Biomedical subjects

Craig R Miller

Publications and source records attributed to Craig R Miller.

4 recordsLinked to original sources

PubMed2005

A new method for estimating the size of small populations from genetic mark-recapture data.

The use of non-invasive genetic sampling to estimate population size in elusive or rare species is increasing. The data generated from this sampling differ from traditional mark-recapture data in that individuals may be captured multiple times within a session or there may only be a single sampling event. To accommodate this type of data, we develop a method, named capwire, based on a simple urn model containing individuals of two capture probabilities. The method is evaluated using simulations of an urn and of a more biologically realistic system where individuals occupy space, and display heterogeneous movement and DNA deposition patterns. We also analyse a small number of real data sets. The results indicate that when the data contain capture heterogeneity the method provides estimates with small bias and good coverage, along with high accuracy and precision. Performance is not as consistent when capture rates are homogeneous and when dealing with populations substantially larger than 100. For the few real data sets where N is approximately known, capwire's estimates are very good. We compare capwire's performance to commonly used rarefaction methods and to two heterogeneity estimators in program capture: Mh-Chao and Mh-jackknife. No method works best in all situations. While less precise, the Chao estimator is very robust. We also examine how large samples should be to achieve a given level of accuracy using capwire. We conclude that capwire provides an improved way to estimate N for some DNA-based data sets.

Computer Simulation↗

PubMed2003

The history of effective population size and genetic diversity in the Yellowstone grizzly (Ursus arctos): implications for conservation.

Protein, mtDNA, and nuclear microsatellite DNA analyses have demonstrated that the Yellowstone grizzly bear has low levels of genetic variability compared with other Ursus arctos populations. Researchers have attributed this difference to inbreeding during a century of anthropogenic isolation and population size reduction. We test this hypothesis and assess the seriousness of genetic threats by generating microsatellite data for 110 museum specimens collected between 1912 and 1981. A loss of variability is detected, but it is much less severe than hypothesized. Variance in allele frequencies over time is used to estimate an effective population size of approximately 80 across the 20th century and >100 currently. The viability of the population is unlikely to be substantially reduced by genetic factors in the next several generations. However, gene flow from outside populations will be beneficial in avoiding inbreeding and the erosion of genetic diversity in the future.

Alleles↗

PubMed2003

Pedigree-based assignment tests for reversing coyote (Canis latrans) introgression into the wild red wolf (Canis rufus) population.

The principal threat to the persistence of the endangered red wolf (Canis rufus) in the wild is hybridization with the coyote (Canis latrans). To facilitate idengification and removal of hybrids, assignment tests are developed which use genotype data to estimate identity as coyote, 1/4, 1/2, 3/4 or full red wolf. The tests use genotypes from the red wolves that founded the surviving population and the resulting pedigree, rather than a contemporary red wolf sample. The tests are evaluated by analysing both captive red wolves at 18 microsatellite loci, and data simulated under a highly parameterized, biologically reasonable model. The accuracy of assignment rates are generally high, with over 95% of known red wolves idengified correctly. There are, however, tradeoffs between ambiguous assignments and misassignments, and between misidengifying red wolves as hybrids and hybrids as red wolves. These result in a compromise between limiting introgression and avoiding demographic losses. The management priorities and level of introgression determine the combination of test and removal strategy that best balances these tradeoffs. Ultimately, we conclude that the use of the assignment tests has the capacity to arrest and reverse introgression. To our knowledge, the presented approach is novel in that it accounts for genetic drift when the genotypes under analysis are temporally separated from the reference populations to which they are being assigned. These methods may be valuable in cases where reference databases for small populations have aged substantially, pedigree information is available or data are generated from historical samples.

Animals↗

PubMed2002

Assessing allelic dropout and genotype reliability using maximum likelihood.

A growing number of population genetic studies utilize nuclear DNA microsatellite data from museum specimens and noninvasive sources. Genotyping errors are elevated in these low quantity DNA sources, potentially compromising the power and accuracy of the data. The most conservative method for addressing this problem is effective, but requires extensive replication of individual genotypes. In search of a more efficient method, we developed a maximum-likelihood approach that minimizes errors by estimating genotype reliability and strategically directing replication at loci most likely to harbor errors. The model assumes that false and contaminant alleles can be removed from the dataset and that the allelic dropout rate is even across loci. Simulations demonstrate that the proposed method marks a vast improvement in efficiency while maintaining accuracy. When allelic dropout rates are low (0-30%), the reduction in the number of PCR replicates is typically 40-50%. The model is robust to moderate violations of the even dropout rate assumption. For datasets that contain false and contaminant alleles, a replication strategy is proposed. Our current model addresses only allelic dropout, the most prevalent source of genotyping error. However, the developed likelihood framework can incorporate additional error-generating processes as they become more clearly understood.

Alleles↗