University
of California, Irvine
Ecology and Evolutionary Biology
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| Research Interests |
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Dr. Long's research focuses on the underlying genetic basis of variation in complex traits. Approaches range from the use of cutting edge modern genomic tools, to the use of computer simulations based on population genetics theory, to purely statistical approaches that make maximal use of experimental data. Below a subset of projects are outlined: Using linkage disequilibrium to dissect complex traits (empirical). By sequencing 16 alleles of the Delta gene region (approximately 30 kb of contiguous DNA) we have identified every common Single Nucleotide Polymorphism (SNP) in this gene region that could contribute to standing variation in bristle number. We are now using identified SNPs to carry out large scale association studies in large samples of wild caught flies. These studies will be important as a validation of the association study methods that human geneticists plan to carry out. (Long et al., 1997; Long et al., 2000). Using linkage disequilibrium to dissect complex traits (theoretical). We are carrying out large scale simulation based on the gene coalescent theory of population genetics to assess the power of association studies to detects gene regions contributing to complex traits. Some of this work has already been published and more recent work has focused on extended the simulations to examine: additional statistical tests, more realistic models of human population structure and demography, and the case control design. (Long et al, 1997; Long and Langley, 1999). Using high density arrays to study adaptation (empirical). We are using high density arrays to examine the genetic architecture of thermal adaptation in experimentally evolved populations of E. coli. We have observed that adaptation in quite replicable at the molecular level and involves a region that is tandemly duplicated in 3 out of 6 experimental replicates. The duplicated regions contain candidate genes important in adaptation, that were not a priori strong candidates for adaptation to high temperature. We are now extending these results and looking at genome wide patterns of gene expression in these and other evolved lines of E. coli. We have also examined expression changes in lines of Drosophila adapted to resist high levels of ammonia in the larval environment (i.e., their food!). Again, we were able to identify a set of genes important in adaptation that were not strong a priori. In both projects, the discovery of genes and pathways important in adaptation raise the exciting possibility of interactions between physiologists, molecular biologists, and experimental evolutionists. (Riehle et al., 2001). Statistical inference from high
density arrays (theory): We have developed statistical
methods that employ the t-test, but incorporate a Bayesian prior
into estimates of the within treatment variance. This approach results
in improved statistical inference with minimal levels of replication.
We have implemented this approach into a set of web-based statistical
tools that are widely used at UCI, and beginning to see use at an
international level. (Arfin et al., 2000, Baldi and Long, 2001;
Long et al., 2001). |
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Degrees |
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B. Arts&Sci, M.Sc., Ph.D. all at McMaster University (Canada) |
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Honors and Awards |
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Current Graduate Students |
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Current Post-docs |
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| Stuart
MacDonald Patricia Beldade |
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Recent Courses Taught |
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Links |
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Recent Papers |
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Long, A.D., and C.H. Langley. 1999. The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits. Genome Research 9: 720-731.
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