664
Study Section
Special Emphasis Panel (NSS)
Program Officer
Lehner, Thomas
Project Start
1998-09-30
Project End
2011-07-31
Budget Start
2007-08-01
Budget End
2008-07-31
Support Year
10
Fiscal Year
2007
Total Cost
$726,673
Indirect Cost
Name
Texas Biomedical Research Institute
Department
Type
DUNS #
007936834
City
San Antonio
State
TX
Country
United States
Zip Code
78245
Knowles, Emma E M; Mathias, Samuel R; Mollon, Josephine et al. (2018) A QTL on chromosome 3q23 influences processing speed in humans. Genes Brain Behav :e12530
Blondell, Lucy; Blackburn, August; Kos, Mark Z et al. (2018) Contribution of Inbred Singletons to Variance Component Estimation of Heritability and Linkage. Hum Hered 83:92-99
Knowles, Emma E M; Curran, Joanne E; Meikle, Peter J et al. (2018) Disentangling the genetic overlap between cholesterol and suicide risk. Neuropsychopharmacology 43:2556-2563
Hodgson, Karen; Poldrack, Russell A; Curran, Joanne E et al. (2017) Shared Genetic Factors Influence Head Motion During MRI and Body Mass Index. Cereb Cortex 27:5539-5546
Kos, Mark Z; Carless, Melanie A; Peralta, Juan et al. (2017) Exome sequences of multiplex, multigenerational families reveal schizophrenia risk loci with potential implications for neurocognitive performance. Am J Med Genet B Neuropsychiatr Genet 174:817-827
Hodgson, Karen; Almasy, Laura; Knowles, Emma E M et al. (2017) The genetic basis of the comorbidity between cannabis use and major depression. Addiction 112:113-123
Knowles, E E M; Huynh, K; Meikle, P J et al. (2017) The lipidome in major depressive disorder: Shared genetic influence for ether-phosphatidylcholines, a plasma-based phenotype related to inflammation, and disease risk. Eur Psychiatry 43:44-50
Zhou, Hua; Blangero, John; Dyer, Thomas D et al. (2017) Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data. Genet Epidemiol 41:174-186
Hodgson, Karen; Carless, Melanie A; Kulkarni, Hemant et al. (2017) Epigenetic Age Acceleration Assessed with Human White-Matter Images. J Neurosci 37:4735-4743
Kulkarni, Hemant; Mamtani, Manju; Wong, Gerard et al. (2017) Genetic correlation of the plasma lipidome with type 2 diabetes, prediabetes and insulin resistance in Mexican American families. BMC Genet 18:48

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