Structure In Genetic Data Research Articles

Exploration of the Genetic Diversity of Solina Wheat and Its Implication for Grain Quality.

Different Solina wheat accessions (n = 24) collected in the Abruzzo region (Italy) were studied using 45,000 SNP markers generated from the DarTseq platform. The structure of genetic data was analyzed by Principal Component Analysis and Hierarchical Cluster analysis that revealed the existence of two main clusters (Clu1 and Clu2) characterized by samples with different geographical origin. The Solina genetic dataset was further merged and analyzed with a public genetic one provided by CIMMYT containing 25,963 genotypes from all over the world. The Solina accessions occupied a vast space, thus confirming a high heterogeneity of this landrace that, nevertheless, is considerably unique and placed quite far from other clusters. Clu1 and Clu2 divergence were clearly visible. Solina clusters were genetically closer to landraces from Turkey and the central fertile crescent than to the Italian genotypes present in the dataset. Selected commercial quality traits of accessions of the two Solina clusters were analyzed (yield, thousand kernel weight, test weight, and protein content), and significant differences were found between clusters. The results of this investigation did not highlight any relationships of Solina with Italian genotypes, and confirmed its wide genetic diversity by permitting to identify two genetic groups with distinct origin and quality traits.

Range-wide phylogeography of the flightless steppe beetle Lethrus apterus (Geotrupidae) reveals recent arrival to the Pontic Steppes from the west

The Eurasian Steppe belt is one of the largest biomes in the Northern Hemisphere. We provide here a range-wide phylogeography of the flightless steppe beetle Lethrus apterus that inhabits the western part of the Steppe belt through the study of population-level variance of mitochondrial cytochrome oxidase I sequences and nuclear microsatellites. We detected a concordant geographic structure of genetic data with a significant isolation-by-distance pattern. We found more genetic variation in the western part of the area and identified Northern Bulgaria and the Pannonian Basin as possible refugia. Genetic clusters were separated by main rivers in the eastern part of the area. This implies west-to-east colonisation and argues for an evolutionarily recent arrival of this species to its current main distribution area, the Pontic Steppes. This contradicts the classical biogeographical wisdom that assumed an east-to-west colonisation pattern.

Biclustering with heterogeneous variance

In cancer research, as in all of medicine, it is important to classify patients into etiologically and therapeutically relevant subtypes to improve diagnosis and treatment. One way to do this is to use clustering methods to find subgroups of homogeneous individuals based on genetic profiles together with heuristic clinical analysis. A notable drawback of existing clustering methods is that they ignore the possibility that the variance of gene expression profile measurements can be heterogeneous across subgroups, and methods that do not consider heterogeneity of variance can lead to inaccurate subgroup prediction. Research has shown that hypervariability is a common feature among cancer subtypes. In this paper, we present a statistical approach that can capture both mean and variance structure in genetic data. We demonstrate the strength of our method in both synthetic data and in two cancer data sets. In particular, our method confirms the hypervariability of methylation level in cancer patients, and it detects clearer subgroup patterns in lung cancer data.

A model-based approach for analysis of spatial structure in genetic data

Characterizing genetic diversity within and between populations has broad applications in studies of human disease and evolution. We propose a new approach, spatial ancestry analysis, for the modeling of genotypes in two- or three-dimensional space. In spatial ancestry analysis (SPA), we explicitly model the spatial distribution of each SNP by assigning an allele frequency as a continuous function in geographic space. We show that the explicit modeling of the allele frequency allows individuals to be localized on the map on the basis of their genetic information alone. We apply our SPA method to a European and a worldwide population genetic variation data set and identify SNPs showing large gradients in allele frequency, and we suggest these as candidate regions under selection. These regions include SNPs in the well-characterized LCT region, as well as at loci including FOXP2, OCA2 and LRP1B.

Population structure and eigenanalysis.

Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general “phase change” phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like FST) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure.