Identification of population structure, a primary goal in population genetics, is easily performed because there is a number of methods available, implemented by user-friendly software packages. However, the user must be cautious when inferring population structure because spurious results may be obtained when there is strong linkage disequilibrium. With recent development of high-density SNPs we have now more power to interrogate the honey bee genome. However, the greater the number of loci genotyped the greater the chance of scoring loci that are linked. In addition, events such as population bottleneck, small effective population size, genetic drift, and admixture may also generate strong linkage disequilibrium. According to Kaeuffer et al. (2007), correlation rLD is the best way to deal with linkage disequilibrium. These authors recommend removing loci with rLD higher than 0.5 when inferring structure. In this study we used the GoldenGate Assay of Illumina to genotype over 1221 loci in individuals sampled from populations of A.m. iberiensis and A.m. mellifera. In this dataset we used the genetic distance between SNPs and rLD to test the effect of linkage in the number of clusters and the introgression level inferred by the clustering method implemented in the software STRUCTURE.Fundação para a Ciência e Tecnologi

Chávez-Galarza, Julio

De la Rúa, Pilar

Henriques, Dora

Johnston, J. Spencer

Kryger, Per

Pinto, M. Alice

Rufino, José

Biblioteca Digital do IPB

5 th EUROPEAN CONFERENCE OF APIDOLOGY 3-71h September 2012 Halla an der Saale, Germany edite;d by Benjamin Barth, Holger Scharpenberg and Robin FA Moritz [6] Pop Genet POSTER ~ Genetic-morphological variability of Melipona subnitida (Apidae, ~ Meliponini) in northeast Brazil. Bonatti V, Francisco FO, Arias MC, Simiies ZLP, Francoy TM* Email: tfrancoy@usp.br 'l11e Melipona subnitida bee is an endemic stingless bee to northeastern Brazil. In spite of its importance to local people, these bees are currently largely threatened due to the destruc-tion of their habitat and their natural nests for honey collection. Nevertheless, population studies of stingless bee species are still very scarce, as well as the urgency in understanding the structure and dynamics of their populations for monitoring and species conservation. Under this scenario, we aimed to evaluate the variability of M. subnitida in different localities in northeastern Brazil through geometric morphometries of the forewing and sequencing of COl mitochondrial gene fragment. To ;malyse the pattern of wing venation, we used appro-ximately ten workers per colony, and eleven landmarks were plotted on each wing. 'I he results showed groups structures with separation statistically significant (a<O,OOOl ). 'I he cross-validation test correctly identified 88,89% of individuals within their respective areas. Molecular analysis identifted eleven haplotypes and presence of high number of exclusive haplotypes among the populations. '!he nucleotide diversity (it) of 0,00543 and haplotype diversity (Hd) of 0,79. Both morphological and molecular results indicated that variability among the sampled populations is related both to the environment in which samples were collected as to the geographical distance between these locations, indicating the existence of locally adapted ccotn)es. ~ Effect of linkage disequilibrium on inferences of population ~ structure and introgression of Iberian and black honeybees. Chavcz-Galarza J*, Henriques D, Kryger P, De la Rim P, Johnston JS, Rufino J, Pinto MA Email: jcbavez@ipb.pt ® Identi fication of populat ion structure, a primary goal in population genetics, is easily perfor-med because there is a number of methods available, implemented by user-friendly software packages. However, the user must be cautious when inferring population structure because spurious results may be obtained when there is strong linkage disequilibrium. With recent development of high-density SNPs we have now more power to interrogate the honeybee genome. However, the greater the number of loci genotyped the greater the chance of sco-ring loci that arc linked. In addition, events such as population bottleneck, small elfective population size, genetic drift , and admixture may also generate strong linkage disequilibri-um. According to Kaeufl'er et al. (2007), correlation rLD is the best way to deal with linkage disequilibrium. 1h ese authors recommend removing loci with rLD higher than 0.5 when inferring structure. Tn this study we used the Golden Gate Assay oflllumina to genotype over 1221 loci in individuals sampled from populations of A . m . iberiensis and A. m. mellifera. In this dataset we used the genetic distance between SNPs and rLD to lest the elTect of linkage in the number of clusters and the introgression level inferred by the clustering method im-plemented in the software STRUCTURE. 242 

Effect of linkage disequilibrium on inferences of population structure and introgression of iberian and black honey bees

Effect of  Linkage Disequilibrium on inferences of population Structure and Introgression of Iberian and Black Honey Bees Julio Chávez‐Galarza, Dora Henriques, Per Kryger, Pilar de la Rúa, J. Spencer Johnston, José Rufino, M. Alice PintoIntroductionP6.200351932805831jchavez@ipb.ptIdentification of population structure, a primary goal in population genetics, is easily performed because there is a number of methods available, implemented by user-friendly software packages.However, the user must be cautious when inferring population structure because spurious results may be obtained when there is strong linkage disequilibrium. With recent development of high-density SNPs we have now more power to interrogate the honey bee genome. However, the greater the number of loci genotyped the greater the chance of scoring loci that are linked. In addition,events such as population bottleneck, small effective population size, genetic drift, and admixture may also generate strong linkage disequilibrium. According to Kaeuffer et al. (2007), correlationrLD is the best way to deal with linkage disequilibrium. These authors recommend removing loci with rLD higher than 0.5 when inferring structure. In this study we used the GoldenGate Assay ofIllumina to genotype over 1221 loci in individuals sampled from populations of A.m. iberiensis and A.m. mellifera. In this dataset we used the genetic distance between SNPs and rLD to test theeffect of linkage in the number of clusters and the introgression level inferred by the clustering method implemented in the software STRUCTURE.Sampling AnalysesGenotypingA total of 824 drone samples (each representing a singlecolony) was collected as following: 711 A. m. iberiensis, 77A. m. mellifera (France, Holland, England, Scotland,Denmark, Norway), 17 A. m. ligustica (Italy) and 19 A. m.carnica (Serbia, Croatia) (Figure 1).A panel of 1536 SNP’s was genotyped using IlluminaBeadStation 500G and a custom Oligo Pool Assay.Individuals were scored using Illumina’s Genome Studiosoftware.A final dataset of 367 SNP loci for A.m. iberiensis and1183 for A. m. mellifera was obtained after excludingmonomorphic loci (2% cutoff) and non-calls.Linkage disequilibrium between pairs of loci was assessedusing the correlation coefficient (rLD) calculated by PLINKsoftware (Purcell et al., 2007).Population structure and introgression was inferred usingSTRUCTURE (Pritchard et al. 2000).STRUCTURE was run using the following settings:admixture and linkage models, correlated allele frequency,Figure 1 – Location of  sampled individuals and populationsResults and DiscussionThe SNP position in the honey bee genome wasidentified using NCBI database.125000 burnin steps, 375000 MCMC iterations, numberof clusters K=2 to 4, and 3 repetitions per K.The analyses were run using four different locicombination: (1) all loci, (2) all loci after excluding thosetightly linked (within 50000 bp distance), (3) all loci afterexcluding those exhibiting an rLD>0.50 (as recommendedby Kaeuffer et al. 2007), (4) only loci exhibiting anrLD>0.50.Figure 3 – Assignment probabilities of belonging to the red cluster (Figure 2) generated by STRUCTURE using different models and loci • STRUCTURE analyses of A. m. iberiensis identified two clusters (best K=2 according to Evano’s test, data not shown) forming asouthwestern-northeastern cline (Figure 2), a pattern which is congruent with mtDNA data (Cánovas et al. 2008). Introgressionlevels of A. m. carnica/A. m. ligustica into A. m. iberiensis were negligible (data not shown).Figure 2 – STRUCTURE analysis for A. m. iberiensis using all loci and the admixture modelAtlantic transect Central transect Mediterranean transectcombinations for A. m. iberiensis• The assignment probabilities (Q) of A. m. iberiensis generated by admixture and linkage models and using the different locicombinations (all loci with and without tightly linked SNPs).were very similar (Figure 3).• Linkage disequilibrium may increase the probability of detecting spurious clustering (Falush et al. 2003; Kaeuffer et al. 2007). Todeal with this potential clustering bias the authors of STRUCTURE recommend removing tightly linked loci (<1 cM) whereasKaeuffer et al. (2007) suggest using the correlation rLD. These authors recommend removing loci with rLD>0.5 when inferringstructure. In this study, rLD values obtained for A.m. iberiensis were < 0.5, in contrast with those of A.m. mellifera (Table 1). Nº SNP loci Average Maximum MinimumA.m.iberiensis 367 ‐0.00032 0.46 ‐0.19A. m. mellifera 1183 0.17 1 ‐048A. m.  367 0 036 0 86 ‐0 39Table 1. Statistics of rLD for A. m. iberiensis and A. m. melliferaFigure 4 – STRUCTURE analysis for A. m. mellifera using all loci (except those tightly linked) and the linkage model• Structure analyses revealed varying levels of A. m. carnica/A. m. ligustica introgression into A. m. mellifera across itsgeographical distribution (Figure 4).Figure 5 – Proportion of introgression generated by STRUCTURE  for A. m. mellifera melliferaa . . .aValues of rLD were calculated using only the SNP loci shared with A.m. iberiensisFrance Denmark Netherlands Switzerl. Scotland Norway England A.m.ligustica A.m.carnica•The proportion of A m ligustica/A m carnica introgression (Q) at the A m mellifera individual level generated using the linkage•The average values of Q for the A. m. mellifera population, which can be interpreted in this case as the introgression level of A. m.ligustica/A. m. carnica into A. m. mellifera, generated by different models and loci combinations are shown in Figure 5. While there arevirtually no differences between admixture and linkage models, the proportion of introgression varies across loci combinations.Exclusion of loci with rLD>0.5 yields a lower level of introgression whereas inference using only loci with rLD>0.5 generates higherintrogression values.Figure 6 ‐ Proportion of introgression generated by STRUCTURE  for each A. m. mellifera  individual. . . . . .model for different loci combinations is shown in Figure 6. Introgression estimates is very similar across different loci combinations forA.m. mellifera individuals of greater purity. However, when the introgression level increases, the estimates vary with the locicombination used. In general, using only loci with rLD>0.5 tends to overestimate the proportion of introgression.AcknowledgmentsWe owe special thanks to beekeepers from Spain and Portugal for their valuable help in obtaining samples. DNA extractions and SNP genotyping wasperformed by Colette Abbey, who was deeply committed to this project. Special thanks to Dr Clare Gill for providing the facilities for SNPs genotyping and forassistance with analysis. Julio Chávez Galarza is supported by Fundação para a Ciência e Tecnologia through the scholarship SFRH/BD/68682/2010. Thisresearch was funded by Fundação para a Ciência e Tecnologia and COMPETE/QREN/EU through the project PTDC/BIA-BEC/099640/2008.LiteratureCánovas, F., De Le Rúa, P.,Serrano, J. Galián, J. 2008. Geographical patterns ofmitochondrial DNA variation in Apis melliferaiberiensis(Hymenoptera: Apidae). Journal of Zoological Systematics and Evolutionary Research, 46: 24-30.Chávez-Galarza, J., Johnston, J. S., Azevedo, J. C., Muñoz, I., de la Rúa, P., Patton, J. C., Costa, F., Pinto, M. A. 2011. Population structureas revealed by SNPs in the Iberian honey bee (Apis mellifera iberiensis). Trends in Biodiversity and Evolution. CIBIO. Vairão, Portugal.Kaeuffer, R., Réale, D., Coltman, D.W., Pontier, D. 2007. Detecting population structure using STRUCTURE software: effect of backgroundlinkage disequilibrium. Heredity. 99: 374-380.Pritchard, J.K., M. Stephens, and P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945–959.Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., Sham, P.C.2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 81(3): 559-575.France Denmark Netherlands Switzerl. Scotland Norway England

http://bibliotecadigital.ipb.pt/bitstream/10198/8954/3/Abstract_Chavez-Galarza%20et%20al.pdf

Effect of linkage disequilibrium on inferences of population structure and introgression of iberian and black honey bees

Abstract

Similar works

Full text

Available Versions

Biblioteca Digital do IPB

Biblioteca Digital do IPB