Valdar Lab

Quantitative and Statistical Genetics

William Valdar


  • June 2017: Probilistically-informed database of CC haplotypes and variants, ISVdb, now online [database] [paper in G3].
  • Jan 2017: Drug-by-genetics interaction paper using the CC is published [paper in Toxicological Sciences]
  • Jul/Nov 2016: Will's talk at The Allied Genetics Conference (TAGC) is online [video]
  • Aug 2016: Greg Keele wins best BCB student presentation award at The UNC 2016 Genetics/BCB Retreat [picture]
  • May 2016: Robert Corty awarded F30 NIMH Predoctoral Fellowship [BCB link]
  • Mar 2016: Dan Oreper awarded PhRMA Foundation Pre-Doctoral Fellowship. [BCB link] [PhRMA link]
  • Oct 2015: Dan Oreper and Sarah Schoenrock jointly win best BCB student presentation award at The UNC 2015 Genetics/BCB Retreat [picture]
  • Mar 2014: Press coverage on recent paper (Crowley, Kim, Lenarcic et al, 2014) on the genetics of drug side-effects: UNC-Endeavors, HealthCanal, MedicalXpress, Newswise
  • Jun 2013: Valdar Lab co-recipient of R01 grant to study the effect of maternal diet on psychiatric disease [click for details]
  • Oct 2012: Valdar Lab receives R01 grant to develop statistical genetics methods for new mouse models [click for details]
  • Lab head

    William Valdar, PhD
    Associate Professor
    Department of Genetics
    University of North Carolina at Chapel Hill

    Adjunct Associate Professor
    Departments of Biostatistics & Computer Science

    Associate Director
    Bioinformatics and Computational Biology Graduate Curriculum

    Research Interests

    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:
    1. Modeling the effects of heritable variation on disease outcomes.
    2. Bayesian hierarchical modeling of latent quantities in experimental animal models.
    3. Designing genetic populations that are optimal for identifying disease genes.
    4. Causal modeling of treatment effect heterogeneity induced by varying genetic and epigenetic background.
    5. Statistical variable selection for reprioritizing disease gene signals in human GWAS.
    6. Bayesian hierarchical modeling of dietary influences on epigenetic transmission.
    7. 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 Publications

    Selected from 59 papers (see all publications).

    Oreper DG, Cai Y, Tarantino LM, Pardo-Manual de Villena F, Valdar W (2017) Inbred Strain Variant Database (ISVdb): A repository for probabilistically informed sequence differences among the Collaborative Cross strains and their founders. G3:Genes|Genomes|Genetics 7(6):1623-1630 [database link]

    Mosedale M*, Kim Y*, Brock WJ, Roth SE, Wiltshire T, Eaddy JS, Keele GR, Corty RW, Xie Y, Valdar W*, and Watkins PB* (2017) Candidate Risk Factors and Mechanisms for Tolvaptan-Induced Liver Injury Are Identified Using a Collaborative Cross Approach. Toxicological Sciences 156(2):438-454 PMID:28115652

    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

    © 2009 William Valdar based on this template by Andreas Viklund