Patterns of genetic diversity and linkage disequilibrium in a highly structured Hordeum vulgare association-mapping population for the Mediterranean basin

Population structure and genome-wide linkage disequilibrium (LD) were investigated in 192 Hordeum vulgare accessions providing a comprehensive coverage of past and present barley breeding in the Mediterranean basin, using 50 nuclear microsatellite and 1,130 DArT® markers. Both clustering and principal coordinate analyses clearly sub-divided the sample into five distinct groups centred on key ancestors and regions of origin of the germplasm. For given genetic distances, large variation in LD values was observed, ranging from closely linked markers completely at equilibrium to marker pairs at 50 cM separation still showing significant LD. Mean LD values across the whole population sample decayed below r 2 of 0.15 after 3.2 cM. By assaying 1,130 genome-wide DArT® markers, we demonstrated that, after accounting for population substructure, current genome coverage of 1 marker per 1.5 cM except for chromosome 4H with 1 marker per 3.62 cM is sufficient for whole genome association scans. We show, by identifying associations with powdery mildew that map in genomic regions known to have resistance loci, that associations can be detected in strongly stratified samples provided population structure is effectively controlled in the analysis. The population we describe is, therefore, shown to be a valuable resource, which can be used in basic and applied research in barle

Venetian local corn (Zea mays L.) germplasm: Disclosing the genetic anatomy of old landraces suited for typical cornmeal mush production.

Due to growing concern for the genetic erosion of local varieties, four of the main corn landraces historically grown in Veneto (Italy) \u2014 Sponcio, Marano, Biancoperla and Rosso Piave \u2014 were characterized in this work. A total of 197 phenotypically representative plants collected from field populations were genotyped at 10 SSR marker loci, which were regularly distributed across the 10 genetic linkage groups and were previously characterized for high polymorphism information content (PIC), on average equal to 0.5. The population structure analysis based on this marker set revealed that 144 individuals could be assigned with strong ancestry association (>90%) to four distinct clusters, corresponding to the landraces used in this study. The remaining 53 individuals, mainly from Sponcio and Marano, showed admixed ancestry. Among all possible pairwise comparisons of individual plants, these two landraces exhibited the highest mean genetic similarity (approximately 67%), as graphically confirmed through ordination analyses based on PCoA centroids and UPGMA trees. Our findings support the hypothesis of direct gene flow between Sponcio and Marano, likely promoted by the geographical proximity of these two landraces and their overlapping cultivation areas. Conversely, consistent with its production mainly confined to the eastern area of the region, Rosso Piave scored the lowest genetic similarity (<59%) to the other three landraces and firmly grouped (with average membership of 89%) in a separate cluster, forming a molecularly distinguishable gene pool. The elite inbred B73 used as tester line scored very low estimates of genetic similarity (on average <45%) with all the landraces. Finally, although Biancoperla was represented at K = 4 by a single subgroup with individual memberships higher than 80% in almost all cases (57 of 62), when analyzed with an additional level of population structure for K = 6, it appeared to be entirely (100%) constituted by individuals with admixed ancestry. This suggests that the current population could be the result of repeated hybridization events between the two accessions currently bred in Veneto. The genetic characterization of these heritage landraces should prove very useful for monitoring and preventing further genetic erosion and genetic introgression, thus preserving their gene pools, phenotypic identities and qualitative traits for the future

A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions

Abstract Motivation: The systematic integration of expression profiles and other types of gene information, such as chromosomal localization, ontological annotations and sequence characteristics, still represents a challenge in the gene expression arena. In particular, the analysis of transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with transcriptional imbalances often characterizing cancer. Results: A computational tool named locally adaptive statistical procedure (LAP), which incorporates transcriptional data and structural information for the identification of differentially expressed chromosomal regions, is described. LAP accounts for variations in the distance between genes and in gene density by smoothing standard statistics on gene position before testing the significance of their differential levels of gene expression. The procedure smoothes parameters and computes p-values locally to account for the complex structure of the genome and to more precisely estimate the differential expression of chromosomal regions. The application of LAP to three independent sets of raw expression data allowed identifying differentially expressed regions that are directly involved in known chromosomal aberrations characteristic of tumors. Availability: Functions in R for implementing the LAP method are available at Contact: [email protected] Supplementary Information

Structured penalized regression for drug sensitivity prediction

Large-scale {\it in vitro} drug sensitivity screens are an important tool in personalized oncology to predict the effectiveness of potential cancer drugs. The prediction of the sensitivity of cancer cell lines to a panel of drugs is a multivariate regression problem with high-dimensional heterogeneous multi-omics data as input data and with potentially strong correlations between the outcome variables which represent the sensitivity to the different drugs. We propose a joint penalized regression approach with structured penalty terms which allow us to utilize the correlation structure between drugs with group-lasso-type penalties and at the same time address the heterogeneity between omics data sources by introducing data-source-specific penalty factors to penalize different data sources differently. By combining integrative penalty factors (IPF) with tree-guided group lasso, we create the IPF-tree-lasso method. We present a unified framework to transform more general IPF-type methods to the original penalized method. Because the structured penalty terms have multiple parameters, we demonstrate how the interval-search Efficient Parameter Selection via Global Optimization (EPSGO) algorithm can be used to optimize multiple penalty parameters efficiently. Simulation studies show that IPF-tree-lasso can improve the prediction performance compared to other lasso-type methods, in particular for heterogenous data sources. Finally, we employ the new methods to analyse data from the Genomics of Drug Sensitivity in Cancer project.Comment: Zhao Z, Zucknick M (2020). Structured penalized regression for drug sensitivity prediction. Journal of the Royal Statistical Society, Series C. 19 pages, 6 figures and 2 table

INVESTIGATING INVASION IN DUCTAL CARCINOMA IN SITU WITH TOPOGRAPHICAL SINGLE CELL GENOME SEQUENCING

Synchronous Ductal Carcinoma in situ (DCIS-IDC) is an early stage breast cancer invasion in which it is possible to delineate genomic evolution during invasion because of the presence of both in situ and invasive regions within the same sample. While laser capture microdissection studies of DCIS-IDC examined the relationship between the paired in situ (DCIS) and invasive (IDC) regions, these studies were either confounded by bulk tissue or limited to a small set of genes or markers. To overcome these challenges, we developed Topographic Single Cell Sequencing (TSCS), which combines laser-catapulting with single cell DNA sequencing to measure genomic copy number profiles from single tumor cells while preserving their spatial context. We applied TSCS to sequence 1,293 single cells from 10 synchronous DCIS patients. We also applied deep-exome sequencing to the in situ, invasive and normal tissues for the DCIS-IDC patients. Previous bulk tissue studies had produced several conflicting models of tumor evolution. Our data support a multiclonal invasion model, in which genome evolution occurs within the ducts and gives rise to multiple subclones that escape the ducts into the adjacent tissues to establish the invasive carcinomas. In summary, we have developed a novel method for single cell DNA sequencing, which preserves spatial context, and applied this method to understand clonal evolution during the transition between carcinoma in situ to invasive ductal carcinoma

Evolution at two time frames : Polymorphisms from an ancient singular divergence event fuel contemporary parallel evolution

When environments change, populations may adapt surprisingly fast, repeatedly and even at microgeographic scales. There is increasing evidence that such cases of rapid parallel evolution are fueled by standing genetic variation, but the source of this genetic variation remains poorly understood. In the salt-marsh beetle Pogonus chalceus, short-winged 'tidal' and long-winged 'seasonal' ecotypes have diverged in response to contrasting hydrological regimes and can be repeatedly found along the Atlantic European coast. By analyzing genomic variation across the beetles' distribution, we reveal that alleles selected in the tidal ecotype are spread across the genome and evolved during a singular and, likely, geographically isolated divergence event, within the last 190 Kya. Due to subsequent admixture, the ancient and differentially selected alleles are currently polymorphic in most populations across its range, which could potentially allow for the fast evolution of one ecotype from a small number of random individuals, as low as 5 to 15, from a population of the other ecotype. Our results suggest that cases of fast parallel ecological divergence can be the result of evolution at two different time frames: divergence in the past, followed by repeated selection on the same divergently evolved alleles after admixture. These findings highlight the importance of an ancient and, likely, allopatric divergence event for driving the rate and direction of contemporary fast evolution under gene flow. This mechanism is potentially driven by periods of geographic isolation imposed by large-scale environmental changes such as glacial cycles.Peer reviewe

Genomic Signatures of Local Adaptation under High Gene Flow in Lumpfish—Implications for Broodstock Provenance Sourcing and Larval Production

Aquaculture of the lumpfish (Cyclopterus lumpus L.) has become a large, lucrative industry owing to the escalating demand for “cleaner fish” to minimise sea lice infestations in Atlantic salmon mariculture farms. We used over 10K genome-wide single nucleotide polymorphisms (SNPs) to investigate the spatial patterns of genomic variation in the lumpfish along the coast of Norway and across the North Atlantic. Moreover, we applied three genome scans for outliers and two genotype–environment association tests to assess the signatures and patterns of local adaptation under extensive gene flow. With our ‘global’ sampling regime, we found two major genetic groups of lumpfish, i.e., the western and eastern Atlantic. Regionally in Norway, we found marginal evidence of population structure, where the population genomic analysis revealed a small portion of individuals with a different genetic ancestry. Nevertheless, we found strong support for local adaption under high gene flow in the Norwegian lumpfish and identified over 380 high-confidence environment-associated loci linked to gene sets with a key role in biological processes associated with environmental pressures and embryonic development. Our results bridge population genetic/genomics studies with seascape genomics studies and will facilitate genome-enabled monitoring of the genetic impacts of escapees and allow for genetic-informed broodstock selection and management in Norway.publishedVersio