The long-term goal of this project continues to be the development and evaluation of appropriate new methodologies for the resolution of genetic and non-genetic determinants of complex diseases and disease-related traits, and an investigation of how genes and environments interact to product phenotypic variation. Arguably, this may be the single most outstanding challenge in genetic epidemiology today. Towards this end, the specific aims cover theoretical/methodological issues in linkage and association analysis, optimum study designs suitable for complex traits, and evaluations of important methodological issues through simulations and occasional applications to read data. The proposed theoretical methodological studies include the development of mathematical and statistical tools for the analysis of phenotypic and genotypic data: extensions of the powerful variance components linkage models in SEGPATH to incorporate categorical phenotypes (logistic model), survival phenotypes with censored data (proportional hazards), semi-quantitative traits, multilocus etiological models, and multivariate phenotypes; development and extensions of suitable meta-analysis methods for pooling results from multiple genome-wide linkage/association scans; optimum study designs and sampling strategies suitable for detection of complex trait genes of modest effect sizes in the face of gene-gene and gene-environment interactions; development of methods for the resolution of genetic heterogeneity by subdividing a sample into relatively more homogeneous subgroups (CART and cluster analysis); development of novel methods for sub-localization of trait genes using a two-stage design and dense maps of single nucleotide polymorphisms (SNPs); development of non-parametric methods for linkage analysis of sibships where some sibs are """"""""affected"""""""" while others have a quantitative measurement (semi-quantitative); development of novel sequential methods for analysis and interpretation of a genome wide scans, with particular attention to a balance between false positives and false negatives. A number of simulations (and some real data) studies are proposed to evaluate the potential strengths and weaknesses of various theoretical models developed so as to identify specific areas for further methodological research, and to evaluate some outstanding methodological issues that do not readily lend themselves to theoretical investigations. We believe that consideration of multiple approaches to the same complex problem as proposed here constitutes a strength and improves robustness of the inference, and further, that the proposed research will contribute significantly to the advancement of genetic epidemiology.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM028719-21
Application #
6179449
Study Section
Mammalian Genetics Study Section (MGN)
Program Officer
Eckstrand, Irene A
Project Start
1980-06-01
Project End
2003-06-30
Budget Start
2000-07-01
Budget End
2001-06-30
Support Year
21
Fiscal Year
2000
Total Cost
$296,826
Indirect Cost
Name
Washington University
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
062761671
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Simino, Jeannette; Kume, Rezart; Kraja, Aldi T et al. (2014) Linkage analysis incorporating gene-age interactions identifies seven novel lipid loci: the Family Blood Pressure Program. Atherosclerosis 235:84-93
Simino, Jeannette; Shi, Gang; Weder, Alan et al. (2014) Body mass index modulates blood pressure heritability: the Family Blood Pressure Program. Am J Hypertens 27:610-9
Sung, Yun J; Gu, C Charles; Tiwari, Hemant K et al. (2012) Genotype imputation for African Americans using data from HapMap phase II versus 1000 genomes projects. Genet Epidemiol 36:508-16
Sung, Yun Ju; Wang, Lihua; Rankinen, Tuomo et al. (2012) Performance of genotype imputations using data from the 1000 Genomes Project. Hum Hered 73:18-25
Rice, Treva K; Sarzynski, Mark A; Sung, Yun Ju et al. (2012) Fine mapping of a QTL on chromosome 13 for submaximal exercise capacity training response: the HERITAGE Family Study. Eur J Appl Physiol 112:2969-78
Sun, Yan V; Sung, Yun Ju; Tintle, Nathan et al. (2011) Identification of genetic association of multiple rare variants using collapsing methods. Genet Epidemiol 35 Suppl 1:S101-6
Shi, Gang; Boerwinkle, Eric; Morrison, Alanna C et al. (2011) Mining gold dust under the genome wide significance level: a two-stage approach to analysis of GWAS. Genet Epidemiol 35:111-8
Shi, Gang; Gu, Chi C; Kraja, Aldi T et al. (2009) Genetic effect on blood pressure is modulated by age: the Hypertension Genetic Epidemiology Network Study. Hypertension 53:35-41
Neuman, Rosalind J; Sung, Yun Ju (2009) Multistage analysis strategies for genome-wide association studies: summary of group 3 contributions to Genetic Analysis Workshop 16. Genet Epidemiol 33 Suppl 1:S19-23
Rice, Treva K (2008) Familial resemblance and heritability. Adv Genet 60:35-49

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