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      Wing Geometric Morphometrics of Workers and Drones and Single Nucleotide Polymorphisms Provide Similar Genetic Structure in the Iberian Honey Bee ( Apis mellifera iberiensis)

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          Abstract

          Wing geometric morphometrics has been applied to honey bees ( Apis mellifera) in identification of evolutionary lineages or subspecies and, to a lesser extent, in assessing genetic structure within subspecies. Due to bias in the production of sterile females (workers) in a colony, most studies have used workers leaving the males (drones) as a neglected group. However, considering their importance as reproductive individuals, the use of drones should be incorporated in these analyses in order to better understand diversity patterns and underlying evolutionary processes. Here, we assessed the usefulness of drone wings, as well as the power of wing geometric morphometrics, in capturing the signature of complex evolutionary processes by examining wing shape data, integrated with geographical information, from 711 colonies sampled across the entire distributional range of Apis mellifera iberiensis in Iberia. We compared the genetic patterns reconstructed from spatially-explicit shape variation extracted from wings of both sexes with that previously reported using 383 genome-wide SNPs (single nucleotide polymorphisms). Our results indicate that the spatial structure retrieved from wings of drones and workers was similar (r = 0.93) and congruent with that inferred from SNPs (r = 0.90 for drones; r = 0.87 for workers), corroborating the clinal pattern that has been described for A. m. iberiensis using other genetic markers. In addition to showing that drone wings carry valuable genetic information, this study highlights the capability of wing geometric morphometrics in capturing complex genetic patterns, offering a reliable and low-cost alternative for preliminary estimation of population structure.

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          Revealing cryptic spatial patterns in genetic variability by a new multivariate method.

          Increasing attention is being devoted to taking landscape information into account in genetic studies. Among landscape variables, space is often considered as one of the most important. To reveal spatial patterns, a statistical method should be spatially explicit, that is, it should directly take spatial information into account as a component of the adjusted model or of the optimized criterion. In this paper we propose a new spatially explicit multivariate method, spatial principal component analysis (sPCA), to investigate the spatial pattern of genetic variability using allelic frequency data of individuals or populations. This analysis does not require data to meet Hardy-Weinberg expectations or linkage equilibrium to exist between loci. The sPCA yields scores summarizing both the genetic variability and the spatial structure among individuals (or populations). Global structures (patches, clines and intermediates) are disentangled from local ones (strong genetic differences between neighbors) and from random noise. Two statistical tests are proposed to detect the existence of both types of patterns. As an illustration, the results of principal component analysis (PCA) and sPCA are compared using simulated datasets and real georeferenced microsatellite data of Scandinavian brown bear individuals (Ursus arctos). sPCA performed better than PCA to reveal spatial genetic patterns. The proposed methodology is implemented in the adegenet package of the free software R.
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            Advances in molecular marker techniques and their applications in plant sciences.

            Detection and analysis of genetic variation can help us to understand the molecular basis of various biological phenomena in plants. Since the entire plant kingdom cannot be covered under sequencing projects, molecular markers and their correlation to phenotypes provide us with requisite landmarks for elucidation of genetic variation. Genetic or DNA based marker techniques such as RFLP (restriction fragment length polymorphism), RAPD (random amplified polymorphic DNA), SSR (simple sequence repeats) and AFLP (amplified fragment length polymorphism) are routinely being used in ecological, evolutionary, taxonomical, phylogenic and genetic studies of plant sciences. These techniques are well established and their advantages as well as limitations have been realized. In recent years, a new class of advanced techniques has emerged, primarily derived from combination of earlier basic techniques. Advanced marker techniques tend to amalgamate advantageous features of several basic techniques. The newer methods also incorporate modifications in the methodology of basic techniques to increase the sensitivity and resolution to detect genetic discontinuity and distinctiveness. The advanced marker techniques also utilize newer class of DNA elements such as retrotransposons, mitochondrial and chloroplast based microsatellites, thereby revealing genetic variation through increased genome coverage. Techniques such as RAPD and AFLP are also being applied to cDNA-based templates to study patterns of gene expression and uncover the genetic basis of biological responses. The review details account of techniques used in identification of markers and their applicability in plant sciences.
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              A worldwide survey of genome sequence variation provides insight into the evolutionary history of the honeybee Apis mellifera.

              The honeybee Apis mellifera has major ecological and economic importance. We analyze patterns of genetic variation at 8.3 million SNPs, identified by sequencing 140 honeybee genomes from a worldwide sample of 14 populations at a combined total depth of 634×. These data provide insight into the evolutionary history and genetic basis of local adaptation in this species. We find evidence that population sizes have fluctuated greatly, mirroring historical fluctuations in climate, although contemporary populations have high genetic diversity, indicating the absence of domestication bottlenecks. Levels of genetic variation are strongly shaped by natural selection and are highly correlated with patterns of gene expression and DNA methylation. We identify genomic signatures of local adaptation, which are enriched in genes expressed in workers and in immune system- and sperm motility-related genes that might underlie geographic variation in reproduction, dispersal and disease resistance. This study provides a framework for future investigations into responses to pathogens and climate change in honeybees.
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                Author and article information

                Journal
                Insects
                Insects
                insects
                Insects
                MDPI
                2075-4450
                30 January 2020
                February 2020
                : 11
                : 2
                : 89
                Affiliations
                [1 ]Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Sta. Apolónia, 5300-253 Bragança, Portugal; dorasmh@ 123456ipb.pt (D.H.); jchavez@ 123456undc.edu.pe (J.C.-G.); helenamf93@ 123456gmail.com (H.F.);
                [2 ]Escola de Agronomia, Universidad Nacional de Cañete, Urb. Rosa de Hualcará, Calle Canal Maria Angola s/n, San Vicente de Cañete, Lima 15701, Peru
                [3 ]Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Av Bandeirantes, 3900, Ribeirão Preto 14040-900, Brazil; s.julianat@ 123456gmail.com
                [4 ]Escola de Artes, Ciências e Humanidades, Universidade de São Paulo, Rua Arlindo Béttio, 1000, São Paulo 03828-000, Brazil; tfrancoy@ 123456usp.br
                Author notes
                [* ]Correspondence: apinto@ 123456ipb.pt ; Tel.: +351-273-303-389
                Author information
                https://orcid.org/0000-0002-8899-2862
                https://orcid.org/0000-0003-2090-4528
                https://orcid.org/0000-0002-6469-1141
                https://orcid.org/0000-0002-2413-966X
                https://orcid.org/0000-0001-9663-8399
                Article
                insects-11-00089
                10.3390/insects11020089
                7074445
                32019106
                669dea9a-37f5-4b29-997b-93c98cf93107
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 December 2019
                : 23 January 2020
                Categories
                Article

                iberian honey bee,spatial population structure,spatial principal component analysis (spca),snps

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