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Eric C Anderson

Publications and source records attributed to Eric C Anderson.

6 recordsLinked to original sources
PubMed2005

The power of single-nucleotide polymorphisms for large-scale parentage inference.

Likelihood-based parentage inference depends on the distribution of a likelihood-ratio statistic, which, in most cases of interest, cannot be exactly determined, but only approximated by Monte Carlo simulation. We provide importance-sampling algorithms for efficiently approximating very small tail probabilities in the distribution of the likelihood-ratio statistic. These importance-sampling methods allow the estimation of small false-positive rates and hence permit likelihood-based inference of parentage in large studies involving a great number of potential parents and many potential offspring. We investigate the performance of these importance-sampling algorithms in the context of parentage inference using single-nucleotide polymorphism (SNP) data and find that they may accelerate the computation of tail probabilities >1 millionfold. We subsequently use the importance-sampling algorithms to calculate the power available with SNPs for large-scale parentage studies, paying particular attention to the effect of genotyping errors and the occurrence of related individuals among the members of the putative mother-father-offspring trios. These simulations show that 60-100 SNPs may allow accurate pedigree reconstruction, even in situations involving thousands of potential mothers, fathers, and offspring. In addition, we compare the power of exclusion-based parentage inference to that of the likelihood-based method. Likelihood-based inference is much more powerful under many conditions; exclusion-based inference would require 40% more SNP loci to achieve the same accuracy as the likelihood-based approach in one common scenario. Our results demonstrate that SNPs are a powerful tool for parentage inference in large managed and/or natural populations.

Algorithms↗
PubMed2005

An efficient Monte Carlo method for estimating Ne from temporally spaced samples using a coalescent-based likelihood.

This article presents an efficient importance-sampling method for computing the likelihood of the effective size of a population under the coalescent model of Berthier et al. Previous computational approaches, using Markov chain Monte Carlo, required many minutes to several hours to analyze small data sets. The approach presented here is orders of magnitude faster and can provide an approximation to the likelihood curve, even for large data sets, in a matter of seconds. Additionally, confidence intervals on the estimated likelihood curve provide a useful estimate of the Monte Carlo error. Simulations show the importance sampling to be stable across a wide range of scenarios and show that the N(e) estimator itself performs well. Further simulations show that the 95% confidence intervals around the N(e) estimate are accurate. User-friendly software implementing the algorithm for Mac, Windows, and Unix/Linux is available for download. Applications of this computational framework to other problems are discussed.

Algorithms↗
PubMed2005

AFP gene expression after acute diethylnitrosamine intoxication is not Afr2 regulated.

The level of alpha-fetoprotein (AFP) gene expression during liver regeneration in mice is regulated by the Afr2 gene. C3H/HeJ mice express 10-fold higher levels of AFP than C57BL/6J mice. We show that AFP gene expression is not Afr2 regulated after intoxication with the carcinogen diethylnitrosamine (DEN). Peak levels of AFP gene expression of the 2 strains were identical, although reached at different times following treatment. Analysis of the expression of AFP derived transgenes not subject to Afr2 regulation and genetic analyses showed that the difference in timing of peak AFP gene expression after DEN intoxication was independent of Afr2 regulation.

Alkylating Agents↗
PubMed2003

Population-genetic basis of haplotype blocks in the 5q31 region.

We investigated patterns of nucleotide variation in the 5q31 region identified by Daly et al. as containing haplotype blocks, to determine whether the blocklike pattern requires the assumption of hotspots in recombination. Using extensive simulations that generate data matched to the Daly et al. data set in (a) the method of ascertainment of single-nucleotide polymorphisms, (b) the heterozygosity of ascertained markers, (c) the number of block boundaries, and (d) the diversity of haplotypes within blocks, we show that the patterns found in the Daly et al. data are not consistent with the assumption of uniform recombination in a population of constant size but are consistent either with the presence of hotspots in a population of constant size or with the absence of hotspots if there was a period of rapid population growth. We further show that estimates of local recombination rate can distinguish between population growth and hotspots as the primary cause of a blocklike pattern. Estimates of local recombination rates for the Daly et al. data do not indicate the presence of recombination hotspots.

Chromosome Mapping↗
PubMed2003

Finding haplotype block boundaries by using the minimum-description-length principle.

We present a method for detecting haplotype blocks that simultaneously uses information about linkage-disequilibrium decay between the blocks and the diversity of haplotypes within the blocks. By use of phased single-nucleotide polymorphism data, our method partitions a chromosome into a series of adjacent, nonoverlapping blocks. The partition is made by choosing among a family of Markov models for block structure in a chromosomal region. Specifically, in the model, the occurrence of haplotypes within blocks follows a time-inhomogeneous Markov process along the chromosome, and we choose among possible partitions by using the two-stage minimum-description-length criterion. When applied to data simulated from the coalescent with recombination hotspots, our method reliably situates block boundaries at the hotspots and infrequently places block boundaries at sites with background levels of recombination. We apply three previously published block-finding methods to the same data, showing that they either are relatively insensitive to recombination hotspots or fail to discriminate between background sites of recombination and hotspots. When applied to the 5q31 data of Daly et al., our method identifies more block boundaries in agreement with those found by Daly et al. than do other methods. These results suggest that our method may be useful for designing association-based mapping studies that exploit haplotype blocks.

Algorithms↗