The broad long-term objective of this project is to use a new paradigm to develop a set of statistical methods for mapping disease genes. The methods developed will be most relevant in preliminary genome-scan studies. The main thrust of the paradigm lies in its formulation of the hypotheses for linkage. Traditionally, hypotheses for linkage are usually set up with the null hypothesis being no linkage and the alternative hypothesis being linkage. In our new formulation, the null and alternative hypotheses are being reversed, with the null hypothesis being tight linkage and the alternative hypothesis being loose linkage or no linkage. Two of the fundamental advantages with this new paradigm are: first, multiplicity adjustment for the number of tests performed in a genome-scan study is unnecessary, and second, the location of a disease gene can be narrowed down to a small genomic region, even at the stage of a preliminary genome-scan study.
The specific aims are: 1. To develop methods for constructing confidence sets of markers or confidence regions (intervals) of disease gene locations based on parametric tests of the new formulation of hypotheses. Single-marker and multiple-marker approaches will be developed for data from general pedigrees. 2. To develop methods, parallel to those in specific aim 1, for constructing confidence sets of markers or confidence regions (intervals) of disease gene locations based on nonparametric tests using allele-sharing statistics. Again, single-marker and multiple-marker approaches will be developed for a wide variety of data types (including sib-ships of arbitrary size and general pedigrees) and statistics. 3. To carry out simulation studies to extensively evaluate and compare the methods developed under a variety of settings, including the underlying disease model, data type, marker density, and marker polymorphism, and to apply the developed methods to a wide range of real data sets available to the PI. 4. To implement the methodology developed in user friendly, well-documented programs, and to make the package of the programs available to other interested researchers.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG002657-03
Application #
7002200
Study Section
Genome Study Section (GNM)
Program Officer
Brooks, Lisa
Project Start
2004-01-20
Project End
2008-12-31
Budget Start
2006-01-01
Budget End
2008-12-31
Support Year
3
Fiscal Year
2006
Total Cost
$144,034
Indirect Cost
Name
Ohio State University
Department
Microbiology/Immun/Virology
Type
Schools of Medicine
DUNS #
832127323
City
Columbus
State
OH
Country
United States
Zip Code
43210
Zhou, Ji-Yuan; Ding, Jie; Fung, Wing K et al. (2010) Detection of parent-of-origin effects using general pedigree data. Genet Epidemiol 34:151-8
Zhou, Ji-Yuan; Hu, Yue-Qing; Lin, Shili et al. (2009) Detection of parent-of-origin effects based on complete and incomplete nuclear families with multiple affected children. Hum Hered 67:1-12
Lin, Shili; Ding, Jie (2009) Integration of ranked lists via cross entropy Monte Carlo with applications to mRNA and microRNA Studies. Biometrics 65:9-18
Zhou, Ji-Yuan; Lin, Shili; Fung, Wing K et al. (2009) Detection of parent-of-origin effects in complete and incomplete nuclear families with multiple affected children using multiple tightly linked markers. Hum Hered 67:116-27
Ding, Jie; Lin, Shili (2008) XMCPDT does have correct type I error rates. Am J Hum Genet 82:528-30;author reply 530-1
Liu, Zhenqiu; Lin, Shili; Tan, Ming (2006) Genome-wide tagging SNPs with entropy-based Monte Carlo method. J Comput Biol 13:1606-14
Papachristou, Charalampos; Lin, Shili (2006) A two-step procedure for constructing confidence intervals of trait loci with application to a rheumatoid arthritis dataset. Genet Epidemiol 30:18-29
Ding, Jie; Lin, Shili; Liu, Yang (2006) Monte Carlo pedigree disequilibrium test for markers on the X chromosome. Am J Hum Genet 79:567-73
Papachristou, Charalampos; Lin, Shili (2006) A comparison of methods for intermediate fine mapping. Genet Epidemiol 30:677-89
Lin, Shili; Ding, Jie; Dong, Crystal et al. (2005) Comparisons of methods for linkage analysis and haplotype reconstruction using extended pedigree data. BMC Genet 6 Suppl 1:S76

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