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 multiple QTL.

  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) Correction

• 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 Supplement Erratum

• 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 here

  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

Research Interests

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 populations.

Some Recent Publications

Theory and Methodology:

1. Zeng, 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.
2. Zeng, Z.-B. (1994) Precision mapping of quantitative trait loci. Genetics 136:1457-1468.
3. Jiang, C. and Z.-B. Zeng (1995) Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140: 1111-1127.
4. Cockerham, C. C. and Z.-B. Zeng (1996) Design III with marker loci. Genetics 143:1437-1456.
5. Jiang, 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.
6. Kao, 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 53:653-665.
7. Kao, C.-H.,Z.-B. Zeng and R. D. Teasdale (1999) Multiple interval mapping for quantitative trait loci. Genetics 152:1203-1216.
8. Zeng, Z.-B., C.-H. Kao, and C. J. Basten (1999) Estimating the genetic architecture of quantitative traits. Genetical Research 74:279-289.
9. Liu, 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.
10. Luo, 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.
11. Wu, R.L., and Z.-B. Zeng (2001) Joint linkage and linkage disequilibrium mapping in natural populations . Genetics 157: 899-909.
12. Wu, 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 159: 869-882.
13. Kao, C.-H. and Z.-B. Zeng (2002) Modeling epistasis of quantitative trait loci using Cockerham's model. Genetics 160: 1243-1261.
14. 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.
15. Zeng, Z.-B., T. Wang and W. Zou (2005) Modeling quantitative trait loci and interpretation of models. Genetics 169:1711-1725.
16. Zeng, Z.-B. (2005) QTL mapping and the genetic basis of adaptation: recent developments. Genetica 123: 25-37.
17. Liu, L. and Z.-B. Zeng (2005) Mixture model equations for marker-assisted genetic evaluation. Anim. Breed. Genet. 122: 229-239.
18. Wang, T., and Z.-B. Zeng (2006) Models and partition of variance for quantitative trait loci with epistasis and linkage disequilibrium. BMC Genetics 7:9.
19. Wang, 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 Genetics 70: 506-523.
20. Li, J., S. Wang and Z.-B. Zeng (2006) Multiple interval mapping for ordinal traits. Genetics 173: 1649-1663.
21. 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.
22. 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.
23. Aylor, D.L. and Z.-B. Zeng (2008) From classic genetics to quantitative genetics to systems biology: Modeling epistasis. PLoS Genetics 4(3).
24. 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. Supplement Erratum
25. Zou, W. and Z-.B Zeng (2008) Statistical methods for mapping multiple QTL. International Journal of Plant Genomics (Article ID 286561).
26. 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).

Applications:

1. Dragani, 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.
2. Nuzhdin, S. V., E. G. Pasyukova, C. L. Dilda, Z.-B. Zeng and T. F. C. Mackay (1997) Sex-specific quantitative trait loci affecting longevity in Drosophila melanogaster. Proceedings of the National Academy of Science USA 94: 9734-9739. [Data]
3. Weber, K., R. Eisman, S. Higgins, L. Kuhl, A. Patty, J. Sparks and Z.-B. Zeng (1999) An analysis of polygenes affecting wing shape on chromosome three in Drosophila melanogaster. Genetics 153: 773-786. [Data]
4. Vieira, 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]
5. Zeng, Z.-B., J. Liu, L. F. Stam, C.-H. Kao, J. M. Mercer and C.C. Laurie (2000) Genetic architecture of a morphological shape difference between two Drosophila species. Genetics 154: 299-310. [Data]
6. Weber, K., R. Eisman, S. Higgins, L. Morey, A. Patty, M. Tausek and Z.-B. Zeng (2001) An analysis of polygenes affecting wing shape on chromosome 2 in Drosophila melanogaster. Genetics 159: 1045-1057. [Data]
7. Tao, 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.
8. Kirst, 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 169:2295-2303.
9. 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.
10. 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.

Software:

1. Basten, C., B.S. Weir and Z.-B. Zeng (1995-2006) QTL Cartographer. Department of Statistics, North Carolina State University, Raleigh, NC.
2. Wang, S., C. Basten and Z.-B. Zeng (1999-2006) WINDOWS QTL Cartographer. Department of Statistics, North Carolina State University, Raleigh, NC
3. Zou, W., D.L. Aylor and Z.-B. Zeng (2006) eQTL Viewer. Bioinformatics Research Center, North Carolina State University, Raleigh, NC