Eric C. Anderson

Research Interests:

I am interested in statistical and computational methods for inference from genetic data. Most of the methods I work on are directly relevant to management and conservation of fish species, however, since the methods I develop involve genetic principles shared across many taxa, they are also applicable to many other species. At present, much of my work is directed toward the use of single nucleotide polymorphisms (SNPs) for fisheries management. We are particularly interested in the use of SNPs for large-scale parentage inference and for genetic stock identification in mixed-stock fisheries.

More about my research: http://users.soe.ucsc.edu/~eriq/eric_ams/Home.html

Education:

• Ph.D. Quantitative Ecology and Resource Management, University of Washington, 2001
• M.S. Fisheries, University of Washington, 1998

Additional Information:

Curriculum Vitae

Software:

 SNPPIT 

SNPPIT is a program for performing fast and accurate, likelihood-based, parentage inference with single nucleotide polymorphisms (SNPs).

SPIP

SPIP simulates the transmission of genes from parents to offspring in a population having demographic structure defined by the user.

CoNe

CoNe computes the likelihood of Ne given data on two temporally spaced genetic samples.

FPG (snpSumPed and TrioTests)

snpSumPed and TrioTests are used to compute false positive and false negative rates for SNP-based parentage inference.

gsi_sim

gsi_sim is useful for estimating the expected accuracy of genetic stock identification given genetic baseline samples from different source populations.