Experimental crosses to identify the genetic loci (called quantitative trait loci, QTL) that influence quantitative traits will continue to play an important role in efforts to uncover the genetic basis of complex human diseases. Recently developed tools (including the joint consideration of multiple crosses, haplo- type analysis, and the use of gene expression information) have led to improved prospects for getting from a QTL to the gene. Our long-term goal is to develop improved statistical methods for mapping QTL in experimental crosses. We focus particularly on mouse and rat models of human disease, and on methods for studies of evolution. Critical to the work is the development and distribution of software implementing such methods, so that the best QTL mapping methods are widely available to geneticists. Toward this goal, our current aims are to (1) Develop methods for the dissection of trans-eQTL hotspots in expression genetics studies, (2) Develop methods to map QTL to a phylogenetic tree through the joint analysis of multiple crosses, (3) Continue the development of practical model selection procedures for mapping multiple QTL in the presence of epistatic interactions, in particular to handle QTL W covariate interactions, the X chromosome, and binary traits, (4) Continue the development of methods for the analysis of multiple-strain recombinant inbred lines (such as the Collaborative Cross), heterogeneous stock (HS), and outbred crosses, and (5) Continue the development of the comprehensive QTL mapping software, R/qtl.
Complex diseases are influenced by numerous interacting genes as well as important environmental factors. Experimental crosses with model organisms (such as the mouse) provide a powerful means for uncovering the genetic etiology underlying important diseases. Through this research effort, we will develop improved statistical methods and software, to enable biologists to make better use of their data to tease apart and ultimately identify the many interacting genes that contribute to complex diseases.
|Tian, Jianan; Keller, Mark P; Broman, Aimee Teo et al. (2016) The Dissection of Expression Quantitative Trait Locus Hotspots. Genetics 202:1563-74|
|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|
|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|
|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|
|Broman, Karl W (2015) R/qtlcharts: interactive graphics for quantitative trait locus mapping. Genetics 199:359-61|
|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|
|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|
|Broman, Karl W (2014) Fourteen Years of R/qtl: Just Barely Sustainable. J Open Res Softw 2:|
|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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