Quantitative and Statistical Genetics
Bioinformatics and Computational Biology Graduate Curriculum
We are a quantitative genetics lab interested the relationship between genes and complex disease. Most of our work focuses on using animal models, such as mice, to study complex human diseases, such as psychiatric disorders, cancer susceptibility, diabetes, and many others.Current research topics include:
- Modeling the effects of heritable variation on disease outcomes.
- Bayesian hierarchical modeling of latent quantities in experimental animal models.
- Designing genetic populations that are optimal for identifying disease genes.
- Causal modeling of treatment effect heterogeneity induced by varying genetic and epigenetic background.
- Statistical variable selection for reprioritizing disease gene signals in human GWAS.
- Bayesian hierarchical modeling of dietary influences on epigenetic transmission.
- Estimating multilayered gene expression networks.
A lot of our current effort 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 Intercrosses and derived populations.
Although work tends to be theoretical or analytic in nature, it is typically strongly motivated by scientific problems that arise in our collaborations on projects with experimental groups. In theory, experimental design, and analysis, we take a systems genetics perspective: specifically, that "inferences about biological phenomena are rarely separable from the genetic system in which they are embedded; thus, to generalize results across genetic backgrounds, experiments must be carried out across genetic backgrounds" (WV quoted in Nature Reviews Genetics PMID:24296534).
Selected from 56 papers (see all publications).
Xie Y, Liu Y, Valdar W (2016) Joint estimation of multiple dependent Gaussian graphical models with applications to mouse genomics. Biometrika 103(3):493-511 arxiv
Sabourin J, Nobel AB, Valdar W (2015) Fine-mapping additive and dominant SNP effects using group-LASSO and Fractional Resample Model Averaging. Genetic Epidemiology 39(2):77-88
Sabourin J, Valdar W, Nobel AB (2015) A permutation approach for selecting the penalty parameter in penalized model selection. Biometrics 71(4):1185-1194 arXiv
Zhang Z, Wang W, Valdar W (2014) Bayesian modeling of haplotype effects in multiparent populations. Genetics 198:139-156
Crowley JJ*, Kim Y*, Lenarcic AB*, Quackenbush CR, Barrick C, Adkins DE, Shaw GS, Miller DR, Pardo Manuel de Villena F, Sullivan PF, Valdar W (2014) Genetics of adverse reactions to haloperidol in a mouse diallel: A drug-placebo experiment and Bayesian causal analysis. Genetics 196(1):321-47 [Issue Highlight for Jan 2014] [Other press coverage: UNC-Endeavors, HealthCanal, MedicalXpress, Newswise]
Phillippi J*, Xie Y*, Miller DR, Bell TA, Zhang Z, Lenarcic AB, Aylor DL, Krovi SH, Threadgill DW, Pardo-Manuel de Villena F, Wang W, Valdar W*, Frelinger JA* (2014) Using the Collaborative Cross to probe the immune system. Genes and Immunity 15(1):38-46
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
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 [Editors Picks for Jan 2013]
Rönnegård L, Valdar W (2012) Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability. BMC Genetics 13(1):63 [Editors Picks for Sep 2012; Highly Accessed]
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.
Valdar W, Holmes C, Mott R, Flint J (2009) Mapping in structured populations by resample model averaging. Genetics 182(4):1263-1277
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. [Commentary in Nature Genetics News and Views]
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.
Valdar Lab-related websites:
- Biological & Biomedical Sciences Program, a graduate program for PhD students.
- Bioinformatics & Computational Biology, a subprogram of BBSP.
- School of Public Health
- Department of Genetics
- Lineberger Comprehensive Cancer Center
- Reach NC
- North Carolina REACH NC research hub