William Neal Reynolds Distinguished Professor
Bioinformatics Research Center
Department of Satistics
Department of Genetics
zeng AT stat.ncsu.edu
New Research Results:
Model selection criterion for multiple interval mapping: Appropriate determination of criterion
for model selection is important for multiple interval mapping in analyzing multiple QTL. This study
extended the score statistic method of Zou et al. (2004, Genetics 168:2307-2316) to multiple interval
mapping, and studied its performance through extensive simulations. Based on the simulation results,
the paper made some general recommendations on model selection procedure and criterion for mapping
Manuscript: Laurie, C., S. Wang, L.A. Carlini-Garcia and Z.-B. Zeng
Use of score statistics for model selection in mapping multiple quantitative trait loci. (submitted)
Genetic basis of heterosis in maize and rice: Heterosis, particularly in maize and rice, is
largely responsible for the Green Revolution. By employing multiple interval mapping on two data sets,
one for maize and one for rice, this article reveals that the genetic basis for heterosis in yield is
quite different for maize and for rice. In maize, the evidence points to dominant quantitative trait
loci as the main cause for the heterosis. In rice, epistasis appears to be the main reason. This
distinction seems to be related to open or self pollination of the respective species.
Manuscript: Garcia, A.A.F., S. Wang, A.E. Melchinger and Z.-B. Zeng (2008)
Quantitative trait loci mapping and the genetic basis of heterosis in maize and rice. Genetics 180:1707-1724
Modeling epistasis for the inference of gene pathway: This is a conceptual study and has
potentially wide implications for constructing gene pathways. In this study, we used the classical
genetics argument to interprete a quantitative genetic model for the inference of genetic pathway, and
tested it in a gene knock-out study in Dictyostellium discoideum.
Manuscript: Aylor, D.L. and Z.-B. Zeng (2008)
From classic genetics to quantitative genetics to systems biology:
Modeling epistasis. PLoS Genetics 4(3)
Estimating high order linkage disequilibrium: We proposed to use multiple order Markov chains
to partition and estimate different levels of linkage disequilibrium among multiple markers and
applied it to the HapMap data. To our knowledge, this is the first method to estimate the amount
of different orders of linkage disequilibrium.
Manuscript: Kim, Y., S. Feng and Z.-B. Zeng (2008)
Measuring and partitioning the high order linkage disequilibrium by multiple order Markov chains.
Genetic Epidemiology 32:301-312
Multiple Interval Mapping for eQTL mapping: We have developed an efficient procedure specifically
for gene expression QTL analysis. The method uses our previously developed MIM to search for eQTL and
uses the false discovery rate (FDR) (the estimated proportion among the declared eQTL for all
expression profiles that are falsely positive) to justify the model selection procedure.
In this method, we proceed to scan the genome for one or multiple QTL on each expression trait stepwisely.
In each step, we make the decision whether to continue the search for more QTL or stop the process based
on a criterion that can be tuned up from FDR calculation. This process is similar to that proposed
by Storey et al (2005, PLoS Biology 3:e267), but with a few critical differences or improvements.
(1) The search is not restricted to markers, but covers the whole genome in the fashion of interval mapping.
This improvement may not be very significant for dense markers, but is a nice generalization and can
be important for less dense markers. (2) Our search is not restricted to two steps, which is the case
of Storey et al and can proceed to multiple steps (eQTL) as justified. (3) The search in the second
and subsequent steps is restricted to those expression traits that the previous search step is significant.
This is quite different from Storey et al's procedure that performs the two or multiple step searches
for all expression traits. In this study, we show that our conditional search is actually much more
powerful statistically than Storey et al and we found more eQTL and eQTL epistasis on the same yeast
data (Brems and Krugleyak, 2005 PNAS 102:1572-1577) that Storey et al
also analyzed with the same FDR level.
Manuscript: Zou, W. and Z.-B. Zeng,
Multiple interval mapping for gene expression QTL analysis. (submitted)
Program codes: An R package to estimate FDR in sequenctial genome scan that complements QTL Cartographer
MIM modules is developed. Information about the codes is
Results: The mapping results are displayed
using eQTL Viewer tool. You need to install SVG tool to show the results. See the information at
eQTL Viewer site.
- eQTL Viewer: The eQTL analysis will produce a list of eQTL (i.e. genomic regions) for all
the typed and analyzed expression traits. The genomic region for each eQTL can be defined by a 1.5 LOD-support
interval calculated from MIM-eQTL, and the genes in each region can be listed if the genome is sequenced and
annotated. So, essentially the final results of eQTL analysis could be summarized in a gene list for each eQTL
that are matched to its expression gene. Then it would be necessary to come up an efficient and informative
way to display, annotate and interpret the results. Using the Scalable Vector Graphics (SVG) technology,
we have developed a very informative and useful tool, called eQTL Viewer, for displaying eQTL mapping results.
The tool is a dynamic database of the gene lists with a graphic 2D display with x-axis for the genome location
of eQTL genes and y-axis for the genome location of expression genes. Each gene in the database can be linked
to the public genome databases. The scalable feature allows us to zoom-in to look at the detail of a particular
region and zoom-out to look at the overall patterns. Many genomics annotation features can be superinposed on the viewer.
Manuscript: Zou, W., D.L. Aylor and Z.-B. Zeng, 2007 eQTL Viewer:
visualizing how sequence variation affects genome-wide transcription. BMC Bioinformatics 8:7
Tool: See information at eQTL Viewer website
My research interest is generally in the area of theoretical
and statistical quantitative genetics. This includes research on developing
theoretical models and statistical methods to map quantitative trait loci (QTL)
and to estimate basic genetic parameters of quantitative trait variation, such
as number, genomic positions, effects, interaction, and pleiotropy of genes
responsible for the variation. Currently, this research is mostly concentrated
on developing statistical methods for analyzing genetic architecture of
quantitative traits as a whole using multiple interval mapping approach.
Research topics include efficient and robust model selection, analysis for
complex epistasis, multiple trait analysis, complex QTL by environment
interaction analysis, mixed and mixture models, general plant breeding
population QTL analysis, full-sib family analysis, linkage disequilibrium
mapping. We also develop software (QTL Cartographer)
for QTL mapping data analysis. The long-term goal of the research is to develop
quantitative genetic theories and statistical methods for characterizing and
analyzing variation of quantitative traits and to learn genetic and
evolutionary bases of the variation within and between natural and experimental
Z.-B. (1993) Theoretical
basis of separation of multiple linked gene effects on mapping
quantitative trait loci. Proceedings of the National Academy of
Science USA 90:10972-10976.
Z.-B. (1994) Precision
mapping of quantitative trait loci. Genetics 136:1457-1468.
C. and Z.-B. Zeng (1995) Multiple trait
analysis of genetic mapping for quantitative trait loci. Genetics
C. C. and Z.-B. Zeng (1996) Design III with
marker loci. Genetics 143:1437-1456.
C. and Z.-B. Zeng (1997) Mapping
quantitative trait loci with dominant and missing markers in various
crosses from two inbred lines. Genetica 101:47-58.
C.-H. and Z.-B. Zeng (1997) General formulae for
obtaining the MLEs and the asymptotic variance-covariance matrix in
mapping quantitative trait loci when using the EM algorithm. Biometrics
C.-H.,Z.-B. Zeng and R. D. Teasdale (1999) Multiple interval
mapping for quantitative trait loci. Genetics 152:1203-1216.
Z.-B., C.-H. Kao, and C. J. Basten (1999) Estimating
the genetic architecture of quantitative traits. Genetical Research
Y. and Z.-B. Zeng (2000) A general
mixture model approach for mapping quantitative trait loci from diverse
cross experimental designs involving multiple inbred lines. Genetical
Research 75: 345-355.
Z.L., S.H. Tao and Z.-B. Zeng (2000) Inferring
linkage disequilibrium between a polymorphic marker locus and a trait
locus in natural populations .Genetics 156:457-467.
R.L., and Z.-B. Zeng (2001) Joint
linkage and linkage disequilibrium mapping in natural populations .
Genetics 157: 899-909.
R.L., M. Gallo-Meagher, R. C. Littell, and Z.-B. Zeng (2001) A general polyploid model for analyzing gene
segregation in qutcrossing tetraploid species. Genetics
C.-H. and Z.-B. Zeng (2002) Modeling epistasis
of quantitative trait loci using Cockerham's model. Genetics
- Wu, R.L., C.-X. Ma, I. Painter and Z.-B.
Zeng (2002) Simultaneous maximum likelihood
estimation of linkage and linkage-phases in outcrossing species. Theoretical
Population Biology 61: 349-363.
Zeng, Z.-B., T. Wang and W. Zou (2005)
Modeling quantitative trait loci and interpretation of models. Genetics 169:1711-1725.
Z.-B. (2005) QTL mapping and
the genetic basis of adaptation: recent developments. Genetica 123:
L. and Z.-B. Zeng (2005) Mixture model equations for
marker-assisted genetic evaluation. Anim. Breed. Genet. 122: 229-239.
T., and Z.-B. Zeng (2006) Models
and partition of variance for quantitative trait loci with epistasis and linkage disequilibrium.
BMC Genetics 7:9.
T., B.S. Weir and Z-.B. Zeng (2006) A
population-based latent variable approach for association mapping of
quantitative trait loci. Annals of Human
Li, J., S. Wang and Z.-B. Zeng (2006)
Multiple interval mapping for ordinal traits. Genetics 173: 1649-1663.
- Melchinger, A.E., H.F. Utz, H.P. Piepho, Z.-B. Zeng, and C.C. Schon (2007)
Quantitative genetic theory to elucidate the role of epistasis in the manifestation of heterosis.
Genetics. 177: 1815-1825.
Yang, R., H. Gao, X. Wang, J. Zhang, Z.-B. Zeng and R. Wu (2007)
A semiparametric approach for composite functional mapping of dynamic quantitative traits.
Genetics 177: 1859-1870.
- Aylor, D.L. and Z.-B. Zeng (2008)
From classic genetics to quantitative genetics to systems biology:
Modeling epistasis. PLoS Genetics 4(3).
- Kim, Y., S. Feng and Z.-B. Zeng (2008)
Measuring and partitioning the high order linkage disequilibrium by multiple order Markov chains..
Genetic Epidemiology 32:301-312.
Zou, W. and Z-.B Zeng (2008)
Statistical methods for mapping multiple QTL. International Journal of Plant Genomics (Article ID 286561).
Wang T., H. Jacob, S. Ghosh, X.J. Wang and Z.-B. Zeng (2008)
A joint association test for multiple SNPs in genetic case-control studies. Genetic epidemiology (in press).
T. A., Z.-B. Zeng, F. Canzian, M. Gariboldi, G. Manenti and M. A.
Pierotti (1995) Molecular mapping of body weight loci on mouse chromosome
X. Mammalian Genome 6: 778-781.
S. V., E. G. Pasyukova, C. L. Dilda, Z.-B. Zeng and T. F. C. Mackay
quantitative trait loci affecting longevity in Drosophila melanogaster.
Proceedings of the National Academy of Science USA 94:
K., R. Eisman, S. Higgins, L. Kuhl, A. Patty, J. Sparks and Z.-B. Zeng
analysis of polygenes affecting wing shape on chromosome three in Drosophila
melanogaster. Genetics 153: 773-786. [Data]
C., E. G. Pasyukova, Z.-B. Zeng, J. B. Hackett, R. F. Lyman and T.
F. C. Mackay (2000) Genotype-environment
interaction for quantitative trait loci affecting lifespan in Drosophila
melanogater. Genetics 154: 213-227. [Data]
Z.-B., J. Liu, L. F. Stam, C.-H. Kao, J. M. Mercer and C.C. Laurie
architecture of a morphological shape difference between two Drosophila
species. Genetics 154: 299-310.
K., R. Eisman, S. Higgins, L. Morey, A. Patty, M. Tausek and Z.-B. Zeng
analysis of polygenes affecting wing shape on chromosome 2 in Drosophila
melanogaster. Genetics 159: 1045-1057. [Data]
Y., Z.-B. Zeng, J. Li, D. L. Hartl and C. C. Laurie (2003) Genetic
dissection of hybrid incompatibilities between Drosophila simulans
and Drosophila mauritiana, II. Mapping hybrid male sterility loci
on the third chromosome. Genetics 164:1399-1418.
M., C. J. Basten, A. A. Myburg, Z.-B. Zeng and R. R. Sederoff (2005)
Genetic architecture of transcript level variation in
differentiating xylem of an Eucalyptus hybrid. Genetics
Lai, C.-Q., J. Leips, W. Zou, J. M. Roberts, K. R. Wollenberg, L. D. Parnell, Z.-B. Zeng,
J. M. Ordovas and T. F. C. Mackay (2007)
Speed-mapping quantitative trait loci using microarrays. Nature Methods 4(10):839-841.
Huang, L., A. S. Heinloth, Z.-B. Zeng. R. S. Paules and P.R. Bushel (2008)
Genes related to apoptosis predict necrosis of the liver as a phenotype observed in rats exposed
to a compendium of hepatotoxicants. BMC Genomics 9:288.
C., B.S. Weir and Z.-B. Zeng (1995-2006) QTL Cartographer.
Department of Statistics, North Carolina State University, Raleigh, NC.
S., C. Basten and Z.-B. Zeng (1999-2006) WINDOWS QTL
Cartographer. Department of Statistics, North Carolina State
University, Raleigh, NC
- Zou, W., D.L. Aylor and Z.-B. Zeng (2006) eQTL Viewer.
Bioinformatics Research Center, North Carolina State
University, Raleigh, NC