Abstract

Many human diseases are inherently quantitative (e.g., hypertension). Others are generally viewed as dichotomous (e.g., diabetes) but arc closely associated with intermediate quantitative phenotypes (e.g., glucose tolerance). Quantitative traits are influenced by multiple genetic loci (called quantitative trait loci, QTLs) as well as the environmental. Our long-term goal is to develop improved statistical methods for mapping multiple QTLs in experimental crosses. We focus on mouse and rat models of human disease. The central statistical problem in QTL mapping is one of model selection: one seeks to identify an appropriate QTL model, including the number and locations of QTLs and the identity of QTL:QTL interactions (called epistasis). The simultaneous mapping of multiple QTLs (versus methods, such as interval mapping, which model a single QTL at a time) has the advantage of better separating linked QTLs and allows the identification of interactions between QTLs. We further seek to develop and distribute computer software implementing such methods, in order to make the best QTL mapping methods widely available to geneticists. Toward these goals, the current proposal has the following specific aims: (1) Develop practical model selection procedures for mapping multiple QTLs in the presence of epistatic interactions and missing genotype data. (2) Develop improved methods for the analysis of recombinant inbred (RI) lines, including RIX lines and RI lines developed from multiple parental strains. (3) Develop and distribute the comprehensive QTL mapping software, R/qtl. Software development is a particularly important aspect of this work, as QTL mapping methods, no matter how refined,, will not be used if they are not implemented in user-friendly computer software. Further, the proper assessment off the performance of the methods developed towards aims (1) and (2), via large-scale computer simulations and the analysis of experimental data, requires their implementation in efficient computer software.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM074244-03
Application #
7238677
Study Section
Genome Study Section (GNM)
Program Officer
Eckstrand, Irene A
Project Start
2005-06-01
Project End
2007-06-30
Budget Start
2007-06-01
Budget End
2007-06-30
Support Year
3
Fiscal Year
2007
Total Cost
$19,363
Indirect Cost

  Institution

Name
Johns Hopkins University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218

  Publications

Palus, Martin; Sohrabi, Yahya; Broman, Karl W et al. (2018) A novel locus on mouse chromosome 7 that influences survival after infection with tick-borne encephalitis virus. BMC Neurosci 19:39
Tian, Jianan; Keller, Mark P; Broman, Aimee Teo et al. (2016) The Dissection of Expression Quantitative Trait Locus Hotspots. Genetics 202:1563-74
Tian, Jianan; Keller, Mark P; Oler, Angie T et al. (2015) Identification of the Bile Acid Transporter Slco1a6 as a Candidate Gene That Broadly Affects Gene Expression in Mouse Pancreatic Islets. Genetics 201:1253-62
Broman, Karl W; Keller, Mark P; Broman, Aimee Teo et al. (2015) Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study. G3 (Bethesda) 5:2177-86
Whitney, Kenneth D; Broman, Karl W; Kane, Nolan C et al. (2015) Quantitative trait locus mapping identifies candidate alleles involved in adaptive introgression and range expansion in a wild sunflower. Mol Ecol 24:2194-211
Broman, Karl W (2015) R/qtlcharts: interactive graphics for quantitative trait locus mapping. Genetics 199:359-61
Kwak, Il-Youp; Moore, Candace R; Spalding, Edgar P et al. (2015) Mapping Quantitative Trait Loci Underlying Function-Valued Traits Using Functional Principal Component Analysis and Multi-Trait Mapping. G3 (Bethesda) 6:79-86
Kwak, Il-Youp; Moore, Candace R; Spalding, Edgar P et al. (2014) A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes. Genetics 197:1409-16
Gatti, Daniel M; Svenson, Karen L; Shabalin, Andrey et al. (2014) Quantitative trait locus mapping methods for diversity outbred mice. G3 (Bethesda) 4:1623-33
Huang, B Emma; Raghavan, Chitra; Mauleon, Ramil et al. (2014) Efficient imputation of missing markers in low-coverage genotyping-by-sequencing data from multiparental crosses. Genetics 197:401-4

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