Haplotype Estimation from Fuzzy Genotypes Using Penalized Likelihood

The Composite Link Model is a generalization of the generalized linear model in which expected values of observed counts are constructed as a sum of generalized linear components. When combined with penalized likelihood, it provides a powerful and elegant way to estimate haplotype probabilities from observed genotypes. Uncertain (“fuzzy”) genotypes, like those resulting from AFLP scores, can be handled by adding an extra layer to the model. We describe the model and the estimation algorithm. We apply it to a data set of accurate human single nucleotide polymorphism (SNP) and to a data set of fuzzy tomato AFLP scores

AWclust: point-and-click software for non-parametric population structure analysis

<p>Abstract</p> <p>Background</p> <p>Population structure analysis is important to genetic association studies and evolutionary investigations. Parametric approaches, e.g. STRUCTURE and L-POP, usually assume Hardy-Weinberg equilibrium (HWE) and linkage equilibrium among loci in sample population individuals. However, the assumptions may not hold and allele frequency estimation may not be accurate in some data sets. The improved version of STRUCTURE (version 2.1) can incorporate linkage information among loci but is still sensitive to high background linkage disequilibrium. Nowadays, large-scale single nucleotide polymorphisms (SNPs) are becoming popular in genetic studies. Therefore, it is imperative to have software that makes full use of these genetic data to generate inference even when model assumptions do not hold or allele frequency estimation suffers from high variation.</p> <p>Results</p> <p>We have developed point-and-click software for non-parametric population structure analysis distributed as an R package. The software takes advantage of the large number of SNPs available to categorize individuals into ethnically similar clusters and it does not require assumptions about population models. Nor does it estimate allele frequencies. Moreover, this software can also infer the optimal number of populations.</p> <p>Conclusion</p> <p>Our software tool employs non-parametric approaches to assign individuals to clusters using SNPs. It provides efficient computation and an intuitive way for researchers to explore ethnic relationships among individuals. It can be complementary to parametric approaches in population structure analysis.</p

Traditional home-garden conserving genetic diversity: a case study of Acacia pennata in southwest China

Conserving biodiversity in human-dominated systems requires research into mechanisms that can maintain biodiversity in fragmented landscapes. Home-garden as traditional agroforestry system in many regions has shown great value in maintaining a wide range of species. Here we show that home-garden populations are also capable of maintaining high level of genetic variation. Using six polymorphic microsatellite DNA markers, we have genotyped 260 individuals of Acacia pennata, a popular wild vegetable in the tropical region of southeast Asia. Samples were collected from home-gardens and wild populations in Xishuangbanna, southwest China. Microsatellite DNA diversity in planted populations were compared with that in geographically nearby wild populations with similar population size. Over 90 % of microsatellite genetic variation in wild populations was also present in planted populations. Pairwise comparison of planted and adjacent wild population showed no significant difference in allelic diversity and heterozygosity. Analysis revealed no significant genetic differences between wild and planted populations, while four home-garden populations showed sign of bottleneck. We conclude that home-gardens show great promise in maintaining genetic diversity, and that these managed patches could be of significant conservation value in tropical regions