Quantitative and Statistical Genetics
We are a quantitative genetics lab interested the relationship between genes and complex disease. In particular, we are interested in animal models of complex human disease, such as cancer susceptibility, diabetes, anxiety disorders, and many others. Our current research topics include:
- Modeling the effects of heritable variation on disease outcomes.
- Statistical variable selection for reprioritizing disease gene signals in human GWAS.
- Bayesian hierarchical modeling of latent quantities in experimental animal models.
- Causal modeling of drug response under varying genetic and epigenetic background.
- Bayesian hierarchical modeling of dietary influences on epigenetic transmission.
- Designing genetic populations that are optimal for identifying disease genes.
- Estimating multilayered gene expression networks.
A lot of our current work focuses on the design and analysis of genetic resource populations for medical research. In particular, we are looking at outbred and recombinant inbred populations of rodents including the Collaborative Cross (see Scientific American, UNC news, local news; scientific overviews: Genetics, G3), Heterogeneous Stocks (HS), Advanced Intercross Lines and derived populations. Although much of our work is theoretical or analytic in nature, it is typically strongly motivated by scientific problems that arise in our collaborations on projects with experimental groups.
[See also all publications]
Valdar W*, Sabourin J*, Nobel A, Holmes C (2012) Reprioritizing genetic associations in hit regions using LASSO-based resample model averaging. Genetic Epidemiology 36(5):451-462 PMID:22549815
Solberg Woods LC, Holl KL, Oreper D, Xie Y, Tsaih S-W, Valdar W (2012) Fine-mapping diabetes-related traits, including insulin resistance, in heterogeneous stock rats Physiological Genomics 44(21):1013-26 PMID:22947656 PMCID:PMC3524769
Rönnegård L, Valdar W (2012) Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability. BMC Genetics 13(1):63 PMID:22827487 PMCID:PMC3493319
Lenarcic AB, Svenson KL, Churchill GA, Valdar W (2012) A general Bayesian approach to analyzing diallel crosses of inbred strains. Genetics 190(2):413-435.
Rönnegård L, Valdar W (2011) Detecting major genetic loci controlling phenotypic variability in experimental crosses. Genetics 188:435-447. PMID:21467569
Valdar W, Holmes C, Mott R, Flint J (2009) Mapping in structured populations by resample model averaging. Genetics 182(4):1263-1277
Wallis-Owen SAG, Valdar W (2009) Deciphering gene-environment interactions through mouse models of allergic asthma. J Allergy Clin Immunol 123(1):14-23.
Valdar W, Solberg LC, Gaugier D, Burnett S, Klenerman P, Cookson WO, Taylor M, Rawlins JNP, Mott R, Flint J (2006) Genome-wide genetic association of complex traits in outbred mice. Nature Genetics 38(8):879-87.
Valdar W, Solberg LC, Gaugier D, Cookson WO, Rawlins JNP, Mott R, Flint J (2006) Genetic and environmental effects on complex traits in mice. Genetics 174(2):959-84.
Valdar W, Flint J, Mott R (2006) Simulating the Collaborative Cross: power of QTL detection and mapping resolution in large sets of recombinant inbred strains of mice. Genetics 172(3):1783-97.
Will Valdar's other UNC webpages:
- Biological & Biomedical Sciences Program, a graduate program for PhD students.
- Bioinformatics & Computational Biology, a subprogram of BBSP.
- Biostatistics & Epidemiology
- School of Public Health
- Department of Genetics
- Lineberger Comprehensive Cancer Center