A comparison of individual-based genetic distance metrics for landscape genetics.

A major aim of landscape genetics is to understand how landscapes resist gene flow and thereby influence population genetic structure. An empirical understanding of this process provides a wealth of information that can be used to guide conservation and management of species in fragmented landscapes and also to predict how landscape change may affect population viability. Statistical approaches to infer the true model among competing alternatives are based on the strength of the relationship between pairwise genetic distances and landscape distances among sampled individuals in a population. A variety of methods have been devised to quantify individual genetic distances, but no study has yet compared their relative performance when used for model selection in landscape genetics. In this study, we used population genetic simulations to assess the accuracy of 16 individual-based genetic distance metrics under varying sample sizes and degree of population genetic structure. We found most metrics performed well when sample size and genetic structure was high. However, it was much more challenging to infer the true model when sample size and genetic structure was low. Under these conditions, we found genetic distance metrics based on principal components analysis were the most accurate (although several other metrics performed similarly), but only when they were derived from multiple principal components axes (the optimal number varied depending on the degree of population genetic structure). Our results provide guidance for which genetic distance metrics maximize model selection accuracy and thereby better inform conservation and management decisions based upon landscape genetic analysis.

sGD: software for estimating spatially explicit indices of genetic diversity.

Anthropogenic landscape changes have greatly reduced the population size, range and migration rates of many terrestrial species. The small local effective population size of remnant populations favours loss of genetic diversity leading to reduced fitness and adaptive potential, and thus ultimately greater extinction risk. Accurately quantifying genetic diversity is therefore crucial to assessing the viability of small populations. Diversity indices are typically calculated from the multilocus genotypes of all individuals sampled within discretely defined habitat patches or larger regional extents. Importantly, discrete population approaches do not capture the clinal nature of populations genetically isolated by distance or landscape resistance. Here, we introduce spatial Genetic Diversity (sGD), a new spatially explicit tool to estimate genetic diversity based on grouping individuals into potentially overlapping genetic neighbourhoods that match the population structure, whether discrete or clinal. We compared the estimates and patterns of genetic diversity using patch or regional sampling and sGD on both simulated and empirical populations. When the population did not meet the assumptions of an island model, we found that patch and regional sampling generally overestimated local heterozygosity, inbreeding and allelic diversity. Moreover, sGD revealed fine-scale spatial heterogeneity in genetic diversity that was not evident with patch or regional sampling. These advantages should provide a more robust means to evaluate the potential for genetic factors to influence the viability of clinal populations and guide appropriate conservation plans.

Inferring landscape effects on gene flow: a new model selection framework.

Populations in fragmented landscapes experience reduced gene flow, lose genetic diversity over time and ultimately face greater extinction risk. Improving connectivity in fragmented landscapes is now a major focus of conservation biology. Designing effective wildlife corridors for this purpose, however, requires an accurate understanding of how landscapes shape gene flow. The preponderance of landscape resistance models generated to date, however, is subjectively parameterized based on expert opinion or proxy measures of gene flow. While the relatively few studies that use genetic data are more rigorous, frameworks they employ frequently yield models only weakly related to the observed patterns of genetic isolation. Here, we describe a new framework that uses expert opinion as a starting point. By systematically varying each model parameter, we sought to either validate the assumptions of expert opinion, or identify a peak of support for a new model more highly related to genetic isolation. This approach also accounts for interactions between variables, allows for nonlinear responses and excludes variables that reduce model performance. We demonstrate its utility on a population of mountain goats inhabiting a fragmented landscape in the Cascade Range, Washington.

Mutation of a putative protein degradation gene LITAF/SIMPLE in Charcot-Marie-Tooth disease 1C.

BACKGROUND: Charcot-Marie-Tooth (CMT) neuropathy is a heterogeneous group of inherited disorders of the peripheral nervous system. The authors recently mapped an autosomal dominant demyelinating form of CMT type 1 (CMT1C) to chromosome 16p13.1-p12.3. OBJECTIVE: To find the gene mutations underlying CMT1C. METHODS: The authors used a combination of standard positional cloning and candidate gene approaches to identify the causal gene for CMT1C. Western blot analysis was used to determine relative protein levels in patient and control lymphocyte extracts. Northern blotting was used to characterize gene expression in 1) multiple tissues; 2) developing sciatic nerve; and 3) nerve-crush and nerve-transection experiments. RESULTS: The authors identified missense mutations (G112S, T115N, W116G) in the LITAFgene (lipopolysaccharide-induced tumor necrosis factor-alpha factor) in three CMT1C pedigrees. LITAF, which is also referred to as SIMPLE, is a widely expressed gene encoding a 161-amino acid protein that may play a role in protein degradation pathways. The mutations associated with CMT1C were found to cluster, defining a domain of the LITAF protein having a critical role in peripheral nerve function. Western blot analysis suggested that the T115N and W116G mutations do not alter the level of LITAF protein in peripheral blood lymphocytes. The LITAF transcript is expressed in sciatic nerve, but its level of expression is not altered during development or in response to nerve injury. This finding is in stark contrast to that seen for other known genes that cause CMT1. CONCLUSIONS: Mutations in LITAF may account for a significant proportion of CMT1 patients with previously unknown molecular diagnosis and may define a new mechanism of peripheral nerve perturbation leading to demyelinating neuropathy.